Efficient heuristic for solving non-permutation flow-shop scheduling problems with maximal and minimal time lags

Ye, Song; Zhao, Ning; Li, Kaidian; Lei, Chuanjin

doi:10.1016/j.cie.2017.08.024

Cited by 10 publications

(1 citation statement)

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“…In addition, for a problem with a batch machine followed by a discrete machine, a hybrid membrane computing metaheuristic algorithm was developed in [34] to minimize the makespan. Additionally, there are recent studies considering flow shop problems with various scheduling requirements as well as limited waiting times [35][36][37][38][39][40][41].…”

Section: Previous Studiesmentioning

confidence: 99%

Metaheuristics for a Flow Shop Scheduling Problem with Urgent Jobs and Limited Waiting Times

2021

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This study considers a scheduling problem for a flow shop with urgent jobs and limited waiting times. The urgent jobs and limited waiting times are major considerations for scheduling in semiconductor manufacturing systems. The objective function is to minimize a weighted sum of total tardiness of urgent jobs and the makespan of normal jobs. This problem is formulated in mixed integer programming (MIP). By using a commercial optimization solver, the MIP can be used to find an optimal solution. However, because this problem is proved to be NP-hard, solving to optimality requires a significantly long computation time for a practical size problem. Therefore, this study adopts metaheuristic algorithms to obtain a good solution quickly. To complete this, two metaheuristic algorithms (an iterated greedy algorithm and a simulated annealing algorithm) are proposed, and a series of computational experiments were performed to examine the effectiveness and efficiency of the proposed algorithms.

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