Traditionally, process planning and scheduling functions are performed sequentially, where scheduling is implemented after process plans has been generated. Recent research works have shown that the integration of the...
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ISBN:
(纸本)9783319897431;9783319897424
Traditionally, process planning and scheduling functions are performed sequentially, where scheduling is implemented after process plans has been generated. Recent research works have shown that the integration of these two manufacturing system functions can significantly improve scheduling objectives. In this paper, we present a new hybrid method that integrates the two functions in order to minimize the makespan. This method is made up of a Shifting Bottleneck Heuristic as a starting solution, Tabu Search (TS) and the kangaroo algorithm metaheuristics as a global search. The performance of this newly hybrid method has been evaluated and compared with an integrated approach based on a Genetic algorithm. Thereby, the characteristics and merits of the proposed method are highlighted.
The permutation flowshop scheduling problem under a position-based learning effect is addressed in this study. Minimization of the maximum completion time (make span) is considered for the identified problem. The math...
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The permutation flowshop scheduling problem under a position-based learning effect is addressed in this study. Minimization of the maximum completion time (make span) is considered for the identified problem. The mathematical programming model is established to find optimal solutions for small-sized problems. Furthermore, meta-heuristics are developed to achieve effective solutions for large-sized problems encountered in real applications. These meta-heuristics are the genetic algorithm which is a population-based solution approach, the kangaroo and the variable neighborhood search algorithms which both are single-solution-based solution approaches. In addition, different solution methods, which are in the literature for similar problem structures, are also used. Improved heuristics are evaluated according to optimal results for small-sized problems and according to performance differences between each other for large-sized problems.
This paper concerns with the total weighted tardiness on a single machine scheduling problem with the concept of learning effect and unequal release dates. A mathematical model is proposed with binary variables and on...
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This paper concerns with the total weighted tardiness on a single machine scheduling problem with the concept of learning effect and unequal release dates. A mathematical model is proposed with binary variables and only small size problems can be solved efficiently due to its NP-hardness. Therefore, four heuristic methods are developed to solve real size applications including the size of 1000 jobs. Proposed heuristics are: genetic, genetic with solution combination, kangaroo and genetic-kangaroo hybrid algorithms. Results denote that developed heuristics are efficient for the considered problem. Research on this topic shows that no study exists on the total weighted tardiness problem with learning effect and unequal release dates simultaneously tackled in this paper.
This paper concerns the total weighted tardiness on single machine scheduling problem with the concept of learning effect and unequal release dates. A mathematical model is proposed with binary variables and only smal...
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This paper concerns the total weighted tardiness on single machine scheduling problem with the concept of learning effect and unequal release dates. A mathematical model is proposed with binary variables and only small size problems can be solved efficiently due to its NP-hardness. Therefore, 4 (four) heuristic methods are developed to solve real size applications including the size of 1000 jobs. The applied heuristics are: genetic, genetic with solution combination, kangaroo and genetic-kangaroo hybrid algorithms. Solutions denote that developed heuristics are efficient for the proposed model. Research of this topic shows that, no study exists on the total weighted tardiness problem with learning effect and unequal release dates together asserted in this paper.
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