A Genetic Algorithm for the Batch Scheduling with Sequence-Dependent Setup Times

Tsiushuang, Chen; Лонг, Линг; Fung, Richard Y. K.

doi:10.1007/11816515_147

Cited by 1 publication

(1 citation statement)

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“…A similar problem with additional sequence-dependent setup times was proposed by Chen et al [3] so as to minimize the maximum lateness. Using problem properties, an effective binary coding-based genetic algorithm was developed and then a heuristic was devised for sequencing the established batches.…”

Section: Intelligent Optimizationmentioning

confidence: 96%

Scheduling of printed circuit board (PCB) assembly systems with heterogeneous processors using simulation-based intelligent optimization methods

Noroozi

Mokhtari

2014

Neural Comput & Applic

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The complexity of printed circuit boards (PCBs), as an important sector of the electronics manufacturing industry, has increased over the last three decades. This paper focuses on a practical application observed at a PCB assembly line of electronics manufacturing facility. It is shown that this problem is equivalent to a flowshop scheduling with multiple heterogeneous batch processors where processors can perform multiple tasks as long as the sizes of jobs in a batch do not violate the processors' capacity. The equivalent problem is mathematically formulated as a mixed integer programming model. Then, a Monte Carlo simulation is incorporated into high-level genetic algorithm-based intelligent optimization techniques to assess the performance of makespan-oriented system under uncertain processing times. At each iteration of algorithm, the output of simulator is used by optimizers to provide online feedbacks on the progress of the search and direct the search toward a promising solution zone. Furthermore, various parameters and operators of the algorithm are discussed and calibrated by means of Taguchi statistical technique. The result of extensive computational experiments shows that the solution approach gives high-quality solutions in reasonable computational time.

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Section: Intelligent Optimizationmentioning

confidence: 96%