KR20170087355A - Meta heuristic based production planning method considering allocation rate conformance - Google Patents

Abstract

The present invention relates to a meta-heuristic-based production planning method considering compliance with an allocation ratio, and more particularly, to a production plan providing method performed in a production plan providing apparatus, Arranging the order data in the order of the delivery date and the maximum order quantity into an order list; Selecting an order to be allocated from the sorted order data and selecting a raw material having a maximum consumption number in each material replacement group of the selected first order; Confirming whether the maximum consumption number of the selected raw materials is equal to or greater than an order quantity; Calculating an allocation ratio when the maximum number of consumables of the raw material is equal to or greater than the order quantity and allocating an order quantity to the raw material having the maximum allocation ratio among the calculated allocation ratios to generate initial solution information of the production plan; And improving the initial solution information of the generated production plan using a meta heuristic algorithm.

Description

META HEURISTIC BASED PRODUCTION PLANNING METHOD CONSIDERING ALLOCATION RATE CONFORMANCE,

More particularly, the present invention relates to a meta-heuristic-based production planning method that considers allocation ratio compliance, and more particularly, to a meta-heuristic-based production planning method that generates initial solution information of a production plan considering allocation ratios on the basis of a meta-heuristic algorithm, The present invention relates to a heuristic-based production planning method that can generate improved solution information of an improved production plan, taking account of allocation ratio.

Production planning methods are becoming a core competence of global manufacturers. The production planning method determines how many items should be produced per given period to meet customer demand.

In particular, production plans can be categorized according to several criteria such as production structure, uncertainty management, and material requirements management. Production plans can be classified into single-level production plans or multi-level production plans depending on the production structure. A single-level production plan considers the cost and the availability of each site at a single production level. In contrast, a multi-level production plan considers movement between production levels.

According to uncertainty management, a production plan can be classified into a deterministic production plan or a stochastic production plan. A deterministic production plan assumes that demand, raw material procurement, and item lead time and delivery decisions are determined. On the contrary, the stochastic production plan considers these variables to be uncertain.

According to material requirements management, a production plan can be classified as a material requirements plan or a material allocation plan. The material requirements plan assumes that all materials are available and the requirements for each material are calculated according to the production plan. Conversely, a material allocation plan creates plans using materials that are available or readily available.

A common heuristic-based production planning technique considers multi-level, deterministic, and material allocation production planning together.

Several studies have been proposed to find optimal solutions for these types of production plans. The main idea for solving a production plan is to minimize costs. This goal can be studied using mathematical models and mixed integer programming (MIP).

However, the cost-based optimization method has two limitations. The two limitations include intensive allocation to sites with low production costs and violations of production chain coordination agreements.

Centralized allocation is caused by an unstable production chain. This occurs because the production chain is strict when changes are made to the plan to which external or internal accidents are assigned. In addition, intensive allocation of small manufacturing sites with small usable capacity requires overtime, is inefficient, and increases cost. Cost-oriented optimization can easily breach contracts. Because it is not appropriate to consider the constraints of the contract. It is necessary to eliminate all unfair and intensive allocations that violate the coordination contract of the production chain and cause an unstable production chain.

Production planning techniques that take into account allocation ratios can meet these needs. It is assumed that the heuristic-based production planning method is a multi-level, deterministic, material allocation production plan. Thus, the production chain in heuristic-based production planning techniques has several production levels.

Higher level items are produced from lower level items. Some items have the same characteristics, such as appearance, quality, and functionality, while others have different item codes (IDs). If the item is separated by the physical site of the manufacturing site or warehouse, different item codes are assigned. The upper level item has replaceable lower level items. Heuristic-based production planning techniques can choose one of them. When a higher-level item has a non-zero allocation rate for a lower-level item, the heuristic-based production planning methodology should be considered.

In contrast, when a higher level item has a zero allocation rate for lower level items, a heuristic based production planning technique can be produced without considering the allocation ratio. The appropriate allocation rate following a contract prevents unfair and intensive allocation, since it prohibits the allocation rate from exceeding the limit specified by the contract. Production scheduling techniques based on allocation ratios can be optimized for multi-purposes.

So heuristic based production planning techniques should be evaluated by several criteria. These few criteria are to minimize the total cost of each solution and the number of spoofed orders, and to maximize adherence to the allocation ratios. Optimization for a multi-objective implies a mathematical optimization problem involving one or more objective functions to be continuously optimized.

However, general mathematical model-based techniques or mixed integer programming (MIP) are not suitable for multi-purpose optimization. Because solving the optimization problem for multi-purpose requires calculating all Pareto's optimal solutions or calculating a representative set of Pareto's optimal solutions. Calculating all possible Pareto solutions is very time consuming and involves high computational costs. This general approach is inadequate for use in manufacturing areas. Therefore, a different approach is urgently needed.

Korean Patent Publication No. 10-2011-0061983 (published on June 10, 2011)

Embodiments of the present invention produce initial solution information based on meta heuristic rules and generate improved solution information of the improved production plan from the generated initial solution information based on tabu search We propose a meta-heuristic-based production planning method that can solve the optimal solution of the production plan considering the allocation ratios.

According to the first aspect of the present invention, in the production plan providing method performed in the production plan providing apparatus, the input order data is analyzed and the order data is sorted in order of the highest order priority, the shortest delivery date, Aligning; Selecting an order to be allocated from the sorted order data and selecting a raw material having a maximum consumption number in each material replacement group of the selected first order; Confirming whether the maximum consumption number of the selected raw materials is equal to or greater than an order quantity; Calculating an allocation ratio when the maximum number of consumables of the raw material is equal to or greater than the order quantity and allocating an order quantity to the raw material having the maximum allocation ratio among the calculated allocation ratios to generate initial solution information of the production plan; And improving the initial solution information of the generated production plan by using a meta heuristic algorithm. The meta-heuristic-based production plan providing method may be provided.

The method may further include shifting the allocation time if the maximum number of consumed raw materials is less than the order quantity as a result of the checking.

The method further includes the step of deleting the selected order from the order list and resetting the time position to the start of the planned period of time when the allocated time moved in the step of moving the allocated time reaches the end of the planned period of time can do.

The method may further include performing a step of selecting a raw material having the maximum consumption number if the ordered list is empty (Null).

The step of improving the initial solution information of the generated production plan may include generating the solution solution information within the time limit from the generated initial solution information, and if the executable solution information does not exist, Can be generated.

The step of improving the initial solution information of the generated production plan may improve the initial solution information of the generated production plan by using a meta-heuristic algorithm based on Tabu search.

The step of improving the initial solution information of the generated production plan may generate the improved solution information by performing a neighbor solution search process that repeatedly moves from the initial solution information of the generated production plan to the improved neighbor solution information .

In the step of improving the initial solution information of the generated production plan, the neighbor solution search process may be performed using a tabu list that guides the search for a solution area that has not been searched.

Embodiments of the present invention produce initial solution information based on meta heuristic rules and generate improved solution information of the improved production plan from the generated initial solution information based on tabu search The optimal solution of the production plan considering the allocation ratio can be solved.

Embodiments of the present disclosure can quickly respond to frequent order changes at production production sites by quickly generating improved solution information for a production plan for large data sets as well as data sets of other sizes.

FIG. 1 is a block diagram of a meta-heuristic-based production planning apparatus considering the allocation ratio according to the embodiment of the present invention.
FIG. 2 is a flowchart of a meta-heuristic-based production planning method that takes account of allocation ratio compliance according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. Will be described in detail with reference to the portions necessary for understanding the operation and operation according to the present specification. In describing the embodiments of the present invention, description of technical contents which are well known in the technical field to which the present invention belongs and which are not directly related to the present specification will be omitted. This is for the sake of clarity without omitting the unnecessary explanation and without giving the gist of the present invention.

In describing the components of the present specification, the same reference numerals may be given to components having the same name, and the same reference numerals may be given to different drawings. However, even in such a case, it does not mean that the corresponding component has different functions according to the embodiment, or does not mean that it has the same function in different embodiments, and the function of each component is different from that of the corresponding embodiment Based on the description of each component in FIG.

FIG. 1 is a block diagram of a meta-heuristic-based production planning apparatus considering the allocation ratio according to the embodiment of the present invention.

1, the meta-heuristic-based production planning apparatus 100 considering the allocation ratio according to the embodiment of the present invention includes a data input unit 110, an order arrangement unit 120, a raw material confirmation unit 130, An initial solution generation unit 140, an improvement solution generation unit 150, and a data storage unit 160.

Hereinafter, the specific configuration and operation of each component of the meta-heuristic-based production planning apparatus 100 considering the allocation ratio of FIG. 1 will be described.

The data input unit 110 receives data related to the meta-heuristic-based production plan considering the allocation ratio. Here, the data related to the production plan is input to the data input unit 110, and the data related to the production plan includes the procurement records of the raw materials, the bill of materials (BOM) of the product, and the subcontract rate . Accordingly, the data input unit 110 can generate virtual order data. Here, the virtual order data can be converted into BOM data, resource plan and order data. The production planning apparatus 100 considering the allocation ratio according to the embodiment of the present invention can generate the production plan based on the meta-heuristic using at least one of the virtual order data or the converted BOM data, the resource plan and the order data.

First, the BOM data input from the data input unit 110 indicates the product configuration. The BOM data structure is organized in a tree structure. The tree structure has parent and child nodes. The BOM data includes parent nodes with parent ID, child nodes with child ID, demand count, replaceable ID, and sub contract ratio. In order to produce one parent note item, the number of child node items is required.

Here, each parent-child relationship has a replaceable ID that represents a material replacement group. If the child nodes have the same replaceable ID, the child nodes have an equal relationship to their parent node. So that child nodes belong to the same material replacement group, and parent nodes can be assembled from any of their group members.

If the replaceable IDs are different among the child nodes, then the nodes are not exchangeable. For example, a parent node with three different replaceable IDs requires three types of items for production, such as a camera group, a battery group, and a cover group.

An interchangeable group can have two relationship types depending on the subcontracting rate of the substitutable raw materials within the material substitution group. If the sub contract rate has a zero value for the items of all the material replacement groups, the items can be freely allocated as freely as possible regardless of the sub contract ratio.

If the material replacement group has only one item, the item is automatically considered as the first type of item. However, if the material substitution group has a non-zero sub contract ratio, the sum of the sub contract rates should be one. The subcontracting rate between the material substitution groups should be adjusted to the maximum subcontracting rate as much as possible.

As such, the production planning apparatus 100 according to the embodiment of the present invention can generate the production plan using the BOM data including the parent nodes, the child nodes, the demand count, the replaceable ID, and the sub contract ratio have.

The resource plan data converted by the data input unit 110 will be described below.

Resource plan data includes additional numbers of raw materials with a specific time. Resource planning data includes information on the availability of raw materials in terms of time and quantity. Resource planning data includes time when specific raw materials are replenished to a factory or warehouse. In addition, the resource planning data includes supplemental quantities of certain raw materials. From a time perspective, raw materials are available after raw materials are stored. In terms of quantity, raw materials are available as far as raw materials are replenished. Raw materials are consumed to produce semifinished products. If raw materials are scarce, semi-finished products can not be produced. Production plans are considered according to the resource plan.

The order data converted by the data input unit 110 will be described below.

Order data includes information on an order quantity, an order priority, and a due date of order. The order quantity is equal to the number of products that satisfy the demand. Order priorities are related to customer importance, which is considered important by the customer's company placing these orders. The delivery date of the order is the deadline of the production. In general, the delivery date of an order is considered the highest priority. However, in the situation where the order priority is considered first, the order priority is considered before the due date.

Meanwhile, the order arranging unit 120 analyzes the order data inputted from the data input unit 110 and obtains the highest order priority, the closest due date, and the order of the largest order quantity Sort order data as order list.

Then, the raw material verifying unit 130 selects an order to be allocated from the ordered data arranged in the order arranging unit 120. Then, Then, the material confirmation unit 130 selects the raw material having the maximum consumption number in each material replacement group of the selected first order.

It is assumed that the order is not divided in the production planning apparatus 100 according to the embodiment of the present invention. For example, if the order quantity is 100, the production planning apparatus 100 has to allocate all 100 assemblies to one sub contract item. Partitioned order quantities result in lower tracking and production management difficulties in the production chain. If the order is divided, the delivery arrival time of the divided order quantity is not unified among the sub contract items. This requires extra inventory storage and management.

In addition, the production planning apparatus 100 generates a production plan on the assumption that the order priority is not violated. When an order has a low priority, if there are uncommitted orders with a high priority, then the materials can not be used. However, if the production planning device 100 is constrained by their due dates, they can skip higher priority orders.

Therefore, the production planning apparatus 100 according to the embodiment of the present invention generates a production plan based on the metaheuristic for the following two reasons.

First, the production planning apparatus 100 according to the embodiment of the present invention generates a production plan required to satisfy a time constraint. Conventional production planning methods using MIP solvers have the problem that the solution can not be generated within a limited time. if. Once the production planning method is completed, the MIP can ensure that the solution information of the production plan is optimal. However, it can not be guaranteed to be optimal if it is prematurely terminated by time constraints.

Second, the production planning apparatus 100 according to the embodiment of the present invention can generate solution information for a production plan even if it is impossible to execute, if it is required to generate an unexecutable solution. If there is no viable solution information, the metaheuristic based production planning apparatus 100 should produce at least non-executable solution information. It means that solution information violates the minimum possible constraints. A typical MIP-based production planning technique searches around solution boundaries that are limited by constraints. So these production planning methods can not at least search for non-feasible solutions.

Hereinafter, the production planning apparatus 100 according to the embodiment of the present invention performs a meta-heuristic-based production planning process in two steps. The production planning apparatus 100 performs a generation step of the initial solution information and an improvement solution generation step in which the initial solution information is improved. The initial solution information generation step and the improvement solution generation step will be described.

First, the initial solution information generation step will be described as follows.

The production planning apparatus 100 generates a production plan based on the meta-heuristic algorithm. The production planning apparatus 100 arranges the order data having the highest order priority in the first order and arranges the order data in the order of the nearest delivery date and the largest order quantity.

For each order having the highest priority, the production planning apparatus 100 calculates the quantity of consumable materials according to the raw material consumption amount and the supplementary data. The production planning apparatus 100 then searches for the maximum consumable materials for each material replacement group and selects the largest consumable materials for each material replacement group. The production planning apparatus 100 can calculate the latest allocation ratio in the material substitution group for the maximum consumed raw materials of each material substitution group as shown in the following equation (1). That is, the production planning apparatus 100 can calculate the latest allocation rate of the i-th item by subtracting a value obtained by dividing the planned quantity of the i-th item by the entire planned quantity in the substitutable ratio of the i-th item.

Here, the recent allocation rate is the recent allocation rate, ( _i ) the alternative rate, ( _i ) the substitutable rate of the ith item, ( _ii ) planned quantity _i is the planned quantity of the _ith item, and ( _iii ) the total planned quantity is the total planned quantity.

Next, the production planning apparatus 100 allocates the order quantity to the item having the latest highest allocation ratio. If the allocation is not possible, that is, if the quantity of the largest raw materials is smaller than the ordered quantity, the production planning apparatus 100 moves the time. If the moved time reaches the end of the planned period of time, the production planning apparatus 100 repeats this process for all levels of order priority in the downward order.

The initial solution generation unit 140 confirms whether the maximum consumption number of the raw materials selected in the raw material confirmation unit 130 is equal to or greater than the order quantity.

As a result of the determination, the initial solution generation unit 140 calculates the allocation ratio when the maximum consumption amount of the raw materials is equal to or greater than the order quantity. Then, the initial solution generation unit 140 generates initial solution information of the production plan by assigning the order quantity to the raw material having the maximum allocation ratio among the calculated allocation ratios.

Meanwhile, as a result of the checking, the initial solution generation unit 140 can shift the allocation time if the maximum consumption amount of the raw materials is less than the order quantity. The initial solution generation unit 140 deletes the order selected in the raw material confirmation unit 130 from the order list when the allocated allocation time reaches the end of the planned object period. Then, the initial solution generation unit 140 may reset the time position to the start of the planned object period.

Then, the initial solution generating unit 140 performs a process of selecting a raw material having the maximum consumption number when the ordered list ordered by the order arranging unit 120 is empty (Null).

The remediation solution generation unit 150 can improve from the initial solution and the solution using the meta-heuristic. A metaheuristic based production plan can generate a solution to improve the production plan based on the tabu search algorithm.

Specifically, the improvement solution generation unit 150 improves the initial solution information of the production plan generated by the initial solution generation unit 140 using the meta-heuristic algorithm.

The improvement process of the initial solution using the meta-heuristic algorithm will be described in detail.

The improvement solution generation unit 150 generates the improvement solution information within the time limit from the initial solution information generated by the initial solution generation unit 140. [ At this time, the improvement solution generating unit 150 may generate the solution information that is not executable if the executable solution information does not exist.

The improvement solution generation unit 150 improves the initial solution information of the production plan by using the meta-heuristic algorithm based on the tabu search algorithm. The improvement solution generation unit 150 performs a neighbor solution search process that repeatedly moves from the initial solution information of the production plan generated by the initial solution generation unit 140 to the improved neighbor solution information. The improvement solution generation unit 150 may generate improved solution information through the neighbor solution improvement process. The improvement solution generation unit 150 may perform the neighbor solution search process using the table list. The tabular list represents a list that guides the remediation solution generation unit 150 to search for a solution area that has not been searched when searching for a neighbor solution.

Here, the tabu search basically represents a method of repeatedly moving the improved neighbor solution information from one initial solution information to retrieve neighbor solution information. Compared with normal neighbor search algorithms, the improved solution generation unit 150 prohibits the search of neighbor solution information from the target list, thereby performing the target search using the target list guiding the search of the unsearched solution area.

Further, the remediation solution generation unit 150 retrieves the neighbor solution information using the tabu search according to the limitation referred to as local optimization. Here, the local optimization means that a stuck occurs in the search space of limited neighbor solution information due to the same set of repeated neighbor solutions. However, the improvement solution generation unit 150 according to the embodiment of the present invention prohibits searching in the neighborhood solution information area in the tablet list in a certain period of time, thereby performing the tablet search. At this time, the possibility of local optimization is rapidly increased.

Meanwhile, the data storage unit 160 stores data related to the production plan based on the meta-heuristic in the production planning apparatus 100 based on the meta-heuristic. For example, the data storage unit 160 stores the data related to the meta-heuristic-based production plan considering the allocation ratio into the input data, order data, order list, order priority, delivery date, order quantity, Data, raw material consumption count, data related to the allocation ratio calculation, and the like.

FIG. 2 is a flowchart of a meta-heuristic-based production planning method that takes account of allocation ratio compliance according to an embodiment of the present invention.

The production planning apparatus 100 receives the order data and sorts the input order data in order of the highest order priority, the shortest delivery date, and the maximum order quantity (S202).

Then, the production planning apparatus 100 selects an order to be allocated from the ordered order data (S204).

Then, the production planning apparatus 100 selects a raw material having the maximum consumption number in each of the substitute raw material groups of the selected order (S206).

Then, the production planning apparatus 100 confirms whether the maximum consumption number is equal to or greater than the order quantity (S208).

If the maximum number of consumed products is equal to or greater than the order quantity, the production planning apparatus 100 generates the initial solution information of the production plan by allocating the order quantity to the raw material having the maximum allocation ratio (S210).

Then, the production planning apparatus 100 confirms whether the order list is empty (S212).

On the other hand, if it is determined in step S208 that the maximum number of consumptions is less than the order quantity, the production planning apparatus 100 moves the allocation time in step S214.

Then, the production planning apparatus 100 confirms whether it reaches the end of the planned object period (S216).

When the confirmation result (S216) reaches the end of the planned object period, the production planning apparatus 100 deletes the selected order and resets the time position to the start position of the planned object period (S218). On the other hand, if the confirmation result (S216) does not reach the end of the planning target period, the production planning apparatus 100 performs the S204 process of selecting the order to be allocated from the ordered order data.

On the other hand, if the order list is empty (S212), the production plan apparatus 100 improves the initial solution information of the production plan using the meta-heuristic algorithm (S220).

On the other hand, if the order list is not empty (S212), the production planning apparatus 100 performs step S204 of selecting an order to be allocated from the ordered order data.

After step S220, the production planning apparatus 100 generates the improved solution information of the fish plan through the improvement process of the initial solution information of the production plan (S222).

On the other hand, the production planning method and other techniques according to the embodiment of the present invention will be compared from various viewpoints such as order fulfillment level, delivery date violation days, and allocation ratio suitability. Other algorithms to be compared are production planning methods based on mathematical models.

The mathematical model-based production planning method does not stop the production plan generation before the optimal solution information is retrieved for the production plan. Therefore, the production planning apparatus 100 sets a time limit for one day. In the experiment, the production planning method was repeated 50 times to obtain the average of each criterion.

The order fulfillment level is measured as follows: " (2) " That is, the order fulfillment level is calculated by dividing the total fulfilled orders by the total orders.

Where total satisfied orders is the order fulfillment level, and total orders is the total orders.

The delivery date violation days are measured as follows. That is, the number of days for the delivery date violation is calculated by summing the values obtained by subtracting the production date of the i-th item order from the delivery date of the i-th item order and the maximum value of 0 from all i.

Here, D _i represents the delivery date of the i-th item order and P _i represents the production date of the i-th item order.

The allocation ratio suitability is measured as follows: " (4) " That is, the allocation ratio suitability is calculated by summing the squares of the absolute values obtained by subtracting the allocation ratios of the i-th site in the j-th material substitution group from the contract ratio of the i-th site in the j-th material substitution group to all i, j do.

CR _ij represents the contract ratio of the i-th site in the j-th material substitution group, and AR _ij represents the allocation ratio of the i-th site in the j-th material substitution group.

On the other hand, in order to compare the production planning method and other techniques according to the embodiment of the present invention, experiments were performed with three data sets.

The first data set is the simplest set of 100 orders, 10 commodity sites, 1 manufacturing site and 2 substitutable groups. The second data set is a set of 1,000 orders, 50 commodity sites, 2 manufacturing sites, and 10 substitutable groups. The third data set is a set of 10,000 orders, 100 commodity sites, 2 manufacturing sites and 20 substitutable groups.

First, the experimental result of the first data set having a small size will be described.

For a small size dataset, each algorithm shows a slight difference between a 100% order fulfillment level, a similar level of delivery date violation days, and allocation ratio suitability, as shown in Table 1 below.

algorithm Criteria Order fulfillment level Number of days due Average Standard Deviation Average Standard Deviation Mathematical model 100.0 (optimum) - 2 (optimal) - Invention 100 0 2 0 Mathematical model
Adhering to the allocation ratio 0.57 - 0.62 - The
Adhering to the allocation ratio 0.39 0.26 0.39 0.26

Here, for the production planning method, N is set to 50 and iteratively calculated, and the performance evaluation is carried out through the mean and variance. The mean and standard deviation of order fulfillment level, the number of days due for delivery date, and the allocation ratio suitability were calculated using the mathematical model for the first data set and the production planning method according to the embodiments of this specification.

Second, the experimental result of the second data set having the medium size will be described.

For medium sized datasets, the mathematical modeling technique shows the highest order fulfillment level and the shortest days of due date violations. However, the production planning method according to the embodiment of the present invention shows statistically minor differences and provides excellent allocation ratio suitability with reference to [Table 1] below.

algorithm Criteria Order fulfillment level Number of days due Average Standard Deviation Average Standard Deviation Mathematical model 92.0 (optimum) - 13 (Optimal) - Invention 86.3 5.6 17.3 0.96 Mathematical model
Adhering to the allocation ratio 4.71 - 3.92 - The
Adhering to the allocation ratio 0.65 0.17 0.65 0.17

Here, for the production planning method, N was set to 50 and it was experimented. The mean and standard deviation of the order fulfillment level, the number of days due for the delivery date, and the allocation ratio suitability were calculated using the mathematical model for the second data set and the production planning method according to the embodiments of the present specification.

Third, the experimental result of the third data set of large size will be described.

For large data sets, the mathematical modeling technique failed to find the optimal solution within four hours. The production planning method according to the embodiment of the present invention thus provided the latest best solution. In the large-sized data set, the production planning method according to the present invention provided a top-quality solution in comparison with other algorithms with reference to Table 3 below.

algorithm Criteria Order fulfillment level Number of days due Average Standard Deviation Average Standard Deviation Mathematical model 84.3 - 307 - Invention 82.2 3.68 298 16 Mathematical model
Adhering to the allocation ratio 95.2 - 107.4 - The
Adhering to the allocation ratio 32.1 3.17 32.1 3.17

Here, for the production planning method, N was set to 50 and it was experimented. The mean and standard deviation of the order fulfillment level, the number of days due for the delivery date, and the allocation ratio suitability were calculated using the method using the mathematical model for the third data set and the production planning method according to the embodiments of the present specification.

Thus, the heuristic-based production planning apparatus 100 compares the order fulfillment level, delivery date violation days, and allocation ratio suitability of the two algorithms with three types of data sets.

For small data sets, the production planning method according to the present invention showed the same results compared with algorithms using a mathematical model. Because it is a small data set, all algorithms can search for the optimal solution. The production planning method according to the embodiment of the present invention can easily solve a small-sized planning problem.

For the intermediate data set, the production planning method according to the embodiment of the present disclosure creates a solution of the minimum allocation ratio violation and generates a little shortage of order fulfillment level and delivery date violation days. Mathematical modeling techniques have slightly better results. However, it takes more time than the production planning method according to the embodiment of the present specification. In addition, the production planning method according to the embodiment of the present invention can search for the best solution in the allocation ratio violation criterion.

For large data sets, the production planning method according to the embodiment of the present invention shows the best results on each criterion. The mathematical model failed to retrieve the optimal solution within four hours. The production planning method according to the embodiment of the present invention shows excellent results in terms of the number of days for violation of the due date and the violation of the allocation ratio while the order fulfillment level of the production planning method according to the embodiment of the present invention is slightly different from the experimental result of the mathematical model Giving. The production planning method according to embodiments of the present disclosure can be very effective when using large data sets.

In general, mathematical models and production planning methods according to embodiments of the present disclosure show the best results in each criterion. The production planning method according to the embodiment of the present invention generates the optimal solution information for the production plan as soon as the experiment results. The time spent was always less than 10 minutes. These results also support the utility of the production planning method according to the embodiments of the present disclosure. Because order changes occur very frequently in the manufacturing area, and production planning methods must respond quickly to order changes. In this situation, the mathematical modeling technique is not appropriate.

In view of the allocation ratio violation, the production planning method according to embodiments of the present disclosure is suitable for creating a balanced allocation. Balanced allocation can increase the sustainability and stability of the supply chain by preventing asymmetric allocation and avoiding forced allocation.

As described above, the production planning method according to embodiments of the present disclosure can solve the optimal allocation of alternative problems that can be substituted in the production schedule process. Here, order satisfaction and delivery date violation are also considered.

The production planning method according to embodiments of the present disclosure shows a similar level of order fulfillment level and delivery date violation days and allocation rate violations compared to mathematical model based techniques. While mathematical model-based techniques provide the optimal solution, they have not found an optimal solution for large data sets in four hours. The production planning method according to the embodiment of the present invention can always generate appropriate production schedules within 10 minutes. And the schedule quality of the production planning method according to embodiments of the present disclosure provides better results than the schedule quality of the mathematical model based techniques for large data sets.

By solving the optimal allocation of alternative selection problems using production planning methods according to embodiments of the present disclosure, appropriate quantities of products and parts can be allocated to prevent disproportionate and intensive allocations that can lead to unstable supply chains . The production planning method according to the embodiment of the present invention has a short execution period for a large data set, so that it is possible to quickly respond to frequent order changes at a manufacturing site.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or essential characteristics thereof. Therefore, the embodiments disclosed in the present invention are intended to illustrate rather than limit the scope of the present invention, and the scope of the technical idea of the present invention is not limited by these embodiments. The scope of protection of the present invention should be construed according to the following claims, and all technical ideas within the scope of equivalents should be construed as falling within the scope of the present invention.

100: Production planning device
110; Data input section
120: order arrangement unit
130: Raw material confirmation unit
140: initial solution generation unit
150:
160: Data storage unit

Claims (8)

A production plan providing method performed in a production plan providing apparatus,
Analyzing the inputted order data and sorting the order data into an order list in order of the highest order priority, the shortest delivery date, and the maximum order quantity;
Selecting an order to be allocated from the sorted order data and selecting a raw material having a maximum consumption number in each material replacement group of the selected first order;
Confirming whether the maximum consumption number of the selected raw materials is equal to or greater than an order quantity;
Calculating an allocation ratio when the maximum number of consumables of the raw material is equal to or greater than the order quantity and allocating an order quantity to the raw material having the maximum allocation ratio among the calculated allocation ratios to generate initial solution information of the production plan; And
Improving the initial solution information of the generated production plan using a meta heuristic algorithm
A meta - heuristic - based production planning method considering compliance ratio. The method according to claim 1,
As a result of the checking, if the maximum number of consumed raw materials is less than the order quantity,
A meta heuristic based production planning method considering the allocation ratio. 3. The method of claim 2,
Removing the selected order from the order list and resetting the time position to the start of the planned object period when the allocated time moved in the step of moving the allocated time reaches the end of the planned object period
A meta heuristic based production planning method considering the allocation ratio. The method according to claim 1,
If the sorted order list is empty (Null), selecting the raw material having the maximum consumption number
A meta heuristic based production planning method considering the allocation ratio. The method according to claim 1,
The step of improving the initial solution information of the generated production plan
Provides a meta-heuristic-based production plan that takes into consideration the allocation ratio to generate remediation solution information within the time limit from the generated initial solution information, and to generate remedial solution information as remediation solution information if executable solution information does not exist Way. The method according to claim 1,
The step of improving the initial solution information of the generated production plan
A meta-heuristic-based production planning method considering adhering ratio compliance to improve initial solution information of the generated production plan using a metabolic heuristic algorithm based on Tabu search. The method according to claim 6,
The step of improving the initial solution information of the generated production plan
A meta-heuristic-based production planning method that considers allocation ratio compliance to perform a neighbor solution search process that repeatedly moves from the initial solution information of the generated production plan to the improved neighbor solution information to generate improved solution information. 8. The method of claim 7,
The step of improving the initial solution information of the generated production plan
A meta-heuristic-based production plan providing method that takes into account the allocation ratio to perform the neighbor solution search process using a tabu list that guides the search for a solution area that has not been searched.

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