• Industrial Engineering and Management Systems
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• v.14 no.1
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• pp.22-31
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• 2015

In this paper, a two-stage hybrid flow shop problem is considered. Specifically, there exist identical parallel machines at stage 1 and two dedicated machines at stage 2, and the objective of the problem is to minimize makespan. After being processed by any machine at stage 1, a job must be processed by a specific machine at stage 2 depending on the job type, and one type of jobs can have different processing times on each machine. First, we introduce the problem and establish complexity of several variations of the problem. For some special cases, we develop optimal polynomial time solution procedures. Then, we establish some simple lower bounds for the problem. In order to solve this NP-hard problem, three heuristics based on simple rules such as the Johnson's rule and the LPT (Longest Processing Time first) rule are developed. For each of the heuristics, we provide some theoretical analysis and find some worst case bound on relative error. Finally, we empirically evaluate the heuristics.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.59
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• pp.11-20
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• 2000

The Job Shop Scheduling Problem(JSSP) is one of the most general and difficult of all traditional scheduling problems. The goal of this research is to develop an efficient scheduling method based on single genetic algorithm(SGA) and parallel genetic algorithm (PGA) to address JSSP. In this scheduling method, new genetic operator, generating method of initial population are developed and island model PGA are proposed. The scheduling method based on PGA are tested on standard benchmark JSSP. The results were compared with SGA and another GA-based scheduling method. The PGA search the better solution or improves average of solution in benchmark JSSP. Compared to traditional GA, the proposed approach yields significant improvement at a solution.

• Management Science and Financial Engineering
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• v.15 no.2
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• pp.69-100
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• 2009

In this paper, we consider a relatively new two-machine flow shop scheduling problem where the unit time WIP cost increases as a job passes through various stages in the production process, and the objective is to minimize the total WIP (work-in-process) cost. Specifically, we study three special cases of the problem. First, we consider the problem where processing times on machine 1 are identical. Second, the problem with identical processing times on machine 2 is examined. The recognition version of the both problems is unary NP-complete (or NP-complete in strong sense). For each problem, we suggest two simple and intuitive heuristics and find the worst case bound on relative error. Third, we consider the problem where the processing time of a job on each machine is proportional to a base processing time. For this problem, we show that a known heuristic finds an optimal schedule.

• Industrial Engineering and Management Systems
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• v.12 no.2
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• pp.151-160
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• 2013

In the past decades, several algorithms based on evolutionary approaches have been proposed for solving job shop scheduling problems (JSP), which is well-known as one of the most difficult combinatorial optimization problems. Most of them have concentrated on finding optimal solutions of a single objective, i.e., makespan, or total weighted tardiness. However, real-world scheduling problems generally involve multiple objectives which must be considered simultaneously. This paper proposes an efficient particle swarm optimization based approach to find a Pareto front for multi-objective JSP. The objective is to simultaneously minimize makespan and total tardiness of jobs. The proposed algorithm employs an Elite group to store the updated non-dominated solutions found by the whole swarm and utilizes those solutions as the guidance for particle movement. A single swarm with a mixture of four groups of particles with different movement strategies is adopted to search for Pareto solutions. The performance of the proposed method is evaluated on a set of benchmark problems and compared with the results from the existing algorithms. The experimental results demonstrate that the proposed algorithm is capable of providing a set of diverse and high-quality non-dominated solutions.

• Journal of the Korea Society for Simulation
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• v.25 no.4
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• pp.1-12
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• 2016

The industry producing the parts of aircraft engines is a traditional order-made system with highly variety and small quantity, and the manufacturing system is the typical job shop with identical or non-identical multiple machines in a workstation. Furthermore, there are many alternative operations and alternative machines allowed in machining processes, and tremendous routings and assembly operations should be considered. Usually simulation is the most efficient technology to analyze such a complex system, and high modeling skills are required for developing the simulation models. In this paper, a case study on a company which produces the parts of aircraft engines is introduced, specially focused on simulation modeling methodologies for the complex system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2993-3010
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• 2021

The job shop scheduling problem (JSSP) plays a critical role in smart manufacturing, an effective JSSP scheduler could save time cost and increase productivity. Conventional methods are very time-consumption and cannot deal with complicated JSSP instances as it uses one optimal algorithm to solve JSSP. This paper proposes an effective scheduler based on deep learning technology named self-supervised long-short term memory (SS-LSTM) to handle complex JSSP accurately. First, using the optimal method to generate sufficient training samples in small-scale JSSP. SS-LSTM is then applied to extract rich feature representations from generated training samples and decide the next action. In the proposed SS-LSTM, two channels are employed to reflect the full production statues. Specifically, the detailed-level channel records 18 detailed product information while the system-level channel reflects the type of whole system states identified by the k-means algorithm. Moreover, adopting a self-supervised mechanism with LSTM autoencoder to keep high feature extraction capacity simultaneously ensuring the reliable feature representative ability. The authors implemented, trained, and compared the proposed method with the other leading learning-based methods on some complicated JSSP instances. The experimental results have confirmed the effectiveness and priority of the proposed method for solving complex JSSP instances in terms of make-span.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.46-49
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• 1996

This paper presents a multi-pass heuristic for job shop scheduling with due dates. The proposed heuristic iteratively improves solutions based on operation due dates. To find a good solution efficiently, a method for searching the neighborhood of current schedule is developed. The heuristic is compared with two existing heuristics as well as several dispatching rules in terms of solution quality and computation time. The experimental results show that the proposed approach is promising.

• The Journal of Korean Society for School & Community Health Education
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• v.2 no.1
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• pp.67-84
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• 2001

This study was conducted to examine beauty shop workers' views of job. Data were collected from the workers in Seoul, Daegu, Pohang, Junjoo, and Kimhae from June 1, 2000 to August 31, 2000. The results of this study are summarized as follows. 1. According to general characteristics of the subjects, 28,7% of them was female; 94.2% 'specialized in hair'; 46.4% 'below twenty nine years old'; 47.1% 'married'; 59.7% 'highschool graduate'; 33.9% 'worked for below three years'; 28.5% 'monthly income of five hundred thousand to nine hundred ninety thousand won'; 62.3% 'working for above twelve hours a day' ; 41.0% 'above five workers' ; 40.6% 'working in city'. 2. 54.8% of the respondents thought that they were in good health. 76.3% of them smoked and 54.8% drank. 62.8% of them did not exercise and 78.7% was under stress. 61.5% responded that they chose the job because of its possibility of professional vocation. 91.0% of them obtained the beauty skill from beauty schools. 3. Among the factors which influenced job satisfaction, 'stable job and life security' was highest(43.9%), while 'interest in the job and amount of pay' was lowest(3.2%). 'Personal ability and use of originality' was 19.4% and 'harmonious relationship with fellow workers' was 18.1%. 'Job environment' was 7.1% and 'harmonious relationship with higher workers' was 4.5%. 4. The level workers' view of job was $113.8{\pm}17.3$ points on the basis of 150 points. On the basis of 75 points, each item showed it points in order of self-development($22.3{\pm}3.8$), service for customers($20.1{\pm}3.1$), vocational mission($15.6{\pm}3.1$), harmony with the others($18.9{\pm}3.5$), working environment($18.6{\pm}3.6$), and working condition($14.3{\pm}5.1$). 5. Among the reasons why they considered leaving the job, 24.0% of them considered it because they could not free time, while 15.4% considered it because undesirable living environment or long distance from home. 15.0% thought it because they could not receive proper treatment as much as they worked and 12.8% thought they overworked. 6. When they move into new working places, they consider such factors as good working environment(24.1%), good place to open their own beauty shops(16.7%), good beauty shop to learn beauty skill(15.6%), chance to have job training(9.5%), and close place from home(9.0%). 7. 40.6% of the respondents wanted to leave the job, while 32.3% of them did not want to leave the job. The intention of leaving the displayed significant difference in the variables of age, working period, monthly income, marital status, the number of workers, location of the shop, rank, and reason of selecting the job. 8. According to the results of a regression analysis of factors which influenced job satisfaction, it was affected significantly by intention of leaving job, the number of workers, health condition, level of stress, and monthly income. The beauty shop workers showed low satisfaction level with working environment, working condition, and working mission, They considered leaving the job because of lack of free time, overwork, poor working environment, improper treatment, etc. Therefore, related professionals and organizations must device adequate measures in order to make them work with pride as creators of beauty.

• IE interfaces
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• v.12 no.4
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• pp.617-624
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• 1999

In order to maximize an availability of machine and utilization of space, the parallel machines scheduling problem with space limit is frequently discussed in the industrial field. In this paper, we consider a scheduling problem for assembly machine in ship engine assembly shop. This paper considers the parallel machine scheduling problem in which n jobs having different release times, due dates and space limits are to be scheduled on m parallel machines. The objective function is to minimize the sum of earliness and tardiness. To solve this problem, a heuristic is developed. The proposed heuristic is divided into three modules hierarchically: job selection, machine selection and job sequencing, solution improvement. To illustrate its effectiveness, a proposed heuristic is evaluated with a large number of randomly generated test problems based on the field situation. Through the computational experiment, we determine the job selection rule that is suitable to the problem situation considered in this paper and show the effectiveness of our heuristic.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.3
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• pp.47-57
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• 1992

The purpose of this paper is to improve the operation performance of unit-load Automated Guided Vehicles (AGV's) used as a carrier and mobile workstation in a flow-shop type flexible manufacturing system. An algorithm is developed to determine the optimal job sequence which minimizes the vehicle idle time on the line and the production makespan by the use of the entering interval and travel time between workcenters. An entering times of AGV's and the minimum number of AGV's required are calculated by optimal job sequence. When the numbe rof AGV's is limited, enterling times of AGV's are adjusted to maximize the efficient use of vehicles. A numerical example is given to illustrate the application of the algorithm.