• 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.

• Korean Chemical Engineering Research
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• v.58 no.2
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• pp.209-223
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• 2020

Executions of SCM in a chemical company of which divisions produce petrochemicals, compounds, batteries, IT material and medicine directly affect their own profit. Execution level of SCM or optimization is very important. This work presents activities of SCM and optimization of inefficient issues in several industrial divisions using mathematical optimization method. The meaning is not only academic research but also making a useful tool which active partner deals with in his work. It is explained how to do beforehand and afterward optimization problem. The benefits are mentioned in the sections. The first of examples would be cover supply plan optimization, optimal profit business plan, and scheduling of a stretching process of polarizer based on minimizing raw material loss in polarizer production. The second example would be cover the optimization of production/packaging plans to maximize productivity of Poly Olefin processes, and the third example is minimization of transition loss in the production of battery electrodes. The fourth example would be cover scheduling of vessel approaching to berth. Because transportation of large portion of raw material and products of petrochemical industry is dealt with vessel, scheduling of vessel approaching to berth is important at the shore of large difference of tide. The final example would be scheduling problem to minimization of change over time of ABS semi products.