A Lévy flight embedded particle swarm optimization for multi-objective parallel-machine scheduling with learning and adapting considerations

Pakzad-Moghaddam, S.H.

doi:10.1016/j.cie.2015.10.019

Cited by 21 publications

(13 citation statements)

References 42 publications

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“…Below we present a brief literature review of the parallel machine scheduling problems with learning effect. Since the references [5,11,17,18] gave a detailed review for such a scheduling problems until 2014, we just mention the other ones until nowadays.…”

Section: Literature Reviewmentioning

confidence: 99%

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Near-optimal solutions and tight lower bounds for the parallel machines scheduling problem with learning effect

Hidri

Jemmali

2020

RAIRO-Oper. Res.

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In this paper, the parallel machines scheduling problem with Dejong’s learning effect is addressed. The considered problem has a practical interest since it models real-world situations. In addition, this problem is a challenging one because of its NP-Hardness. In this work, a set of heuristics are proposed. The developed heuristics are categorized into two types. The first category is based on the dispatching methods, with new enhancement variants. The second type is more sophisticated and requires solving NP-Hard problems. Furthermore, several lower bounds are developed in order to assess the performance of the proposed heuristics. These lower bounds are based on solving the problem of the determination of the minimum average load under taking into account some observations. Among these observations, the existence of a limit position that the jobs are not allowed to exceed in any optimal schedule. Finally, an extensive experimental study is conducted over benchmark test problems, with up to 1500 jobs and 5280 instances. The obtained results are outperforming those proposed in the literature.

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Section: Literature Reviewmentioning

confidence: 99%

“…In addition, a polynomial approximation algorithm heuristic is presented. Authors in [18] considered the multiobjective uniform parallel machine scheduling problem with learning effect. An efficient particle swarm based approximation scheme is provided to solve this problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Near-optimal solutions and tight lower bounds for the parallel machines scheduling problem with learning effect

Hidri

Jemmali

2020

RAIRO-Oper. Res.

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“…When the problem scale is expanded, it is hard to obtain the optimal solution within a reasonable time using mathematical programming. In the face of this problem, most scholars often use a meta-heuristic to obtain the approximate solution; the tabu search method [14][15][16], genetic algorithm [17], particle swarm optimization [18], variable neighborhood search [19], and ant colony optimization [20] are some examples.…”

Section: Introductionmentioning

confidence: 99%

Application of a Non-Dominated Sorting Genetic Algorithm to Solve a Bi-Objective Scheduling Problem Regarding Printed Circuit Boards

Chang

Zheng

2022

Mathematics

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An unrelated parallel machine scheduling problem motivated by the scheduling of a printed circuit board assembly (PCBA) under surface mount technology (SMT) is discussed in this paper. This problem involved machine eligibility restrictions, sequence-dependent setup times, precedence constraints, unequal job release times, and constraints of shared resources with the objectives of minimizing the makespan and the total job tardiness. Since this scheduling problem is NP-hard, a mathematical model was first built to describe the problem, and a heuristic approach using a non-dominated sorting genetic algorithm (NSGA-II) was then designed to solve this bi-objective problem. Multiple near-optimal solutions were provided using the Pareto front solution and crowding distance concepts. To demonstrate the efficiency and effectiveness of the proposed approach, this study first tested the proposed approach by solving test problems on a smaller scale. It was found that the proposed approach could obtain optimal solutions for small test problems. A real set of work orders and production data was provided by a famous hardware manufacturer in Taiwan. The solutions suggested by the proposed approach were provided using Gantt charts to visually assist production planners to make decisions. It was found that the proposed approach could not only successfully improve the planning time but also provide several feasible schedules with equivalent performance for production planners to choose from.

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“…Li et al [9] provided a hybrid nondominated sorting genetic algorithm-II (NSGA-II [10]) with fuzzy logic controller and an exact method. Pakzad-Moghaddam [11] presented a Lévy flight embedded particle swarm optimization for PMSP with learning and adapting. Afzalirad and Rezaeian [12] reported a multiobjective ant colony optimization to minimize mean weighted flow time and mean weighted tardiness.…”

Section: Introductionmentioning

confidence: 99%

A New Imperialist Competitive Algorithm for Multiobjective Low Carbon Parallel Machines Scheduling

Pan

Lei

Zhang

2018

Mathematical Problems in Engineering

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This paper considers low carbon parallel machines scheduling problem (PMSP), in which total tardiness is regarded as key objective and total energy consumption is a non-key one. A lexicographical method is used to compare solutions and a novel imperialist competitive algorithm (ICA) is presented, in which a new strategy for initial empires is adopted. Some new improvements are also added in ICA to obtain high quality solutions, which are adaptive assimilation, adaptive revolution, imperialist innovation, and alliance and the novel way of imperialist competition. Extensive experiments are conducted to test the search performance of ICA by comparing it with methods from literature. Computational results show the promising advantages of ICA on low carbon PMSP.

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A Lévy flight embedded particle swarm optimization for multi-objective parallel-machine scheduling with learning and adapting considerations

Cited by 21 publications

References 42 publications

Near-optimal solutions and tight lower bounds for the parallel machines scheduling problem with learning effect

Near-optimal solutions and tight lower bounds for the parallel machines scheduling problem with learning effect

Application of a Non-Dominated Sorting Genetic Algorithm to Solve a Bi-Objective Scheduling Problem Regarding Printed Circuit Boards

A New Imperialist Competitive Algorithm for Multiobjective Low Carbon Parallel Machines Scheduling

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