• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.85-88
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• 2003

In case that any jobs in a Job Shop can be scheduled on more than one machine and may have flexible operation sequences, with considering such case it is very difficult and complex to make the optimal process plans and scheduling. But they should be considered for an integrated model to perform more effective process planning and scheduling in this job shop problem. In this paper, we propose GA-based scheduling method to integrate effectively the problem of alternative machines, alternative operation sequences and scheduling. The performance of proposed GA is evaluated through comparing integrated scheduling with not integrated scheduling in molding company with alternative machines and operation sequences. Also, we use benchmark problems to evaluate performance. The scheduling method in this research will apply usefully to real world scheduling problems.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.2
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• pp.97-109
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• 2002

Manufacturing environments in the real world are subject to many sources of change and uncertainty, such as new job releases, job cancellations, a chance in the processing time or start time of some operation. Thus, the realistic scheduling method should Properly reflect these dynamic environment. Based on the release times of jobs, JSSP (Job Shoe Scheduling Problem) can be classified as static and dynamic scheduling problem. In this research, we mainly consider the dynamic JSSP with continually arriving jobs. The goal of this research is to develop an efficient scheduling method based on GA (Genetic Algorithm) to address dynamic JSSP. we designed scheduling method based on SGA (Sing1e Genetic Algorithm) and PGA (Parallel Genetic Algorithm) The scheduling method based on GA is extended to address dynamic JSSP. Then, This algorithms are tested for scheduling and rescheduling in dynamic JSSP. The results is compared with dispatching rule. In comparison to dispatching rule, the GA approach produces better scheduling performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.191-199
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• 2001

This paper presents a new genetic algorithm for job-shop scheduling problems. When we design a genetic algorithm for difficult ordering problems such as job-shop scheduling problems, it is important to design encoding/crossover that is excellent in characteristic preservation and to maintain a diversity of population. We used Job-based order crossover(JOX). Since the schedules generated by JOX are not always active-schedule, we proposed a method to transform them into active schedulesby using the GT method with c)laracteristic preservation. We introduce strategies for maintaining a diversity of the population by eliminating same individuals in the population. Furthermore, we are not used mutation. Experiments have been done on two examples: Fisher s and Thompson s $lO\timeslO and 20\times5$ benchmark problem.

• Journal of Industrial Convergence
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• v.1 no.2
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• pp.69-79
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• 2003

In order to solve scheduling, many techniques including optimization and heuristics methods have been proposed. However, these conventional approaches are usually inadequate to obtain a satisfactory solution because of a NP-Complete which requires extensive computation effort. The aim of this paper is to develop an expert system for job shop scheduling using an AI technique. Through this system, the user can choose one of several performances. If one performance is chosen, then the system solves a scheduling in order to satisfy that performance. Then, this system decides urgent job. This system can utilize production resources efficiently and minimize work-in-process inventories and tardy jobs.

• Journal of Korean Institute of Industrial Engineers
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• v.21 no.3
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• pp.409-428
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• 1995

Job shop scheduling with m different machines and n different jobs is a NP-hard problem of combinatorial optimization. The purpose of the paper is to develop the heuristic method using tabu search for job shop scheduling to minimize makespan or mean flowtime. To apply tabu search to job shop scheduling problem, in this paper we propose the several move methods that employ insert moves in order to generate the neighbor solutions, and present the efficient rescheduling procedure that yields active schedule for a changed operation sequence by a move of operations. We also discuss the tabu search techniques of diversifying the search of solution space as well as the simple tabu search. By experiments, we find the appropriate tabu list size and tabu attributes, and analyze the proposed tabu search techniques with respect to the quality of solutions and the efforts of computation. The experimental results show that the proposed tabu search techniques using long-term memory function have the ability to search a good solution, and are more efficient in the mean flowtime minimization problem than in the makespan minimization.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.43
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• pp.127-137
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• 1997

This paper proposes a heuristic method for job shop scheduling with alternative machines. Our heuristic suggests two machine-selecting rules and two priority dispatching rules for modifying existent ones considering alternative machines, and then it extends existing nondelay/active job shop schedule generation. This heuristic provides good criteria(rules) in the selection of a proper machine among those performing a specific operation and for the dispatch of an operation to a selected machine and thus these rules permit the efficient job shop scheduling with alternative machines. The performances of our two machine-selecting rules in addition to the two priority dispatching rules, applied together with the existing 17 rules, are experimented and evaluated, respectively.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.45-54
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• 2022

In this study, a multi-objective robust job-shop scheduling (JSS) model was developed. The model considered multi-jobs and multi-machines. The model also considered uncertain processing times for all tasks. Each job was assigned a specific due date and a tardiness penalty to be paid if the job was not delivered on time. If any job was completed early, holding expenses would be assigned. In addition, the model added idling penalties to accommodate the idling of machines while waiting for jobs. The problem assigned was to determine the optimal start times for each task that would minimize the expected penalties. A numerical problem was solved to minimize both the makespan and the total penalties, and a comparison was made between the results. Analysis of the results produced a prescription for optimizing penalties that is important to be accounted for in conjunction with uncertainties in the job-shop scheduling problem (JSSP).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.39
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• pp.275-284
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• 1996

It can be made a definition that scheduling is a imposition of machinery and equipment to perform a collection of tasks. Ultimately scheduling is an assessment of taking order for which would be perform. So it is called "sequencing" in other words. In a job shop scheduling, the main object is to making delivery in accordance with the due date and order form customer, not to producing lots of quantity with minimizing mean flow time in a given time. Actually, in a company, they concentrate more in the delivery than minimizing the mean flow time. Therefore this paper suggest a new priority dispatching rule under consideration as below in a n/m job shop scheduling problem with due date. 1. handling/transportation time, 2. the size of customer order With this algorithm, we can make a scheduling for minimizing the tardiness of delivery which satisfy a goal of production.roduction.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.2
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• pp.92-101
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• 2002

The Job Shop Scheduling Problem(JSSP) is concerned with schedule of m different machines and n jobs where each job consists of a chain of operations, each of which needs to be processed during an uninterrupted time period of a given length on a given machine. The purpose of this paper is to develop the efficient heuristic method for solving the minimum makespan problem of the large scale job shop scheduling. The proposed heuristic method is based on a Tabu Search(TS) and on a Constraint Satisfaction Technique(CST). In this paper, ILOG libraries is used to embody the job shop model, and a CST is developed for this model to generate the increased solution. Then, TS is employed to overcome the increased search time of CST on the increased problem size md to refine the next-current solution. Also, this paper presents the new way of finding neighbourhood solution using TS. On applying TS, a new way of finding neighbourhood solution is presented. Computational experiments on well known sets of MT and LA problem instances show that, in several cases, our approach yields better results than the other heuristic procedures discussed In literature.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.303-306
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• 1996

This paper proposes a heuristic method for job shop scheduling with alternative machines. Our heuristic suggests four machine-selecting rules and two priority dispatching rules for modifying existent ones considering alternative machines, and then it extends existing nondelay/active job shop schedule generation. This heuristic provides good criteria(rules) in the selection of a proper machine among those performing a specific operation and for the dispatch of an operation to a selected machine and thus these rules permit the efficient job shop scheduling with alternative machines. The performances of our four machine-selecting rules in addition to the two priority dispatching rules, applied together with the existing 17 rules, are experimented and evaluated, respectively.