Description:TECHNICAL FIELD
The present disclosure relates generally to a field of delivery network management, and more specifically, to a system and method for optimising delivery network based on enhanced rider profile.
BACKGROUND
In a field of delivery network management, a delivery network is managed in order to coordinate the various aspects of delivering goods or services to customers. The management of the delivery network involves tasks such as receiving delivery requests, communicating with riders, scheduling a delivery, dispatching, route planning, tracking deliveries, and conducting business analysis. However, with the expansion of delivery networks and increase in delivery requests, the system struggles to handle the growing volume of delivery goods or services. Hence, such systems result in overwhelmed dispatchers, delays in assigning riders, and difficulties in optimizing routes. Thus, the system is inefficient and has difficulty in coordinating and optimizing the delivery process.
Existing technology in the field of delivery network optimization primarily focuses on basic allocation algorithms that consider factors such as distance, delivery volume, and vehicle availability. While these approaches offer some level of optimization, they fail to address the diverse characteristics and capabilities of individual riders. Thus, current systems manifest suboptimal resource allocation, missed personalization opportunities, inconsistent workload distribution, and limited adaptability in terms of delivery patterns, trends, seasonality in demand, changing circumstances or dynamic shifts in workload.
Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks.
SUMMARY
The present disclosure provides a system for optimising a delivery network. The present disclosure provides a solution to the existing problem of inefficient delivery allocation within a delivery network due to the lack of comprehensive rider profiles and historical data. An aim of the present invention is to provide a solution that overcomes at least partially the problems encountered in prior art and provide an improved system that optimizes delivery network based on an enhanced rider profile. Thus, in turn, allows for generating the enhanced rider profile based on preferences, and needs of individual riders. There is further provided an improved method for optimising delivery network optimization, which incorporates enhanced rider profiles into the allocation process, which enables a more data-driven, personalized, and efficient allocation of delivery tasks. It allows for better resource utilization, optimized routing, improved performance monitoring, and increased adaptability to changing conditions, ultimately leading to a more effective and streamlined delivery network.
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 optimising delivery network. The system includes a delivery network optimising server that comprises a hardware processor and a calibration component. The hardware processor is configured to obtain a historical delivery dataset of each rider of a plurality of riders operating a delivery vehicle from a first database. The historical delivery dataset comprises a type of entity serviced by each rider of the plurality of riders. The hardware processor is further configured to obtain a behavioural dataset of each rider of the plurality of riders from a second database. The behavioural dataset comprises self-cancellation information pertaining to deliveries, active time periods in a day when each rider is active, and dormant time periods in the day for each rider. The hardware processor is further configured to obtain a vehicle dataset associated with a plurality of delivery vehicles assigned to the plurality of riders from a vehicle database. The hardware processor is further configured to generate an enhanced rider profile based on the historical delivery dataset, the behavioural dataset, and the vehicle dataset. The calibration component is configured to automatically calibrate allocation of deliveries to each of the plurality of riders in correlation with the enhanced rider profile for a defined geographical area taking into account at least the type of entity serviced by each rider of the plurality of riders, the self-cancellation information pertaining to deliveries, the active time periods in the day when each rider is active, causing re-balancing of the delivery network as well as a total allocation number of the plurality of riders for the defined geographical area.
By generating the enhanced rider profile of each rider, the system prepares a comprehensive profile of each rider by considering factors such as the type of entity serviced by each rider, self-cancellation information, and active time periods. Thus, the system intelligently distributes deliveries, ensuring riders are assigned appropriate tasks based on their capabilities and preferences.
The automatic calibration of the allocation of deliveries is performed in correlation with the generated enhanced rider profile that takes various factors into consideration. For example, the riders who are not familiar with a particular area may be more likely to get lost or involved in an accident. Thus, the system may help to increase safety of the delivery network by ensuring that the riders are only allocated the deliveries that do not pose a safety risk. The system may further identify the riders who are most likely to be available to work during certain times of the day. Thus, the automatic calibration of the allocation of deliveries performed by the system improves efficiency of the delivery network by ensuring that the deliveries are allocated to the riders who are most likely to complete the deliveries under predetermined time and in a cost-effective manner. By ensuring that the deliveries are made in a timely manner, the system may help to improve customer satisfaction. The system may reduce costs associated with the delivery network by ensuring that riders are only allocated deliveries that the riders may complete. Further, the automatic calibration of allocation of deliveries reduces a number of deliveries that are cancelled or delayed and hence the system may save costs in the long run.
Advantageously, the hardware processor integrates the historical delivery dataset, behavioral dataset, and vehicle dataset to create an enhanced rider profile. This integration allows for a holistic view of each rider, considering their past performance, behavioral characteristics, and the vehicles they are assigned to. By combining these datasets, a more complete and accurate profile of each rider is generated. This enriched profile serves as a valuable resource for allocation decisions and optimizing the delivery process. The behavioral dataset, including active and dormant time periods, provides valuable information for route planning and scheduling. The enhanced rider profile allows for the consideration of individual rider availability and behavior patterns when assigning tasks and planning routes. It enables efficient routing and scheduling decisions, reducing travel time, optimizing delivery sequences, and enhancing overall productivity. The availability of an enhanced rider profile enables the system to adapt to changing circumstances, such as shifts in rider behavior, vehicle availability, familiarity of a work area, or customer demand. By continuously updating and analyzing the profile based on new data, the system can dynamically adjust task assignments, routing, and scheduling to ensure optimal performance in response to evolving conditions.
In an implementation form, the historical delivery dataset includes a number of deliveries performed by each rider of the plurality of riders in the delivery network, a frequency of deliveries performed by each rider in a predefined period of time, working hours of each rider, and a time taken for completing each of the one or more deliveries by each rider. With the help of information stored in the historical delivery dataset, the system may assess performance of each rider. The system may identify the riders who are performing well and the riders who may need additional training or support. The system with the information stored in the historical delivery dataset, may identify the riders who are most likely to complete the deliveries on time.
In a further implementation form, the first database is configured to store date and time of each delivery, location of each delivery, a type of delivery package belonging to each of the one or more entities, a name of each rider who performed the delivery, or other type of rider information. Advantageously, the first database assists the system in identifying major hotspots for each of the one or more entity. The system may identify the major pickup locations and major drop locations for each of the one or more entities. Further, the system may identify the time at which the one or more entities possess highest delivery density.
In a further implementation form, the type of entities corresponds to a warehouse, a food-based entity, a medicine-based entity, a vehicle-allocation entity, or other type of supplier entity. For example, Dunzo, Grofers, Blinkit, Zomato, Flipkart, Amazon, Swiggy, 1mg, and more.
In a further implementation form, the behavioural dataset comprises a skill set of each rider of the plurality of riders, a punctuality information of each rider, a number of deliveries completed within a pre-determined period of time associated with each rider, a set of feedbacks obtained from each type of entities serviced by each rider, a leave trend of each rider, a set of riding or service preferences of each rider, and a plurality of trainings undertaken by each rider. Advantageously, the system prioritizes the deliveries for each rider according to their service preferences and the feedback obtained from the one or more entities.
In a further implementation form, the second database is configured to store a skill set of each rider, a login and a logout time of each rider, delivery completion information foreach delivery completed within a pre-determined period of time, self-cancellation information pertaining to each delivery self-cancelled by each rider, feedback information obtained from each type of entities serviced by each rider, leave information comprising leaves taken by each rider, a set of riding or service preferences of each rider, a plurality of trainings undertaken by each rider, or other type of behavioural information. The system may determine efficiency and scope of improvements for each rider by analyzing information stored in the second database.
In a further implementation form, the pre-determined period of time corresponds to an expected period of time calculated by the system for completing each delivery.
In a further implementation form, the vehicle dataset comprises a unique vehicle identifier of each vehicle of the plurality of delivery vehicles, a mileage of each vehicle, a last service date of each vehicle, or other types of vehicle information associated with the plurality of delivery vehicles. The system may identify a working condition of each delivery vehicle by analyzing information analyzed using vehicle dataset.
In a further implementation form, the hardware processor is configured to identify each of the plurality of riders through an image recognition module. The identification is performed via at least one combination of a face and a logo in a dress worn by the rider. The hardware processor is configured to allocate the deliveries to each of the plurality of riders based on identification of each of the plurality of riders. The hardware processor is further configured to update the historical delivery dataset and the behavioural dataset corresponding to each of the plurality of riders based on successful completion of the allocated deliveries. Identification of each of the plurality of riders ensure that deliveries are allocated to correct riders.
In another aspect, the present disclosure provides a method for optimising delivery network. The method includes obtaining a historical delivery dataset of each rider of a plurality of riders operating a delivery vehicle from a first database. The historical delivery dataset comprises a type of entity serviced by each rider of the plurality of riders. The method further includes obtaining a behavioural dataset of each rider of the plurality of riders from a second database. The behavioural dataset comprises self-cancellation information pertaining to deliveries, active time periods in a day when each rider is active, and dormant time periods in the day for each rider. The method further includes obtaining a vehicle dataset associated with a plurality of delivery vehicles assigned to the plurality of riders from a vehicle database. The method further includes generating an enhanced rider profile based on the historical delivery dataset, the behavioural dataset, and the vehicle dataset. The method further includes automatically calibrating allocation of deliveries to each of the plurality of riders in correlation with the enhanced rider profile for a defined geographical area taking into account at least the type of entity serviced by each rider of the plurality of riders, the self-cancellation information pertaining to deliveries, the active time periods in the day when each rider is active, causing re-balancing of the delivery network as well as a total allocation number of the plurality of riders for the defined geographical area.
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. Even if, in the following description of specific embodiments, a specific functionality or step to be performed by external entities is not reflected in the description of a specific detailed element of that entity which performs that specific step or functionality, it should be clear for a skilled person that these methods and functionalities can be implemented in respective software or hardware elements, or any kind of combination thereof. 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
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:
FIG. 1 is a block diagram illustrating a system for optimising delivery network based on enhanced rider profile, in accordance with an embodiment of the present disclosure;
FIG. 2 is a block diagram illustrating a delivery network optimising (DNO) server, in accordance with an embodiment of the present disclosure;
FIG. 3 is a diagram of an exemplary scenario of the allocation of work instructions to a plurality of riders based on enhanced rider profile, in accordance with an embodiment of the present disclosure; and
FIG. 4 is a flowchart of a method for optimising delivery network based on enhanced rider profile, in accordance with an embodiment of the present disclosure.
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.
As used throughout this disclosure, the word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to.
The phrases “at least one”, “one or more”, and “and/or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B and C”, “at least one of A, B, or C”, “one or more of A, B, and C”, “one or more of A, B, or C” and “A, B, and/or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together.
The term “a” or “an” entity refers to one or more of that entity. As such, the terms “a” (or “an”), “one or more” and “at least one” can be used interchangeably herein. It is also to be noted that the terms “comprising”, “including”, and “having” can be used interchangeably.
The present subject matter may have a variety of modifications and may be embodied in a variety of forms, and specific embodiments will be described in more detail with reference to the drawings. It should be understood, however, that the embodiments of the present subject matter are not intended to be limited to the specific forms, but include all modifications, equivalents, and alternatives falling within the spirit and scope of the present subject matter.
FIG. 1 is a block diagram illustrating a system for optimising a delivery network based on enhanced rider profile, in accordance with an embodiment of the present disclosure. With reference to FIG. 1, there is shown a block diagram that includes a system 100. The system 100 includes a delivery network optimising (DNO) server 102 communicatively coupled with a plurality of riders 110 through a communication network 116. The DNO server 102 includes a hardware processor 104 and a calibration component 118. The DNO server 102 further includes a historical delivery dataset 108A, a behavioural dataset 108B, and a vehicle dataset 108C that is obtained from a first database 106A, a second database 106B, and a vehicle database 106C respectively. The plurality of riders 110 are operating in defined geographical areas 114A, 114B, and 114C. The defined geographical areas 114A, 114B, and 114C belong to a delivery network 112.
The present disclosure provides the system 100, that is adapted to optimize the delivery network 112 by allocating deliveries to the plurality of riders 110 based on enhanced rider profile corresponding to each of the plurality of riders 110 within different geographical areas. The system 100 receives an information about the deliveries to be allocated to the plurality of riders 110 transmitted by a type of entity and automatically calibrate the allocation of the deliveries to each of the plurality of riders 110 in correlation with the enhanced rider profile for a defined geographical area 114A-114C taking into account the type of entity serviced by each rider of the plurality of riders 110, the self-cancellation information of each of the plurality of riders 110 pertaining to deliveries, active time periods in a day when each rider is active. The automatic calibration of the allocation of deliveries causes re-balancing of the delivery network 112 as well as a total allocation number of the plurality of riders 110 for the defined geographical area 114A-114C. The deliveries refer to an order or requirement that is indicative of a demand for goods and/or services.
In an implementation, the DNO server 102 includes suitable logic, circuitry, interfaces, and code that may be configured to communicate with the plurality of riders 110 via the communication network 116. In an implementation, the DNO server 102 may be a master server or a master machine that is a part of a data center 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 communication network 116 includes a medium (e.g., a communication channel) through which the plurality of riders 110 communicates with the DNO server 102. The communication network 116 may be a wired or wireless communication network. Examples of the communication network 116 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.
The hardware processor 104 refers to a computational element that is operable to respond to and processes instructions that drive the DNO server 102 in the system 100. The hardware processor 104 refers 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 DNO server 102 in the system 100. Examples of the hardware processor 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.
Each rider in the plurality of riders 110 refers to a delivery personnel who operates a delivery vehicle in the delivery network 112 being managed. Each rider in the plurality of riders 110 possesses specific preferences, requirements, and characteristics. The plurality of riders 110 includes a first rider 110A, a second rider 110B, and up to a Nth rider 110N. In an implementation, the first rider 110A, the second rider 110B, and a third rider 110C are assigned in a defined geographical area 114A. A fourth rider 110D, a fifth rider 110E, and a sixth rider 110F are assigned in a defined geographical area 114B. A seventh rider 110G, an eighth rider 110H, and up to the Nth rider 110N are assigned in a defined geographical area 114C. The defined geographical areas 114A, 114B, and 114C together constitute the delivery network 112.
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. As discussed above, the delivery network 112 includes the plurality of riders 110 placed in the defined geographical areas 114A, 114B, and 114C. In some examples, the delivery network 112 includes, but is not limited to, the physical locations, such as warehouses, distribution centers, stores, restaurants, and pickup points, where goods are stored or made available for delivery.
In an implementation, the first database 106A refers to a database that contains information about the delivery history of each rider, including details such as the type of entity serviced by each rider within the delivery network 112. The first database 106A is configured to store date and time of each delivery, location of each delivery, a type of delivery package belonging to each of the type of entities, a name of each rider who performed the delivery, or other type of rider information. The first database 106A may assist the system 100 in identifying major hotspots for each of the type of entities. The system 100 may identify the major pickup locations and major drop locations for each type of entity. Further, the system 100 may identify the time at which the type of entities possess highest delivery density.
In an implementation, the second database 106B refers to a database that contains information about the behavior and actions of each rider within the delivery network 112. The second database 106B is configured to store the skill set of each rider, a login and a logout time of each rider, delivery completion information for each delivery completed within the pre-determined period of time, the self-cancellation information pertaining to each delivery self-cancelled by each rider, the feedback information obtained from each type of entities serviced by each rider, the leave information comprising leaves taken by each rider, the set of riding or service preferences of each rider, a plurality of trainings undertaken by each rider, or other type of behavioural information. The system 100 may increase efficiency and determine scope of improvements for each rider by analyzing information stored in the second database 106B.
In an implementation, the vehicle database 106C refers to a database that contains information about the vehicles assigned to the plurality of riders 110 within the delivery network 112. The vehicle database 106C is configured to store the unique vehicle identifier of each vehicle of the plurality of delivery vehicles, the mileage of each vehicle, the last service date of each vehicle, or other types of vehicle information associated with the plurality of delivery vehicles.
In an implementation, the historical delivery dataset 108A corresponds to an information concerning each of the plurality of riders 110, specifically, types of entities to which the plurality of riders 110 have provided services in a past time period. For example, the first rider 110A may have serviced the food-based clients such as Zomato, Swiggy etc., the second rider 110B may have serviced the warehouse entity such as Flipkart, Amazon etc. In an implementation, the historical delivery dataset 108A further includes a number of deliveries performed by each rider of the plurality of riders 110 in the delivery network 112, a frequency of deliveries performed by each rider in a predefined period of time, working hours of each rider, and a time taken for completing each of the one or more deliveries by each rider.
In an implementation, the behavioural dataset 108B includes a skillset of each rider of the plurality of riders 110, a punctuality information of each rider, a number of deliveries completed within a pre-determined period of time associated with each rider, a set of feedbacks obtained from each type of entities serviced by each rider, a leave trend of each rider, a set of riding or service preferences of each rider, and a plurality of trainings undertaken by each rider. The skillset of each rider of the plurality of riders 110 includes information about skills possessed by each rider from the plurality of riders 110. The skillset describes the individual abilities and proficiencies of each rider. For example, languages known by the rider and more. The punctuality information of each rider refers to information about how consistently each rider adheres to scheduled or expected timeframes. The pre-determined period of time corresponds to an expected period of time calculated by the system 100 for completing each delivery. The behavioural dataset 108B tracks the productivity or efficiency of the riders in terms of completing deliveries. The set of feedback obtained from each type of entities serviced by each rider refers to a collection of feedback received from different entities (such as Flipkart, Dunzo, Zomato etc.) who have been serviced by each rider. It implies that feedback regarding the rider's performance has been recorded. The leave trend of each rider refers to information about the leave patterns or trends of each rider. The leave trend includes data on when and how often each rider takes day off. The set of riding or service preferences of each rider refers to information about the specific preferences or choices of each rider regarding riding or service-related aspects. The plurality of training undertaken by each rider refers to information about multiple trainings that each rider has undergone. Based on the plurality of training undertaken by each rider, the hardware processor 104 tracks the various training programs or courses that the riders have undergone.
In an implementation, the vehicle dataset 108C refers to the collection of information related to the delivery vehicles assigned to the plurality of riders. The vehicle dataset 108C includes a unique vehicle identifier of each vehicle of the plurality of delivery vehicles, a mileage of each vehicle, a last service date of each vehicle, or other types of vehicle information associated with the plurality of delivery vehicles.
The calibration component 118 is a processing unit, which is configured to automatically calibrate the allocation of deliveries to the plurality of riders 110 based on an information received through the hardware processor 104. Examples of the calibration component 118 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.
In operation, the hardware processor 104 is configured to obtain the historical delivery dataset 108A of each rider of the plurality of riders 110 operating the delivery vehicle from the first database 106A. The historical delivery dataset 108A includes a type of entity serviced by each rider of the plurality of riders 110. In an implementation, the type of entities corresponds to a warehouse, a food-based entity, a medicine-based entity, a vehicle-allocation entity, or other type of supplier entity such as, Dunzo, Grofers, Blinkit, Zomato, Flipkart, Amazon, Swiggy, 1mg, and more.
With the help of information provided by the historical delivery dataset 108A, the system 100 may assess performance of each rider. The system 100 may identify the riders who are performing well and the riders who may need additional training or support. The system 100 with the information provided by the historical delivery dataset 108A may identify the riders who are most likely to complete the deliveries on time.
Further, the hardware processor 104 is configured to obtain a behavioural dataset 108B of each rider of the plurality of riders 110 from a second database 106B. The behavioural dataset 108B includes self-cancellation information of each rider pertaining to deliveries, active time periods in a day when each rider is active, and dormant time periods in the day for each rider. The behavioural dataset 108B specifically refers to a dataset that contains information about the behavior of each of the plurality of riders 110. The self-cancellation information refers to an information that contains details about when each rider cancels deliveries on their own, and hence indicates instances where they choose not to complete or accept a delivery. The self-cancellation information is useful in tracking the types of delivery which are most likely to be cancelled by each of the rider. The active time periods in a day are indicative of an information about specific time periods during the day when each rider is actively engaged in accepting and completing the allocated deliveries. The dormant time periods in the day are indicative of an information about specific periods of time during the day when each rider is inactive and not accepting any allocation of the deliveries.
Furthermore, the hardware processor 104 is configured to obtain a vehicle dataset 108C associated with a plurality of delivery vehicles assigned to the plurality of riders from the vehicle database 106C. The system 100 may identify the working condition of each delivery vehicle by analyzing information stored in the vehicle dataset 108C.
Furthermore, the hardware processor 104 is configured to generate an enhanced rider profile based on the historical delivery dataset 108A, the behavioural dataset 108B, and the vehicle dataset 108C. The enhanced rider profile of each rider of the plurality of riders 110 is generated by combining and analyzing the historical delivery dataset 108A, the behavioural dataset 108B, and the vehicle dataset 108C. The enhanced rider profile provides a detailed understanding of each rider. The enhanced rider profile may include information such as the rider's delivery history, behavior patterns (including the self-cancellation information and active/dormant time periods), and vehicle assignment details. It provides a holistic view of the rider's performance, behavior, and working conditions, which are important for decision-making for optimization of the DNO server 102.
In addition, the calibration component 118 is configured to automatically calibrate allocation of deliveries to each of the plurality of riders 110 in correlation with the corresponding enhanced rider profile for the defined geographical area 114A-114C considering at least the type of entity serviced by each rider of the plurality of riders 110, the self-cancellation information of each rider pertaining to deliveries, the active time periods in the day when each rider is active. The automatic calibration of the allocation of deliveries further assists in re-balancing of the delivery network 112 as well as the total allocation number of the plurality of riders 110 for the defined geographical area 114A-114C.
By generating the enhanced rider profile of each rider, the system 100 prepares a comprehensive profile of each rider by considering factors such as the type of entity serviced by each rider, self-cancellation information, and active time periods. Thus, the system 100 intelligently distributes deliveries, ensuring riders are assigned appropriate tasks based on their capabilities and preferences.
The automatic calibration of the allocation of deliveries is performed in correlation with the generated enhanced rider profile that takes various factors into consideration. For example, the riders who are not familiar with a particular area may be more likely to get lost or involved in an accident. Thus, the system 100 may help to increase safety of the delivery network 112 by ensuring that the riders are only allocated the deliveries that do not pose a safety risk. The system 100 may further identify the riders who are most likely to be available to work during certain times of the day. Thus, the automatic calibration of the allocation of deliveries performed by the system 100 improves efficiency of the delivery network 112 by ensuring that the deliveries are allocated to the riders who are most likely to complete the deliveries under predetermined time and in a cost-effective manner. By ensuring that the deliveries are made in a timely manner, the system 100 may help to improve customer satisfaction. The system 100 may reduce costs associated with the delivery network 112 by ensuring that riders are only allocated deliveries that the riders may complete. Further, the automatic calibration of allocation of deliveries reduces a number of deliveries that are cancelled or delayed and hence the system 100 may save costs in a long run.
Advantageously, the hardware processor 104 integrates the historical delivery dataset 108A, the behavioral dataset 108B, and the vehicle dataset 108C to create the enhanced rider profile. This integration allows for a holistic view of each rider, considering their past performance, behavioral characteristics, and the operating vehicles they are assigned to. By combining these datasets, a more complete and accurate profile of each rider is generated. This enriched rider profile serves as a valuable resource for allocation decisions and optimizing the delivery process. The behavioral dataset 108B, including active and dormant time periods, provides valuable information for route planning and scheduling. The enhanced rider profile allows for the consideration of individual rider availability and behavior patterns when assigning tasks and planning routes. It enables efficient routing and scheduling decisions, reducing travel time, optimizing delivery sequences, and enhancing overall productivity. The availability of the enhanced rider profile enables the system 100 to adapt to changing circumstances, such as shifts in rider behavior, vehicle availability, familiarity of a work area, or customer demand. By continuously updating and analyzing the enhanced rider profile based on new data, the system 100 can dynamically adjust task assignments, routing, and scheduling to ensure optimal performance in response to evolving conditions.
FIG. 2 is a block diagram illustrating a delivery network optimising (DNO) server, in accordance with an embodiment of the present disclosure. With reference to FIG. 2, there is shown a block diagram 200 of the DNO server 102 for optimising the delivery network 112. As depicted in FIG. 2, the DNO server 102 includes the hardware processor 104, the calibration component 118, a network interface 202, a primary storage 204, and an image recognition module 206, communicatively coupled to each other.
The network interface 202 refers to a communication interface to enable communication of the DNO server 102 to any other external device, such as a user device belonging to the plurality of rider 110. Examples of the network interface 202 include, but are not limited to, a network interface card, a transceiver, and the like.
The primary storage 204 is configured to store the first database 106A, the second database 106B, and the vehicle database 106C (of FIG. 1). Examples of implementation of the primary storage 204 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 hardware processor 104 is further configured to implement the image recognition module 206 to identify each of the plurality of riders 110. In an implementation, the identification is performed via at least one of a combination of a face and a logo in a dress worn by the rider. In an exemplary embodiment, the face and the logo in the dress worn by the rider is captured by a camera and further compared with the images of the face and the logo stored in the primary storage 204.
The hardware processor 104 is further configured to allocate the deliveries to each of the plurality of riders based on the identification of each of the plurality of riders 110. In an implementation, the hardware processor 104 is configured to allocate the deliveries to each of the plurality of riders 110 based on automatically calibration of allocation of deliveries performed by the calibration component 118 and the identification of each of the plurality of rider 110 performed by the image recognition module 206.
In addition, the hardware processor 104 is further configured to update the first database 106A and the second database 106B corresponding to each of the plurality of riders 110 based on successful completion of the allocated deliveries. In an implementation, the first database 106A and the second database 106B are updated in the DNO server 102 in order to generate an enhanced rider profile accordingly.
FIG. 3 is a diagram of an exemplary scenario of the allocation of work instructions to a plurality of riders based on enhanced rider profile, in accordance with an embodiment of the present disclosure. With reference to FIG. 3, there is shown an exemplary scenario diagram 300 of the system 100 for optimising the delivery network 112 based on the enhanced rider profile 302.
In an implementation, the system 100 receives a delivery instruction from the types of entities through the DNO server 102. The DNO server 102 and the calibration component 118 automatically calibrate 304 the allocation of deliveries to each of the plurality of riders 110 in correlation with the enhanced rider profile 302 for a defined geographical area 114A. After the calibration 304 of the allocation of deliveries, the deliveries are allocated 306 to each rider 110A of the plurality of riders 110. After successful allocation of the deliveries, the rider 110A proceeds to complete delivery 308 within the predefined geographical areas 114A and 114B. Once the delivery has been successfully completed, the first database 106A and the second database 106B are updated 310 based on details of the successful completion of the allocated deliveries.
FIG. 4 is a flowchart of a method for optimising delivery network based on enhanced rider profile, in accordance with an embodiment of the present disclosure. FIG. 4 is explained in conjunction with elements from FIGs. 1, 2, and 3. With reference FIG. 4, there is shown a flowchart of a method 400. The method 400 is executed in the DNO server 102 (shown in FIGs. 1 and 2). The method 400 may include steps 402 to 410.
At step 402, the method 400 includes obtaining the historical delivery dataset 108A of each rider of a plurality of riders 110 operating a delivery vehicle from the first database 106A. The historical delivery dataset 108A comprises a type of entity serviced by each rider of the plurality of riders 110.
At step 404, the method 400 further includes obtaining the behavioural dataset 108B of each rider of the plurality of riders 110 from the second database 106B. The behavioural dataset 108B includes the self-cancellation information pertaining to deliveries, the active time periods in a day when each rider is active, and the dormant time periods in the day for each rider.
At step 406, the method 400 further includes obtaining the vehicle dataset 108C associated with the plurality of delivery vehicles assigned to the plurality of riders 110 from the vehicle database 106C. In an implementation, the method 400 may identify the working condition of each delivery vehicle by analyzing information stored in the vehicle dataset 108C.
At step 408, the method 400 further includes generating the enhanced rider profile 302 based on the historical delivery dataset 108A, the behavioural dataset 108B, and the vehicle dataset 108C. The enhanced rider profile of each rider of the plurality of riders 110 provides a holistic view of the rider's performance, behavior, and working conditions, which are important for decision-making for optimization of the DNO server 102.
At step 410, the method 400 further includes automatically calibrating 304 allocation of deliveries to each of the plurality of riders 110 in correlation with the enhanced rider profile 302 for a defined geographical area 114A-114C taking into account at least the type of entity serviced by each rider of the plurality of riders 110, the self-cancellation information pertaining to deliveries, the active time periods in the day when each rider is active, causing re-balancing of the delivery network 112 as well as a total allocation number of the plurality of riders 110 for the defined geographical area 114A-114C. The automatic calibration of allocation of deliveries performed by the system 100 improves efficiency of the delivery network 112 by ensuring that the deliveries are allocated to the riders who are most likely to complete the deliveries under predetermined time and in a cost-effective manner.
The steps 402 to 412 are only illustrative and other alternatives can also be provided where one or more steps are added, one or more steps are removed, or one or more steps are provided in a different sequence without departing from the scope of the claims herein. Various embodiments and variants disclosed with the aforementioned system (such as the system 100) apply mutatis mutandis to the aforementioned method 400.
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 optimising delivery network (112), the system comprises:
a delivery network optimising (DNO) server (102) comprising a hardware processor (104), wherein the hardware processor (104) is configured to:
obtain a historical delivery dataset (108A) of each rider of a plurality of riders (110) operating a delivery vehicle from a first database (106A), wherein the historical delivery dataset (108A) comprises a type of entity serviced by each rider of the plurality of riders;
obtain a behavioural dataset (108B) of each rider of the plurality of riders (110) from a second database (106B), wherein the behavioural dataset (108B) comprises self-cancellation information of each rider pertaining to deliveries, active time periods in a day when each rider is active, and dormant time periods in the day for each rider;
obtain a vehicle dataset (108C) associated with a plurality of delivery vehicles assigned to the plurality of riders (110) from a vehicle database (106C);
generate an enhanced rider profile (302) based on the historical delivery dataset (108A), the behavioural dataset (108B), and the vehicle dataset (108C); and
a calibration component (118) configured to automatically calibrate (304) allocation of deliveries to each of the plurality of riders (110) in correlation with the enhanced rider profile (302) for a defined geographical area (114A, 114B, 114C) taking into account at least the type of entity serviced by each rider of the plurality of riders (110), the self-cancellation information of each rider pertaining to deliveries, the active time periods in the day when each rider is active, causing re-balancing of the delivery network (112) as well as a total allocation number of the plurality of riders (110) for the defined geographical area (114A, 114B, 114C).

2. The system (100) as claimed in claim 1, wherein the historical delivery dataset (108A) comprises a number of deliveries performed by each rider of the plurality of riders in the delivery network (112), a frequency of deliveries performed by each rider in a predefined period of time, working hours of each rider, and a time taken for completing each of the one or more deliveries by each rider.

3. The system (100) as claimed in claim 1, wherein the first database (106A) is configured to store date and time of each delivery, location of each delivery, a type of delivery package belonging to each of the type of entities, a name of each rider who performed the delivery, or other type of rider information.

4. The system (100) as claimed in claim 1, wherein the type of entities corresponds to a warehouse, a food-based entity, a medicine-based entity, a vehicle-allocation entity, or other type of supplier entity.

5. The system (100) as claimed in claim 1, wherein the behavioural dataset (108B) comprises a skill set of each rider of the plurality of riders, a punctuality information of each rider, a number of deliveries completed within a pre-determined period of time associated with each rider, a set of feedbacks obtained from each type of entities serviced by each rider, a leave trend of each rider, a set of riding or service preferences of each rider, and a plurality of trainings undertaken by each rider.

6. The system (100) as claimed in claim 1, wherein the second database (106B) is configured to store a skill set of each rider, a login and a logout time of each rider, delivery completion information foreach delivery completed within a pre-determined period of time, self-cancellation information pertaining to each delivery self-cancelled by each rider, feedback information obtained from each type of entities serviced by each rider, leave information comprising leaves taken by each rider, a set of riding or service preferences of each rider, a plurality of trainings undertaken by each rider, or other type of behavioural information.

7. The system (100) as claimed in claim 6, wherein the pre-determined period of time corresponds to an expected period of time calculated by the system for completing each delivery (308).

8. The system (100) as claimed in claim 1, wherein the vehicle dataset (108C) comprises a unique vehicle identifier of each vehicle of the plurality of delivery vehicles, a mileage of each vehicle, a last service date of each vehicle, or other types of vehicle information associated with the plurality of delivery vehicles.

9. The system (100) as claimed in claim 1, the hardware processor (104) is configured to:
identify each of the plurality of riders (110) through an image recognition module (206), wherein the identification is performed via at least one of a combination of a face and a logo in a dress worn by the rider;
allocate (306) the deliveries (308) to each of the plurality of riders based on the identification of each of the plurality of riders (110); and
update (310) the historical delivery dataset (108A) and the behavioural dataset (108B) corresponding to each of the plurality of riders (110) based on successful completion of the allocated deliveries (308).

10. A method (400) for optimising delivery network, wherein the method (400) comprising:
in a delivery network optimising (DNO) server (102):
obtaining a historical delivery dataset (108A) of each rider of a plurality of riders (110) operating a delivery vehicle from a first database (106A), wherein the historical delivery dataset (108A) comprises a type of entity serviced by each rider of the plurality of riders;
obtaining a behavioural dataset (108B) of each rider of the plurality of riders (110) from a second database (106B), wherein the behavioural dataset (108B) comprises self-cancellation information of each rider pertaining to deliveries, active time periods in a day when each rider is active, and dormant time periods in the day for each rider;
obtaining a vehicle dataset (108C) associated with a plurality of delivery vehicles assigned to the plurality of riders (110) from a vehicle database (106C);
generating an enhanced rider profile (302) based on the historical delivery dataset (108A), the behavioural dataset (108B), and the vehicle dataset (108C); and
automatically calibrating (304) allocation of deliveries to each of the plurality of riders (110) in correlation with the enhanced rider profile (302) for a defined geographical area (114A, 114B, 114C) taking into account at least the type of entity serviced by each rider of the plurality of riders (110), the self-cancellation information of each rider pertaining to deliveries, the active time periods in the day when each rider is active, causing re-balancing of the delivery network (112) as well as a total allocation number of the plurality of riders (110) for the defined geographical area (114A, 114B, 114C).