• IE interfaces
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• v.25 no.3
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• pp.357-364
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• 2012

This paper considers a unrelated parallel machine scheduling problem with ready times, due times and sequence and machine-dependent setup times. The objective of this problem is to determine the allocation of jobs and the scheduling of machines to minimize the total tardy time. A mathematical model for optimal solution is derived. An in-depth analysis of the model shows that it is very complicated and difficult to obtain optimal solutions as the problem size becomes large. Therefore, a genetic algorithm using an effective dispatching method is proposed. The performance of the proposed genetic algorithm is evaluated using several randomly generated examples.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.2
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• pp.158-168
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• 2001

We present a tabu search (TS) algorithm to minimize maximum lateness on a single machine in the presence of sequence dependent setup times and dynamic job arrivals. The TS algorithm starts with a feasible schedule generated by a modified ATCS (Apparent Tardiness Cost with Setups) rule, then through a series of search steps it improves the initial schedule. Results of extensive computational experiments show that the TS algorithm significantly outperforms a well-known RHP heuristic by Ovacik and Uzsoy, both on the solutions quality and the computation time. The performance advantage is particularly pronounced when there is high competition among jobs for machine capacity.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.2
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• pp.69-83
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• 2001

The problem of minimizing total tardiness on a batch processing machine with incompatible job families when there are sequence independent setup times between families is studied where all jobs of the same family have identical processing times and jobs of different families cannot be processed together. A batch processing machine can process a number of jobs, within a maximal batch size, simultaneously as a batch. The processing time required of each batch is equal to the one of jobs. A dynamic programming algorithm which gives the optimal solution, and several heuristics are presented. Performance of simple dispatching rules based on due dates are compared, and the best of them is used as an initial solution for the decomposition algorithm, which is shown to give good schedules in relatively short computational time.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.3
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• pp.305-312
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• 2014

This research deals with a problem that minimizes makespan in a non-identical parallel machine system with sequence and machine dependent setup times and machine dependent processing times. We first present a new mixed integer programming formulation for the problem, and using this formulation, one can easily find optimal solutions for small problems. However, since the problem is NP-hard and the size of a real problem is large, we propose four heuristic algorithms including genetic algorithm based heuristics to solve the practical big-size problems in a reasonable computational time. To assess the performance of the algorithms, we conduct a computational experiment, from which we found the heuristic algorithms show different performances as the problem characteristics are changed and the simple heuristics show better performances than genetic algorithm based heuristics for the case when the numbers of jobs and/or machines are large.

• Management Science and Financial Engineering
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• v.13 no.2
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• pp.117-139
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• 2007

This paper contributes by incorporating works addressing supply chain coordination and investing in setup reduction program. Consider a two-echelon, EOQ-like inventory system consisting of a supplier and a buyer. We assume that both the supplier and the buyer can invest in setup cost reduction programs in order to benefit from small order sizes. However, the costs of investing in setup cost reduction programs are different for the two parties, leading to mismatches in individually optimal setup costs and order cycle times. We propose a supply chain coordination contract that makes use of quantity discount as an incentive transfer scheme for supply chain coordination.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.32
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• pp.121-126
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• 1994

Setup Time Reduction is an important aspect of the Just-in-Time(JIT) and Zero Inventory(Zl) Concepts since it supports reductions in manufacturing lead times and inventories. It also enables small lot sizes and kanban systems implementation for material flow - achieving major improvements in production floor management. One concept fundamental to the pursuit of JIT production in Japan and other countries is adoption of a setup time reduction. This paper looks at the necessities of setup time reduction and the relations to machine utilization. By using an EOQ model for evaluate the effect of setup time reduction, we get the results that over 75 % reduction in setup time is obtain the desired results in the lot size reduction.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.12 no.19
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• pp.79-88
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• 1989

This characteristics of process industries are high capital intensity, relatively long and sequence dependent setup times, and extremely limited capacity resources. The lot sizing, sequencing and limited capacity resources factors must he considered for production scheduling in these industries. This paper presents a mixed integer programming model for production scheduling. The economic trade offs between capacitated lot sizing flow shop scheduling and sequence dependent setup times also be compared with SMITH-DANIELS's model. As a results, it is shown that this paper has lower total cost, more efficient throughput than SMITH-DANIELS's model.

• ETRI Journal
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• v.36 no.3
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• pp.367-373
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• 2014

With high bandwidth, low interference, and low power consumption, optical network-on-chip (ONoC) has emerged as a highly efficient interconnection for the future generation of multicore system on chips. In this paper, we propose a new path-setup method for ONoC to mitigate contentions, such as packets, by recycling the setup packet halfway to the destination. A new, strictly non-blocking $6{\times}6$ optical router is designed to support the new method. The simulation results show the new path-setup method increases the throughput by 52.03%, 41.94%, and 36.47% under uniform, hotspot-I, and hotspot-II traffic patterns, respectively. The end-to-end delay performance is also improved.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.4
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• pp.125-135
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• 1993

An Unrelated Parallel Processing with Weighted jobs and Setup times scheduling prolem is studied. We consider a parallel processing in which a group of processors(machines) perform a single operation on jobs of a number of different job types. The processing time of each job depends on both the job and the machine, and each job has a weight. In addition each machine requires significant setup time between processing jobs of different job types. The performance measure is to minimize total weighted flow time in order to meet the job importance and to minimize in-process inventory. We present a 0-1 Mixed Integer Programming model as an optimizing algorithm. We also present a simple heuristic algorithm. Computational results for the optimal and the heuristic algorithm are reported and the results show that the simple heuristic is quite effective and efficient.

• Industrial Engineering and Management Systems
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• v.11 no.1
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• pp.114-122
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• 2012

This paper considers a non-identical parallel machine scheduling problem with sequence and machine dependent setup times. The objective of this problem is to determine the allocation of jobs and the scheduling of each machine to minimize makespan. A mathematical model for optimal solution is derived. An in-depth analysis of the model shows that it is very complicated and difficult to obtain optimal solutions as the problem size becomes large. Therefore, two meta-heuristics, genetic algorithm (GA) and a new population-based evolutionary meta-heuristic called self-evolution algorithm (SEA), are proposed. The performances of the meta-heuristic algorithms are evaluated through compare with optimal solutions using randomly generated several examples.