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• W1517181285 abstract "Scheduling operations problems arise in diverse areas such as flexible manufacturing, production planning and scheduling, logistics, supply chain problem, etc. A common feature of many of these problems is that no efficient solution algorithms are known that solve each instance to optimality in a time bounded polynomially in the size of the problem, Dorndorf and Pesch [22]. Discrete optimization can help to overcome these difficulties. This paper presents an optimization approach to solve the complex scheduling problem in a job shop environment. This problem is also known as the Job Shop Scheduling Problem (JSSP). The JSSP is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. The proposed approach is based on a genetic algorithm technique. Genetic algorithms are an optimization methodology based on a direct analogy to Darwinian natural selection and mutations in biological reproduction. The scheduling rules such as SPT and MWKR are integrated into the process of genetic evolution. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities and delay times of the operations are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed approach." @default.
• W1517181285 created "2016-06-24" @default.
• W1517181285 creator A5036136815 @default.
• W1517181285 date "2010-02-01" @default.
• W1517181285 modified "2023-10-17" @default.
• W1517181285 title "Complex scheduling problems using an optimization methodology" @default.
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