Robust scheduling of parallel machines with sequence-dependent set-up costs

Anglani, A.; Grieco, A; Guerriero, Emanuela; Musmanno, Roberto

doi:10.1016/j.ejor.2003.08.059

Cited by 48 publications

(16 citation statements)

References 22 publications

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“…They propose a hybrid intelligent algorithm to solve the proposed models where fuzzy makespan, fuzzy lateness and fuzzy idleness are minimized. Anglani et al [48] propose a robust approach to minimize total setup cost in PMSP with sequence-dependent setups. Then, a fuzzy mathematical programming model is formulated to provide optimal solution as a trade-off between total setup cost and the necessity degree.…”

Section: Pmsm Under Uncertaintymentioning

confidence: 99%

A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem

Torabi

Sahebjamnia

Mansouri

et al. 2013

Applied Soft Computing

106

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Section: Pmsm Under Uncertaintymentioning

confidence: 99%

A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem

Torabi

Sahebjamnia

Mansouri

et al. 2013

Applied Soft Computing

106

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“…Daniels and Carrillo (1997) proposed another model of robust scheduling; i.e., to find a scheduling such that its largest-probability performance under processing uncertainty is not worse than the expected performance. Anglani et al (2005) described the processing uncertainty in terms of a fuzzy number and modeled the robust scheduling problem of a parallel machines system by a mixed-integer programming model with the objective of optimizing the robustness of satisfying the demand. For uncertainty created by sudden fluctuations in production capacity-e.g., the failure of machines- Jensen (2003) defined the robustness measurement as the average performances of all of the schedules and proposed a genetic algorithm to optimize the robustness measurement.…”

Section: Robust Production Scheduling Under Uncertaintymentioning

confidence: 99%

Robust production control policy for a single machine and single part-type manufacturing system with inaccurate observation of production surplus

Wang

2012

IIE Transactions

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The optimal production control policy (i.e., the hedging point policy) for a single machine and single part-type manufacturing system for the case where the production surplus is accurately known has been reported in the literature. However, the production surplus is often observed inaccurately in practice. Ignoring this inaccuracy in using the optimal policy leads to not only non-robust situations but also high production costs. To consider the effect of the inaccurate observation, robustness of the production control policy is required. The robustness can be evaluated in terms of the difference between the production surplus trajectory under the policy with an inaccurate observation and the trajectory under the policy with an accurate production surplus (i.e., the hedging point policy). A difference of zero is the ideal case; however, this is generally impossible to achieve since the extent of the observation error is generally unknown. This article proposes a new robust production control policy that can give a small difference between the two trajectories. Compared with the hedging point policy, the proposed policy is more insensitive to production surplus inaccuracy in the neighborhood of the hedging point but responds more slowly to a failure of the machine. Simulation studies show that the proposed policy has a better robustness than the hedging point policy for cases where, just as in actual industrial practice, the machine failure time is much shorter than the up time of the machine. In these cases, with an increase in the observation error, the robustness of the proposed policy becomes increasingly better than that for the hedging point policy.

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“…Stochastic approaches for tackling scheduling problem under uncertainties are available [10], but some of these approaches have their limitation due to the strict prerequisite and assumption [5]. For example, stochastic approaches require certain information on probability distribution of processing time or release time of each job, which can be inferred on the condition that a substantial amount of historical data is available [9,11]. However, such amount of historical data is unavailable in highly uncertain environment and the only information is an educated guess of the lower bound and upper bound of some parameters, such as processing time and ready time [12].…”

Section: Literature Reviewmentioning

confidence: 99%

Robust Parallel Machine Scheduling Problem with Uncertainties and Sequence-Dependent Setup Time

Qin

2016

Scientific Programming

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A parallel machine scheduling problem in plastic production is studied in this paper. In this problem, the processing time and arrival time are uncertain but lie in their respective intervals. In addition, each job must be processed together with a mold while jobs which belong to one family can share the same mold. Therefore, time changing mold is required for two consecutive jobs that belong to different families, which is known as sequence-dependent setup time. This paper aims to identify a robust schedule by min–max regret criterion. It is proved that the scenario incurring maximal regret for each feasible solution lies in finite extreme scenarios. A mixed integer linear programming formulation and an exact algorithm are proposed to solve the problem. Moreover, a modified artificial bee colony algorithm is developed to solve large-scale problems. The performance of the presented algorithm is evaluated through extensive computational experiments and the results show that the proposed algorithm surpasses the exact method in terms of objective value and computational time.

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Robust scheduling of parallel machines with sequence-dependent set-up costs

Cited by 48 publications

References 22 publications

A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem

A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem

Robust production control policy for a single machine and single part-type manufacturing system with inaccurate observation of production surplus

Robust Parallel Machine Scheduling Problem with Uncertainties and Sequence-Dependent Setup Time

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