CA3166341A1 - Delivery path planning method and system taking order aggregation degree into consideration - Google Patents

Abstract

Disclosed are a delivery path planning method and system taking an order aggregation degree into consideration. The method comprises: extracting an aggregation distance describing an order aggregation degree of each path, and taking the sum of the aggregation distances of all the paths as a total aggregation distance; simultaneously optimizing a total distance and the total aggregation distance to enable same to be the shortest; generating a new feasible path by using an adaptive large-scale neighborhood search algorithm; and updating the path by means of a weighted single-target policy based on a solution performance increment. In the present invention, it can be ensured that all orders on each path are close to each other, and the requirement of secondary delivery can be completed when the total distance, the total time and the total cost are increased as little as possible. By means of the method, the weight does not need to be re-selected according to different environments, and the method is not influenced by different quantities of targets and different target dimensions, and same can overcome the defect of a common means of converting multiple targets into a single target by means of weighting same.

Description

DELIVERY PATH PLANNING METHOD AND SYSTEM TAKING ORDER
AGGREGATION DEGREE INTO CONSIDERATION
BACKGROUND OF THE INVENTION
Technical Field [0001] The present invention relates to planning for delivery routes, and more particularly to an order-aggregation-based delivery routing method and a system thereof.
Description of Related Art

[0002] Express delivery plays a very important role in the whole merchandise logistics process.
After a list of orders to be delivered is confirmed, a delivery dispatcher will assign operational work sheets to suitable carriers and their vehicles. Most companies plan dispatching and vehicle scheduling manually, so the process is time-consuming and tends to have errors, leading to waste of time for waiting and prolonged overall operation in the subsequent picking up and loading procedures.

[0003] At present, some companies have used algorithms to replace human labor for dispatching and scheduling. Most existing approaches to route planning take the shortest total distance, the shortest total time, and the lowest total income costs as their optimization objectives.
However, the actual applications are usually complicated by sudden incidents.
For example, when a customer is not available during the first attempt of delivery of the delivery person, a second attempt of delivery has to be arranged. With such a change, the earlier set route optimized for the foregoing objective may be no more the best, shortest route, and the total distance, the total driving time, and the total costs may increase significantly.

[0004] In addition, the practical issues are usually about multiple objectives, but there has not been a general strategy for turning multi-objective planning into single-objective planning that could ensure that the set weight combination is applicable to various scenarios regardless of different objective dimensions.
SUMMARY OF THE INVENTION

[0005] The objective of the present invention is to provide an order-aggregation-based delivery routing method and a system thereof.

[0006] To achieve the foregoing objective, the present invention implements the following Date Regue/Date Received 2022-06-29 technical schemes. The present invention provides an order-aggregation-based delivery routing method, which comprises:

[0007] acquiring distances from all orders to a warehouse;

[0008] generating an initial route using a best insertion heuristic algorithm;

[0009] generating a new feasible route using an adaptive large neighborhood search algorithm;

[0010] computing fitness value of the new solution to aggregation objective and fitness value of the new solution to total distance objective; and

[0011] simultaneously optimizing a total distance shortest and a total aggregative distance shortest, and update the optimal route.

[0012] Further, the distances from all said orders to the warehouse are bi-directional navigation distances, and the method further comprises acquiring vehicle travelling speeds at different time points and in different road sections, and computing bi-directional a navigation time length from each said orders to the warehouse.

[0013] Further, the step of generating an initial route using a best insertion heuristic algorithm is achieved by:

[0014] during initialization, processing the currently operated route, so as to remove order points that are re-arrangeable;

[0015] processing the orders in an order collection one by one according to a chronological order of arrival of their requests, and traversing all vehicles to identify the vehicle and the route that request a least insertion cost from the order, wherein the insertion cost is obtained by subtracting a route cost for the original route before the order is inserted from a route cost for an optimal route after the order is inserted;

[0016] if there is not any said vehicle having a feasible route, refusing a request to deliver the order; and

[0017] accepting the order and inserting it into the route only when the insertion cost of the optimal route of the order is smaller than a profit made by the order.

[0018] Further, the adaptive large neighborhood search algorithm uses plural removal operators and insertion operators for search, in which the removal operators include a random Date Regue/Date Received 2022-06-29 removal operator, a worst removal operator, and a Shaw removal operator, and the insertion operators include a random insertion operator, a greedy insertion operator, a Regret-2 insertion operator, and a Regret-3 insertion operator; and

[0019] by giving weights to different operators, selecting a set of said removal operator and said insertion operator to be used during a particular iteration from the removal operators and the insertion operator, respectively, in a roulette-like manner.

[0020] Further, the weights are automatically adjusted according to data obtained in the previous iteration during the algorithm process, and the entire search is divided into plural passages so that every 100 iterations form a said passage, and at an end of every said passage, the weights are updated according to scores of the operators; in which at beginning of every said passage, the score of an operator is initialized as 0; when update is performed in every said passage, the used removal operator and the used insertion operator have their scores increased correspondingly; and the selected weights of the removal operator and the insertion operator for the next passage are acquired according to an equation:

[0021] w = w (1 ¨ r) + r

[0022] where, 7rg and Og are the score and a use frequency of the operator g in the present passage, and the parameter r is used to control how fast the weight adjusting strategy provides a validity feedback for the operator.

[0023] Further, the step of computing fitness value of the new solution to aggregation objective comprises:

[0024] Step 5.1. selecting a route in order

[0025] wherein there are N vehicles to be routed in total, in which the ith vehicle delivers Mi orders, and when the ith vehicle is selected, the ith route is selected;

[0026] Step 5.2. figuring out a virtual aggregation center of the route

[0027] which is achieved by: acquiring map coordinates of all orders int the route, in which the Jill order coordinates of the ith vehicle are (xipyij), and its location is expressed as:

[0028] Pij = (xij, yij)

[0029] where, i = 1,2, = = = , N,j = 1,2, = = = , Mi;

Date Regue/Date Received 2022-06-29

[0030] then defining a navigation distance and a Euclidean distance;

[0031] expressing the navigation distance between two said locations Pj and Pk as G(Pj, Pk)

[0032] expressing the Euclidean distance between two said locations Pj and Pk as E(pi, Pk)

[0033] at last, taking an average of all coordinates in the route as the virtual center of the route;

[0034] computing the average coordinate of the all Mi orders of the ill' vehicle

[0035] x, = Ejm. iix,j/M, , y,

[0036] defining (1õ37,) as the virtual center of the route, expressed as

[0037] P, = (;,j)

[0038] Step 5.3. selecting the order that is closest to the virtual aggregation center of the route as an actual aggregation center;

[0039] figuring out the Euclidean distances of all said orders in the route to the virtual center successively, and recording the order t c [1,2, = = = , Mi} having the shortest distance, and expressing its location Pit as:
\ 2

[0040] Pit = minf\[(xij ¨ .11)2 (yij ¨ 37,) = 1,2, = = = ,

[0041] defining Pit as the actual center of the route;

[0042] Step 5.4. taking a sum of the navigation distances from all of the orders in the route to its actual aggregation center as its aggregative distance;

[0043] which comprises figuring out the aggregative distance C, of the route, or a sum of the navigation distances of all of the orders to the actual center order, in which since the actual center order is separated from itself by 0, the average navigation distances of the other M, ¨ 1 orders are considered,

[0044] Ci =E7i iG(Pij,Pit) = Mil(Mi¨ 1)

[0045] Step 5.5. traversing all of the routes

[0046] Step 5.6. figuring out a sum of the aggregative distances of all said routes, or a total aggregative distance, as:

Date Regue/Date Received 2022-06-29

[0047] C = Eliv-i

[0048] Further, computing fitness value of the new solution to total distance objective, as:

[0049] Assuming that driving distance for each route (including the distance for returning to the warehouse distance) is Si, the sum of the distances of all routes is

[0050] S = Eliv¨i S

[0051] Further, a weighted single-objective strategy for solution-based incremental performance simultaneously optimizes two objectives, namely a total distance S shortest and a total aggregative distance C shortest; and

[0052] whether a new solution Tnet, can replace the current best solution Tbõt is determined using an equation:

[0053] AT = toi Snew¨Sbest to2Cnew¨Cbest Sbest Cbest

[0054] where, col and to 2 are weight coefficients of the two objectives; and

[0055] if AT < 0, Tnew replaces Tbõt; or

[0056] if AT > 0, with simulate anneal, Tnew replaces Tbõt by a certain probability.

[0057] Further, when the algorithm reaches a maximum count of iterations or when a number of successive un-updated iterations reaches a predetermined threshold, the algorithm terminates and the system outputs all of the routes with the best solution.

[0058] The present invention further provides an order-aggregation-based delivery routing system, which comprises:

[0059] a distance acquiring module, for acquiring distances from all orders to a warehouse;

[0060] an initial route generating module, for generating an initial route using a best insertion heuristic algorithm;

[0061] a feasible route generating module, for generating a new feasible route using an adaptive large neighborhood search algorithm; and

[0062] an optimal route generating module, for computing fitness value of the new solution to aggregation objective and fitness value of the new solution to total distance objective, Date Regue/Date Received 2022-06-29 simultaneously optimizing a total distance shortest and a total aggregative distance shortest, and generating the optimal route.

[0063] As compared to the prior art, the present invention has the following significant advantages: (1) With consideration of order aggregation, the present invention secures short distances between successive orders arranged in the same route, so as to provide a second attempt of delivery with minimized increases in total distance, total time, and total cost; and (2) The present invention proposes a novel, weighted single-objective strategy based on solution of performance increment, which eliminates the need of reselecting weights according to different environments and is independent of changes in the number of objectives or objective dimensions, thereby addressing the two defects of the conventional approach to turning multi-objective to single-objective with weighting.
BRIEF DESCRIPTION OF THE DRAWINGS

[0064] FIG. 1 is a schematic drawing of a route generated without considering aggregation degree.

[0065] FIG. 2 is a schematic drawing of a route generated with considering aggregation degree.

[0066] FIG. 3 is a flowchart of the complete technical scheme of the present invention.

[0067] FIG. 4 shows comparison of a navigation distance and a Euclidean distance according to the present invention.

[0068] FIG. 5 is a flowchart of updating fitness value of the new solution to aggregation objective according to the present invention.

[0069] FIG. 6 shows a virtual center of routes according to the present invention.

[0070] FIG. 7 shows an actual center of routes according to the present invention.

[0071] FIG. 8 shows all routes before neighborhood update according to the present invention.

[0072] FIG. 9 shows all routes after neighborhood update according to the present invention.

[0073] FIG. 10 is a routing result for a given area in a given city according to the present invention.

[0074] FIG. 11 is a routing result for the given area in the given city from an existing routing system.

Date Regue/Date Received 2022-06-29 DETAILED DESCRIPTION OF THE INVENTION

[0075] At present, some companies have used algorithms to replace human labor for dispatching and scheduling. Most existing approaches to route planning take the shortest total distance, the shortest total time, and the lowest total income costs as their optimization objectives.
However, the actual applications are usually complicated by sudden incidents.
For example, when a customer is not available during the first attempt of delivery of the delivery person, a second attempt of delivery has to be arranged. With such a change, the earlier set shortest route optimized for the foregoing objective may be no more the -optimal", route, and the total distance, the total driving time, and the total costs may increase significantly. As shown in FIG. 1, there are one warehouse (a rectangle), 12 orders (rounds), and 2 routes. One of the routed is expressed as Pii-P16, with the delivery sequence of P11¨>P16. If Pii requires a second visit, a long return has to be made.

[0076] The problem can be avoided if all orders in the route are relatively close. As shown in FIG. 2, which is similar to FIG. 1 except for route planning, there are 2 routes having good order aggregation, so that if any order needs a second attempt of delivery, only a short return is needed.

[0077] Additionally, a conventional approach to generating single-objective weighting from multi-objective weighting is directly summing up the weights of all objectives.
Nevertheless, the conventional approach can cause a huge risk. That is, once the application scenario changes, the set weights have to be re-selected.

[0078] In view of the forgoing issues, the present invention provides a novel, weighted single-objective strategy for solution-based incremental performance. The strategy eliminates the need of re-selecting weight for changes in the application environment, and is independent of changes in the number of objectives or objective dimensions.
The strategy comprises: Step 1. proposing a concept of an aggregative distance that describes order aggregation in every route, and taking the sum of the aggregative distances of all routes as the total aggregative distance; Step 2. simultaneously optimizing a total distance shortest and a total aggregative distance shortest; Step 3. generating a new feasible route using an adaptive large neighborhood search algorithm; Step 4. Proposing a novel, weighted single-objective strategy for solution-based incremental performance that is used to update the routes; and Step 5. having the logistic business end perform delivery Date Regue/Date Received 2022-06-29 according to the route recommended by the system.

[0079] The steps of the order-aggregation-based delivery routing method of the present invention will be detailed below.

[0080] As shown in FIG. 3, the delivery routing method comprises the following steps.

[0081] Step 1: System receiving order information

[0082] After a list of orders to be delivered is confirmed, information of the orders, available vehicles, and the warehouse is entered into the system. The order information includes latitudes and longitudes, volume, mass, required delivery time of the order.
The available vehicle information includes the working time limits, the maximum load limits, and the maximum capacity limits of the vehicles. The warehouse information includes the latitude and the longitude of the warehouse.

[0083] Step 2: Acquiring navigation distances between all orders and warehouse

[0084] A special software is used to acquire bi-directional navigation distances from all orders to the warehouse. With a delivery speed prediction system built empirically, vehicle speeds at different time points in different road sections are acquired.
According to this, the bi-directional navigation time of all orders and the warehouse can be figured out.

[0085] In real-world environments, there are factors that cause the navigation distance between two points to be far greater than the Euclidean distance, such as rivers and railways, as shown in FIG. 4. Therefore, in the present invention, the distances from all said orders to the warehouse are all navigation distances.

[0086] Step 3: Generating initial route using best insertion heuristic algorithm

[0087] The best insertion heuristic (BIS) algorithm is a common heuristic algorithm, and is fast and simple. The method processes all orders one by one in a chronological order, and considers all feasible routes so as to identify the route requires the least insertion cost for insertion of orders.

[0088] During initialization, the currently operated route is processed, so as to remove order points that are re-arrangeable. Afterward, the orders in an order collection are processed one by one according to a chronological order of arrival of their requests, and all vehicles are traversed so as to identify the vehicle and the route that request least insertion cost Date Regue/Date Received 2022-06-29 from the order. The insertion cost is obtained by subtracting a route cost for the original route before the order is inserted from a route cost for a best route after the order is inserted (satisfying all limits). If there is not any said vehicle having a feasible route, the request to deliver the order is refused. At last, in the present invention, the order is accepted and inserted it into the route only when the insertion cost of the best route of the order is smaller than a profit made by the order.

[0089] Step 4: Generating new feasible route using adaptive large neighborhood search algorithm

[0090] The adaptive large neighborhood search (ALNS) algorithm derives from the large neighborhood search (LNS) algorithm. According to LNS, during every iteration, some orders are first removed from the present solution, and then reinserted to generating a new solution. This process is repeated until a certain terminating condition is satisfied.
The ALNS algorithm uses various operators for removal and insertion during search. The use propagability of these operators correspond to their historical performances. By contrast, LNS only uses one removal operator and one insertion operator.

[0091] The present invention uses three removal operators, including the random removal operator, the worst removal operator, the Shaw removal operator, and four insertion operators, including the random insertion operator, the greedy insertion operator, the Regret-2insertion operator, and the Regret-3inserti on operator, to obtain quality, feasible solutions.

[0092] To select a combination of a removal operator and an insertion operator for a given iteration, the present invention assigns weights to the operators, and then makes selection from four removal operators and four insertion operators in a roulette-like manner.

[0093] The weights are automatically adjusted according to data obtained in the previous iteration during computation based on the algorithm. This is achieved by tracking the scores of every operator across iterations. The scores indicate the performance of the operator, and a better performance associates with a higher score.

[0094] The entire search is divided into plural passages so that every 100 iterations form a said passage, and at an end of every said passage, the weights are updated according to scores of the operators; in which at beginning of every said passage, the score of an initialization operator is 0; when update is performed in every said passage, the used removal operator Date Regue/Date Received 2022-06-29 and the used infix operator have their scores increased correspondingly; and the selected weights of the removal operator and the infix operator for the next passage are acquired according to an equation:

[0095] wg = wg (1 ¨ r) + r ¨Thefl

[0096] where, n-fl and 19g are the score and a use frequency of the operator g in the present passage, and the parameter r is used to control how fast the weight adjusting strategy provides a validity feedback for the operator.

[0097] Step 5: Computing fitness value of the new solution to aggregation objective

[0098] FIG. 5 is the flowchart of updating the fitness value to aggregation objective, comprising the following steps:

[0099] Step 5.1 Selecting route in order

[0100] there are N vehicles to be routed in total, in which the ith vehicle delivers Mi orders, and when the ith vehicle is selected, the ith route is selected.

[0101] Step 5.2 Figuring out virtual aggregation center of routes

[0102] First, acquiring map coordinates of all orders int the route. The fh order coordinates of the ill' vehicle are (xi j, yij), whose location is expressed as:

[0103] Pij = (xij, yij)

[0104] where, i = 1,2, = = = , N,j = 1,2, = = = , Mi.

[0105] Then defining a navigation distance and a Euclidean distance.

[0106] The navigation distance between two locations Pj and Pk is qpi, Pk) and

[0107] the Euclidean distance between two locations Pj and Pk is expressed as E(pi,Pk), so

[0108] E(, Pk) = ¨ xk) + (yi ¨ yk)

[0109] At last, taking an average of all coordinates in the route as the virtual center of the route.

[0110] The average coordinates of the all Mi orders of the ith vehicle are Date Regue/Date Received 2022-06-29 [01 1 11 Xi = i Xi j/Mi y, [0112] (.1õ yi) is taken as the virtual center of the routes, as shown in FIG.
6, and is expressed as [0113] P, = (K,y,) [0114] Step 5.3. Selecting order closest to virtual aggregation center of route as actual aggregation center [0115] figuring out the Euclidean distances of all said orders in the route to the virtual center successively, and recording the order t c [1,2, = = = , Mi} having the shortest distance, and expressing its location Pit as:
[0116] Pit = minf\[(xij ¨ 2+ (yij ¨ 37,)2J = 1,2, === , Mil [0117] Pit is named as the actual center of the routes, as shown in FIG. 7.
[0118] Step 5.4. taking sum of navigation distances from all orders in route to its actual aggregation center as its aggregative distance [0119] This comprises figuring out the aggregative distance Ci of the route, or a sum of the navigation distances of all of the orders to the actual center order, in which since the actual center order is separated from itself by 0, the average navigation distances of the other Mi ¨ 1 orders are considered, [0120] Ci =ElPiG(Pij,Pit)= Mil(Mi¨ 1) [0121] Step 5.5. Traversing all route [0122] Step 5.6. Computing sum of aggregative distances of all routes, namely total aggregative distance [0123] The sum of the aggregative distances of all routes is [0124] C =Eliv¨iCi [0125] Step 5.7. Taking total aggregative distance shortest as optimization objective [0126] The sum of the aggregative distances of all routes is taken as the optimization objective Date Regue/Date Received 2022-06-29 for the algorithm, i.e., [0127] minC
[0128] Step 6: Computing fitness value of the new solution to total distance objective [0129] Assuming that the driving distance for each route (including the distance for returning to the warehouse distance) is Si [0130] the sum of the distances of all routes is [0131] S = Eliv¨i S
[0132] The total distance shortest is taken as the optimization objective for the algorithm, i.e., [0133] minS
[0134] Step 7: Computing algorithm optimization objective [0135] The present invention has to optimize two objectives simultaneously, namely the total distance S shortest and the total aggregative distance C shortest.
[0136] Generally, to generate single-objective weighting from the multi-objective weighting, the corresponding weight is added before each objective directly, such as toiS +
to2C.
Nevertheless, the conventional approach can cause a huge risk. That is, once the application scenario changes, the set weights have to be re-selected.
[0137] Hence, the present invention provides a novel, weighted single-objective strategy for solution-based incremental performance.
[0138] Whether a new solution Tnet, can replace the current best solution Tbõt is determined using an equation:
[0139] AT = toi Snew¨S best to2Cnew¨Cbest Sbest Cbest [0140] where, col and to2 are weight coefficients of the two objectives, summing up to 1;
S best, C best are the sum of all routes of the present best solution and the sum of the aggregative distances of all routes, and Snew , Cnew are the sum of distances of all routes of the new solution, and the sum of the aggregative distances of all routes.
[0141] The strategy eliminates the need of reselecting weights according to different environments and is independent of changes in the number of objectives or objective Date Regue/Date Received 2022-06-29 dimensions.
[0142] Step 8: Optimal route updating [0143] if AT < 0, Tõ,,, replaces Tbõt; or [0144] if AT > 0, with simulate anneal, Tnet, replaces Tbõt by a certain probability.
[0145] Taking the total aggregative distance for example, the process of updating the solution is shown in FIG. 8 and FIG. 9. The updated routes are more reasonable.
[0146] Step 9: System outputting all routes when terminating condition is satisfied [0147] when the algorithm reaches a maximum count of iterations or when a number of successive un-updated iterations reaches a predetermined threshold, the algorithm terminates and the system outputs all of the routes with the best solution.
[0148] Step 10: Logistic business end performing delivery following system-recommended route [0149] Based on the foregoing order-aggregation-based delivery routing method, the present invention further provides a delivery routing system, comprises:
[0150] a distance acquiring module, for acquiring distances from all orders to a warehouse;
[0151] an initial route generating module, for generating an initial route using a best insertion heuristic algorithm;
[0152] a feasible route generating module, for generating a new feasible route using an adaptive large neighborhood search algorithm;
[0153] an optimal route generating module, for computing fitness value of the new solution to aggregation objective and fitness value of the new solution to total distance objective, simultaneously optimizing a total distance shortest and a total aggregative distance shortest, and generating an optimal route.
[0154] The present invention innovatively uses aggregation as a routing objective and defines corresponding objective functions, thereby solving the problem of single-objective and multi-objective vehicle routing with consideration of aggregation, and forming the method and the system to support delivery. The present invention innovatively provides a novel, weighted single-objective strategy for solution-based incremental performance to form an all-purpose method for turning multi-objective into single-objective Date Regue/Date Received 2022-06-29 optimization.
[0155] The following description is made with reference to an embodiment and the accompanying drawings to further explain the present invention.
[0156] Embodiment [0157] The example is staged in a given area in a given city for delivering major appliances. The system receives the vehicle information. There are 7 vehicles with different size and loading capacities. The system also receives the order information. There are 110 orders, and some addresses correspond to multiple orders.
[0158] For facilitating comparison, the same orders are submitted to the routing system of the present invention and a conventional routing system for route planning. The arrangements of the two system are shown in FIG. 10 and FIG. 11, respectively. Therein, every point in the route represents a latitude-longitude location. Since some latitude-longitude locations correspond to multiple orders, the number of points shown is smaller than 110.
[0159] The objective fitness values for the algorithms are computed. As to the total distance, it is 388.8km with the present invention, representing a 4 % improvement as compared to 405.2km resulted from the conventional system. As to the total aggregative distance, it is 305km with the present invention, representing a 34% improvement as compared to 461.8km resulted from the conventional system. This demonstrates that the present invention system can shorten the total distance to some extent and can shorten the total aggregative distance significantly.
[0160] People of ordinary skill in the art would appreciate that some or all steps of the method as described in the precious embodiments may be realized by using a computer program to direct relevant hardware. The computer program may be stored in a nonvolatile computer-readable storage medium, and when executed may implement the flows as described in the embodiments of the disclosed method. Therein, the memory, storage, database, or any of the other medium as referred in the embodiments of the present invention may include nonvolatile and/or volatile memories. The nonvolatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM) or a flash memory. The volatile memory may be a random-access memory (RAM) or an external cache memory. As examples but not limitations, the RAM may be available in Date Regue/Date Received 2022-06-29 various forms, such as a static RAM (SRAM), a dynamic RAM (DRAM), a synchronous DRAM (SDRAM), a double data rate SDRAM (DDRSDRAM), an enhanced SDRAM
(ESDRAM), a synchronous link (Synchlink) DRAM (SLDRAM), a memory bus (Rambus) direct RAM (RDRAM), a direct memory rambus dynamic RAM (DRDRAM), a memory rambus dynamic RAM (RDRAM), or the like.
[0161] The technical features in the foregoing embodiments may be combined in any manner.
While not all these possible combinations are described herein for succinctness, any combination not causing conflicts among these technical features shall be included in the scope of the present invention.
[0162] The present invention has been described with reference to the preferred embodiments and it is understood that the embodiments are not intended to limit the scope of the present invention. Moreover, as the contents disclosed herein should be readily understood and can be implemented by a person skilled in the art, all equivalent changes or modifications which do not depart from the concept of the present invention should be encompassed by the appended claims. Hence, the scope of the present invention shall only be defined by the appended claims.
Date Regue/Date Received 2022-06-29

Claims (10)

What is claimed is:

1. An order-aggregation-based delivery routing method, comprising:
acquiring distances from all orders to a warehouse;
generating an initial route using a best insertion heuristic algorithm;
generating a new feasible route using an adaptive large neighborhood search algorithm;
computing fitness value of the new solution to aggregation objective and fitness value of the new solution to total distance objective; and simultaneously optimizing a total distance shortest and a total aggregative distance shortest, and updating the optimal route.

2. The order-aggregation-based delivery routing method of claim 1, wherein the distances from all said orders to the warehouse are bi-directional navigation distances, and the method comprises acquiring vehicle travelling speeds at different time points and in different road sections, and computing bi-directional a navigation time length from each said orders to the warehouse.

3. The order-aggregation-based delivery routing method of claim 1, wherein the step of generating an initial route using a best insertion heuristic algorithm is achieved by:
during initialization, processing the currently operated route, so as to remove order points that are re-arrangeable;
processing the orders in an order collection one by one according to a chronological order of arrival of their requests, and traversing all vehicles to identify the vehicle and the route that request a least insertion cost from the order, wherein the insertion cost is obtained by subtracting a route cost for the original route before the order is inserted from a route cost for an optimal route after the order is inserted;

if there is not any said vehicle having a feasible route, refusing the request to deliver the order;
and only when the insertion cost of the optimal route of the order is smaller than a profit made by the order, accepting the order and inserting it into the route.

4. The order-aggregation-based delivery routing method of claim 1, wherein the adaptive large neighborhood search algorithm uses plural removal operators and insertion operators for search, in which the removal operators include a random removal operator, a worst removal operator, and a Shaw removal operator, and the insertion operators include a random insertion operator, a greedy insertion operator, a Regret-2 insertion operator, and a Regret-3 insertion operator; and by giving weights to different operators, selecting a set of said removal operator and said insertion operator to be used during a particular iteration from the removal operators and the insertion operator, respectively, in a roulette-like manner.

5. The order-aggregation-based delivery routing method of claim 4, wherein the weights are automatically adjusted according to data obtained in the previous iteration during the algorithm process, and the entire search is divided into plural passages, every 100 iterations form a said passage, and at an end of every said passage, the weights are updated according to scores of the operators; in which at beginning of every said passage, the score of an operator is initialized to be 0; when update is performed in every said passage, the used removal operator and the used insertion operator have their scores increased correspondingly; and the selected weights of the removal operator and the insertion operator for the next passage are acquired according to an equation:
where, n-fl and 19fl are the score and a use frequency of the operator g in the present passage, and the parameter r is used to control how fast the weight adjusting strategy provides a validity feedback for the operator.

Date Regue/Date Received 2022-06-29

6. The order-aggregation-based delivery routing method of claim 1, wherein the step of computing fitness value of the new solution to aggregation objective comprises:
Step 5.1. selecting a route in order wherein there are N vehicles to be routed in total, in which the i th vehicle delivers M i orders, and assuming the i th vehicle is selected, i.e., the i th route is selected;
Step 5.2. figuring out a virtual aggregation center of the route firstly, acquiring map coordinates of all orders int the route, in which the j th order coordinates of the i th vehicle are (xij, yij), and its location is expressed as:
where, i= 1,2, .cndot..cndot..cndot., N,j = 1,2, .cndot..cndot..cndot. , Mi;
then defining a navigation distance and a Euclidean distance;
expressing the navigation distance between two said locations P j and P k as G(P j,P k) expressing the Euclidean distance between two said locations P j and P k as E(P j,P k) at last, taking an average of all coordinates in the route as the virtual center of the route;
computing the average coordinate of the all M i orders of the i th vehicle defining (~l,~l) as the virtual center of the route, expressed as Step 5.3. selecting the order that is closest to the virtual aggregation center of the route as an actual aggregation center;
figuring out the Euclidean distances of all said orders in the route to the virtual center successively, and recording the order t .epsilon.{[1,2,.cndot..cndot..cndot. , M i) having the shortest distance, and expressing its location P it as:

defining Pit as the actual center of the route;
Step 5.4. taking a sum of the navigation distances from all of the orders in the route to its actual aggregation center as its aggregative distance;
figuring out the aggregative distance Ci of the route, i.e., a sum of the navigation distances of all of the orders to the actual center order, since the actual center order is separated from itself by 0, the average navigation distances of the other Mi ¨ 1 orders are considered, Step 5.5. traversing all of the routes Step 5.6. figuring out a sum of the aggregative distances of all said routes, i.e., a total aggregative distance, as:

7. The order-aggregation-based delivery routing method of claim 1, wherein the step of figuring out fitness value of the new solution to total distance objective comprises:
defining a driving distance of every said route as Si, so that a sum of the distances of all of the routes is

8. The order-aggregation-based delivery routing method of claim 1, wherein based on a weighted single-objective strategy for solution performance incremental, simultaneously optimizing two objectives, namely a total distance S shortest and a total aggregative distance C shortest; and Date Regue/Date Received 2022-06-29 whether a new solution Tnew can replace the current best solution Tbõt is determined using an equation:
where, col and to2 are weight coefficients of the two objectives; and if AT < 0, Tnew replaces Tbõt;
if AT > 0 , in combination with simulated annealing algorithm, Tnew replaces Tbõt by a certain probability.

9. The order-aggregation-based delivery routing method of claim 8, wherein when the algorithm reaches a maximum count of iterations or when a number of successive un-updated iterations reaches a predetermined threshold, the algorithm terminates and the system outputs all of the routes of the optimal solution.

10. A system using the order-aggregation-based delivery routing method of any of claims 1-9, comprising:
a distance acquiring module, for acquiring distances from all orders to a warehouse;
an initial route generating module, for generating an initial route using a best insertion heuristic algorithm;
a feasible route generating module, for generating a new feasible route using an adaptive large neighborhood search algorithm; and an optimal route generating module, for computing fitness value of the new solution to aggregation objective and fitness value of the new solution to total distance objective, simultaneously optimizing a total distance shortest and a total aggregative distance shortest, and generating the optimal route.
Date Regue/Date Received 2022-06-29