Heuristic algorithms for the unrelated parallel machine scheduling problem with one scarce additional resource

Villa, Fulgencia; Vallada, Eva; Fanjul-Peyro, Luis

doi:10.1016/j.eswa.2017.09.054

Cited by 49 publications

(40 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The aforementioned literature sections proved the importance of hybrid algorithms to solve UPMSP. Although metaheuristic algorithms are one of the most attractive methodologies in optimization, they cannot guarantee the optimal solution [31]. In this instance, obtaining optimal solutions seems to be very hard, particularly in the case of large-scale problems.…”

Section: Research Gapmentioning

confidence: 99%

A New Approach Based on Matching Theory and Simulated Annealing for Parallel Machine Scheduling Problems

Moussa

Azar

Ghatari

2021

Preprint

View full text Add to dashboard Cite

As an extension of the classical Parallel Machine Scheduling Problem (PMSP), Unrelated Parallel Machine Scheduling Problem (UPMSP) is a much substantial issue in the modern manufacturing environment. It has been demonstrated to be a NP-hard problem. This research suggests a hybrid algorithm that combines Matching Theory (MT) and Simulated Annealing (SA) for solving an UPMSP with sequence-dependent setup time aimed at minimizing the total completion time. The hybrid algorithm is based on allocation of works to the best machine that can do it, and the determination of the order in which jobs have to be handled on the machines. The hybridization of MT and SA that integrates the features of these two individual parts is the main innovation aspect of the strategy. MT encourages the convergence, while SA promotes the diversity. Therefore, the designed algorithm can balance the intensification and diversification very well. Some tests were conducted using 16 tests for two problems to assess the efficiency of the suggested algorithm. Furthermore, the execution of the suggested algorithm with that of other meta-heuristic methods was contrasted. The outcome revealed that the performance dimensions of the suggested algorithm overrated those of other techniques.

show abstract

Section: Research Gapmentioning

confidence: 99%

A New Approach Based on Matching Theory and Simulated Annealing for Parallel Machine Scheduling Problems

Moussa

Azar

Ghatari

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…As extensions of this work, we recommend the use of meta-heuristics to improve the solutions generated by the proposed algorithms. Future studies could also investigate the behavior of the proposed meta-heuristic with other objective functions or other variants, as well as, resource-constrained problems (Afzalirad and Rezaeian, 2016;Villa et al, 2018). Finally, it is worth studying multi-objective variants of the UPMSPST because of the fact that in many real industrial settings, the scheduling problems have a multi-objective nature.…”

Section: Genetic Algorithmmentioning

confidence: 99%

A genetic algorithm with neighborhood search procedures for unrelated parallel machine scheduling problem with sequence-dependent setup times

Abreu

Prata

2020

JM2

View full text Add to dashboard Cite

Purpose The purpose of this paper is to present a hybrid meta-heuristic based on genetic algorithms (GAs), simulated annealing, variable neighborhood descent and path relinking for solving the variant of the unrelated parallel machine scheduling problem considering sequence-dependent setup times. Design/methodology/approach The authors carried out computational experiments on literature problem instances proposed by Vallada and Ruiz (2011) and Arnaout et al. (2010) to test the performance of the proposed meta-heuristic. The objective function adopted was makespan minimization, and the authors used relative deviation, average and population standard deviation as performance criteria. Findings The results indicate the competitivity of the proposed approach and its superiority in comparison with several other algorithms. In small instances proposed by Vallada and Ruiz (2011) and on small and large instances proposed by Arnaout et al. (2010), the proposed approach presented the best results in most tested problem instances. Practical implications In small instances proposed by Vallada and Ruiz (2011) and on small and large instances proposed by Arnaout et al. (2010), the proposed approach presented the best results in most tested problem instances. Originality/value The proposed approach presented high-quality results, with an innovative hybridization of a GA and neighborhood search algorithms, tested in diverse instances of literature. Furthermore, the case study demonstrated that the proposed approach is recommended for solving real-world problems.

show abstract

“…A variable neighborhood search algorithm and a simulated annealing algorithm Scheduling problems were also proposed. Villa et al [19] proposed several heuristics based on resources and assignments to minimizing the makespan on unrelated parallel machines with one renewable additional resource. Abdeljaoued et al [20] provide two new heuristics and a simulated annealing metaheuristic for the parallel machine scheduling problem with a set of renewable resources.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimize Unrelated Parallel Machines Scheduling Problems With Multiple Limited Additional Resources, Sequence-Dependent Setup Times and Release Date Constraints

Al-Harkan

Qamhan

2019

IEEE Access

View full text Add to dashboard Cite

This paper considers the problem of scheduling a set of jobs on unrelated parallel machines subject to several constraints which are non-zero arbitrary release dates, limited additional resources, and non-anticipatory sequence-dependent setup times. The objective function is to minimize the maximum completion time. In order to find an optimal solution for this problem, a new mixed-integer linear programming model (MILP) is presented. Moreover, a two-stage hybrid metaheuristic based on variable neighborhood search hybrid and simulated annealing (TVNS_SA) is proposed. In the first stage, a developed heuristic is used to find an initial solution with good quality. At the second stage, the obtained initial solution is used as the first neighborhood structures in the proposed metaheuristic, for further progress different neighborhood structures and effective resolution schemes are also presented. The computational results indicate that the proposed metaheuristic is capable of obtaining optimal solutions for most of the instances when compared to the solution obtained by the developed mixed-integer linear programming model. In addition, the metaheuristic dominated the MILP with respect to computing time. The overall evaluation of the proposed algorithm shows its efficiency and effectiveness when compared with other algorithms. Finally, in order to obtain rigorous and fair conclusions, a paired t-test has been conducted to test the significant differences between the five variants of the TVNS_SA.

show abstract

Heuristic algorithms for the unrelated parallel machine scheduling problem with one scarce additional resource

Cited by 49 publications

References 33 publications

A New Approach Based on Matching Theory and Simulated Annealing for Parallel Machine Scheduling Problems

A New Approach Based on Matching Theory and Simulated Annealing for Parallel Machine Scheduling Problems

A genetic algorithm with neighborhood search procedures for unrelated parallel machine scheduling problem with sequence-dependent setup times

Optimize Unrelated Parallel Machines Scheduling Problems With Multiple Limited Additional Resources, Sequence-Dependent Setup Times and Release Date Constraints

Contact Info

Product

Resources

About