• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.617-620
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• 2000

In this paper, we show an application of constraint logic programming to the operation scheduling on machines in a job shop. Constraint logic programming is a new genre of programming technique combining the declarative aspect of logic programming with the efficiency of constraint manipulation and solving mechanisms. Due to the latter feature, combinatorial search problems like scheduling may be resolved efficiently. In this study, the jobs that consist of a set of related operations are supposed to be constrained by precedence and resource availability. We also explore how the constraint solving mechanisms can be defined over a scheduling domain. Thus the scheduling approach presented here has two benefits: the flexibility that can be expected from an artificial intelligence tool by simplifying greatly the problem; and the efficiency that stems from the capability of constraint logic programming to manipulate constraints to prune the search space in an a priori manner.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.609-612
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• 2000

Job-shop scheduling problem(JSSP) is one of the best-known machine scheduling problems and essentially an ordering problem. A new encoding scheme which always give a feasible schedule is presented, by which a schedule directly corresponds to an assigned-operation ordering string. It is initialized with G&T algorithm and improved using the developed genetic operator; APMX or BPMX crossover operator and mutation operator. and the problem of infeasibility in genetic generation is naturally overcome. Within the framework of the newly designed genetic algorithm, the NP-hard classical job-shop scheduling problem can be efficiently solved with high quality. Moreover the optimal solutions of the famous benchmarks, the Fisher and Thompson's 10${\times}$10 and 20${\times}$5 problems, are found.

• Korean Management Science Review
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• v.8 no.1
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• pp.51-70
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• 1991

This paper discusses planning and scheduling problems for efficient utilization of an FMS and presents an integrated decision support system for FMS production planning and scheduling problems. The decision support system, FMSDS(Flexible Manufacturing Systems Decision Support System), includes of data of handling module, part selection module, loading module, load adjusting module, scheduling module and simulation module etc. This paper includes the solution methodology of each subproblem. And an integrated interface scheme between the subproblems is presented. The interface scheme considers the relationships between the subproblems and generates solution using hierarchical and looping approaches. FMSDS is made up of six alternative models considering 3 loading objectives and 2 production order processing strategies. Performance comparisons among 6 alternatives and other decision support systems are shown using the non-terminating simulation techniques.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.410-420
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• 1994

In many project-oriented production systems, e.g., shipyards or large-scale steel products manufacturing, resource loading by an activity is flexible, and the activity duration is a function of resource allocation. For example, if one doubles the size of the crew assigned to perform an activity, it may be feasible to complete the activity in half the duration. Such flexibility has been modeled by Weglarz [131 and by Leachman, Dincerler, and Kim [7] in extended formulations of the resource-constrained project scheduling problem. This paper presents a new algorithmic approach to the problem that combines the ideas proposed by the aforementioned authors. The method we propose involves a two-step approach: (1) solve the resource-constrained scheduling problem using a heuristic, and (2) using this schedule as an initial feasible solution, find improved resource allocations by solving a linear programming model. We provide computational results indicating the superiority of this approach to previous methodology for the resource-constrained scheduling problem. Extensions to the model to admit overlap relationships of the activities also are presented.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.131-134
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• 1996

Application of machine learning to scheduling problems has focused on improving system performance based on opportunistic selection among multitudes of simple rules. This study proposes a new method of learning scheduling rules, which first establishes qualitatively meaningful criteria and quantitatively optimizes the use of them, a similar way as human scheduler accumulate their expertise. The weighting of these criteria is trained in response to the system states through simulation. To mimic human quantitative feelings, distributed fuzzy sets are used for assessing the system state. The proposed method was applied to job dispatching in a simulated FMS environment. The job-dispatching criteria used were the length of the processing time of a job and the situation of the next workstation. The results show that the proposed method can develop efficient and robust scheduling strategies, which can also provide understandable and usable know-hows to the human scheduler.

• 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.

• 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 Sensor Science and Technology
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• v.25 no.2
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• pp.91-102
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• 2016

The objective of this paper is to propose a heuristic algorithm to optimize the railway track maintenance scheduling, a NP-hard problem, by reflecting conditions of the actual field more quickly and easily. We develop the mechanism based on Binary Artificial Bee Colony (BABC) and Binary Harmony Search (BHS), and verify their performance through simulation experiments. Our proposed BABC and BHS mechanisms were applied to problems composed of 30, 60, 100, and 200 operations for railway track maintenance scheduling to carry out experiments and analysis. On comparing it with the results solved by CPLEX, it is found that the mechanism could present an optimal solution within limited time by user.

• Management Science and Financial Engineering
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• v.9 no.2
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• pp.47-68
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• 2003

We consider the customer order scheduling problem with job capacity restriction where the number of jobs in the shop at the same time is fixed. In the customer order scheduling problem, each job is part of some batch (customer order) and the composition of the jobs (product) in the batch is pre-specified. The objective function is associated with the completion time of the batches instead of the completion time of the jobs. We first summarize the known results for the general customer order scheduling problems. Then, we establish some new properties for the problems with job capacity restriction. For the case of unit processing time with the objective of minimizing makespan, we develop a polynomial-time optimal procedure for the two machine case. For the same problem with a variation of no batch alternation, we also develop a polynomial-time optimal procedure. Then, we show that the problems with the objectives of minimizing makespan and minimizing average batch completion time become NP-hard when there exist arbitrary number of machines. Finally, We propose optimal solution procedures for some special cases.