Description:TECHNICAL FIELD
The present disclosure relates generally to the field of delivery network optimization and more specifically, to a system and method for optimizing delivery network by defining rest and work areas of riders.
BACKGROUND
In the field of modern logistics and e-commerce technologies, efficient delivery services are crucial for timely and cost-effective order fulfilment. However, several challenges exist in the general technical domain, such as inefficient routing, inefficient allocation of resources, like vehicles and riders, and lack of technologies that can support real-time adaptation to changing conditions. Such challenges may lead to delays, increased costs, and customer dissatisfaction.
Existing technology in delivery network optimization involves traditional routing algorithms and static assignment of work orders to delivery personnel. Additionally, the conventional delivery networks involve allocation of work orders to the delivery personnel based only on a current geographical location of the delivery personnel, which is not always a best use case. For example, this may result in an imbalanced distribution of orders, with some delivery persons overloaded while others have insufficient orders. As a result, there is a need for an improved system and method that overcomes the limitations of the prior art and optimizes the delivery network by considering dynamic factors such as real-time rider preferences, and varying workload. Current systems are limited to use of global positioning systems, like GPS, and riders’ availability for work order optimisation, which is error-prone, and flawed in nature. For example, in scenarios where riders make multiple deliveries during a single trip, accurately determining work area becomes more complex. Furthermore, geocoding is the process of converting addresses into geographic coordinates. However, the accuracy of geocoding services may vary, and errors in mapping addresses to exact locations can lead to inefficiencies in routing or incorrect identification work areas leading to inefficient delivery network.
Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks associated with the conventional viscosity density sensors.
SUMMARY
The present disclosure provides a system and method for optimizing a delivery network by defining rest and work areas of riders. The present disclosure provides a solution to the technical problem of segmented approach (not holistic) currently used in optimizing a delivery network, resulting in inefficient, error-prone, and flawed management and usage of resources while attempting to optimize the delivery network. An aim of the present disclosure is to provide a solution that overcomes at least partially the problems encountered in the prior art and provide an improved system and method for optimizing delivery network, that considers preferred rest and work areas of the riders in real time and near-real time. Thus, providing a technical effect of accurately and automatically defining a rest area and a work area for each rider, which surprisingly improves and further optimizes the network delivery operations practically in real word, by more than 20-30%. Additionally, beneficially, the productivity and efficiency of delivery operations are improved, delivery times are reduced, and optimal utilization of resources is ensured.
One or more objectives of the present disclosure is achieved by the solutions provided in the enclosed independent claims. Advantageous implementations of the present disclosure are further defined in the dependent claims.
In one aspect, the present disclosure provides a system for optimizing a delivery network. The system includes a delivery network optimising (DNO) server including a hardware processor and an allocation component. The hardware processor is configured to obtain a historical location dataset of a plurality of delivery vehicles and a plurality of riders from a location database. The hardware processor is further configured to obtain a first location dataset of the plurality of delivery vehicles in real time or near real time from a geolocation module configured in each of the plurality of delivery vehicles, and a second location dataset of the plurality of riders in real time or near real time from a mobile navigation device positioned proximate to each of the plurality of riders. Each of the plurality of delivery vehicles is assigned to a corresponding rider from the plurality of riders. The hardware processor is further configured to determine a stationary status of each rider and the assigned delivery vehicle at specific coordinates or geographical areas for a predefined duration based on the first location dataset and the second location dataset. The hardware processor is further configured to dynamically define a rest area for each rider based on the determined stationary status of each rider and the assigned delivery vehicle. The hardware processor is further configured to identify one or more frequently visited locations of each rider during work hours or work-related activities, locations of a set of entities from where a plurality of incoming work instructions are received and a quantum of work instructions are received, based on the historical location dataset, the first location dataset, and the second location dataset. The hardware processor is further configured to dynamically define a work area for each rider based on the determined frequently visited locations of each rider during work hours or work-related activities, the locations of the set of entities from where the plurality of incoming work instructions are received and the quantum of work instructions are received. The allocation component is configured to automatically allocate the plurality of incoming work instructions to the plurality of riders based on locations of the rest area and the work area for each rider.
It is observed during experimentation that knowing the work and rest areas of riders can help to improve the overall fleet management, such as vehicles and riders and related resources. Conventional systems are limited to use of geocoding and geofencing, which uses GPS to create virtual boundaries around specific locations so that when a rider enters or leaves a geofenced area, a fleet manging entity can be notified. The disclosed system includes the delivery network optimising (DNO) server, which takes into account the stationary status of both the rider’s mobile device as well as the assigned delivery vehicle to the rider for a predefined duration. Further, the rest area for each rider is automatically and dynamically assigned based on the determined stationary status of each rider and the assigned delivery vehicle for the predefined duration, say 1-3 hours. Additionally, the system is able to dynamically define the work area for each rider using a holistic approach and taking into account the determined frequently visited locations of each rider during work hours or work-related activities, the locations of the set of entities from where the plurality of incoming work instructions are received and the quantum of work instructions are received.
It is observed that by accurately defining the work area and rest area, the system is able to achieve or provides following technical effects:
a) Efficient Resource Allocation: By accurately and dynamically defining a rest area in a city, for example, delivery vehicles may be assigned to nearby rest areas. This approach optimizes resource allocation by minimizing unnecessary travel distances, reducing fuel consumption, and maximizing the utilization of available vehicles that may be picked from the rest area itself.
b) Real-Time Optimization: By continuously monitoring the work area and rest area, the system may dynamically adjust the rest areas and work areas to adapt to changing conditions. This real-time optimization enables the network to respond quickly to fluctuations in demand, traffic conditions, or other factors that may affect the delivery process.
c) Real-time route planning and allocation improvements: By dynamically assigning work areas to riders, the system ensures that riders operate within familiar territories, where the rider has a better understanding of local routes, traffic patterns, and customer preferences. As a result, the system can optimize route planning and allocate deliveries efficiently, reducing travel distances and improving delivery times. Furthermore, dynamically defining the work area based on frequently visited locations allows riders to operate within a familiar and localized region. It is observed that this reduces the travel time by about 10-30% percent between delivery locations, as riders are more likely to be familiar with the routes, traffic patterns, and potential shortcuts within their designated work area.
d) Resource Allocation optimisation: Dynamically defining the work area enables better resource allocation by matching the number of riders to the quantum of work instructions received in specific areas. It enables the system to distribute the workload evenly among riders, ensuring efficient utilization of resources and avoiding overloading or underutilization of riders in different regions.
e) Improved adaptability to workload changes: The system may dynamically adjust the work areas based on changes in workload or demand patterns. If there is a surge in orders from a particular area, the system can expand the work area for riders or reassign riders from less busy regions to maintain a balanced distribution of workload. With real-time adaptability, the system promptly adjusts rest areas, work areas, and work allocations to address changes or disruptions in the delivery network. The system is flexible and scalable, accommodating any number of geographical areas and entities within the network. Ultimately, such optimizations result in improved customer satisfaction, with timely deliveries, accurate tracking, and enhanced service quality.
In another aspect, the present disclosure provides a method for optimizing delivery network. The method includes obtaining, by a hardware processor, a historical location dataset of a plurality of delivery vehicles and a plurality of riders from a location database. The method further includes obtaining, by the hardware processor, a first location dataset of the plurality of delivery vehicles in real time or near real time from a geolocation module configured in each of the plurality of delivery vehicles, and a second location dataset of the plurality of riders in real time or near real time from a mobile navigation device positioned proximate to each of the plurality of riders. The method further includes determining, by the hardware processor, a stationary status of each rider and the assigned delivery vehicle at specific coordinates or geographical areas for a predefined duration based on the first location dataset and the second location dataset. The method further includes dynamically defining, by the hardware processor, a rest area for each rider based on the determined stationary status of each rider and the assigned delivery vehicle. The method further includes identifying, by the hardware processor, one or more frequently visited locations of each rider during work hours or work-related activities, locations of a set of entities from where a plurality of incoming work instructions are received and a quantum of work instructions are received, based on the historical location dataset, the first location dataset, and the second location dataset. The method further includes dynamically defining, by the hardware processor, a work area for each rider based on the determined frequently visited locations of each rider during work hours or work-related activities, the locations of the set of entities from where a plurality of incoming work instructions are received and a quantum of work instructions are received.
The method achieves all the advantages and technical effects of the system of the present disclosure.
It is to be appreciated that all the aforementioned implementation forms can be combined. All steps which are performed by the various entities described in the present application as well as the functionalities described to be performed by the various entities are intended to mean that the respective entity is adapted to or configured to perform the respective steps and functionalities. It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.
Additional aspects, advantages, features, and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative implementations construed in conjunction with the appended claims that follow.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram of a system for optimizing a delivery network, in accordance with an embodiment of the present disclosure;
FIG. 2 is a block diagram of a server of the system for optimizing delivery network, in accordance with another embodiment of the present disclosure;
FIG. 3 is a scenario-based diagram that depicts an exemplary scenario of dynamically defining a rest area and a work area of a rider and of allocation of work instructions the rider based on the rest area and the work area, in accordance with an embodiment of the present disclosure; and
FIG. 4 is a flowchart that depicts a method of optimizing delivery network, in accordance with an embodiment of the present disclosure.

The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those skilled in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.
Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:
In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.
DETAILED DESCRIPTION OF EMBODIMENTS
The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.
FIG. 1 is a block diagram of a system for optimizing a delivery network, in accordance with an embodiment of the present disclosure. With reference to FIG. 1, there is shown a system 100 for delivery network optimization. The system 100 includes a delivery network optimizing (DNO) server 102. The DNO server 102 includes a hardware processor 104 and an allocation component 106. The DNO server 102 further includes a primary storage 108 communicatively coupled with the hardware processor 104 and the allocation component 106. The system 100 further includes a location database 110 and a delivery network 112 including a set of entities 114, a plurality of delivery vehicles 116 and a plurality of riders 118. The DNO server 102, the location database 110 and the delivery network 112 are communicatively coupled to each other through a communication network 120.
The present disclosure provides the system 100 for optimizing the delivery network 112 by dynamically defining rest and work areas of the plurality of riders 118 based on real time or near real time locations of each delivery vehicles of the plurality of delivery vehicles 116 and each rider of the plurality of riders 118, and locations of the set of entities 114 from where a plurality of incoming work instructions are received. The rest area refers to a designated location or zone where the riders may take breaks, rest, or perform non-work-related activities. The rest area may serve as a place for the riders to recharge, relax, or attend to personal needs during their working hours. In some examples, the rest areas may serve as a charging station for the plurality of delivery vehicle 116 assigned to the corresponding riders. In some other examples, the rest areas are defined based on factors such as rider preferences, proximity to the work areas, accessibility, and facilities available to ensure the well-being and efficiency of the plurality of riders 118 within the delivery network 112. The work areas refer to specific locations or zones where the plurality of riders 118 carry out their work-related activities. In some examples, the work areas are designated based on the riders' frequent visitation patterns, the locations of the set of entities 114 from where work instructions are received, and the volume of work instructions received. The term “dynamically defining the rest and work areas” refers to automatically determining and establishing the rest and work areas for the riders in a flexible and adaptive manner based on factors such as rider availability, order density in different networks, nature of the business, and other relevant parameters. By dynamically defining the rest and work areas, the system 100 optimizes the allocation of tasks and resources, ensuring that the plurality of riders 118 operate efficiently and effectively within their designated areas. Further, allowing for adaptability to changing conditions, rider preferences, and network demands, ultimately enhancing the performance and productivity of the delivery network 112.
The DNO server 102 includes suitable logic, circuitry, interfaces, and code that may be configured to communicate with the location database 110, the set of entities 114 and the delivery network 112 via the communication network 120. In an implementation, the DNO server 102 may be a master server or a master machine that is a part of a data centre that controls an array of other cloud servers communicatively coupled to it for load balancing, running customized applications, and efficient data management. Examples of the DNO server 102 may include, but are not limited to a cloud server, an application server, a data server, or an electronic data processing device.
The hardware processor 104 refers to a computational element that is operable to respond to and processes instructions that drive the system 100. The hardware processor 104 may refer to one or more individual processors, processing devices, and various elements associated with a processing device that may be shared by other processing devices. Additionally, the one or more individual processors, processing devices, and elements are arranged in various architectures for responding to and processing the instructions that drive the system 100. In some implementations, the hardware processors 104 may be an independent unit and may be located outside the DNO server 102 of the system 100. Examples of the hardware processors 104 may include but are not limited to, a hardware processor, a digital signal processor (DSP), a microprocessor, a microcontroller, a complex instruction set computing (CISC) processor, an application-specific integrated circuit (ASIC) processor, a reduced instruction set (RISC) processor, a very long instruction word (VLIW) processor, a state machine, a data processing unit, a graphics processing unit (GPU), and other processors or control circuitry.
The allocation component 106 is another processing unit, which is configured to allocate the plurality of work instructions 124 to the plurality of riders 118 based on an information received through the hardware processor 104. Examples of the allocation component 106 may include but are not limited to, a hardware processor, a digital signal processor (DSP), a microprocessor, a microcontroller, a complex instruction set computing (CISC) processor, an application-specific integrated circuit (ASIC) processor, a reduced instruction set (RISC) processor, a very long instruction word (VLIW) processor, a state machine, a data processing unit, a graphics processing unit (GPU), and other processors or control circuitry.
The primary storage 108 is configured to store a historical location dataset 122 of the plurality of delivery vehicles 116 and the plurality of riders 118. The historical location dataset 122 is obtained from the location database 110. The primary storage 108 is further configure to a plurality of incoming work instructions 124 obtained from the set of entities 114. The primary storage 108 is further configured to store a first location dataset 126 obtained from the plurality of delivery vehicles 116, and a second location dataset 128 obtained from the plurality of riders 118. Examples of implementation of the primary storage 108 may include, but are not limited to, an Electrically Erasable Programmable Read-Only Memory (EEPROM), Dynamic Random-Access Memory (DRAM), Random Access Memory (RAM), Read-Only Memory (ROM), Hard Disk Drive (HDD), Flash memory, a Secure Digital (SD) card, Solid-State Drive (SSD), and/or CPU cache memory.
The location database 110 refers to a structured collection of data that stores information about various geographical locations. The location database 110 is a repository that contains specific details such as latitude, longitude, addresses, and other relevant attributes associated with different places. In an implementation, the location database 110 contains the historical location dataset 122 related to the geographical areas relevant to the delivery network 112. The historical location dataset 122 includes historical information about rest areas, work areas, pickup points, drop-off locations, customer addresses, and other relevant points of interest.
The delivery network 112 refers to a comprehensive system that enables the transportation and distribution of goods or services from various sources to their intended destinations. The delivery network 112 includes, but is not limited to, the physical locations, such as warehouses, distribution centres, stores, restaurants, and pickup points, where goods are stored or made available for delivery. In some examples, the delivery network 112 also encompasses the routes, transportation methods, and vehicles used to transport the goods, as well as the technology systems, communication channels, and software applications that facilitate the management and coordination of the delivery process. In some implementations, the delivery network 112 encompasses all the components, entities, and processes involved in the delivery operations. Specifically, the delivery network 112 includes the set of entities 114, the plurality of delivery vehicles 116, and the plurality of riders 118.
The set of entities 114 refers to various locations, establishments, or points of interest that play a role in the delivery operations. The set of entities 114 include but are not limited to warehouses, distribution centres, stores, restaurants, pickup points, or any other locations that are involved in the delivery network 112. The set of entities 114 may serve as important hubs or nodes within the delivery network 112 where goods or services are stored, processed, or exchanged. The set of entities 114 includes the first entity 114A, the second entity 114B, and up to the Nth entity 114N. Each entity of the set of entities 114 includes an incoming work instruction. For example, the first entity 114A includes a first incoming work instruction 124A, the second entity 114B includes a second incoming work instruction 124B, and so on up to the Nth entity 114N includes a Nth incoming work instruction 124N.
The plurality of delivery vehicles 116 refers to vehicles that are specifically designated or employed for the purpose of transporting goods or providing delivery services within the delivery network 112. The plurality of delivery vehicles 116 play a crucial role in facilitating the movement of items from one location to another, ensuring timely and efficient deliveries. In some implementations, the plurality of delivery vehicles 116 includes various types of transportation means, such as trucks, vans, cars, motorcycles, bicycles, or any other suitable mode of transport. The plurality of delivery vehicles 116 includes a first delivery vehicle 116A, a second delivery vehicle 116B, and up to a Nth delivery vehicle 116N. Each of the plurality of delivery vehicle 116 includes a geolocation module from a plurality of geolocation module 130. The plurality of geolocation module 130 includes a first geolocation module 130A, a second geolocation module 130B, and up to a Nth geolocation module 130N. For example, the first delivery vehicle 116A includes the first geolocation module 130A, the second delivery vehicle 116B includes the second geolocation module 130B, and the Nth delivery vehicle 116N includes the Nth geolocation module 130N. In some implementations, the plurality of geolocation module 130 refers to electronic systems or technologies that utilize satellite-based positioning, wireless networks, or other geospatial technologies to gather real-time or near-real-time location data. Examples of each of the plurality of geolocation modules 130 may include GPS receivers, cellular network-based location trackers, RFID tags, or any other suitable means of obtaining geolocation information.
The plurality of riders 118 refers to individuals who are involved in the delivery operations within the delivery network 112. The plurality of riders 118 are responsible for carrying out the actual delivery tasks, which may include picking up items from designated locations and transporting them to their respective destinations. The plurality of riders 118 includes a first rider 118A, a second rider 118B, and up to a Nth rider 118N. Each rider of the plurality of riders 118 further includes a mobile navigation device from a plurality of mobile navigation devices 132. The plurality of mobile navigation devices 132 includes a first mobile navigation device 132A, a second mobile navigation device 132B, and up to a Nth mobile navigation device 132N. the plurality of mobile navigation devices 132 refers to portable electronic devices that are designed to provide navigation assistance and guidance to users while they are on the move. The plurality of mobile navigation devices 132 is typically handheld or mounted in vehicles and offer functionalities such as mapping, route planning, turn-by-turn directions, and real-time traffic updates. The plurality of mobile navigation devices 132 are utilized within the delivery network 112 to support the plurality of riders 118 in efficiently navigating the assigned routes and reaching the pickup and drop-off locations. Examples of the mobile navigation devices 132 may include smartphones, tablets, dedicated GPS devices, or any other portable devices equipped with navigation capabilities.
The communication network 120 includes a medium (e.g., a communication channel) through which the location database 110 and the delivery network 112 communicates with the DNO server 102. The communication network 120 may be a wired or wireless communication network. Examples of the communication network 120 may include, but are not limited to, Internet, a Local Area Network (LAN), a wireless personal area network (WPAN), a Wireless Local Area Network (WLAN), a wireless wide area network (WWAN), a cloud network, a Long-Term Evolution (LTE) network, a plain old telephone service (POTS), a Metropolitan Area Network (MAN), and/or the Internet.
In operation, the hardware processor 104 is configured to obtain the historical location dataset 122 of the plurality of delivery vehicles 116 and the plurality of riders 118 from the location database 110. For instance, let's assume that the location database 110 stores GPS data, timestamps, and other relevant information related to the plurality of delivery vehicles 116 and riders 118. The hardware processor 104 retrieves this data to create the historical location dataset 122.
The hardware processor 104 is further configured to obtain the first location dataset 126 of the plurality of delivery vehicles 116 in real time or near real time from the geolocation module of the plurality of geolocation modules 130 configured in each of the plurality of delivery vehicles 116. For example, the hardware processor 104 obtains location of the first delivery vehicle 116A in the real time or near-real time from the first geolocation module 130A configured in the first delivery vehicle 116A and further obtains location of the second delivery vehicle 116B in the real time or near-real time from the second geolocation module 130B configured in the second delivery vehicle 116B. The hardware processor 104 is further configured to obtain the second location dataset 128 of the plurality of riders 118 in real time or near real time from the mobile navigation device of the plurality of mobile navigation devices 132 positioned proximate to each of the plurality of riders 118. For example, the hardware processor 104 obtains location of the first rider 118A in the real time or near-real time from the first mobile navigation device 132A positioned proximate to the first rider 118A, and the hardware processor 104 obtains location of the second rider 118B in the real time or near-real time from the second mobile navigation device 132B positioned proximate to the second rider 118B. In some implementations, each of the plurality of delivery vehicles 116 is assigned to a corresponding rider from the plurality of riders 118.
The hardware processor 104 is further configured to determine a stationary status of each rider and the assigned delivery vehicle at specific coordinates or geographical areas for a predefined duration based on the first location dataset 126 and the second location dataset 128. In order to determine the stationary status of each rider and the assigned delivery vehicle, the hardware processor 104 is further configured to analyze the first location dataset 126 and the second location dataset 128. After analyzing the first location dataset 126 and the second location dataset 128, the hardware processor 104 is further configured to define a stationary period of time of each rider based on an inactivity or limited movement of a corresponding rider of the plurality of riders 118 and the assigned delivery vehicle for a significant period within a geographical area. Then, the hardware processor 104 is further configured to assign the stationary status to the corresponding rider when the stationary period of time is greater than the predefined duration. For instance, let's say a rider and the assigned delivery vehicle is in the same geographical area for a considerable amount of time without significant movement or activity. The hardware processor 104 analyzes the data from the first and second location datasets 126, 128 and identifies a prolonged period of inactivity or limited movement within that specific area. In some implementations, to define the stationary period for the rider, the hardware processor 104 compares the collected data with the predefined duration, which represents the expected threshold for a significant stationary period. If the accumulated time of inactivity or limited movement exceeds the predefined duration, the hardware processor 104 concludes that the rider and their assigned vehicle are stationary. Once the stationary status is determined, the hardware processor 104 assigns this status to the corresponding rider.
The hardware processor 104 is further configured to dynamically define a rest area for each rider based on the determined stationary status of each rider and the assigned delivery vehicle. Using the above example, let’s say one of the riders, Rider A, is identified as stationary for a significant period within a specific geographical area. The hardware processor 104 detects this prolonged inactivity or limited movement of Rider A and their assigned delivery vehicle. Based on this information, the hardware processor 104 dynamically defines that specific geographical area as a rest area for Rider A. The rest area may be a designated location where Rider A can take breaks, rest, or perform non-delivery-related tasks while waiting for the next assignment. In another example, the hardware processor 104 calculates and determines the most suitable location for the rest area based on factors such as proximity to Rider A's assigned delivery area, accessibility, amenities, and other relevant criteria.
The hardware processor 104 is further configured to identify one or more frequently visited locations of each rider during work hours or work-related activities, locations of the set of entities 114 from where the plurality of incoming work instructions 124 are received or a quantum of work instructions are received, based on the historical location dataset 122, the first location dataset 126, and the second location dataset 128. In an example, Rider A frequently visits a location X during work hours. The location X may be a specific pickup point, a delivery destination, or a particular entity within an exemplary delivery network. Further, Rider B is often assigned work instructions from Entity Y, which is a popular hub for incoming work within the exemplary delivery network. By analyzing the historical location dataset 122, the first location dataset 126, and the second location dataset 128, the hardware processor 104 identifies rider patterns and determines the one or more frequently visited locations for each rider and the locations of the set of entities 114.
The hardware processor 104 is further configured to dynamically define a work area for each rider based on the determined frequently visited locations of each rider during work hours or work-related activities, the locations of the set of entities 114 from where the plurality of incoming work instructions 124 are received or the quantum of work instructions are received. For example, if a rider frequently visits certain neighborhoods or commercial areas during their work shifts and receives a significant number of work instructions from specific pickup points or delivery destinations, the hardware processor would dynamically define those areas as the rider's work area. In some implementations, the work areas encompass the determined frequently visited locations, the locations of the set of entities 114 from where the plurality of incoming work instructions 124 are received and the quantum of work instructions are received.
Further, in operations, the allocation component 106 is configured to automatically allocate the plurality of incoming work instructions 124 to the plurality of riders 118 based on locations of the rest area and the work area for each rider. For example, let's consider a scenario where there are multiple riders within the delivery network 112, each with designated rest areas and work areas. The allocation component 106 continuously monitors the locations of these rest areas and work areas for each rider. When new work instructions (for example: the plurality of work instructions 124) are received, the allocation component 106 utilizes the real-time or near real-time location data of the riders' rest areas and work areas to determine the most suitable rider for each task. The allocation component 106 takes into account factors such as proximity, rider availability, and workload distribution.
In accordance with an embodiment of the present disclosure, the hardware processor 104 is further configured to generate a plurality of optimized delivery routes for one or more assigned riders from the plurality of riders 118 based on the locations of the rest area and the work area for each assigned rider. For example, suppose Rider A is assigned to deliver packages from their designated work area to various destinations within the delivery network 112. The hardware processor 104 takes into account the specific locations of Rider A's rest area and work area. The hardware processor 104 then generates an optimized delivery route for Rider A, considering factors such as distance, traffic conditions, and delivery priorities.
In accordance with an embodiment of the present disclosure, the hardware processor 104 is further configured to automatically update the rest areas and the work areas for each rider associated with the delivery network 112 in real time or near real time, based on analysis of the first location dataset 124 and the second location dataset 126. For example, let's consider a scenario where a delivery network includes multiple riders who need designated rest areas and work areas. The hardware processor 104 continuously monitors the first location dataset (the dataset 124) and the second location dataset (the dataset 126) to gather real-time or near real-time information about the locations and activities of the plurality of riders 118. Based on this analysis, the hardware processor 104 automatically updates the rest areas and work areas for each rider. If a rider's location indicates that they are in a designated rest area, the hardware processor 104 identifies it as their current rest area. Similarly, if a rider's location aligns with a designated work area, it is considered their current work area.
Advantageously, dynamic and real-time updating of rest areas and work areas enables efficient resource allocation and task assignment within the delivery network 112. By ensuring that the plurality of riders 118 are assigned to the most suitable work areas and provided with appropriate rest areas based on their real-time locations, the delivery network 112 may optimize productivity, minimize delays, and enhance overall operational efficiency.
In an embodiment of the present disclosure, the allocation component 106 is further configured to re-balance the delivery network 112 by adjusting the allocation of the plurality of incoming work instructions 124 to the plurality of riders 118 based on real-time or near real time updates in the locations of the rest area and the work area for each rider. For instance, if a rider's real-time location indicates that they are close to a particular work area, the allocation component 106 may prioritize assigning them work instructions from that area. Similarly, if a rider's current location suggests that they are near a designated rest area, the allocation component 106 may consider their proximity when allocating tasks and allowing for appropriate rest periods. By dynamically re-balancing the delivery network 112 based on real-time updates in rest areas and work areas, the system 100 may optimize task distribution, ensure efficient utilization of resources, and respond promptly to changing conditions within the delivery network 112.
FIG. 2 is a block diagram of a delivery network optimizing (DNO) server in a system for optimizing a delivery network, in accordance with another embodiment of the present disclosure. FIG. 2 is described in conjunction with elements of FIG. 1. With reference to FIG. 2, there is shown a block diagram of a delivery network optimizing (DNO) server 202 in the system 100 (of FIG. 1) for optimizing the delivery network 102 (of FIG. 1). The DNO server 202 includes a network interface 204 communicatively coupled to the hardware processor 104 (of FIG. 1). Further, the DNO server 202 includes a primary storage 206 and the allocation component 108 communicatively coupled with the hardware processor 104.
The network interface 204 refers to a communication interface to enable communication of the DNO server 202 to any other external device, such as a user device. Examples of the network interface 204 include, but are not limited to, a network interface card, a transceiver, and the like.
The primary storage 206 is configured to store a profile information regarding the plurality of riders operating the plurality of mobile navigation devices 132. The profile information includes one or more datasets, such as a historical incoming work instructions dataset 208, a behavioural dataset 210, and a vehicle dataset 212. The historical incoming work instructions dataset 208 contains a historical data of previous work instructions fulfilled by each rider from the plurality of riders operating the plurality of mobile navigation devices 132. In an implementation, the historical incoming work instructions dataset 208 includes information such number of work instructions fulfilled by each rider in past time period, number of work instructions refused by each rider in the past time period and the like.
The behavioural dataset 210 includes an information about the behaviour and performance of the plurality of riders within the delivery network 112. The behavioural dataset 210 includes the information such as delivery speed, customer ratings, adherence to delivery schedules and the like for each rider from the plurality of riders in the delivery network 112. The vehicle dataset 212 includes an information about the delivery vehicles assigned to each rider device. The vehicle dataset 212 the information such as vehicle type, capacity, availability, mileage of the vehicle and the like.
In accordance with an embodiment, the allocation of the plurality of incoming work instructions 124 to the plurality of rider 118 by the allocation component 106 is further based on the historical incoming work instructions dataset 208, the behavioural dataset 210, and the vehicle dataset 212 associated with the plurality of delivery vehicles 116 assigned to each rider from the plurality of riders 118. The allocation component 122 is configured to allocate work instructions based on the past performance, the rider behaviour, the vehicle suitability, and the workload distribution to optimize the allocation process. For example, if a rider has consistently demonstrated fast delivery times and high customer ratings in the behavioural dataset, then the allocation component 106 prioritizes the assignment of the time-sensitive or high-priority work instructions to that rider. Similarly, if a particular vehicle associated with a rider device has a larger capacity, then the allocation component 106 assigns bulk work instructions or larger deliveries to optimize the rider’s vehicle usage. By incorporating the historical incoming work instructions dataset 208, the behavioural dataset 210 and the vehicle dataset 212 into the allocation process, the hardware processor 104 makes intelligent decisions while assigning work instructions to the plurality of riders, which helps in optimizing resource utilization, improve delivery efficiency, and enhance overall customer satisfaction.
FIG. 3 is a scenario-based diagram that depicts an exemplary scenario of dynamically defining a rest area and a work area of a rider and of allocation of work instructions the rider based on the rest area and the work area, in accordance with an embodiment of the present disclosure. FIG. 3 is described in conjunction with elements of FIG. 1 and 2. With reference to FIG. 3, there is shown an exemplary scenario including the delivery network 112. In the illustrated example of FIG. 3, the delivery network 112 includes a set of geographical areas. Four of the set of geographical locations includes a first geographical area 302, a second geographical area 304, a third geographical area 306, and a fourth geographical area 308. The first geographical area 302 includes a first entity 114A and a second entity 114B. The second geographical area 304 includes a third entity 114C, a fourth entity 114D, and a fifth entity 114E. Each of the first geographical area 302 and the second geographical area 304 further includes one or more working riders 310 in the first and second geographical areas 302 and 304. The one or more working riders 310 may be either picking-up or delivering a product/parcel/food item(s) based on the work instructions received.
Although, in the delivery network 112 of FIG. 3, only four geographical areas and five entities are shown for illustrative purposes, the delivery network 112 may include any number of geographical areas and any number of entities, without any limitation thereto.
Each of the third geographical area 306 and the fourth geographical area 308 includes one or more resting riders 312. In some implementations, each of the third geographical area 306 and the fourth geographical area 308 includes one or more charging stations 314. The one or more resting riders 312 may charge the assigned delivery vehicle using the one or more charging stations 314 while resting. In an implementation, the plurality of the riders 118 (shown in FIG. 1) includes the one or more working riders 310 and the one or more resting riders 312.
In an implementation, the DNO server 102 receives real time or near-real time location datasets of each delivery vehicle and each rider from the plurality of geolocation modules 130 and the plurality of mobile navigation devices 132, respectively. Further, the DNO server 102 collectively receives the plurality of incoming work instructions 124 from the one or more entities 114A, 114B, 114C, 114D, and 114E located in the first geographical area 302 and the second geographical area 304. The DNO server 102 has information about the incoming work instructions 124 from each individual entity, including the one or more entities 114A, 114B, 114C, 114D, and 114E. Based on the received location datasets and work instructions, the DNO server 102 performs an allocation process to assign work instructions to the working riders 310 within the delivery network 112.
The hardware processor 104, which is a part of the DNO server 102, dynamically define the rest and work areas for each rider based on the analysed location datasets and the plurality of incoming work instructions 124. For example, if the second geographical area 304 is frequently visited by a particular working rider and also a number of work instructions are received from the second geographical area 304, then the second geographical area 304 is defined as the work area for that particular working rider. Similarly, if a particular resting rider is stationary for a predefined time period in the fourth geographical area 308, then the fourth geographical area is defined as the rest area for that particular resting rider.
The allocation component 106, which is also part of the DNO server 102, automatically allocates the plurality of incoming work instructions 124 to the plurality of riders 118 based on the locations of their rest areas and work areas. Each rider's rest area and work area are dynamically defined based on factors such as their frequently visited locations during work hours, the locations of entities where work instructions are received, and other relevant datasets. By considering the rest areas and work areas of each rider, the allocation component 106 ensures that the assigned work instructions are efficiently distributed among the riders. This dynamic allocation process enables optimized delivery routes to be generated for the assigned riders based on the corresponding rest areas and work areas, as well as the locations of the set of entities 114 within the delivery network 112.
FIG. 4 is a flowchart that depicts a method of optimizing delivery network, in accordance with an embodiment of the present disclosure. FIG. 4 is described in conjunction with elements of FIGS. 1 to 4. With reference to FIG. 4, there is shown a method 400 for optimizing the delivery network 112 (of FIG. 1). The method 400 includes steps from 402 to 412.
At step 402, the method 400 includes obtaining, by the hardware processor 106, the historical location dataset 122 of the plurality of delivery vehicles 116 and the plurality of riders 118 from the location database 110.
At step 404, the method 400 further includes obtaining, by the hardware processor 104, the first location dataset 126 of the plurality of delivery vehicles 116 in real time or near real time from the geolocation module of the plurality of geolocation modules 130 configured in each of the plurality of delivery vehicles 116, and the second location dataset 128 of the plurality of riders 118 in real time or near real time from the mobile navigation device of the plurality of mobile navigation devices 132 positioned proximate to each of the plurality of riders 118.
At step 406, the method 400 further includes determining, by the hardware processor 104, the stationary status of each rider and the assigned delivery vehicle at specific coordinates or geographical areas for the predefined duration based on the first location dataset 126 and the second location dataset 128.
At step 408, the method 400 further includes dynamically defining, by the hardware processor 104, the rest area for each rider based on the determined stationary status of each rider and the assigned delivery vehicle.
At step 410, the method 400 further includes identifying, by the hardware processor 104, the one or more frequently visited locations of each rider during work hours or work-related activities, the locations of the set of entities 114 from where the plurality of incoming work instructions 124 are received and the quantum of work instructions are received, based on the historical location dataset 122, the first location dataset 126, and the second location dataset 128.
At step 412, the method 400 further includes dynamically defining, by the hardware processor 104, the work area for each rider based on the determined frequently visited locations of each rider during work hours or work-related activities, the locations of the set of entities 114 from where the plurality of incoming work instructions 124 are received and the quantum of work instructions are received.
In accordance with an embodiment, the method 400 further includes automatically allocating, by the allocation component 106, the plurality of incoming work instructions 124 to the plurality of riders 118 based on the locations of the rest area and the work area for each rider. In accordance with an embodiment, the method 400 is performed in the delivery network optimising (DNO) server 102 including the hardware processor 104 and the allocation component 106.
It is observed during experimentation that knowing the work and rest areas of the plurality of riders 118 may help to improve the overall fleet management, such as vehicles and riders and related resources. The disclosed system 100 and the method 400 includes the delivery network optimising (DNO) server 102, which takes into account the stationary status of both the rider’s mobile device 132 as well as the geolocation module 130 of the assigned delivery vehicle to the rider for a predefined duration. Further, the rest area for each rider is automatically and dynamically assigned based on the determined stationary status of each rider and the assigned delivery vehicle for the predefined duration, say 1-3 hours. Additionally, the system 100 and method 400 is able to dynamically define the work area for each rider using a holistic approach and taking into account the determined frequently visited locations of each rider during work hours or work-related activities, the locations of the set of entities 114 from where the plurality of incoming work instructions 124 are received and the quantum of work instructions are received.
It is observed that by accurately defining the work area and rest area, the system 100 and the method 400 are able to achieve or provides following technical effects:
a) Efficient Resource Allocation: By accurately and dynamically defining the rest area in a geographical location, for example, delivery vehicles may be assigned to nearby rest areas. This approach optimizes resource allocation by minimizing unnecessary travel distances, reducing fuel consumption, and maximizing the utilization of available vehicles that may be picked from the rest area itself.
b) Real-Time Optimization: By continuously monitoring the work area and rest area, the DNO server 102 may dynamically adjust the rest areas and work areas to adapt to changing conditions. This real-time optimization enables the delivery network 112 to respond quickly to fluctuations in demand, traffic conditions, or other factors that may affect the delivery process.
c) Real-time route planning and allocation improvements: By dynamically assigning work areas to the plurality of riders 118, the DNO server 102 ensures that the plurality of riders 118 operate within familiar territories, where the rider has a better understanding of local routes, traffic patterns, and customer preferences. As a result, the system 100 and the method 400 may optimize route planning and allocate deliveries efficiently, reducing travel distances and improving delivery times. Furthermore, dynamically defining the work area based on frequently visited locations allows the plurality of riders 118 to operate within a familiar and localized region. It is observed that this reduces the travel time by about 10-30% percent between delivery locations, as riders are more likely to be familiar with the routes, traffic patterns, and potential shortcuts within their designated work area.
d) Resource Allocation optimisation: Dynamically defining the work area enables better resource allocation by matching the plurality of riders 118 to the quantum of work instructions received in specific areas. It enables the DNO server 102 to distribute the workload evenly among riders, ensuring efficient utilization of resources and avoiding overloading or underutilization of riders in different regions.
e) Improved adaptability to workload changes: The system 100 and the method 400 may include dynamically adjusting the work areas based on changes in workload or demand patterns. If there is a surge in orders from a particular area, the DNO server 102 may expand the work area for riders or reassign riders from less busy regions to maintain a balanced distribution of workload. With real-time adaptability, the DNO server 102 promptly adjusts rest areas, work areas, and work allocations to address changes or disruptions in the delivery network 112. Such approach is flexible and scalable, accommodating any number of geographical areas and entities within the delivery network 112. Ultimately, such optimizations result in improved customer satisfaction, with timely deliveries, accurate tracking, and enhanced service quality.
Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non-exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural. The word "exemplary" is used herein to mean "serving as an example, instance or illustration". Any embodiment described as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments and/or to exclude the incorporation of features from other embodiments. The word "optionally" is used herein to mean "is provided in some embodiments and not provided in other embodiments". It is appreciated that certain features of the present disclosure, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the present disclosure, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable combination or as suitable in any other described embodiment of the disclosure. , Claims:1. A system (100) for optimizing a delivery network (112), the system (100) comprising:
a delivery network optimising (DNO) server (102) comprising a hardware processor (104), wherein the hardware processor (104) is configured to:
obtain a historical location dataset (122) of a plurality of delivery vehicles (116) and a plurality of riders (118) from a location database (110);
obtain a first location dataset (126) of the plurality of delivery vehicles (116) in real time or near real time from a geolocation module of a plurality of geolocation modules (130) configured in each of the plurality of delivery vehicles (116), and a second location dataset (128) of the plurality of riders (118) in real time or near real time from a mobile navigation device of a plurality of mobile navigation devices (132) positioned proximate to each of the plurality of riders (118), wherein each of the plurality of delivery vehicles (116) is assigned to a corresponding rider from the plurality of riders (118);
determine a stationary status of each rider and the assigned delivery vehicle at specific coordinates or geographical areas for a predefined duration based on the first location dataset (126) and the second location dataset (128);
dynamically define a rest area for each rider based on the determined stationary status of each rider and the assigned delivery vehicle;
identify one or more frequently visited locations of each rider during work hours or work-related activities, locations of a set of entities (114) from where a plurality of incoming work instructions (124) are received or a quantum of work instructions are received, based on the historical location dataset (122), the first location dataset (126), and the second location dataset (128); and
dynamically define a work area for each rider based on the determined frequently visited locations of each rider during work hours or work-related activities, the locations of the set of entities (114) from where the plurality of incoming work instructions (124) are received or the quantum of work instructions are received; and
an allocation component (106) configured to automatically allocate the plurality of incoming work instructions (124) to the plurality of riders (118) based on locations of the rest area and the work area for each rider.

2. The system (100) as claimed in claim 1, wherein, in order to determine the stationary status of each rider and the assigned delivery vehicle, the hardware processor (104) is further configured to:
analyze the first location dataset (126) and the second location dataset (128);
define a stationary period of time of each rider based on an inactivity or limited movement of a corresponding rider of the plurality of riders (118) and the assigned delivery vehicle for a significant period within a geographical area; and
assign the stationary status to the corresponding rider when the stationary period of time is greater than the predefined duration.

3. The system (100) as claimed in claim 1, wherein the allocation of the plurality of incoming work instructions (124) to the plurality of riders (118) by the allocation component (106) is further based on a historical incoming work instructions dataset (208), a behavioral dataset (210), and a vehicle dataset (212) associated with the plurality of delivery vehicles (116) assigned to each rider from the plurality of riders (118).

4. The system (100) as claimed in claim 1, wherein the hardware processor (104) is further configured to generate a plurality of optimized delivery routes for one or more assigned riders from the plurality of riders (118) based on the locations of the rest area and the work area for each assigned rider.

5. The system (100) as claimed in claim 1, wherein the work areas encompass the determined frequently visited locations, the locations of the set of entities (114) from where the plurality of incoming work instructions (124) are received and the quantum of work instructions are received.

6. The system (100) as claimed in claim 1, wherein the hardware processor (104) is further configured to automatically update the rest areas and the work areas for each rider associated with the delivery network (112) in real time or near real time, based on analysis of the first location dataset (126) and the second location dataset (128).

7. The system (100) as claimed in claim 6, wherein the allocation component (106) is further configured to re-balance the delivery network (112) by adjusting the allocation of the plurality of incoming work instructions (124) to the plurality of riders (118) based on real-time or near real time updates in the locations of the rest area and the work area for each rider.

8. A method (200) for optimizing a delivery network (112), the method (200) comprising:
obtaining, by a hardware processor (104), a historical location dataset (122) of a plurality of delivery vehicles (116) and a plurality of riders (118) from a location database (110);
obtaining, by the hardware processor (104), a first location dataset (126) of the plurality of delivery vehicles (116) in real time or near real time from a geolocation module of the plurality of geolocation modules (130) configured in each of the plurality of delivery vehicles (116), and a second location dataset (128) of the plurality of riders (118) in real time or near real time from a mobile navigation device of the plurality of mobile navigation devices (132) positioned proximate to each of the plurality of riders (118);
determining, by the hardware processor (104), a stationary status of each rider and the assigned delivery vehicle at specific coordinates or geographical areas for a predefined duration based on the first location dataset (126) and the second location dataset (128);
dynamically defining, by the hardware processor (104), a rest area for each rider based on the determined stationary status of each rider and the assigned delivery vehicle;
identifying, by the hardware processor (104), one or more frequently visited locations of each rider during work hours or work-related activities, locations of a set of entities (114) from where a plurality of incoming work instructions (124) are received and a quantum of work instructions are received, based on the historical location dataset (122), the first location dataset (126), and the second location dataset (128); and
dynamically defining, by the hardware processor (104), a work area for each rider based on the determined frequently visited locations of each rider during work hours or work-related activities, the locations of the set of entities (114) from where the plurality of incoming work instructions (124) are received and the quantum of work instructions are received.

9. The method (200) as claimed in claim 8, further comprising automatically allocating, by an allocation component (106), the plurality of incoming work instructions (124) to the plurality of riders (118) based on locations of the rest area and the work area for each rider.

10. The method (200) as claimed in claim 8, wherein the method (200) is performed in a delivery network optimising (DNO) server (102) comprising the hardware processor (104) and the allocation component (106).