A united search particle swarm optimization algorithm for multiobjective scheduling problem

Lian, Zhigang

doi:10.1016/j.apm.2010.03.001

Cited by 15 publications

(7 citation statements)

References 18 publications

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“…Particle Swarm Optimization. PSO was proposed by Kennedy and Eberhart [21] and Lian [22] pointed out that this method was a stochastic optimization method based on swarm intelligence and PSO was inspired by the social behavior of bird flocking and their means of information exchange. Due to its easy implementation and fast convergence, PSO has been successfully applied to solve nonlinear optimization problems.…”

Section: Proposed Heuristic Methodsmentioning

confidence: 99%

Omni-Channel Product Distribution Network Design by Using the Improved Particle Swarm Optimization Algorithm

Zhang

Zhu

et al. 2019

Discrete Dynamics in Nature and Society

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Distribution network under Omni-Channel integration contains many levels. There are one or more dealers at each level which forms a many-to-many distribution network. Consumers purchase a wide variety of products and their demands are uncertain, which constitutes a complex demand network and increases the complexity of the supply chain network. This paper focuses on the integrated optimization of supply chain distribution network and demand network and constructs the joint randomization planning model of location and routing. The goal is to minimize the total costs of the supply chain network under uncertain customer demands. Based on the traditional particle swarm optimization (PSO), this study introduces the collaborative idea to reduce the coding dimension, improves the boundary processing strategy, and adopts the mutation operator to expand the search space. A case study of distribution under Omni-Channel integration in a large enterprise was done. The validity of the model and the effectiveness of the proposed method were verified by numerical experiments.

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Section: Proposed Heuristic Methodsmentioning

confidence: 99%

Omni-Channel Product Distribution Network Design by Using the Improved Particle Swarm Optimization Algorithm

Zhang

Zhu

et al. 2019

Discrete Dynamics in Nature and Society

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“…The proposed metaheuristics uses a string with the size of n+m-1 where n represents the number of jobs, m denotes the number of machines, and the feasible solutions are integer numbers between one and n+m-1, which is design using the proposed method by Lian (2010). For instance, Fig.…”

Section: Solution Methodsmentioning

confidence: 99%

An imperialist competitive algorithm for a bi-objective parallel machine scheduling problem with load balancing consideration

Madani-Isfahani¹,

Ghobadian²,

Tekmehdash³

et al. 2013

ijiec

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In this paper, we present a new Imperialist Competitive Algorithm (ICA) to solve a bi-objective unrelated parallel machine scheduling problem where setup times are sequence dependent. The objectives include mean completion time of jobs and mean squares of deviations from machines workload from their averages. The performance of the proposed ICA (PICA) method is examined using some randomly generated data and they are compared with three alternative methods including particle swarm optimization (PSO), original version of imperialist competitive algorithm (OICA) and genetic algorithm (GA) in terms of the objective function values. The preliminary results indicate that the proposed study outperforms other alternative methods. In addition, while OICA performs the worst as alternative solution strategy, PSO and GA seem to perform better.

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“…Fang [38] proposed an improved weighted-based multiobjective genetic algorithm for the typical parallel machines scheduling problem. Lian [39] have proposed a united search particle swarm optimization algorithm for the parallel machines problem with different due dates. Chyu and Chang [40] have developed a pareto evolutionary algorithm for a biobjective unrelated parallel machines scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

A Multiobjective Optimization Approach to Solve a Parallel Machines Scheduling Problem

Amodeo

Yalaoui

et al. 2010

Advances in Artificial Intelligence

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A multiobjective optimization problem which focuses on parallel machines scheduling is considered. This problem consists of scheduling n independent jobs on m identical parallel machines with release dates, due dates, and sequence-dependent setup times. The preemption of jobs is forbidden. The aim is to minimize two different objectives: makespan and total tardiness. The contribution of this paper is to propose first a new mathematical model for this specific problem. Then, since this problem is NP hard in the strong sense, two well-known approximated methods, NSGA-II and SPEA-II, are adopted to solve it. Experimental results show the advantages of NSGA-II for the studied problem. An exact method is then applied to be compared with NSGA-II algorithm in order to prove the efficiency of the former. Experimental results show the advantages of NSGA-II for the studied problem. Computational experiments show that on all the tested instances, our NSGA-II algorithm was able to get the optimal solutions.

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A united search particle swarm optimization algorithm for multiobjective scheduling problem

Cited by 15 publications

References 18 publications

Omni-Channel Product Distribution Network Design by Using the Improved Particle Swarm Optimization Algorithm

Omni-Channel Product Distribution Network Design by Using the Improved Particle Swarm Optimization Algorithm

An imperialist competitive algorithm for a bi-objective parallel machine scheduling problem with load balancing consideration

A Multiobjective Optimization Approach to Solve a Parallel Machines Scheduling Problem

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