• The Journal of Information Systems
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• v.25 no.4
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• pp.105-119
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• 2016

Purpose The conventional solution methods for production scheduling problems typically focus on the active schedules, which result in short makespans. However, the active schedules are more difficult to generate than the semi active schedules. In other words, semi active schedule based search strategy may help to reduce the computational costs associated with production scheduling. In this context, this paper aims to compare the performances of active schedule based and semi active schedule based search methods for production scheduling problems. Design/methodology/approach Two decoding approaches, active schedule decoding and semi active schedule decoding, are introduced in this paper, and they are used to implement genetic algorithms for classical job shop scheduling problem. The permutation representation is adopted by the genetic algorithms, and the decoding approaches are used to obtain a feasible schedule from a sequence of given operations. Findings The semi active schedule based genetic algorithm requires slightly more iterations in order to find the optimal schedule, while its execution time is quite shorter than active schedule based genetic algorithm. Moreover, the operations of semi active schedule decoding is easy to understand and implement. Consequently, this paper concludes that semi active schedule based search methods also can be useful if effective search strategies are given.

• 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.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.1-22
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• 2005

This paper presents new scheduling heuristics, namely Mean Progressive Weighted Tardiness Estimator (MPWT) Heuristic Method and modified priority rules with sequence-dependent setup times consideration. These are designed to solve job shop scheduling problems with new performance measures - progressive weighted tardiness penalties. More realistic constraints, which are inter-machine overlapping sequence-dependent setup times, are considered. In real production environments, inter-machine overlapping sequence-dependent setups are significant. Therefore, modified scheduling generation algorithms of active and nondelay schedules for job shop problems with inter-machine overlapping sequence-dependent setup times are proposed in this paper. In addition, new customer-based measures of performance, which are total earliness and progressive weighted tardiness, and total progressive weighted tardiness, are proposed. The objective of the first experiment is to compare the proposed priority rules with the consideration of sequence-dependent setup times and the standard priority rules without setup times consideration. The results indicate that the proposed priority rules with setup times consideration are superior to the standard priority rules without the consideration of setup times. From the second experiment and the third experiment to compare the proposed MPWT heuristic approach with the efficient priority rules with setup times consideration, the MPWT heuristic method is significantly superior to the Batched Apparent Tardiness Cost with Sequence-dependent Setups (BATCS) rule, and other priority rules based on total earliness and progressive weighted tardiness, and total earliness and tardiness.

• IE interfaces
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• v.11 no.3
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• pp.1-13
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• 1998

This paper addresses a scheduling scheme for Flexible Flow Shop(FFS) in the case that a factory is a sub-plant of an electronic device manufacturing plant. Under this environment, job orders for the sub-plants in the production route are generated together with job processing time bucket when the customer places orders for final product. The processing time bucket for each job is a duration from possible release date to permissible due date. A sub-plant modeled FFS should schedule these jobs orders within time bucket. Viewing a Printed Circuit Board(PCB) assembly line as a FFS, the developed scheme schedules an incoming order along with the orders already placed on the scheduled. The scheme consists of the four steps, 1)assigning operation release date and due date to each work cells in the FFS, 2)job grouping, 3)dispatching and 4)machine allocation. Since the FFS scheduling problem is NP-complete, the logics used are heuristic. Using a real case, we tested the scheme and compared it with the John's algorithm and other dispatching rules.

• Journal of Korean Society for Quality Management
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• v.16 no.1
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• pp.32-42
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• 1988

The general n/m job-shop problem is easy to state what is required, but it is extremely difficult to make any progress whatever toward a solution. This paper was first to examine a heuristic procedure of general n/m scheduling generation focused on the procedure of MWRK (Most Work Remaining) presented by Giffler and Thompson (1960) among others. Then modified procedure was proposed to obtain better solution in light of the key measure of performance compared with that of the literature presented by Baker (1974). The modified procedure then has been extended to other example problem to test the better results and to assure the properness of application.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.16 no.28
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• pp.165-171
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• 1993

The algorithm of Giffler and Thompson is modified for solving job shop scheduling problems related to the overlapping production The start-lag and stop-lag are also applied New priority rules for the overlapping production are proposed and the efficiency of them is compared with that of the classical priority rules.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.729-730
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• 2011

• 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.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.64
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• pp.63-76
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• 2001

This paper presents a job-shop type flexible manufacturing system(FMS) scheduling problem and examines the effects of scheduling rules on the performance of FMS. Several machine and AGV scheduling rules are tested against the mean flow-time criterion. In this paper, I compare the rules under various experimental conditions by using an FMS simulation model. One of the objectives of this study is to discuss how the simulation-based scheduling problem can be operated. The other is to measure sensitivity of the rules to changes at inter arrival time, queue capacity, breakdown rates for machines and AGV, and AGV speed. Therefore, the results of simulation experiments were considered on FMS design and operating stages. A comprehensive bibliograph? is also presented in the paper.