JPH01231200A - Vehicle allotment planning method - Google Patents

Abstract

PURPOSE:To easily cope with the change of a restruction condition,and also, to secure the applicability to various vehicle objects by describing the restriction condition related to loading of freight, and the know-how related to allotment of vehicles by a knowledge engineering method, in various vehicle systems in which it is necessary to frame the vehicle allotment schedule of plural cars. CONSTITUTION:The object quantity of freight to be delivered is predicted before receiving an order is closed, and vehicles corresponding thereto are arranged. In this case, a restriction condition, etc., for loading together freight to be delivered are converted to a knowledge base so that the number of vehicles becomes correct, by which the number of vehicles which has been calculated from the total capacity or the total weight of the freight is checked. Subsequently, based on the freight to be delivered which has been ordered and the object quantity, the delivery destination and its number of cases, the freight to be delivered by one car is determined by using the know-how of a car allotment schedule stored in the knowledge base. The delivery sequence of the determined delivery destination is solved by various algorithms which have been described and realized by a mathematical planning method such as minimization of the delivery cost. In such a way, it is possible to cope easily with a change of the restriction condition related to the allotment of cars which is apt to change, and also, a car allotment schedule whose calculation processing time is quick can be executed.

Description

[Detailed Description of the Invention] [Field of Industrial Application] The present invention relates to a computer system that solves various delivery planning problems, and in particular, the present invention relates to a computer system that solves various delivery planning problems. This invention relates to a vehicle allocation planning method suitable for cases where such planning is necessary.

[Conventional technology]

Traditionally, delivery planning has been based on Eiji Abo's ``Practical Logistics Systems Using Case Study Method'', Dobunkan.

As described in 1974, pages 97 to 112, this is a solution method using a mathematical programming method such as the vSP method.

[Problem that the invention seeks to solve]

The vehicle allocation planning problem is a problem of determining the cargo to be loaded on a vehicle, and includes one combination of constraints, constraints on the types of vehicles that can be used, etc., and these constraints differ for each delivery center.

Furthermore, when the cargo handled changes, the control conditions also change.

For this reason, it has been difficult to respond to changes in constraint conditions using conventional mathematical programming methods that formulate and solve problems.

Further, the conventional delivery route determination method is effective when the cargo to be delivered is delivered by one vehicle.

However, in direct sales deliveries by wholesalers and retailers, deliveries by department stores, etc., the delivery source and delivery volume change daily. Furthermore, since the amount to be delivered is so large, it cannot be delivered by one vehicle.

Therefore, delivery routes for multiple vehicles must be determined at the same time. In order to determine the delivery route, the entire delivery area must be divided into areas that can be delivered by - vehicles (hereinafter referred to as delivery areas). This entire delivery area is defined as
town.

One possible method is to divide it by village or ward, etc.
If vehicles are allocated to each divided delivery area in a manner that satisfies constraint conditions such as delivery time specification and vehicle type specification, there are problems such as an increase in the number of vehicles, a decrease in the loading rate of each vehicle, and an increase in delivery costs. Ta.

Furthermore, the problem of assigning a driver to each vehicle is a combination of vehicles and drivers. The constraint conditions for driver assignment include conditions that must be observed and conditions that should be observed as much as possible. And there are conditions that I want to protect. Since it is not possible to determine in advance which of the conditions that must be violated in order to obtain a solution, it has been difficult to formulate and solve the problem using mathematical programming techniques. One possible method is to allocate drivers one by one, but this method has the problem of lengthening the planning time and furthermore making the overall planning unbalanced.

SUMMARY OF THE INVENTION An object of the present invention is to provide a vehicle allocation planning method that solves the above-mentioned problems, allows for easy handling of changes in constraint conditions, and ensures applicability to various delivery targets.

Another object of the present invention is to solve the above-mentioned problems, to be able to easily deal with changes in constraint conditions, to have excellent maintainability, and to provide a method for determining delivery routes that requires a lot of computer processing time. It is about providing.

Another object of the present invention is to provide an area division method that solves the above problems and allows vehicle allocation planning to be carried out more efficiently.

Another object of the present invention is to provide a personnel assignment method that solves the above-mentioned problems and allows for a well-balanced work assignment with respect to the number of drivers.

[Means for solving problems]

The above objective is achieved by a vehicle allocation planning method using a knowledge engineering method. That is, in various vehicle dispatch planning systems,
We create a knowledge base of frequently changing planning conditions, cargo or vehicle constraints, vehicle dispatch planning know-how possessed by experts, and create a vehicle dispatch plan based on this knowledge.We also provide delivery route planning and delivery within divided delivery areas. This is achieved by providing a means to describe and realize cost calculations etc. using mathematical programming techniques.

[Effect]

Said means operate as follows.

The system predicts the amount of cargo to be delivered before the order deadline and arranges appropriate vehicles. At this time, in order to ensure that the number of vehicles is appropriate, constraints on the loading of cargo to be delivered, etc. are created as a knowledge base, and based on this, the number of vehicles calculated from the total volume or total weight of the cargo is checked. Constraints and the like are described in the form, for example, if (condition) then (result), and are made into a knowledge base.

Next, based on the ordered cargo to be delivered, the quantity, the destination and the number of deliveries, we use the vehicle dispatch planning know-how stored in Knowledge Pace in the same way as above.

- Determine the cargo to be delivered by the vehicle. The delivery order for the determined delivery destinations is solved using various algorithms described and realized using mathematical programming methods, such as minimizing delivery costs.

Furthermore, when assigning a driver to each vehicle, the repetition of each driver's delivery area, operating time, etc. are taken into consideration.

This makes it possible to easily deal with changes in constraint conditions related to vehicle allocation, which are subject to change, and to create a vehicle allocation plan with a quick calculation processing time.

〔Example〕

An embodiment of the present invention will be described below with reference to FIGS. 1 to 23, taking shipping of products at a distribution center as an example.

FIG. 1 shows the overall configuration of the present invention. 11 is a computer for controlling this system, and the arranged vehicle determining unit 10
1, a vehicle plan determining section 102, a vehicle allocation determining section 103, and a man-machine interface section 104. Reference numeral 12 denotes a display device for displaying and modifying vehicle allocation planning results, and includes input devices such as a keyboard, a mouse, and a trackball. 13 is a knowledge base that stores vehicle allocation planning know-how, constraints on cargo loading, etc.;
This is a file that stores various data and vehicle allocation planning results.

Hereinafter, one embodiment of the vehicle allocation planning method using the knowledge engineering method will be described in detail according to the operational steps of the flowchart shown in FIG. In this embodiment, a case will be considered in which the delivery destination and delivery amount differ from day to day, and, for example, a case is considered in which cargo for which an order is accepted until 3 o'clock is delivered the next day. At this time, vehicle arrangements are
The number of vehicles required for the next day must be arranged before the order acceptance deadline, for example, by 12:00.

In step 200, the system imports the performance data and order receipt data stored in the file 14 into the performance data table shown in the third session and the order receipt table shown in FIG. From this performance data, for example, the moving average method, exponential smoothing method, etc. are used to predict the amount of cargo that will be ordered by the order deadline, taking into account fluctuations in seasons, days of the week, etc. Next, the process proceeds to step 300.

Step 300: Determine the number of vehicles required to deliver the cargo amount predicted in step 200 above. The required number of vehicles is calculated, for example, by dividing the total transport amount by the capacity or weight as shown in the following equation.

v W: Total delivery amount (weight), W: Vehicle limit weight V: Total delivery amount (volume), V2 Vehicle surface limit volume Based on the calculated number of vehicles, based on the know-how regarding vehicle arrangement shown in Figure 5. ,
Determine whether there are vehicle types and number of vehicles that meet the loading conditions and vehicle type specification conditions for pre-ordered cargo. If a problem occurs, the number of vehicles will be increased or decreased to meet the above conditions.

Step 400: Based on the number of vehicles determined in step 300, all the vehicles in possession are arranged, for example, in the order of arrangement order of arrangement light names shown in FIG. The above steps are repeated until the number of vehicles determined in step 300 is reached. This arrangement order is set so that the arrangement light can be added or changed.

Next, the process proceeds to step 500.

Step 500: Here, the following processing is performed on all orders received by the order deadline time to determine the cargo to be loaded on each vehicle. First, in accordance with the know-how shown in FIG. 7, a slip that is considered to be central when planning each vehicle (hereinafter referred to as a core slip) is extracted. The extracted slips are combined with the slips that satisfy the area constraints and stored in the vehicle allocation plan table shown in FIG. This process is repeated until all orders received are integrated. Next, the process proceeds to step 600.

The details of the delivery route determination method and the delivery area determination method will be described later.

Step 600: Consider the loaded cargo and delivery area of each vehicle determined in step 204 above, and further.

Assign drivers to vehicles, taking into account periodic vehicle inspections, driver rotation, and operating hours. Next step 70
Go to 0. The details of the method of allocating drivers and other personnel will be described later.

Step 7oO: The above vehicle allocation plan is planned using the vehicle allocation planning know-how of the person in charge and mathematical programming methods.
It is difficult to convert all know-how into a knowledge base.

In addition, as mentioned above, constraints include constraints that must be observed and constraints that should be observed if possible, and these constraints also change depending on the planning results, so it is not possible to incorporate all of the constraints. Can not. Therefore.

It is not always possible to obtain a result that satisfies the person in charge. Therefore, the person in charge will be able to modify the vehicle allocation plan results in an interactive manner.

The details of the method of determining the delivery route in step 500 will be explained below using the delivery of the product at the delivery center as an example.
A detailed explanation will be given according to the operation flow shown in the flowchart shown in the figure.

In step 501, the system imports the order data stored in the file 14 for each slip, and stores the product name 1001, quantity 1002, etc. in the order slip table shown in FIG. Customer name 1o03 and delivery destination 1004 are stored. Next, the process advances to step 502.

In step 502, the system sorts the order receipts in the order receipt table shown in FIG. 10 by area according to the delivery destination 1004. For example, a code is provided for each delivery address, and the items are classified by city, town, or village using this code. Next, the process advances to step 503.

In step 503, the system selects, from among the order receipts classified in step 502, documents that satisfy the conditions described in the rules for extracting core documents shown in FIG. 7(a) stored in the knowledge base 13. All documents are extracted as core documents that are used to determine the delivery range. Next, the process advances to step 504.

In step 504, if there are multiple nuclear slips within the area classified in step 502, the knowledge base 1
The documents that satisfy the conditions described in the rules regarding core document integration shown in FIG. 7(b) stored in 3 are integrated. At the time of integration, it is checked whether the product and vehicle conditions shown in FIGS. 11 and 12 are met. Furthermore, Fig. 7(d)
If the nuclear slips of adjacent areas that satisfy the knowledge of the merging possible areas shown in 2 also satisfy the conditions described in the rules, they will be integrated. Next, the process advances to step 505.

In step 505, the system integrates other slips into the integrated core slip using the following procedure.

(i) Vouchers that are in the same area as the core voucher and that satisfy the conditions described in the rules regarding voucher merging shown in FIG. 7(c) are merged.

(n) Area slip that can be integrated with the nuclear slip, as shown in Figure 7(c)
Integrate the documents that meet the conditions described in the rules shown below.

(Mountain) Integrate remaining slips in areas that can be integrated.

(iv) Furthermore, if there are remaining slips that cannot be integrated, the constraints described in the rules are relaxed and the book is executed. For example, the constraint 7 on the number of deliveries described in FIG. 7(c)
Increase the number of cases.

Next, the process advances to step 506.

Step 506: The system determines the delivery route for the slips integrated in step 505, for example, by integer programming using the OR technique with the objective functions of minimizing delivery cost and minimizing delivery distance and time.

Step 507: The system displays the delivery destination list shown in FIG. 13(a) and the delivery destination list shown in FIG. 13(b) on the display device 12.
) The results will be displayed by displaying the amount of delivery on the map shown in (). Next, the process advances to step 508.

Step 508: The user inputs either correction or termination based on the result on the display device 12.

Step 509: The user interactively corrects the results on the display device 12 using an input device such as a mouse. Modification of the results is carried out using functions such as exchanging, moving, and deleting slips using, for example, an icon or a mouse.

The delivery area determination method will be described in detail below according to the operational steps of the flowchart in FIG.

Step 511: The delivery area divided by city, town, village, ward, etc. is further divided into a plurality of areas (hereinafter referred to as blocks) and stored in the delivery area table shown in FIG. Next, the process proceeds to step 512.

Step 512: Import the slips one by one from the order slip table shown in FIG. 17, determine which block the slip belongs to from the delivery destination field and the address field of the delivery area table shown in FIG. The delivery amount of the order slip table shown in FIG. 16 is added to the delivery amount of the block to which the slip belongs in the delivery area table shown in FIG. Next, the process advances to step 513.

Step 513: If there are any remaining order slips that have not been processed in step 512, the process returns to step 512.

If all the steps have been performed, the process advances to step 514.

Step 514: Extract blocks in the delivery area table shown in FIG. 16 whose delivery amount is equal to or greater than a threshold value. The blocks marked with O in the delivery amount determination column of the delivery area table shown in FIG. 16 are the extracted blocks. Next, the process advances to step 515.

Step 515: Take out the block with 0 in the delivery amount determination column of the delivery area table shown in FIG. 16, and if there is a block with 0 in the delivery amount determination column among the plurality of adjacent blocks, Combine two blocks. Next, if there are blocks that can be integrated 5 times, repeat this step.

As a result, it is possible to divide the delivery area into consideration of the amount of delivery, which is more effective for vehicle allocation planning than the division of the delivery area by municipality or ward.

Hereinafter, the method of allocating personnel in step 600 will be described in detail. Here, more general assignment of workpieces to devices will be taken as an example.

Hereinafter, a detailed explanation will be given according to the operational steps of the flowchart shown in FIG.

In step 601, the system displays the schedule table shown in FIG. 22 stored in the storage device 14 on the display device N12 as shown in FIG. 23, and proceeds to step 602.

In step 602, the system determines whether schedule creation is complete. If the process has been completed, the system is terminated; if the process has not been completed, the process proceeds to step 603.

The step 603 system extracts partial problems based on the partial problem extraction know-how shown in FIG. 19, which is stored in the knowledge base 13. less than.

An example of how to extract subproblems is shown below.

(1) As shown in FIG. 20, a set of workpieces and manufacturing equipment is extracted during a period when only one workpiece can be assigned to one manufacturing equipment. In FIG. 20, the 16th to 18th are partial questions.

(2) Extract the combination of manufacturing equipment and workpieces that have fewer workpieces to which they can be assigned, and define them as one-part problems.

(3) Extract pairs of workpieces and manufacturing devices to which few manufacturing devices can be assigned, and use them as partial problems.

Step 6o4 The manufacturing equipment searched in step 603,
Evaluate how good it is to allocate the work, and calculate the benefit C1J as shown in Figure 21.
Create a matrix of Here, even if a partial problem is optimized as in the example shown in step 603, it does not lead to overall optimization. If the schedule of the boundary part of the sub-problem is not taken into account, when finding a solution to the boundary part, the already allocated part will have to be redone frequently, making it meaningless to find the optimal solution. Therefore, for this boundary portion 2102 as well, a matrix of benefit CIJ is created in the same manner as for the partial problem 2101 above.

The benefit C1J taking this boundary portion into consideration is calculated as follows. Rating PTC is the evaluation result for evaluation items,
The weight Wh is the degree of importance of an evaluation item.

CLI" basic point + Σ (rating PkX weight WX) Examples of evaluation items used here are shown below.

(1) Averaging the operating time of the equipment (2) Repetition of the same workpiece 3) Ability to allocate workpieces to determine how many of the workpieces that could have been allocated to the tree can no longer be allocated (4) Workpieces (5) Securing days when equipment can be maintained Step 605: Using this matrix as an assignment problem in mathematical planning, find the combination that maximizes the sum of the values of each element of the matrix as follows. Formulate and solve.

O objective function: ΣΣCI J X I J-+M a X
O Determining variable: This is to ensure that In the example of FIG. 21, the O mark is the solution.

Step 606: The schedule table shown in FIG. 22 is updated based on the results obtained in step 605 above, and the process returns to step 601.

In this embodiment, importance is set when calculating benefit CIJ. By making this degree of importance changeable, the priority of evaluation items can be changed, allowing schedules to be created in accordance with the situation.

In this embodiment, schedules can be created using the same method for allocating work to devices or for allocating driving doors to vehicles.

〔Effect of the invention〕

According to the present invention, there is provided a vehicle dispatch planning system that is highly maintainable and can be easily dealt with even when vehicle dispatch planning know-how, cargo loading constraints, etc. change, and that is applicable to various delivery plans and has excellent versatility. It becomes possible. Additionally, it is possible to plan vehicle allocation and determine delivery routes faster than conventional manual methods.

Furthermore, by converting the know-how of experts into a knowledge base, even inexperienced personnel can plan dispatch plans similar to those of experts.

Vehicles can be arranged and delivery areas can be determined according to the amount of cargo to be delivered, optimizing the number of vehicles and improving vehicle loading efficiency.
Transportation costs can be reduced.

[Brief explanation of the drawing]

FIG. 1 is an overall configuration diagram of one embodiment of the present invention, and FIG. 2 is a
Flowchart of the vehicle allocation planning method, Figure 3 is an example of the actual data table, Figure 4 is an example of the order receipt table, and Figure 5 is an example of the order receipt table.
The figure shows an example of constraints related to product loading, Figure 6 shows an example of a destination priority table, Figure 7 shows an example of vehicle dispatch planning know-how stored in the knowledge base, and Figure 8 shows an example of a vehicle dispatch planning table. , Fig. 9 is a flowchart of the delivery route determination method, Fig. 10 is an example of a slip table, Fig. 11 is an example of a product table, Fig. 12 is an example of a vehicle table, and Fig. 13 is an example of the determined delivery route. 14 is a flowchart of the delivery area determination method, Fig. 15 is an example of dividing the delivery area into blocks, Fig. 16 is an example of the delivery area table, and Fig. 17 is an example of the delivery area table.
The figure shows an example of an order receipt table, Figure 18 is a flowchart of the personnel allocation method, Figure 19 is an example of partial problem extraction know-how, Figure 20 is an example of partial problems, and Figure 21 is a benefit matrix ¥ J Nobe Mine 5 door $2 side 3rd field 4th field rod g door calculation t'' 72 3 ta ``a 7th rho side condolence// times) 6 ノ2 D Uya/J side (a> Ko/4th ・5th time) 4 out of tfi $ 77 page 7 Rienka, 2 illustrations 20 times $ 2 no C4 23 pages in total

Claims (1)

[Scope of Claims] 1. In various delivery systems in which a vehicle allocation plan for a plurality of vehicles must be drawn up, vehicle allocation is characterized in that constraints regarding cargo loading and know-how regarding vehicle allocation are described using a knowledge engineering method. How to plan. 2. In various delivery systems where delivery routes for multiple vehicles must be determined, delivery area determination know-how is described using knowledge engineering methods, delivery areas are determined, and calculation processing parts that require processing time are mathematically planned. A vehicle allocation planning method characterized by determining a delivery route within a delivery area by describing it using a method, and utilizing a combination of these determining processes. 3. The delivery planning method of item 2, wherein the process of determining the delivery area includes a process of dividing the cargo according to the amount of cargo to be delivered. 4. The above-mentioned vehicle allocation plan is the vehicle allocation planning method of item 1, which includes driving processing at the time of vehicle allocation planning. 5. The vehicle allocation planning method according to item 4, wherein the personnel allocation process includes a process of dividing the personnel allocation problem into subproblems that can be solved using a mathematical programming method, and solving the subproblems using a mathematical programming method.