Resource configuration system and resource configuration method

Abstract

To provide a resource arrangement system and a resource arrangement method for providing the optimization result of a resource arrangement plan to which the difficulty of execution is added.SOLUTION: The resource arrangement system includes an arrangement plan receiving unit that receives a basic arrangement plan in which resources are arranged for each work time period of a plurality of work processes in a main storage device, an arrangement plan creating unit that creates a plurality of modified arrangement plans in which the number of resources to be arranged is modified for each work time period of the basic arrangement plan based on the received basic arrangement plan by using a multi-objective optimization algorithm, a simulation unit that obtains a work result from the modified arrangement plan, an availability evaluation unit that obtains a difficulty level of changing resource arrangement in the modified arrangement plan created by the arrangement plan creating unit from the basic arrangement plan, and an output unit that outputs a predetermined number of arrangement plans in which at least one of the work result obtained by the simulation unit and the difficulty level obtained by the availability evaluation unit is excellent in association with the availability.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a resource allocation system and a resource allocation method. [Background technology]

[0002] The logistics industry is expected to face a worsening labor shortage problem, and efforts are being made to utilize fewer personnel and reduce costs by optimizing the allocation of personnel at logistics centers.One initiative is to develop optimal personnel allocation plans using a multi-objective optimization algorithm that utilizes a simulator.

[0003] Personnel allocation plans optimized using simulators to maximize work outcomes have a high degree of freedom in terms of factors such as the frequency of reassignments, which can sometimes place a heavy burden on the field and make them unfeasible in actual work.

[0004] Patent Document 1 discloses a personnel allocation planning device in which an input receiving unit receives simulation conditions, and a simulation executing unit executes multi-objective optimization using a line simulator based on the simulation conditions, thereby obtaining multiple personnel allocation plans that optimize at least one of optimization indices (optimization items) including the number of workers and the number of processing orders.

[0005] In this personnel allocation planning device, the simulation execution unit divides the working hours of one day into multiple parts and performs multi-objective optimization for each division unit, thereby reducing the processing load when formulating multiple appropriate worker allocation plans for each process carried out within a logistics center. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] WO2019 / 064379 publication Summary of the Invention [Problem to be solved by the invention]

[0007] Patent Document 1 discloses that a simulation execution unit executes a process for optimizing personnel allocation using a line simulator based on simulation conditions.

[0008] The simulation execution unit first divides the work for one day into predetermined time periods (30 minutes), and searches for the optimal allocation of workers for each division unit (30 minutes) using a line simulator.

[0009] It is disclosed that the total number of workers and the number of orders to be processed are used as optimization indices, and a personnel allocation plan (how many people to allocate to each process) is created that optimizes the number of orders and number of workers that can be processed between 10:00 and 10:30, for example, and the results of multi-objective optimization (Pareto solution) are obtained.

[0010] However, no information is provided that indicates whether it is possible to change the current staffing plan to the Pareto solution, or how difficult it would be to implement the staffing plan. [Means for solving the problem]

[0011] The above-mentioned object can be achieved by a resource allocation system that allocates resources to work time slots of a plurality of work processes, the system comprising: a placement plan receiving unit that receives a basic placement plan for allocating resources for each work time slot of a plurality of work processes; a placement plan creation unit that creates a plurality of revised placement plans by using a multi-objective optimization algorithm based on the received basic placement plan to modify the number of resources to be allocated for each work time slot of the basic placement plan; a simulation unit that determines the work performance from the revised placement plan; an availability evaluation unit that determines the difficulty of changing the resource allocation in the revised placement plan created by the placement plan creation unit based on the basic placement plan; and an output unit that outputs a predetermined number of placement plans that are excellent in at least one of the work performance determined by the simulation unit and the difficulty determined by the availability evaluation unit, in association with availability; [Effects of the Invention]

[0012] According to the present invention, it is possible to provide an optimization result that takes into account the degree of difficulty of executing a resource allocation plan. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is a diagram illustrating an example of a configuration of a resource allocation system according to an embodiment of the present invention. [Figure 2] An example of a current generation staffing plan used in a genetic algorithm. [Figure 3] An example of a candidate revised staffing plan for use with a genetic algorithm. [Figure 4] A graph explaining the Pareto optimal solution using an example of work performance and slack time. [Figure 5] 10 shows an example of a staffing plan according to an embodiment of the present invention. [Figure 6] 10 is an example of a revised staffing plan according to an embodiment of the present invention. [Figure 7] 10 is an example of the amount of movement (availability A) per process in an embodiment of the present invention. [Figure 8]10 is an example of the amount of movement (availability A) for each work time period in an embodiment of the present invention. [Figure 9] 10 is an example of the number of instructions (availability C) for executing a modified allocation plan in an embodiment of the present invention. [Figure 10] 3 is a diagram illustrating an example of a software configuration according to an embodiment of the present invention. [Figure 11] 10 shows a revised headcount-based allocation plan in an embodiment of the present invention. [Figure 12] 10 shows a modified deployment plan based on capacity (availability D) in an embodiment of the present invention. [Figure 13] 10 is an example of a capability table according to an embodiment of the present invention. [Figure 14] 10 shows an example of constraint information (availability B) in an embodiment of the present invention. [Figure 15] 10 is an example of an index value table according to an embodiment of the present invention. [Figure 16] 1 is an example of a flowchart illustrating processing of a resource allocation system according to an embodiment of the present invention. [Figure 17] 10 is an example of a flowchart of an availability evaluation process according to an embodiment of the present invention. [Figure 18] 10 is an example of an availability display screen for each plan in the embodiment of the present invention. [Figure 19] 10 is an example of a personnel allocation screen for each plan in an embodiment of the present invention. [Figure 20] 10 is an example of a screen for allocating personnel to processes in an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0014] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In each drawing for explaining the embodiments, the same components are given the same names and reference numerals as much as possible, and repeated description thereof will be omitted.

[0015] The present invention is not limited to the following examples, and includes various modifications and equivalent configurations within the spirit and scope of the appended claims. For example, the examples have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations.

[0016] Furthermore, the processing units and processing modules described in the embodiments may be realized in part or in whole in hardware, for example by designing them as integrated circuits, or may be realized in software by having a processor interpret and execute a program that realizes each function.

[0017] The information explained in the embodiment may be a table, a database (DB), or data stored in the main memory.

[0018] FIG. 1 is a diagram showing an example of the configuration of a resource allocation system according to an embodiment of the present invention.

[0019] The resource allocation system 1 is realized by a computer including a CPU (Central Processing Unit) 2, a main memory device 3, an external memory device 4, and an input / output device 5. In this embodiment, an example of realizing the system by a standalone computer will be described, but it may also be realized by using a cloud system that provides computer resource services.

[0020] The main memory device 3 is realized by semiconductor elements such as a ROM (Read Only Memory) and a RAM (Random Access Memory).

[0021] The main memory device 3 stores, as software modules, a deployment plan creation unit 10 that creates a deployment plan, a simulation unit 11 that simulates the created deployment plan, an availability evaluation unit 12 that evaluates availability, a deployment plan reception unit 13 that receives the basic deployment plan necessary to create the deployment plan, and an output unit 23 that outputs the created deployment plan.

[0022] Although this example describes the improvement of allocation plans, it is also possible to apply this to the improvement of work plans that include allocation plans. If applied to the improvement of work plans, it is expected that not only will the work results be improved by changing the allocation of various resources, but also that the work results will be improved by changing the work order.

[0023] These software modules refer to the information stored in the external storage device 4 and are executed by the CPU 2 .

[0024] The external storage device 4 is realized by a hard disk drive (HDD), a solid state drive (SSD), or the like.

[0025] The external storage device 4 is provided with a resource placement plan DB 14 that stores a basic placement plan 15 and a revised placement plan 16. It also has an index value table 19 that stores the indexes obtained as a result of simulating the placement plan, constraint condition information 20 that stores the maximum values ​​of available resources, a capacity table 21 that stores the resource capacities, and optimization parameters 22 that stores parameters such as the number of generations of placement plans to be created, the crossover rate, the mutation rate, etc.

[0026] The input / output device 5 includes a NIC (Network Interface Card) for inputting and outputting data to and from the network, a keyboard, a mouse, a display, and the like through which the user inputs commands.

[0027] Figure 2 shows an example of the current generation of personnel allocation plan used in the genetic algorithm.

[0028] The genetic algorithm is a type of multi-objective optimization algorithm, and other algorithms may also be used.

[0029] The top table shows the base staffing plan, which is a selection of existing staffing plans with high performance. Performance is selected by the user based on the work results, such as revenue, slack time, and the balance between revenue and slack time.

[0030] Default staffing plans are prepared in advance, and a staffing plan may be selected from these. The base staffing plan may be an actually implemented staffing plan or a staffing plan simulated by a simulator. The selected base staffing plan is set as the current generation.

[0031] In this example, the task has processes A to E, and personnel are assigned in 30-minute increments from 9:30 to 12:00.

[0032] To find an improved staffing plan using a genetic algorithm, we use the base staffing plan and the random staffing plan shown below. In this example, the area enclosed by the dotted line in the random staffing plan has been changed.

[0033] The rate of change may be changed by accepting a specification of the crossover rate and mutation rate.

[0034] The allocation plan will be explained using personnel allocation as an example, but it can be applied to the allocation of various resources, not just personnel, such as vehicles, machinery, and equipment.

[0035] Figure 3 shows an example of a candidate revised personnel allocation plan used in the genetic algorithm.

[0036] The parts surrounded by dotted lines in Figure 2, which are obtained by crossover and mutation from the base personnel allocation plan and the random personnel allocation plan, are the changes.

[0037] These revised staffing plan candidates are simulated using a simulator, and the revised staffing plan candidate that achieves high performance is designated as the current generation.

[0038] By repeating this process, it is possible to obtain a personnel allocation plan with better performance.

[0039] Figure 4 is a graph illustrating the Pareto optimal solution using work performance and slack time as an example.

[0040] In tasks where there is a contradictory relationship between increasing work output and decreasing slack time, and increasing slack time decreases work output, it is necessary to find a solution that results in a reasonably good value.

[0041] When finding a solution that balances work results such as revenue and slack time, it is difficult to find a personnel allocation plan that provides the highest performance for all indicators.

[0042] For this reason, we simulate the obtained personnel allocation plan as shown in Figure 4 and create a graph with work results and spare time as the axes.

[0043] The Pareto optimal solution is not a solution like 32, which is close to the origin, but a solution 33, which is far from the origin surrounded by a line. By selecting a personnel allocation plan from this, it is possible to obtain a performance plan that balances work results and slack time.

[0044] FIG. 5 is an example of a personnel allocation plan in an embodiment of the present invention.

[0045] The availability of the present invention will be explained using this personnel allocation plan as an example. A personnel allocation plan with good performance obtained by using a genetic algorithm based on the personnel allocation plan in Figure 5 is the revised personnel allocation plan in Figure 6.

[0046] The revised staffing plan obtained has improved indicators such as revenue and slack time, but it is not necessarily an easy staffing plan to implement. Looking at Figure 6, in Process A, the number of staff increases by one person from the 10:00 time slot to the 10:30 time slot. From the 10:30 time slot to the 11:00 time slot, the number of staff decreases by one person, and then increases by one person again from the 11:00 time slot to the 11:30 time slot.

[0047] By summarizing the increase or decrease for each work process, the movement amount for each process can be calculated as shown in Figure 7.

[0048] In addition, the amount of movement for each work period can be calculated as shown in Figure 8. The amount of movement shown in Figures 7 and 8 is an index of availability. This availability is called availability A.

[0049] Generally, it is difficult for a person to frequently change work processes during a day's work, and changing work processes requires tasks such as moving to another location and preparing the jigs needed for the next process.

[0050] Therefore, if the amount of movement is high for availability A, it will be difficult to implement the revised personnel allocation plan. Depending on the target business, weighting may be applied depending on whether fluctuations in the amount of movement between work periods or the amount of movement for each process affect the execution of the plan.

[0051] Furthermore, even if the increase or decrease is for each work process or between work time periods, if there are processes or work time periods where it is difficult to increase or decrease, the difficulty of execution can be made clearer by weighting.

[0052] FIG. 9 shows an example of the number of instructions for executing a modified allocation plan in an embodiment of the present invention.

[0053] To implement the required revised personnel allocation plan, it is necessary to issue movement instructions to personnel who move between work periods and processes.

[0054] In addition, the allocation plan for the machines to be used may require transporting the machines from their current locations to their new locations and setting them up. In the example of the revised personnel allocation plan in Figure 9, the number of instructions shown by the arrows is required. This number of instructions is called availability C.

[0055] If the number of instructions is small, it can be determined that the availability is high, but if the instructions are complex and difficult to change, the availability may be calculated by weighting.

[0056] FIG. 10 is an example of a software configuration diagram according to an embodiment of the present invention.

[0057] The placement plan receiving unit 13 receives the basic placement plan, and the placement plan creating unit 10 creates the placement plan. Here, a multi-objective optimization algorithm such as a genetic algorithm is used to create multiple improvement plans for the placement plan.

[0058] To create a placement plan, the placement plan stored in the resource placement plan DB 14, the number of generations of the placement plan to be created stored in the optimization parameters 22, the crossover rate, the mutation rate, the upper limit value of the resource stored in the constraint condition information 20, and the placement plan that has already been created and stored in the index value table 19 are referenced.

[0059] The created layout plan is simulated by the simulation unit 11 to obtain the results of the work, such as profits and work completion time.

[0060] The created allocation plan is evaluated for various availability factors by the availability evaluation unit 12, and the evaluation results are stored in the index value table 19.

[0061] The availability may be determined as a single availability or as multiple availability options. The availability options may be selected depending on the type of work to be planned.

[0062] FIG. 11 shows a modified allocation plan based on the number of people in an embodiment of the present invention.

[0063] Three revised allocation plans, Plan A to Plan C, are proposed, listing the number of personnel to be allocated to each process and work time period. These revised allocation plans are based on the number of people, so it is assumed that the capabilities of all personnel are constant. As a result, even if a person has no experience working in the destination process, they will be considered to have the same capabilities as an experienced person.

[0064] If the person in charge had low ability, it would have been difficult to achieve the performance desired in the simulation, but if the person in charge had high ability, it may have been possible to achieve higher performance by changing the placement location.

[0065] Figure 12 shows a capability-based revised allocation plan in an embodiment of the present invention. This revised allocation plan lists the capabilities of personnel to be allocated to each process and work time period. The capability of each person in charge is the number of tasks that can be completed per minute, as shown in capability table 21 in Figure 13. The capabilities of each person in charge are calculated from past work records and registered in capability table 21.

[0066] In this embodiment, a uniform capacity is registered for each person in charge, but if capacity is registered for each person in charge and for each process, more accurate allocation of personnel will be possible.

[0067] Since the abilities of personnel improve as they gain work experience, the ability table needs to be updated periodically based on actual performance.

[0068] It is possible to assign personnel who meet the numerical values ​​set out in the ability-based revised assignment plan based on the ability table 21. If personnel with abilities below the required level are assigned, there will be no room for work, and it is highly likely that the expected work results will not be achieved. However, it is not reasonable to assign personnel with abilities significantly greater than the required ability.

[0069] The criterion indicating whether the created resource number-based allocation plan can be executed with the capacity of the allocated resources is called availability D.

[0070] FIG. 14 shows an example of constraint information in an embodiment of the present invention.

[0071] In this example, the maximum number of workers available for each work time slot is stored as constraint condition information 20. Although the maximum number of workers is stored on a headcount basis, the maximum capacity for each work time slot may also be stored on a capacity basis.

[0072] Furthermore, for highly specialized work, storing the maximum number of workers and maximum capacity for each process makes it possible to determine whether the allocation plan is feasible. Storing constraints other than manpower, such as the facilities, equipment, energy, water, and number of product lines required to perform the work, allows for more accurate judgment. This condition is called availability B.

[0073] FIG. 15 is an example of an index value table in an embodiment of the present invention.

[0074] As a result of the allocation plan creation by the allocation plan creation unit 10 and the simulation by the simulation unit 11, an allocation plan with good work results is stored in association with the work results and availability indicating the degree of difficulty in executing the allocation plan.

[0075] In this example, the work output is the revenue and the cut time slack, which indicates how much time remains until all work must be completed.

[0076] Availability B and Availability D are related to the limit values ​​of the number of people and capabilities, and are therefore likely to be essential conditions that will result in an unfeasible deployment plan if the limit values ​​are exceeded.

[0077] On the other hand, availability A and availability C are feasible if they satisfy availability B and availability D, but as they are divided into plans that are easy to implement and plans that are difficult to implement, these are availability that are likely to be selection criteria.

[0078] With placement plan A, a profit of 10,000 (k yen) is expected, and the cut time margin is 60 (minutes). Availability A, which is the availability of the amount of resource movement, indicates that 12 people will need to be moved, and Availability B, which is the availability of the maximum amount of resource, is 0, indicating that the maximum amount of resource is not exceeded.

[0079] It is shown that availability C, which is the availability of the number of instructions, requires three instructions. Availability D, which is the availability of work capacity, is calculated as follows: First, calculate the work capacity based on the number of resources by adding up the capacities of all resources assigned to each process and time period, assuming that all are 1.

[0080] Next, the capacities of all the assigned resources are referred to in the capacity table 21, and the percentage of normal capacity is calculated to determine the total resource capacity-based work capacity.

[0081] The difference, which is the value obtained by subtracting the resource capacity-based work capacity from the resource number-based work capacity, is the availability D.

[0082] Therefore, if the availability D is a positive value, it means that there is a shortage of work capacity, and if it is a negative value, the work capacity is greater than or equal to the number of resources.

[0083] In this example, 0.1 is the largest value of availability D for the operation and work time slot, indicating that there are operation processes and work time slots with insufficient work capacity.

[0084] FIG. 16 is an example of a flowchart showing the processing of the resource allocation system in the embodiment of the present invention.

[0085] The allocation plan receiving unit 13 receives a basic allocation plan (S10). The basic allocation plan may be the allocation plan currently being used, or may be a specific allocation plan registered in advance in the resource allocation plan DB 14.

[0086] The received basic allocation plan is registered in the resource allocation plan DB 14, a plurality of random allocation plans in the same format as this basic allocation plan are generated, and the basic allocation plan and the generated random allocation plans are combined to form the allocation plan of the current generation (S11).

[0087] A plurality of new placement plans are created from the current generation placement plan by crossover and mutation (S12). The simulation unit 11 performs a simulation of the created placement plan and obtains the work results, work completion time, etc. (S13).

[0088] The availability of the created placement plan is evaluated by the availability evaluation unit 12 (S14). From the current generation placement plan and the created placement plan, a placement plan with the greatest work results and excellent availability is selected using a genetic algorithm and set as the current generation (S15).

[0089] The genetic algorithm may be an algorithm such as NSGA3 (Non-Dominated Sorting Genetic Algorithm 3) or SPEA2 (Strength Pareto Evolutionary Algorithm 2).

[0090] It is determined whether the number of simulations designated by the optimization parameters 22 has been executed (S16), and if the designated number has not been reached, the process returns to step S12 and repeats the process.

[0091] If the specified number of simulations have been performed, a predetermined number of allocation plans are selected from the allocation plans and registered in the resource allocation plan DB 14 (S17).

[0092] At this time, placement plans that are not completed by the completion time specified by the simulation, placement plans that do not produce the specified work results, and placement plans that require resources that greatly exceed the limit value may be deleted.

[0093] The output unit 23 outputs a placement plan designated based on a user request from among the placement plans registered in the resource placement plan DB 14 (S18). In addition, placement plans that produce work results inferior to the work results obtained with the basic placement plan may not be output.

[0094] Specifically, the profit of the work result is higher than the profit obtained by the basic allocation plan, or the work end time of the work result is earlier than the time the work is completed by the basic allocation plan, or alternatively, an allocation plan that achieves both of these may be output.

[0095] By specifying the amount of improvement from the work results of the basic placement plan and outputting only placement plans that are expected to have a certain level of improvement, it is possible to provide only excellent placement plans.

[0096] Also, a placement plan may be output that predicts results that are better than the profit and work completion time determined independently of the work results of the basic placement plan.

[0097] By outputting a layout plan that can obtain independently determined work results, it is possible to avoid confusion at the work site caused by unnecessarily changing the layout plan, and to improve work results.

[0098] FIG. 17 is a flowchart showing an example of availability evaluation processing in an embodiment of the present invention.

[0099] In this example, the availability of ADs is sought, but the availability of only one of ADs may be sought, or a combination of multiple ADs may be sought.

[0100] First, the amount of change between work periods, which is the sum of changes in resource amounts between adjacent work periods, is calculated (S30). The calculated sum of changes between work periods is registered in the index value table 19 as availability A.

[0101] Next, the amount of resources required for the allocation plan is calculated (S32). The required resources are the number of personnel allocated to all processes during a work period. The amount of resources that can be secured for each work period is registered in the constraint information 20, and this information is compared with the amount of resources required for the allocation plan (S33).

[0102] If the amount of reservable resources is greater, availability B is deemed executable and 0 is registered in the index value table 19 (S34). If there are insufficient reservable resources, availability B is deemed unexecutable and 1 is registered in the index value table 19 (S35).

[0103] If the difference between the amount of resources that can be secured registered in the constraint information 20 and the amount of resources required in the placement plan is defined as availability B, then if the amount of resources that can be secured is less than the amount of resources required, the difference can indicate the difficulty of securing the missing resources.

[0104] Next, the number of instructions required to change the amount of resources between work time periods is calculated (S36), and the sum of the calculated number of instructions is taken as availability C and registered in the index value table 19 (S37).

[0105] Finally, the availability evaluation unit 12 calculates the work capacity for each work process and each work time slot based on the number of resources when the resource capacity is set to 1. Next, the capacity table is referenced to calculate the work capacity for each work process and each work time slot based on the resource capacity.

[0106] A difference, which is a value obtained by subtracting the resource capacity-based work capacity from the resource number-based work capacity, is calculated for each work process and each work time period (S38).

[0107] The sum of the differences in work capacity found for all work processes and all work time periods is calculated for each allocation plan, and the calculated sum of the differences is registered in the index value table 19 as availability D (S39).

[0108] By storing the work process, work time zone and availability D value that show the largest difference in work capacity in the index value table 19, it is possible to indicate the work process, work time zone and difficulty level that are difficult to carry out.

[0109] The availability D is an index of the number of resources taking into account the capacity of the resources, and the availability D makes it possible to know the feasibility of the allocation plan.

[0110] In other words, if the availability D is a positive value, it means that the allocation plan allocates resources with less than the required capacity, and there is no room for error. Therefore, there is a high possibility that the work results obtained in the simulation will not be achieved.

[0111] If the availability D is a negative value, it means that the allocation plan allocates high-capacity resources and has a margin of error. Therefore, it is highly likely that the work results obtained in the simulation will be achieved.

[0112] The capacity table 21 may be the number of processes per minute calculated based on past work results, or may use numerical values ​​such as the grade certified for the resource and the processing capacity of the machine.

[0113] FIG. 18 shows an example of an availability display screen for each plan in the embodiment of the present invention.

[0114] The layout plan with good work results stored in the index value table 19 is output from the input / output unit 23 to the output device 5 such as a display.

[0115] It is also possible to output a specified number of layout plans stored in the index value table 19 in order of best work results.

[0116] Furthermore, when there are multiple indices relating to work performance, instructions may be received as to which indices should be given priority in selecting an allocation plan, and an allocation plan with good work performance based on the specified indices may be output.

[0117] When you select a deployment plan on this screen, the details of the deployment plan will be displayed.

[0118] FIG. 19 shows an example of a screen for assigning personnel for each plan in an embodiment of the present invention.

[0119] The number of people to be deployed for each process and each work period of the deployment plan selected on the screen of FIG. 18, the most important availability index 52, its content, and changes in the outcome index (performance) are output.

[0120] In this example, availability index A is 12 people, and the largest increase is in process A, where it increases from 30 to 31 people, while the largest decrease is in process C, where it decreases from 30 to 23 people.

[0121] The next largest change is in availability index C, which indicates the number of instructions that must be issued, and shows that 25 instructions must be issued.

[0122] The simulation shows that if layout plan A is implemented, the cut time margin will be extended from 30 minutes to 1 hour compared to the basic plan, and the performance indicators show that revenue is expected to increase from 7.5 million yen to 10 million yen.

[0123] In the table below, the "Plan" column displays the "Estimated Completion" which is the expected time of completion of the work in the basic plan, the "Remaining Work" which is the total number of personnel movements in "Man-hours", and the number of people moving for each work period, while the "Optimal" column displays the corresponding values ​​for Allocation Plan A.

[0124] However, since the availability D of allocation plan A shown in Figure 18 is 0.1, it can be seen that this is an allocation plan in which personnel with low work capabilities are assigned. Therefore, it is unlikely that the work results will be achieved as simulated, and it can be seen that the margin at the work site is low.

[0125] Furthermore, by specifying a process on this screen, detailed personnel allocation for the specified process can be viewed.

[0126] FIG. 20 is an example of a screen for allocating personnel to processes in an embodiment of the present invention.

[0127] On this screen, you can check who has been assigned to process A by their name, employee number, or other identifiers. You can also see that Person B, who will start work at 8:00 and Person A, who will start work at 17:30, are the personnel who have been changed, as shown by the lines. If the resources to be assigned are machines or equipment, you can also display the identifiers, such as the numbers assigned to each machine or piece of equipment. [Explanation of symbols]

[0128] 1: Resource allocation system 2: CPU 3: Main memory 4: External storage device 5: Input / output device 10: Placement Planning Department 11: Simulation section 12: Availability evaluation section 13: Placement Planning Reception Department 14: Resource allocation plan DB 15: Basic layout plan 16: Revised layout plan 19: Index value table 20: Constraint information 21: Ability Table 22: Optimization parameters 23: Output section

Claims

1. A resource allocation system that allocates resources to work time periods of a plurality of work processes, an allocation plan receiving unit that receives a basic allocation plan for allocating resources for each work time period of a plurality of work processes; a placement plan creation unit that creates a plurality of revised placement plans by modifying the number of resources to be placed for each work time slot of the basic placement plan using a multi-objective optimization algorithm based on the received basic placement plan; a simulation section for determining the work results from the revised layout plan; an availability evaluation unit that calculates the degree of difficulty of changing resource allocation in a revised allocation plan created from the basic allocation plan by the allocation plan creation unit; A resource allocation system comprising: an output unit that outputs a predetermined number of allocation plans that are superior in at least one of the work results determined by the simulation unit and the difficulty determined by the availability evaluation unit, in association with availability; and the allocation plan creation unit that creates a revised allocation plan using a multi-objective optimization algorithm based on at least one of the work results determined by the simulation unit and the difficulty determined by the availability evaluation unit.

2. The resource allocation system according to claim 1 , The availability evaluation unit is a resource allocation system that determines availability as the difference in the number of resources between adjacent work time periods in the allocation plan.

3. The resource allocation system according to claim 1 , The resource allocation system in which the availability evaluation unit determines availability as the difference between the number of resources required to execute the revised allocation plan and the number of resources that can be allocated.

4. The resource allocation system according to claim 1 , The resource allocation system in which the availability evaluation unit determines availability as the number of instructions required to change the number of resources between adjacent work time slots in the allocation plan.

5. The resource allocation system according to claim 1 , The output unit compares the work results obtained from the basic allocation plan with the work results of the revised allocation plan simulated by the simulation unit, and outputs the revised allocation plan when the work results of the revised allocation plan exceed the work results of the basic allocation plan.

6. The resource allocation system according to claim 5, The simulation department determines the work completion time for the revised layout plan, The output unit of the resource allocation system outputs a revised allocation plan when the determined task completion time is earlier than a predetermined time.

7. The resource allocation system according to claim 1 , The deployment plan creation unit creates a revised deployment plan based on the number of resources to be deployed, A resource allocation system in which the availability evaluation unit refers to the capacity table, calculates the number of resources taking into account the capacity of the resources from the revised allocation plan, and determines the difference between the number of resources and the number of resources taking into account the capacity of the resources as availability.

8. The resource allocation system according to claim 1 , The allocation plan creation unit creates a revised allocation plan that associates resource identifiers with work processes and work time periods, The output unit outputs resources of the revised allocation plan that require changes from the basic allocation plan in a manner different from that of resources that do not require changes.

9. A resource allocation method for allocating resources to work time slots of a plurality of work processes, comprising: an allocation plan receiving unit receives a basic allocation plan for allocating resources for each work time period of a plurality of work processes; The placement plan creation unit creates a plurality of revised placement plans by modifying the number of resources to be placed for each work time slot of the basic placement plan using a multi-objective optimization algorithm based on the basic placement plan received by the placement plan reception unit, The simulation department obtains the work results from the revised layout plan, The availability evaluation unit calculates the degree of difficulty of changing the resource allocation in the revised allocation plan created by the allocation plan creation unit from the basic allocation plan, A resource allocation method in which an output unit outputs a predetermined number of allocation plans that are excellent in at least one of the work results determined by the simulation unit and the difficulty level determined by the availability evaluation unit, in association with availability.

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