Robust and stable flow shop scheduling with unexpected arrivals of new jobs and uncertain processing times

Rahmani, Donya; Heydari, Mojtaba

doi:10.1016/j.jmsy.2013.03.004

Cited by 78 publications

(32 citation statements)

References 33 publications

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“…In 327 such cases, the predictive methods are more appropriate. Moreover, the usage of reactive 328 scheduling methods such as SEPT in scheduling the systems with a high degree of 329 uncertainty is recommended [3]. This point is also confirmed here; i.e., whenever the 330 type of machine breakdowns is B6, the objective degradation of the schedule generated 331 by SEPT improves significantly compared to SEPT-OSMH algorithm.…”

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confidence: 62%

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Predictive heuristics to generate robust and stable schedules in single machine systems under disruptions

2019

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confidence: 62%

“…Table 1 150 summarizes the indices used in the model. When the real value of uncertain parameters are not known in advance, surrogate measures are 162 commonly used to obtain robust and stable schedules [3]. In this paper, we arbitrarily consider…”

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confidence: 99%

Predictive heuristics to generate robust and stable schedules in single machine systems under disruptions

2019

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“…In uncertainty condition, Rahmani and Heydari (Rahmani & Heydari, 2014) developed a study of FSS problem to minimize the make-span, while maintaining stability and robustness, and incorporating uncertain processing times and the unexpected arrival of new jobs. Other production scheduling studies have included random processing times (Kamburowski, 2000).…”

Section: Literature Reviewmentioning

confidence: 99%

A robust just-in-time flow shop scheduling problem with outsourcing option on subcontractors

Goli

Tirkolaee

Soltani

2019

Production & Manufacturing Research

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Scheduling is known as a great part of production planning in manufacturing systems. Flow Shop Scheduling (FSS) problem deals with the determination of the optimal sequence of jobs processing on machines in a fixed order. This paper addresses a novel robust FSS problem with outsourcing option where jobs can be either scheduled for inside or outsourced to one of the available subcontractors. Capacity limitation for inside resource, just-in-time delivery policy and uncertain processing time are the key assumptions of the proposed model. The objective is to minimize the total-weighted time required to complete all jobs and the total cost of outsourcing. So, a Robust Mixed-Integer Linear Programming (RMILP) model is proposed to accommodate the problem with the real-world conditions. Finally, the obtained results show the effects of the robustness in optimizing the model under uncertainty condition. Moreover, the comparison analysis demonstrated the superiority of our proposed model against the previous Non-Linear Programming (NLP) model in the literature. ARTICLE HISTORY

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“…Nervousness Schedules are intrinsically nervous and fragile with some unexpected information. This information is not known a priori in the planning phase and is revealed over time; thus, dynamic or on-line scheduling techniques are usually used [27,28]. To show the in uence of nervousness in scheduling problems, another approach is developed in this paper and a new measure, such as the measure of robustness or stability, is de ned.…”

Section: Stabilitymentioning

confidence: 99%

A new proactive-reactive approach to hedge against uncertain processing times and unexpected machine failures in the two-machine flow shop scheduling problems

Rahmani¹

2017

Scientia Iranica

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A new proactive-reactive approach to hedge against uncertain processing times and unexpected machine failures in the two-machine ow shop scheduling problems Abstract. In this paper, a proactive-reactive approach has been considered for achieving stable and robust schedules despite uncertain processing times and unexpected machine failures in a two-machine ow shop system. In the literature, Surrogate Measures (SMs) have been developed for achieving stable and robust solutions against the occurrence of stochastic disruptions. These measures proactively provide an approximation of the real conditions of the system in the event of a disruption. Because of the discrepancies of these measures with their real values, a di erent approach is developed in this paper in a two-step structure. First, an initial robust schedule is produced and then, based on a multi-component measure, an appropriate reaction is adopted against unexpected machine failures. Computational results indicate that this method produces better solutions compared to the other two classical scheduling approaches considering their e ectiveness and performance.

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Robust and stable flow shop scheduling with unexpected arrivals of new jobs and uncertain processing times

Cited by 78 publications

References 33 publications

Predictive heuristics to generate robust and stable schedules in single machine systems under disruptions

Predictive heuristics to generate robust and stable schedules in single machine systems under disruptions

A robust just-in-time flow shop scheduling problem with outsourcing option on subcontractors

A new proactive-reactive approach to hedge against uncertain processing times and unexpected machine failures in the two-machine flow shop scheduling problems

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