A hybrid approach based on the variable neighborhood search and particle swarm optimization for parallel machine scheduling problems—A case study for solar cell industry

Chen, Yin-Yann; Cheng, Chen-Yang; Wang, Li‐Chih; Chen, Tzu‐Li

doi:10.1016/j.ijpe.2012.06.013

Cited by 39 publications

(21 citation statements)

References 32 publications

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“…Based upon the gbest values, various solutions are selected for further proceedings. Then, VNS come in action to improve the results of PSO technique further [37,38,39]. A multi-objective fitness function was designed to reduce the energy consumption rate, makespan and load imbalance rate at a same time [40,41,42].…”

Section: IImentioning

confidence: 99%

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An Efficient Load Balancing Technique for Mutation based Particle Swarm Optimization

Sethi¹

2019

IJRASET

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Load balancing techniques play significant role in cloud computing environment because it directly affects the performance of cloud data centers. An efficient load balancing technique not only provides high availability of resources to cloud users, but also enhances the performance of cloud data centers. Load balancing techniques are a typical NP-hard problem. Currently, many researchers have solved load balancing problem by considering well-known metaheuristic techniques. However, these techniques suffer from one of these issues: premature convergence, poor convergence speed, initially selected random solutions and stuck in local optima. To handle the issues associated with existing metaheuristic techniques, in this paper, a mutation based particle swarm optimization based load balancing technique is proposed. The proposed technique has an ability to overcome several issues associated with existing techniques such as premature convergence, poor convergence speed, initially selected random solutions and stuck in local optima issues. Also, multiobjective fitness function is designed as a minimization problem. Multi-objective fitness function considers energy consumption, makespan and load imbalance rate parameters. The proposed technique outperforms existing load balancing techniques in terms of makespan, speedup, communication overheads, efficiency, utilization, mean gain time, load imbalance rate and energy consumption. Index terms: Load balancing, Particle swarm optimization, Cloud computing, Energy efficiency. I. INTRODUCTION Cloud computing is growing phase having a world view of substantial scale distributed computing in internet era [1]. It is a combination of grid and 'X' as a resources in wireless environment. Here, 'X' may be infrastructure, platform, software, data, hardware etc. [2]. The primary objective of scheduling is the assignment of jobs to available set of servers so that execution time can be minimized. Another feature of scheduling is decision-making process and is mostly used in service and manufacturing organizations. Scheduling takes place by taking help of load balancing algorithms [3]. Job is a term related with scheduling used at application level and is a script or program to execute a specific set of jobs. Load balancing algorithm is a technique to which jobs are optimistically assigned to data center resources. There is no completely perfect scheduling mechanism available due to different scheduling objectives. Scheduling algorithms can be executed or implemented under suitable conditions according to assigned applications by a good scheduler. Scheduling algorithm is a mechanism that solves a problem in seconds, minutes or even hours. Time used for execution of particular algorithm measures the efficiency of that algorithm and so time complexity can be measured from the efficiency. Time complexity plays a significant role in time execution of an algorithm. There are some time complexity algorithms used for job execution. The problem is feasible, traceable, fast and efficient in case ...

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Section: IImentioning

confidence: 99%

“…jIk " range 0 to 1; 8j"(1; 2; :::a); "(1; 2; ::::J j ); k"(1; 2; ::::n)(39) Z jITS "(0:::::1); 8j"(1; 2; ::::a); I"(1; 2; ::::J j ); TS"(1; 2; :::::TS)(40)TSJ j1 "(1; 2; ::::TS); 8j"(1; 2; ::::a); I 2 to(1; 2; :::::J j ) (41) The objective function given in Eq. (4.44) has clearly shown that the earning of 1 and the follow-ing is its cost and condition Eq.…”

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confidence: 99%

An Efficient Load Balancing Technique for Mutation based Particle Swarm Optimization

Sethi¹

2019

IJRASET

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“…The main advantage of PSO consists of its ability in exploitation while it may give weak result in exploring the solution space. In order to boost the performance of PSO, a series of hybrid PSO algorithms, which combine PSO with other performing search techniques, have been developed by literature so far (Sha & Hsu, 2006;Xia & Wu, 2006;Chen et al, 2013;Gao et al, 2014;Javidrad & Nazari, 2017). Notably, Arabameri and Salmasi (2013) demonstrated as the performance of PSO may be significantly improved if it is combined with a proper neighborhood search.…”

Section: Improving the Performance Of Psomentioning

confidence: 99%

Single machine batch processing problem with release dates to minimize total completion time

Beldar¹,

Costa²

2018

10.5267/j.ijiec

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A single machine batch processing problem with release dates to minimize the total completion time (1|rj,batch| j C  ) is investigated in this research. An original mixed integer linear programming (MILP) model is proposed to optimally solve the problem. Since the research problem at hand is shown to be NP-hard, several different meta-heuristic algorithms based on tabu search (TS) and particle swarm optimization (PSO) are used to solve the problem. To find the most performing heuristic optimization technique, a set of test cases ranging in size (small, medium, and large) are randomly generated and solved by the proposed meta-heuristic algorithms. An extended comparison analysis is carried out and the outperformance of a hybrid meta-heuristic technique properly combining PSO and genetic algorithm (PSO-GA) is statistically demonstrated.

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“…Computational results show that the proposed algorithm could get better schedules. Here, we briefly investigated PSO, GA and ICA and to understand better, we refer readers to prior studies like: (Tseng et al 2010;Jolai et al 2012;Rabiee, Zandieh, and Jafarian 2012;Azadeh et al 2013;Chen et al 2013;Ngoc et al 2014;Xiong and Xing 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid particle swarm optimisation for scheduling just-in-time single machine with preemption, machine idle time and unequal release times

Seidgar

Abedi

Tadayonirad

et al. 2014

International Journal of Production Research

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This paper addresses preemption in just-in-time (JIT) single-machine-scheduling problem with unequal release times and allowable unforced machine idle time as realistic assumptions occur in the manufacturing environments aiming to minimise the total weighted earliness and tardiness costs. Delay in production systems is a vital item to be focussed to counteract lost sale and back order. Thus, JIT concept is targeted including the elements required such as machine preemption, machine idle time and unequal release times. We proposed a new mathematical model and as the problem is proven to be NP-hard, three meta-heuristic approaches namely hybrid particle swarm optimisation (HPSO), genetic algorithm and imperialist competitive algorithm are employed to solve the problem in larger sizes. In HPSO, cloud theorybased simulated annealing is employed with a certain probability to avoid being trapped in a local optimum. Taguchi method is applied to calibrate the parameters of the proposed algorithms. A number of numerical examples are solved to demonstrate the effectiveness of the proposed approach. The performance of the proposed algorithms is evaluated in terms of relative percent deviation and computational time where the computational results clarify better performance of HPSO than other algorithms in quality of solutions and computational time.

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A hybrid approach based on the variable neighborhood search and particle swarm optimization for parallel machine scheduling problems—A case study for solar cell industry

Cited by 39 publications

References 32 publications

An Efficient Load Balancing Technique for Mutation based Particle Swarm Optimization

An Efficient Load Balancing Technique for Mutation based Particle Swarm Optimization

Single machine batch processing problem with release dates to minimize total completion time

A hybrid particle swarm optimisation for scheduling just-in-time single machine with preemption, machine idle time and unequal release times

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