Metaheuristics for Solving a Hybrid Flexible Flowshop Problem with Sequence-Dependent Setup Times

Sioud, Aymen; Gagné, Caroline; Gravel, Marc

doi:10.1007/978-3-319-12970-9_2

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2017

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“…Due to the fact that published examples consist of only processing times we needed to complete those examples by generating weights (w i ) and due dates (d i ). We applied the following schema: w i has been generated uniformly from the range [1,10] and d i has been generated uniformly from the range [P (1 − T − R/2), P (1 − T + R/2)] where P denotes the best known value for the makespan for the C max problem, T = 0.3 and R = 0.3 ( [11]).…”

Section: Test Datamentioning

confidence: 99%

Sampling Method for the Flow Shop with Uncertain Parameters

Rajba

Wodecki

2017

Computer Information Systems and Industrial Management

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Part 6: Modelling and OptimizationInternational audienceIn the classic approach for optimization problems modelling well defined parameters are assumed. However, in real life problems we find ourself very often in a situation where parameters are not defined precisely. This may have many sources like inaccurate measurement, inability to establishing precise values, randomness, inconsistent information or subjectivity.In this paper we propose a sampling method for solving optimization problems with uncertain parameters modeled by random variables. Moreover, by applying confidence intervals theory, the execution time has been significantly reduced. We will also show an application of the method for the flowshop problem with deadlines and parameters modeled by random variables with the normal distribution

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Section: Test Datamentioning

confidence: 99%