• Journal of Korean Institute of Industrial Engineers
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• v.30 no.2
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• pp.107-119
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• 2004

A study on the following parallel machine problem is addressed in this research. An order is completed only when a given number of processes (cycle) are repeated. Anew cycle is possible only upon the completion of the previous cycle. Orders are classified into job group according to product feature. For a machine to switch to a different job group from the currently processing one a major setup is required while a minor setup time is inserted in between two jobs of the same job group. The objective of the study is to find a schedule that minimizes total weighted tardiness. An initial solution is obtained by the RATCS(Restricted Apparent Tardiness Cost with Setup) rule, and a Tabu search is applied to improve the solution. Numerical examples are also presented.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.157-160
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• 2000

This study is concerned with how to optimize the Input sequence of product models with sequence -dependent setup time in Mixed-Model Assembly Line (MMAL) using conveyor system. Usually MMAL consists of a number of stations linked by conveyor belt and each station has a work zone limited by upstream and downstream boundaries. To avoid improper interference between operators in the adjacent stations and excess of machine moving range, operators are forced to complete their operations within their predetermined work zone. In this study, our goal is to determine sequence of models for minimizing the total work unfinished within their work zone. A generalized formulation of the product sequencing problem in MMAL is presented and we developed an optimal procedure using Branch & Bound. Also we developed a heuristic procedure using local search.

• The Journal of Bigdata
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• v.9 no.1
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• pp.141-152
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• 2024

In the biopharmaceutical contract manufacturing organization (CMO) business, establishing a production schedule that satisfies the due date for various customer orders is crucial for competitiveness. In a CMO process, each order consists of multiple batches that can be allocated to multiple production lines in small batch units for parallel production. This study proposes a meta-heuristic algorithm to establish a scheduling plan that minimizes the total delivery delay of orders in a CMO process with identical parallel machine. Inspired by biological evolution, the proposed algorithm generates random data structures similar to chromosomes to solve specific problems and effectively explores various solutions through operations such as crossover and mutation. Based on real-world data provided by a domestic CMO company, computer experiments were conducted to verify that the proposed algorithm produces superior scheduling plans compared to expert algorithms used by the company and commercial optimization packages, within a reasonable computation time.