• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.306-309
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• 1996

The job shop scheduling problem has been a major target for many researchers. And, most of the past studies did not consider setup time. In many cases of real manufacturing environment, however, there exists a setup time for each operations. The setup can be divide into two parts, one can be done after job arrival. The setup time based on the latter can be summed together with processing time, but that based on the former can not be. We propose an approximation method based on shifting bottleneck procedure for solving the job shop scheduling problem with sequence independent setup time. It schedules the machines one by one, taking a bottleneck machine among the machines not yet scheduled. Every time after a new machine is scheduled, all schedules previously established are updated. Both the bottlenck search and the schedule updating procedure are based on solving a single machine scheduling problem with ready time, setup time and delivery time iteratively.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.90-93
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• 2000

This paper presents job scheduling for non-identical parallel machines using Simulated Annealing (SA). The scheduling problem accounts for allotting work parts of L lots into M parallel machines, where each lot is composed of N homogeneous jobs. Some lots may have different jobs while every job within each lot has common due date. Each machine has its own performance and set up time according to the features of the machine, and also by job types. A meta-heuristic, SA, is applied in this study to determine the job sequences of the scheduling problem so as to minimize total tardiness of due. The SA method is compared with a conventional steepest descent(SD) algorithm that is a typical tool for finding local optimum. The comparison shows the SA is much better than the SD in terms tardiness while SA takes longer , but acceptable time.

• 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.

• Journal of Korean Society for Quality Management
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• v.23 no.3
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• pp.156-166
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• 1995

Job shop scheduling problem is a complex system and an NP-hard problem. Thus it is natural to look for heuristic method. We consider the multi-part production scheduling problem for quality level in a job shop scheduling under the existence of alternative routings. The problem is more complex if the processing time is imprecision. It requires suitable method to deal with imprecision. Fuzzy set theory can be useful in modeling and solving scheduling problems with uncertain processing times. Li-Lee fuzzy number comparison method will be used to compare processing times that evaluated under fuzziness. This study presents heuristic method for quality level in bottleneck-based job shop scheduling under fuzzy environment. On the basis of the proposed method, an example is presented.

• Journal of Korean Institute of Industrial Engineers
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• v.18 no.1
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• pp.91-104
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• 1992

Flowshop scheduling problem is known to be NP-complete. Since the optimization apporach like branch-and-bound is limited by exponentially growing computation time, many heuristic methods have been developed. Total tardiness is one of the criteria that the researchers have recently considered in flowshop scheduling. There, however, are few literatures which studied the general (m machine)-flowshop scheduling under the total tardiness criterion. In this paper, a heuristic scheduling method to minimize total tardiness at the (m machine, n job)-flowshop is presented. A heuristic value function is proposed to be used as a dispatching criterion in initial schedule generation. And the schedule improving procedure, by pairwise interchange of tardy job with the job right ahead of it, is introduced. Illustrative examles and simulated results are presented.

• Korean Management Science Review
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• v.9 no.1
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• pp.119-137
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• 1992

A two phase heuristic algorithm has been developed for the resolution of resource conflicts in a single project scheduling problem. Phase 1 of the algorithm generates a feasible schedule by repairing resource conflicts. Phase 2 finds local improvements in the schedule found in phase 1. Then, the algorithm has been applied to multi project and job shop scheduling. Computational results are compared with those of dispatching procedures. Index Terms-disjunctive constraints, heuristic algorithm, project scheduling, job-shop scheduling.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.286-289
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• 1989

The purpose of this paper is to design a job scheduling algorithm utilizing intelligent control technique. Rulebase is built through the evaluation of rule-set scheduling. 24 scheduling rule-sets and meta-rules are employed. An appropriate scheduling rule-set is selected based on this rulebase and current manufacturing system status. Six criteria have been used to evaluate the performance of scheduling. The performance of sheduling is dependent on random breakdown of the major FMS components during simulation.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.287-307
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• 2018

There are two targets to schedule parallel jobs in the Cloud: (1) scheduling the jobs as many as possible, and (2) reducing the average execution time of the jobs. Most of previous work mainly focuses on the computing speed of resources without considering other attributes, such as bandwidth, memory and so on. Especially, past work does not consider the supply-demand condition from those attributes. Resources have different attributes, considering those attributes together makes the scheduling problem more difficult. This is the problem that we try to solve in this paper. First of all, we propose a new parallel job scheduling method based on a classification method of resources from different attributes, and then a scheduling method-CPLMT (Cloud parallel scheduling based on the lists of multiple attributes) is proposed for the parallel tasks. The classification method categories resources into different kinds according to the number of resources that satisfy the job from different attributes of the resource, such as the speed of the resource, memory and so on. Different kinds have different priorities in the scheduling. For the job that belongs to the same kinds, we propose CPLMT to schedule those jobs. Comparisons between our method, FIFO (First in first out), ASJS (Adaptive Scoring Job Scheduling), Fair and CMMS (Cloud-Minmin) are executed under different environments. The simulation results show that our proposed CPLMT not only reduces the number of unfinished jobs, but also reduces the average execution time.