Research Article  Open Access
Jianzhong Xu, Song Zhang, Yuzhen Hu, "Research on Construction and Application for the Model of Multistage Job Shop Scheduling Problem", Mathematical Problems in Engineering, vol. 2020, Article ID 6357394, 12 pages, 2020. https://doi.org/10.1155/2020/6357394
Research on Construction and Application for the Model of Multistage Job Shop Scheduling Problem
Abstract
Based on the practical application of an enterprise, we address the multistage job shop scheduling problem with several parallel machines in the first stage (production), a few parallel machines in the second stage (processing and assembly), and one machine in the following stages (including joint debugging, testing, inspection, and packaging). First, we establish the optimization objective model for the first two stages. Then, based on the design of the sequencing algorithm in the first two stages, a correction algorithm is designed between the first stage and the second stage to solve this problem systematically. Finally, we propose two benchmark approaches to verify the performance of our proposed algorithm. Verification of numerical experiments shows that the model and algorithm constructed in this paper effectively improve the production efficiency of the enterprise.
1. Introduction
Multistage scheduling problems are common in the actual production scheduling of enterprises. Since the beginning of the 21st century, with the rapid development of the social economy, the diversified and personalized needs of customers have been increasing daily. The relationship between market supply and demand is gradually changing from a seller’s market to a buyer’s market, and competition among enterprises is intensifying, forcing enterprises to change their production mode towards diversification, multiple varieties, and small batches. Therefore, how to respond quickly, optimize production, and meet customers’ needs efficiently and punctually has become the core factor of competition among enterprises. In this context, most enterprises modularize their production according to process characteristics to form a number of production and assembly workshops with specific functions. At this time, the multistage scheduling problem arises in production scheduling. However, most existing research remains theoretical, with few studies on practical application, so there is an urgent need to establish a set of system methods that can be applied to the multistage production enterprise production scheduling mode.
In this study, we focus on the multistage job shop scheduling problem. The constraints (such as processing time and processing sequence) in the process of multistage job shop scheduling are considered to construct a mathematical model for different stages of production scheduling, and both a multistage separated production scheduling algorithm and an improved multistage relevance production scheduling algorithm are designed to solve the proposed problem. Finally, the improved multistage relevance production scheduling algorithm is verified through a computational experiment based on realworld data. Two contributions are made in this paper: (1) in the process of calculating the solution, the theory of scheduling and a heuristic algorithm are combined to obtain a satisfactory multistage production scheduling solution; (2) compared with previous articles, this paper pays more attention to the practical application of mathematical problems in enterprises. It not only improves the production efficiency of multistage manufacturing enterprises but also provides a scientific reference for the production decisionmaking of enterprises. Furthermore, the approach can create more profits for enterprises.
The remainder of this paper is organized as follows. Section 2 reviews the relevant literature on production scheduling. Section 3 builds a model for the multistage job shop scheduling problem. Section 4 designs the corresponding algorithm for the multistage job shop scheduling mode. Section 5 reports the numerical experiments of the model of multistage job shop scheduling, and conclusions are drawn in Section 6. The findings can be used as a reference for production enterprises’ decisionmaking and have good application value.
2. Literature Review
Production scheduling, which can be defined as to “allocate limited resources to several tasks in time to meet or optimize one or more objectives” [1], is a key task for manufacturing enterprises in implementing production plans. Moreover, production scheduling is an important field in operational research. Since Johnson [2] proposed an effective optimization algorithm for solving N/2/F/Cmax and some special n/3/F/Cmax problems, many scholars have engaged in production scheduling research. Graves [3] systematically summarized the types of production scheduling based on previous research. According to the processing complexity, he divided production scheduling into four types: one processor, parallel processors, flow shop, and job shop. This classification method has been adopted and quoted by later scholars.
Many scholars engaged in oneprocessor scheduling and parallelprocessor scheduling research in the early stage [4–8], and many scholars are continuing research in this field [9–15]. Luo et al. [16] studied the singlemachine scheduling problem with jobdependent machine deterioration. Chen and Yuan [17] studied the classic singlemachine scheduling problem with deadlines based on minimizing the total tardiness and proved that the problem is NPhard. Luo and Liu [18] focused on the singlemachine scheduling problem with workload dependent on maintenance time and extended the previous theoretical results. Atakan et al. [19] studied the singlemachine scheduling problem with uncertain parameters. Zhang et al. [20] proposed an unrelated parallel machine scheduling problem based on the consideration of processing speed and processing time, and they verified the effectiveness of their proposed heuristic evolutionary algorithm. Vallada et al. [21] studied the problem of unrelated parallel machines with one additional resource using a scatter search algorithm. Alidaee et al. [22] used dynamic programming to study a parallel machine scheduling model in which the job processing rates are interdependent and jobs have no priority. Vincent et al. [23] studied the capacityconstrained batch problem with multiple projects, set time and unrelated parallel machines, and compared the results with those of Toledo and Armentano [24] to demonstrate the competitiveness of the existing research results.
Meanwhile, scholars represented by Johnson [2] are also studying the multistage production scheduling problem. The development of production modularization and integration in manufacturing enterprises has led to complex relations among production processes, which often affect the progress of the whole production system. Indeed, the multistage flow shop scheduling and multistage job shop scheduling are more in line with the actual needs of enterprises. Multistage flow shop scheduling is applicable to steel manufacturing [25, 26], printed circuit boards [27], automobile manufacturing [28], and other enterprises whose products must be produced and processed according to a certain technological process. Multistage job shop scheduling is more common in manufacturing enterprises because of its uncertain processing process [29–32]. However, due to the lack of practical application background, most scholars are engaged in research from the perspective of improving the speed [33, 34] and solution quality [35, 36] of multistage production scheduling algorithms. Samarghandi [37] studied the problem of minimizing the maximum completion time of flow shop scheduling under the constraints of a minimum and maximum time delay. By constructing a mixedinteger linear programming model and a constraint programming model, the author proposed a tabu search algorithm and verified the effectiveness of the proposed tabu search algorithm with an example. Gong et al. [38] proposed a hybrid evolutionary algorithm based on the flexibility of workers that considered factors such as energy consumption. Experiments on benchmark examples demonstrated that the proposed algorithm was significantly better than two other wellknown algorithms in terms of solution quality and computational efficiency. Caldeira and Gnanavelbabu [39] used an improved Java algorithm to solve a flexible job shop scheduling problem and compared the solution quality with other famous metaheuristic algorithms to verify the superiority of their algorithm. Dabah et al. [40] paralleled the tabu search algorithm to solve the blocking job shop scheduling problem and used data in the existing literature to verify that the designed algorithm can improve upon the best results achieved by a large number of benchmark methods in the literature. Zhang et al. [41] proposed an improved hybrid particle swarm optimization algorithm to study the multiobjective flexible job shop scheduling problem. The benchmark experiment showed that the algorithm has the advantages of high quality and fast convergence.
A literature review finds many research results related to the production scheduling problem. However, the production scheduling problem is characterized by not only many production constraints but also a large number of production sequencing combinations, so a general production scheduling theory is difficult to develop. Existing scholars focus on improving the solution quality of the algorithms and give less attention to the application of the production scheduling model to solve the actual problems of enterprises. Therefore, the application of multistage production scheduling to better serve real manufacturing enterprises must be studied.
3. Problem Description and Model Formulation
3.1. Problem Description
Quality, cost, and delivery time are the three core issues in the production and operation management of enterprises. This paper focuses on the delivery time to solve a production scheduling problem in which the products go through multiple stages of production before final delivery. In this multistage production, the first two stages are usually the most complex because the product processing techniques of these two stages, which are the key factors affecting the production efficiency, are both independent and influence each other. The subsequent stages are all singlemachine operations, so we simply need to sum the standard working hours of each stage. To summarize, we take the first two stages of the production scheduling process as the key content to be studied.
3.1.1. Problem Description of the FirstStage Production Scheduling
The first stage usually involves producing intermediate products and providing raw materials for subsequent assembly. Let j workstations produce i parts in this stage. In this process, due to the limited number of processing workstations, there is no specific requirement for prioritizing which type of intermediate products to produce, which leads to many plans for production. However, the processing time for different types and models of intermediate products is different, and intermediate products can be processed at any workstation. At any time, an intermediate product can be processed at most at one workstation, and each workstation can process only one intermediate product at a time. The processing of intermediate products in any processing stage is not allowed to be interrupted. This scenario inevitably leads to different processing times for each workstation to complete the intermediate products. For a complete set of products, the production schedule of the first stage directly affects the assembly time of the second stage, so the production of the first stage must be scheduled carefully.
3.1.2. Problem Description of the SecondStage Production Scheduling
The second stage is the process of product system integration, and the speed of its progress directly affects the delivery time of the final product. Therefore, the production of the second stage must also be scheduled. In this stage, l workstation processes workpieces (denoted as set K), and the set K is divided into two subsets (i.e., K = K_{1}∪K_{2}): set K_{1}, in which workpieces can be processed directly without waiting time, and set K_{2}, in which workpieces have a waiting time. The waiting time is the total time spent in the production and conversion section of intermediate products in the first stage, and when the intermediate products are transferred to the second stage, they should be transferred in the form of subsystems.
3.1.3. Relevance between the First Stage and the Second Stage
The relevance of the first stage, the second stage, and the third stage can be seen in Figure 1.
The first stage and second stage have obvious relevance in the process of production scheduling.
First, the intermediate product production in the first stage and the no waiting time workpiece from set K_{1} in the second stage can be conducted in parallel, and their completion time directly affects the start time of the third stage.
Second, the completion time of the intermediate product processing in the first stage directly affects the start time of the workpiece assembly with waiting time in the second stage.
Third, the workpiece with waiting time in the second stage must be assembled in the form of a subsystem.
Finally, only after all the workpieces are assembled can they be transferred to the third stage.
3.2. Model Formulation
3.2.1. Objective
During the modeling process, this paper makes some reasonable simplifications, including that the availability of raw materials does not affect the processing process, workstation equipment failure has little impact on processing and can be ignored, the processing time of all intermediate products is known in standard working hours, and there is no disturbance of the processing time caused by the production personnel’s emotions. For the convenience of subsequent modeling, we call the processing and assembly of the secondstage workpiece the assembly task.
According to the situation of the first two stages, the optimization objective is to minimize the latest completion time of the assembly task for all assembly stations in the second stage to ensure that all tasks are completed as evenly as possible before the third stage.
3.2.2. Models
(1)Parameter definitions are as follows: I: intermediate product set J: intermediate product processing workstation set K′: subsystem set I_{k}: intermediate product set contained in the kth subsystem, k ∈ K′ t_{i}: processing time of intermediate product i on the processing workstation, i ∈ I L: assembly workstation set K_{1}: assembly task set without waiting time K_{2}: assembly task set with waiting time K: assembly task set, K = K_{1}∪K_{2} P_{kl}: assembly time of assembly task k on assembly workstation l, k ∈ K, l ∈ L C_{k}: the end of processing time when assembly task k finishes processing in the first stage of intermediate products, k ∈ K(2)Decision variables definition is as follows: S_{ij}: start processing time of intermediate product i on processing workstation j, i ∈ I, j ∈ J. C_{ij}: end processing time of intermediate product i on processing workstation j, i ∈ I, j ∈ J. S_{kl}: start assembly time of assembly task k on assembly workstation l, k ∈ K, l ∈ L. C_{kl}: end assembly time of assembly task k on assembly workstation l, k ∈ K, l ∈ L.(3)Objective function is as follows:(4)Constraints are as follows:
Objective function (3) is used to minimize the latest end time of each workstation.
Constraint (4) indicates that an intermediate product can be processed by only one processing workstation. Constraint (5) represents the end processing time of intermediate product i on the processing workstation. Constraint (6) indicates that a single processing workstation cannot process two intermediate products simultaneously. Constraint (7) represents that decision variable S_{ij} is a nonnegative continuous number. Constraint (8) indicates that decision variable X_{ij} is an integer of 0 or 1. Constraint (9) indicates that the start time of the assembly task is later than the end time of the previous operation. There are two meanings: one is to take C_{k} = 0 for an assembly task that does not need to wait; the other is to take C_{k} > 0 for an assembly task that needs to wait. Constraint (10) indicates that an assembly task can be assembled by only one assembly workstation. Constraint (11) indicates that a single assembly workstation cannot assemble two assembly tasks simultaneously. Constraint (12) shows that the end assembly time of assembly task k on assembly workstation l is the sum of its start assembly time on assembly workstation l and its assembly time on assembly workstation l. Constraint (13) represents that decision variable S_{kl} is a nonnegative continuous number. Constraint (14) indicates that decision variable X_{kl} is an integer of 0 or 1.
The above models are difficult to solve with commercial software, and enterprises prefer to obtain a satisfactory solution instead of deliberately pursuing the optimal solution when addressing actual production scheduling problems. Thus, a heuristic algorithm is good for multistage production scheduling problems. Multistage production scheduling is usually solved stage by stage, in line with the actual production situation of enterprises. Therefore, a multistage heuristic algorithm is designed in this paper.
4. Scheduling Procedure (Algorithm Design)
According to the actual situation of production, this paper focuses on the first two stages of production scheduling and designs a corresponding scheduling algorithm. Generally, the scheduling process will not start the next stage until the previous stage is completed. Based on this principle, we design twostage algorithms in Sections 4.1 and 4.2, denoted as S1 and S2. Moreover, according to the relevance description in Section 3.1.3, the algorithm can be further optimized, so we propose an improved algorithm (denoted as S1′) for the first stage; see Section 4.3 for details.
4.1. Design of the FirstStage Production Scheduling Algorithm
4.1.1. General Idea of the Production Scheduling Algorithm for the First Stage
In the first stage, the processing sequence of intermediate products and the processing sequence of each subsystem should be determined. The general ideas for the firststage scheduling algorithm (S1) are as follows.
First, we need to identify and summarize the intermediate products to which each subsystem belongs.
Second, because the processed intermediate products need to be transferred to the second stage in the form of a subsystem, one subsystem must be completed before processing another subsystem; that is, if the intermediate products of one subsystem are being processed, the intermediate products of another subsystem cannot be processed.
Finally, during intermediate product processing, the complexity of each subsystem is directly proportional to its processing time, so we prioritize complex subsystems, which should be transferred to the next stage as soon as possible. We give priority to complex systems, that is, tasks with long processing times, because Tang and Zhao [42] proved that the optimal solution of the maximum processing time can be obtained by performing tasks in descending order of processing time in the same speed machine scheduling problem, which will not be demonstrated here.
4.1.2. Steps of S1
Step 1: all intermediate products are classified according to their subsystems Step 2: the processing times of all intermediate products in set I_{k} are added to obtain M_{k}, namely, Step 3: all subsystems are sorted in descending order according to M_{k}, namely, Step 4: all intermediate products in set I_{1} are sorted in descending order of t_{i}, namely, Step 5: let k = 1. The first parts are taken and arranged on workstations in sequence; now, let Step 6: if any workstation is processing, the ith intermediate product in set I_{k} set is assigned to this workstation Step 7: if , then let k = k+1, and go to Step 8; otherwise, go to Step 6, and let i = i+1 Step 8: if , then all intermediate products in set I_{k} are sorted in descending order of t_{i}, namely, t_{i} ≥ t_{i+1}; let i = 1, and go to Step 6; otherwise, go to Step 9 Step 9: end
The time complexity of algorithm S1 is O (mn^{3}), where m represents the number of processing subsystems, that is, , and n represents the maximum number of intermediate products in all subsystems, that is, . In Step 2, the summation of the processing time for each subsystem occurs no more than n times, so it takes at most mn iterations to obtain the total processing time for m subsystems. In Step 6, Step 7, and Step 8, intermediate product assignment of each subsystem to a workstation occurs no more than mn times. In Step 8, the complexity of the bubble sorting algorithm for each subsystem is O (n^{2}). Therefore, the total complexity of algorithm S1 is O (mn) + O (mn^{3}) = O (mn^{3}).
4.2. Design of the SecondStage Production Scheduling Algorithm
4.2.1. General Idea of the Production Scheduling Algorithm for the Second Stage
Compared with that of the firststage scheduling algorithm, the design of the secondstage scheduling algorithm is more complex because the types of tasks in the second stage are different: there are tasks both with and without waiting time. Thus, the assembly sequence must be determined for tasks both with and without waiting time. According to the set optimization objectives, the general ideas for the second stage scheduling algorithm are as follows.
First, we need to identify and distinguish the types of tasks.
Second, the tasks without waiting time are sorted in descending order of assembly time.
Third, for tasks requiring waiting time, the waiting time is defined as the time consumed by the corresponding subsystem in the first stage. Therefore, the scheduling of tasks requiring waiting time is a dynamic process that should be combined with the scheduling situation of the first stage in Section 4.1.1 and should be changed along with changes in the first stage of scheduling. Additionally, once a task is being assembled, it cannot be interrupted, and the assembly of another task can be started only if the previous task is completed.
Finally, in the second stage, the goal is to balance the workload of each assembly workstation as much as possible. Only after each assembly workstation has completed the assembly task can the product be transferred to the next stage.
4.2.2. Steps of S2
Step 1: all the assembly tasks in the second stage are classified according to their type to form the no waiting time task set K_{1} and the waiting time task set K_{2}. Step 2: the tasks in task set K_{1} are sorted in descending order of assembly time P_{k}, namely, . Step 3: the first tasks are arranged on workstations in turn; now, let . Step 4: confirm whether there is an idle assembly workstation; if so, go to Step 5. Otherwise, continue to wait until an idle workstation appears; then, go to Step 5. Step 5: search task set K_{2} to confirm whether there are any tasks to be assembled. If there are tasks to be assembled, assembly task with the longest assembly time is assigned to the idle assembly workstation; then, . Otherwise, the kth task in K_{1} is assigned to the idle assembly workstation. Step 6: if , go to Step 7; otherwise, let k = k + 1, and go to Step 4. Step 7: confirm whether there is an idle assembly workstation; if so, go to Step 8. Otherwise, continue to wait until an idle workstation appears; then, go to Step 8. Step 8: search task set K_{2} to confirm whether there are any tasks to be assembled. If there are tasks to be assembled, assembly task with the longest assembly time is assigned to the idle assembly workstation; then, . Otherwise, continue to wait until a task to be assembled appears and assign it to the idle workstation. Step 9: if , go to Step 10; otherwise, go to Step 7. Step 10: end.
The time complexity of algorithm S2 is , where m′ represents the number of assembly workstations, that is, , and n′ represents the maximum number of the two types of assembly tasks, that is, . Additionally, m steps are required to confirm the existence of an idle assembly workstation in Step 4. Step 5 needs times to search set . If there exist tasks to be assembled, the assignment of the task with the longest assembly time takes no more than iterations; otherwise, it will take no more than iterations to assign the task from set K_{1} in Step 5 and Step 6. Therefore, the complexity of Step 4, Step 5, and Step 6 is . Similarly, the complexity of Step 7, Step 8, and Step 9 is . Therefore, the total complexity of algorithm S1 is .
4.3. Improved Algorithm for the First Stage (S1′)
The scheduling algorithm design of the second stage will change dynamically with the scheduling of the first stage. Here, we give priority to tasks with longer assembly times, which need to be assembled first, while in the scheduling algorithm design of the first stage, we give priority to subsystems with a longer processing time, which need to be processed first. However, the assembly time and processing time are not the same concept; if the scheduling algorithm of the first stage is directly embedded in the scheduling algorithm of the second stage, it is no longer applicable. Therefore, the improved scheduling algorithm of the first stage (S1′) should be adjusted as follows. Step 1: subsystems I_{k} are sorted in descending order of assembly time P_{k}, namely, Step 2: all intermediate products in set I_{1} set are sorted in descending order of t_{i}, namely, Step 3: let k = 1; the first components are arranged on workstations in turn, and now let Step 4: if any workstation finishes processing, the ith intermediate product in set I_{k} is assigned to this workstation Step 5: if , then let k = k + 1, and go to Step 6; otherwise, go to Step 4 and let i = i + 1 Step 6: if , then all intermediate products in set I_{k} are sorted in descending order of t_{i}, namely, t_{i} ≥ t_{i+1}; let i = 1, and go to Step 4; otherwise, go to Step 7 Step 7: end
The only difference between S1 and S1′ is the sorting criterion in Step 1; therefore, the complexity of S1′ is the same as that of S1.
In summary, to solve the twostage production scheduling problem, we propose two combined scheduling algorithms in this chapter: the S1S2 algorithm and the S1′S2 algorithm.
5. Numerical Experiments
Empirical enterprises produce three main types of products (type A, type E, and type F), and all products have to go through multistage production successively. Figure 2 illustrates a schematic of the multistage job shop problem. In the first stage, an intermediate product circuit board is produced, and, in the second stage, cable processing, core component pretreatment, and subsystem assembly are conducted. Then, joint debugging, testing, acceptance checking, and packing are performed in the subsequent stage. The entire production process conforms to the mathematical model and algorithm design built in this paper.
In the enterprise, there are four parallel processing workstations in the first stage. The four processing workstations have no sequence when they perform processing tasks, and they are independent of each other. Once an intermediate product begins processing on one of the workstations, it will not be terminated and transferred to another workstation. Furthermore, in the second stage, there are five assembly workstations. The five assembly workstations have no sequence when they perform assembly tasks, and the workstations are independent of each other. Once an assembly task is begun on a workstation, it will not be terminated and transferred to another workstation.
5.1. Process Data
An Ftype product with complex technology is selected for empirical research. For reasons of confidentiality, the actual names of the subsystems, intermediate products, and assembly tasks are replaced in the following process data.
5.1.1. Process Data in the First Stage
The processing time and subsystem details of the firststage intermediate products are shown in Table 1.

5.1.2. Process Data in the Second Stage
The assembly time and task type of the secondstage assembly tasks are shown in Table 2.

5.1.3. Process Data in the Subsequent Stage
After completing the assembly tasks in the second stage, the products are integrated into a set of system products; then, joint debugging, working environment testing, acceptance checking, and packing are conducted in the subsequent stages. Table 3 shows the process data for the subsequent stages.

5.2. Comparison Analysis
To verify the effectiveness of the S1′S2 algorithm, we propose two benchmark approaches: the S1S2 algorithm and the actual operation approach of the enterprise.
5.2.1. Comparative Analysis between the S1′S2 Algorithm and S1S2 Algorithm
The operation results of the S1′S2 algorithm and the S1S2 algorithm in the first and second stages, which can be seen in Tables 4 and 5, can be obtained using the scheduling algorithm in Section 4. The comparison results are shown in Table 6.



Table 6 shows that 255 working hours are required to complete a single set of Ftype products using the S1S2 algorithm, while 199.5 working hours are required by the S1′S2 algorithm: the completion time of the S1′S2 algorithm is 21.76% higher than that of the S1S2 algorithm.
In terms of the annual output of Ftype products, the S1′S2 algorithm produces 3.59 sets more than the S1S2 algorithm, and the production efficiency is increased by 24.90%. The annual output of Ftype products is obtained as the ratio of the actual working days of the enterprise in the whole year to the production cycle of a single set of Ftype products. Two points need to be explained.
First, the working system of the empirical enterprise is a onedayoff system; that is, there is only one day off (Sunday) every week. Based on 52 weeks in a year and deducting 11 days for China national legal holidays, we can obtain the total actual number of working days for the whole year of the empirical enterprise as follows: actual working days = 365 − 11 − 52 = 302 (days).
Second, the working environment testing in the subsequent stages, which costs 56 hours, is completed continuously, so the working environment test actually takes 3 days.
In terms of the annual profit value of Ftype products, the S1′S2 algorithm yields 1.436 million Yuan greater profit than the S1S2 algorithm, and the profit margin is increased by 24.90%.
The reason for the above differences is the different scheduling rules adopted in the production scheduling process. The S1S2 algorithm isolates the twostage production scheduling into two separate production stages, and its scheduling algorithm guarantees only the minimum of the latest completion time of each stage. The S1′S2 algorithm regards the twostage production scheduling as a whole and forms a link between the stages. Table 4 shows that the latest completion time of the first stage of the S1′S2 algorithm is 122 working hours and that of the S1S2 algorithm is 120 working hours. However, in the second stage, the S1′S2 algorithm gives priority to the assembly time and returns the information of the subsystems with long assembly times to the first stage of the production scheduling, which effectively balances the assembly capacity of each assembly workstation in the second stage. Thus, a more satisfactory solution can be obtained.
5.2.2. Comparative Analysis between the S1′S2 Algorithm and the Actual Operation of the Enterprise
To verify the effectiveness of the S1′S2 algorithm, we obtain the output of Ftype products from 2015 to 2018. The comparison results between the actual operation of the enterprise and the S1′S2 algorithm are provided in Table 7.

In Table 7, the output of Ftype products is the highest in 2018, reaching 9 sets. The S1′S2 algorithm can be used to obtain 14.42 sets of annual production, which is 1.6 times the actual production in 2018. Therefore, the production capacity of Ftype products is improved considerably. In view of the above situation, we analyze the following two reasons for this improvement.
First, in the actual production scheduling process, enterprises usually implement production scheduling according to the production schedule. The production scheduling personnel not only are familiar with the production process of the product but also monitor the firstline production workshop to follow the production progress and take timely and effective adjustment measures to ensure that the production scheduling meets the requirements of the production schedule as much as possible. However, in the process of adjusting production scheduling, due to the different experience of production scheduling personnel, different scheduling rules will be adopted, which results in the balance of orderly production of workstations being broken. In the end, good production capacity cannot be obtained.
Second, the empirical enterprise implements the time work system. Under this condition, production workers inevitably experience inertia that affects their work enthusiasm and initiative and leads to relatively low production efficiency. The S1′S2 algorithm that we highly recommend is designed from the theoretical perspective. It eliminates the influence of human factors in the production scheduling process to improve production capacity.
In summary, the effectiveness of the S1′S2 algorithm is substantially better than that of the S1S2 algorithm and the actual operation of enterprises, which demonstrates that the empirical enterprise can effectively improve its production capacity and earn greater profits by introducing the S1′S2 algorithm. The S1′S2 algorithm has good reference significance for guiding the actual production scheduling of enterprises.
6. Conclusions
This paper constructs a multistage production scheduling model and designs a multistage production scheduling algorithm based on sequencing theory by analyzing the relationships among production stages and optimizing each stage. Meanwhile, to verify the effectiveness of the construction model and design algorithm, we conduct numerical experiments that show that the model and algorithm constructed in this paper not only improve the production efficiency of an empirical enterprise but also provide technical support to improve production capacity.
However, the paper considers only static scheduling and not dynamic scheduling. In the actual production scheduling process, it is impossible for the production personnel to work all the time. Moreover, production equipment cannot operate all the time without faults, and not all the raw materials can be delivered on time. Therefore, there are many uncertain factors in the production scheduling process. Thus, one direction of followup research will be to improve the adaptability of the production scheduling method.
Data Availability
The data used to support the findings of this study can be found in Tables 1–3 of this paper.
Conflicts of Interest
The authors declare no conflicts of interest.
Authors’ Contributions
Conceptualization was performed by Jianzhong Xu and Song Zhang; formal analysis was done by Song Zhang and Yuzhen Hu; investigation was carried out by Song Zhang; methodology was prepared by Song Zhang and Yuzhen Hu; original draft was written by Song Zhang; review and editing were carried out by Song Zhang; supervision was performed by Jianzhong Xu. All authors have read and approved the published version of the manuscript.
Acknowledgments
This research was partially supported by the Natural Science Foundation of Heilongjiang Province (LH2019G014 and QC2016095), Philosophy and Social Science Research Planning Project of Heilongjiang Province (17JYH49 and 18GLC208), Key Research Topics of Economic and Social Development in Heilongjiang Province (19023), the 2019 Annual Basic Project of the Party’s Political Construction Research Center of the Ministry of Industry and Information Technology (19GZY411), the National Natural Science Foundation on Emergency Management Project of China (71841054), the National Natural Science Foundation of China (71801061), the project of Central University Basic Research Fund (3072020CFT0902), and Philosophy and Social Science Research Planning Project of Zhejiang Province (20NDQN297YB).
References
 X. Shao and Y. Rao, Theory and Method of Manufacturing System Operation Optimization, Science Press, Beijing, China, 2010.
 S. M. Johnson, “Optimal two and threestage production schedules with setup times included,” Naval Research Logistics Quarterly, vol. 1, no. 1, pp. 61–68, 1954. View at: Publisher Site  Google Scholar
 S. C. Graves, “A review of production scheduling,” Operations Research, vol. 29, no. 4, pp. 646–675, 1981. View at: Publisher Site  Google Scholar
 J. R. Jackson, “Scheduling a production line to minimize maximum tardiness,” Tech. Rep., UCLA, Los Angeles, CA, USA, 1955, Research Report 43. View at: Google Scholar
 W. E. Smith, “Various optimizers for singlestage production,” Naval Research Logistics Quarterly, vol. 3, no. 12, pp. 59–66, 1956. View at: Publisher Site  Google Scholar
 T. C. Hu, “Parallel sequencing and assembly line problems,” Operations Research, vol. 9, no. 6, pp. 841–848, 1961. View at: Publisher Site  Google Scholar
 R. L. Graham, “Bounds for certain multiprocessing anomalies,” Bell System Technical Journal, vol. 45, no. 9, pp. 1563–1581, 1966. View at: Publisher Site  Google Scholar
 R. L. Graham, “Bounds on multiprocessing timing anomalies,” SIAM Journal on Applied Mathematics, vol. 17, no. 2, pp. 416–429, 1969. View at: Publisher Site  Google Scholar
 Z. Jiang, F. Chen, and X. Zhang, “Singlemachine scheduling with timesbased and jobdependent learning effect,” Journal of the Operational Research Society, vol. 68, no. 7, pp. 809–815, 2017. View at: Publisher Site  Google Scholar
 K. Kianfar, G. Moslehi, and A. S. Nookabadi, “Exact and heuristic algorithms for minimizing Tardy/Lost penalties on a singlemachine scheduling problem,” Computational and Applied Mathematics, vol. 37, no. 2, pp. 867–895, 2018. View at: Publisher Site  Google Scholar
 X. Cao, W.H. Wu, W.H. Wu, and C.C. Wu, “Some twoagent singlemachine scheduling problems to minimize minmax and minsum of completion times,” Operational Research, vol. 18, no. 2, pp. 293–314, 2018. View at: Publisher Site  Google Scholar
 Z. Xu and D. Xu, “Singlemachine scheduling with workloaddependent tool change durations and equal processing time jobs to minimize total completion time,” Journal of Scheduling, vol. 21, no. 4, pp. 461–482, 2018. View at: Publisher Site  Google Scholar
 A. Ekici, M. Elyasi, O. Ö. Özener, and M. B. Sarıkaya, “An application of unrelated parallel machine scheduling with sequencedependent setups at vestel electronics,” Computers & Operations Research, vol. 111, pp. 130–140, 2019. View at: Publisher Site  Google Scholar
 J. C. YepesBorrero, F. Villa, F. Perea, and J. P. CaballeroVillalobos, “GRASP algorithm for the unrelated parallel machine scheduling problem with setup times and additional resources,” Expert Systemswith Applications, vol. 141, Article ID 112959, 2020. View at: Publisher Site  Google Scholar
 M. S. S. Mir, J. Rezaeian, and H. Mohamadian, “Scheduling parallel machine problem under general effects of deterioration and learning with pastsequencedependent setup time: heuristic and metaheuristic approaches,” Soft Computing, vol. 24, no. 2, pp. 1335–1355, 2020. View at: Publisher Site  Google Scholar
 W. Luo, Y. Xu, W. Tong, and G. Lin, “Singlemachine scheduling with jobdependent machine deterioration,” Journal of Scheduling, vol. 22, no. 6, pp. 691–707, 2019. View at: Publisher Site  Google Scholar
 R. Chen and J. Yuan, “Unary NPhardness of singlemachine scheduling to minimize the total tardiness with deadlines,” Journal of Scheduling, vol. 22, no. 5, pp. 595–601, 2019. View at: Publisher Site  Google Scholar
 W. Luo and F. Liu, “On singlemachine scheduling with workloaddependent maintenance duration,” Omega, vol. 68, pp. 119–122, 2017. View at: Publisher Site  Google Scholar
 S. Atakan, B. Kerem, and N. Noyan, “Minimizing valueatrisk in singlemachine scheduling,” Annals of Operations Research, vol. 248, no. 12, pp. 25–73, 2017. View at: Publisher Site  Google Scholar
 L. Zhang, Q. Deng, G. Gong, and W. Han, “A new unrelated parallel machine scheduling problem with tool changes to minimise the total energy consumption,” International Journalof Production Research, pp. 1–20, 2019. View at: Publisher Site  Google Scholar
 E. Vallada, F. Villa, and L. FanjulPeyro, “Enriched metaheuristics for the resource constrained unrelated parallel machine scheduling problem,” Computers & Operations Research, vol. 111, pp. 415–424, 2019. View at: Publisher Site  Google Scholar
 B. Alidaee, H. Wang, R. B. Kethley, and F. Landram, “A unified view of parallel machine scheduling with interdependent processing rates,” Journal of Scheduling, vol. 22, no. 5, pp. 499–515, 2019. View at: Publisher Site  Google Scholar
 B. Vincent, C. Duhamel, L. Ren, and N. Tchernev, “A populationbased metaheuristic for the capacitated lotsizing problem with unrelated parallel machines,” International Journal of Production Research, pp. 1–18, 2020. View at: Publisher Site  Google Scholar
 F. M. B. Toledo and V. A. Armentano, “A Lagrangianbased heuristic for the capacitated lotsizing problem in parallel machines,” European Journal of Operational Research, vol. 175, no. 2, pp. 1070–1083, 2006. View at: Publisher Site  Google Scholar
 N. Ueno, S. Sotojima, and J. Takeda, “Multistage flowshop in steel works,” in Proceedings of the 24th Annual Simulation Symposium, pp. 332–337, IEEE Computer Society Press, New Orleans, LO, USA, 1991. View at: Google Scholar
 S. Yuan, T. Li, B. Wang, and N. Yu, “Model and algorithm for twostage flow shop group scheduling problem with special blocking constraint,” Control and Decision, vol. 35, no. 7, pp. 1773–1779, 2020. View at: Google Scholar
 W. Qin, Z. Zhuang, Y. Liu, and O. Tang, “A twostage ant colony algorithm for hybrid flow shop scheduling with lot sizing and calendar constraints in printed circuit board assembly,” Computers& Industrial Engineering, vol. 138, Article ID 106115, 2019. View at: Publisher Site  Google Scholar
 M. K. Marichelvam, M. Geetha, and O. Tosun, “An improved particle swarm optimization algorithm to solve hybrid flowshop scheduling problems with the effect of human factors—a case study,” Computers& Operations Research, vol. 114, Article ID 104812, 2020. View at: Publisher Site  Google Scholar
 S. Li, Y. Li, and Z. Liu, “Mix optimization scheduling approach formultiresource jobshop,” Journal of Systems Engineering, vol. 22, no. 5, pp. 551–555, 2007. View at: Google Scholar
 S. Wang, Y. Li, and W. Huai, “A threestage decoding method for justintime jobshop scheduling,” Industrial Engineering Journal, vol. 18, no. 3, pp. 98–104, 2015. View at: Google Scholar
 P. Fattahi, N. B. Rad, F. Daneshamooz, and S. Ahmadi, “A new hybrid particle swarm optimization and parallel variable neighborhood search algorithm for flexible job shop scheduling with assembly process,” Assembly Automation, vol. 40, no. 3, p. 419, 2020. View at: Publisher Site  Google Scholar
 W. Huang, C. Yan, X. Wang, and J. Xu, “Job shop scheduling problem for complex engineering project products,” Mechanical Science and Technology for Aerospace Engineering, vol. 39, no. 3, pp. 1–12, 2020. View at: Publisher Site  Google Scholar
 B.h. Zhou, X.m. Liao, and K. Wang, “Kalman filter and multistage learningbased hybrid differential evolution algorithm with particle swarm for a twostage flow shops scheduling problem,” Soft Computing, vol. 23, no. 24, pp. 13067–13083, 2019. View at: Publisher Site  Google Scholar
 M. Liu, X. Yao, and Y. Li, “Hybrid whale optimization algorithm enhanced with Levy flight and differential evolution for job shop scheduling problems,” Applied Soft Computing, vol. 87, Article ID 105954, 2020. View at: Publisher Site  Google Scholar
 D. Ferone, S. Hatami, E. M. GonzalezNeira, A. A. Juan, and P. Festa, “A biasedrandomized iterated local search for the distributed assembly permutation flowshop problem,” International Transactionsin Operational Research, vol. 27, no. 3, 2020. View at: Publisher Site  Google Scholar
 S. Liu, J. Pei, H. Cheng, X. Liu, and P. M. Pardalos, “Twostage hybrid flow shop scheduling on parallel batching machines considering a jobdependent deteriorating effect and nonidentical job sizes,” Applied Soft Computing, vol. 84, 2019. View at: Publisher Site  Google Scholar
 H. Samarghandi, “Minimizing the makespan in a flow shop environment under minimum and maximum timelag constraints,” Computers & Industrial Engineering, vol. 136, pp. 614–634, 2019. View at: Publisher Site  Google Scholar
 G. Gong, R. Chiong, Q. Deng et al., “Energyefficient flexible flow shop scheduling with worker flexibility,” Expert Systemswith Applications, vol. 141, Article ID 112902, 2020. View at: Publisher Site  Google Scholar
 R. H. Caldeira and A. Gnanavelbabu, “Solving the flexible job shop scheduling problem using an improved Jaya algorithm,” Computers& Industrial Engineering, vol. 137, Article ID 106064, 2019. View at: Publisher Site  Google Scholar
 A. Dabah, A. Bendjoudi, A. AitZai, and N. N. Taboudjemat, “Efficient parallel tabu search for the blocking job shop scheduling problem,” Soft Computing, vol. 23, no. 24, pp. 13283–13295, 2019. View at: Publisher Site  Google Scholar
 Y. Zhang, H. Zhu, and D. Tang, “An improved hybrid particle swarm optimization for multiobjective flexible jobshop scheduling problem,” Kybernetes, 2019, in Press. View at: Publisher Site  Google Scholar
 H. Tang and C. Zhao, Introduction to Sequencing Theory, Science Press, Beijing, China, 2002.
Copyright
Copyright © 2020 Jianzhong Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.