Efficient simulation/optimization of dispatching priority with “fake” processing time

Morito, Susumu; Lee, Keun Hyung

doi:10.1145/268437.268668

Cited by 9 publications

(5 citation statements)

References 1 publication

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“…Chan and Smith [6] developed a simulation model using GPSS/H to evaluate the performance of a welding assembly in a car factory. Morito and Lee [7] focus on short period production planning problems. They use simulation to study the dispatching rule optimization with random process time.…”

Section: Simulation Applicationsmentioning

confidence: 99%

A simulation approach to facilitate manufacturing system design

Pita¹,

Wang²

2010

Int. j. simul. model.

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In recent years, the manufacturing sector has faced unparalleled levels of change and its intricacies are also challenging. This has lead to manufacturers acquiring new methods for planning and control of their manufacturing systems. One approach that can give good results in this context is simulation modelling. In this paper, a simulation modelling technique is applied to a bottling process in a winery to analyze the variations in the duration of the process with different changes in the production line.Step changes have been made during the simulation model construction. Based on the results of this study, it is possible to predict in which parts of the process it is best to invest so that a greater yield can be obtained. The objective of this study was achieved, since the process duration could be reduced from a twoday bottling process to attain all the bottling in a single day.

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Section: Simulation Applicationsmentioning

confidence: 99%

A simulation approach to facilitate manufacturing system design

Pita¹,

Wang²

2010

Int. j. simul. model.

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“…Shi et al (1999) discuss the nested partitions method as applied to a simultaneous simulation environment. Morito and Lee (1997) present a local search algorithm to determine optimal dispatching priorities in a job shop simulation model.…”

Section: Large Number Of Feasible Solutionsmentioning

confidence: 99%

A survey of recent advances in discrete input parameter discrete-event simulation optimization

et al. 2004

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Discrete-event simulation optimization is a problem of significant interest to practitioners interested in extracting useful information about an actual (or yet to be designed) system that can be modeled using discrete-event simulation. This paper presents a survey of the literature on discrete-event simulation optimization published in recent years (1988 to the present), with a particular focus on discrete input parameter optimization. The discrete input parameter case differentiates techniques appropriate for small and for large numbers of feasible input parameter values. Examples of applications that illustrate these methods are also discussed.

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“…Simulation has become a popular technique for developing production schedules and dispatch lists in manufacturing environments [3,4,9,10]. Simulation offers the advantage of developing a feasible and accurate schedule using shorter computation times compared to some of the other techniques such as branch and bound, genetic algorithm, tabu search, etc.…”

Section: Conjunctive Simulated Scheduling (Css) Conceptmentioning

confidence: 99%

Single machine scheduling with multiple objectives in semiconductor manufacturing

Gupta‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Sivakumar

2004

Int J Adv Manuf Technol

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To deal with today's stiff competition in the logistics of semiconductor manufacturing, the development of an efficient scheduling approach for the complex semiconductor back-end testing operations is very essential, where more than one objective, such as cycle time, machine utilization and due date accuracy are kept in the focus to various degrees simultaneously. In this paper, the problem of scheduling N independent jobs on a single testing machine with due dates and sequence-dependent setup times is addressed, where the multiple objectives are to minimize average cycle time and average tardiness and to maximize machine utilization. A near optimal solution, which is not inferior to any other feasible solution in terms of all objectives, is generated combining the analytically optimal and conjunctive simulated scheduling approach. First, the machine-scheduling problem is modeled using the discrete event simulation approach and the problem is divided into simulation clock-based lot selection sub-problems. Then, at each decision instance in simulated time, a Pareto optimal lot is selected using the compromise programming technique for multi-objective optimization. With the help of a broad experimental design, this developed solution is then compared with common heuristic-dispatching rules used in industry such as the shortest processing time (SPT) and earliest due date (EDD). The developed scheduling method shows better results for all the objectives over a wide range of problems. It shows approximately 16.7% reduction in average cycle time, 25.6% reduction in average tardiness, and 21.6% improvement in machine utilization over the common dispatching rules, SPT and EDD.

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Efficient simulation/optimization of dispatching priority with “fake” processing time

Cited by 9 publications

References 1 publication

A simulation approach to facilitate manufacturing system design

A simulation approach to facilitate manufacturing system design

A survey of recent advances in discrete input parameter discrete-event simulation optimization

Single machine scheduling with multiple objectives in semiconductor manufacturing

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