Variable neighborhood strategy adaptive search for solving green 2-echelon location routing problem

Pitakaso, Rapeepan; Sethanan, Kanchana; Theeraviriya, Chalermchat

doi:10.1016/j.compag.2020.105406

Cited by 33 publications

(19 citation statements)

References 27 publications

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“…The problem-solving accuracy of the VaNSAS algorithm has been proven by several researchers. Jirasirilerd et al [57] and Pitakaso et al [58,64] used the VaNSAS algorithm for production and planning problem solving. The operating algorithm used in the VaNSAS process can be the differential evolution algorithm, the iterated local search, the swap method, the modified differential evolution algorithm, the large neighborhood search, or the shortest processing time-swap.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition, several heuristic methods were used for optimization prediction to improve the speed and success of the tour routing, transportation, agriculture, and manufacturing processes [53][54][55][56][57][58]. Several studies used heuristic methods for optimized problem solving, such as adaptive large neighborhood search (ALNS), genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), variable neighborhood strategy adaptive search (VaNSAS), etc.…”

Section: Introductionmentioning

confidence: 99%

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Variable Neighborhood Strategy Adaptive Search for Optimal Parameters of SSM-ADC 12 Aluminum Friction Stir Welding

et al. 2021

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In this study, we present a new algorithm for finding the optimal friction stir welding parameters to maximize the tensile strength of a butt joint made of the semisolid material (SSM) ADC12 aluminum. The welding parameters were rotational speed, welding speed, tool tilt, tool pin profile, and rotational direction. The method presented is a variable neighborhood strategy adaptive search (VaNSAS) approach. The process of finding the optimal friction stir welding parameters comprises five steps: (1) identifying the type and range of friction stir parameters using a literature survey; (2) performing experiments according to (1); (3) constructing a regression model using the response surface method optimizer (RSM optimizer); (4) using VaNSAS to find the optimal parameters for the model obtained from (3); and (5) confirming the results from (4) using the parameter levels obtained from (4) to perform real experiments. The computational results revealed that the tensile strength generated from VaNSAS was 3.67% higher than the tensile strength obtained from the RSM optimizer parameters. The optimal parameters obtained from VaNSAS were a rotation speed of 2200 rpm, a welding speed of 108.34 mm/min, a tool tilt of 1.23 Deg, a tool pin profile of a hexagon, and a rotational direction of clockwise.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Variable Neighborhood Strategy Adaptive Search for Optimal Parameters of SSM-ADC 12 Aluminum Friction Stir Welding

et al. 2021

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“…This paper presents a novel method called variable neighborhood strategy adaptive search (VaNSAS) to solve the parallel-machine-scheduling problem in order to minimize energy consumption while considering job priority and makespan control. Although VaNSAS successfully improved solution-search performance in previous studies [17,22,[36][37][38], none had accounted for energy consumption, late delivery charge, and production overhead. The advantage of applying VaNSAS in this study was that its algorithms search for the best possible solution in many different areas by using several searching approaches, thereby moving to find more diversification and intensification at all times depending on the designed blackbox methods.…”

Section: Discussionmentioning

confidence: 96%

“…Computation results showed that VaNSAS could find solutions for all problem sizes in much less processing time than that needed by the exact method. After that, Pitakaso et al [37] presented VaNSAS with another LRP, the green 2-echelon location-routing problem (G2ELRP), which is a variant of the capacitated location-routing problem (CLRP) and the 2-echelon location routing problem (2ELRP). The G2ELRP aims to reduce overall fuel consumption on the basis of distance and road conditions in both echelons.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The differential evolutionary (DE) algorithm is suitable to solve this kind of problem because it can obtain non-dominated solutions in a single run and has been successfully applied to optimization problems [17,20,24,[27][28][29]. In addition, metaheuristics based on local search methods, such as the variable neighborhood strategies adaptive search (VaNSAS), have been successfully applied to solve many combinatorial optimizations problems [30][31][32][33][34][35][36][37][38][39][40], which inspired us to develop a parallel-machine-scheduling model, and to propose VaNSAS and new neighborhood strategies: (1) solution destroy and repair (SDR); (2) track-transition method (TTM); and (3) multiplier factor (MF).…”

Section: Introductionmentioning

confidence: 99%

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Variable Neighborhood Strategy Adaptive Search to Solve Parallel-Machine Scheduling to Minimize Energy Consumption While Considering Job Priority and Control Makespan

et al. 2021

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Environmental concerns and rising energy prices put great pressure on the manufacturing industry to reduce pollution and save energy. Electricity is one of the main machinery energy sources in a plant; thus, reducing energy consumption both saves energy costs and protects our planet. This paper proposes the novel method called variable neighborhood strategy adaptive search (VaNSAS) in order to minimize energy consumption while also considering job priority and makespan control for parallel-machine scheduling problems. The newly presented neighborhood strategies of (1) solution destroy and repair (SDR), (2) track-transition method (TTM), and (3) multiplier factor (MF) were proposed and tested against the original differential evaluation (DE), current practice procedure (CU), SDR, TTM, and MF for three groups of test instances, namely small, medium, and large. Experimental results revealed that VaNSAS outperformed DE, CU, SDR, TTM, and MF, as it could find the optimal solution and the mathematical model in the small test instance, while the DE could only find 25%, and the others could not. In the remaining test instances, VaNSAS performed 16.35–19.55% better than the best solution obtained from Lingo, followed by DE, CU, SDR, TTM, and MF, which performed 7.89–14.59% better. Unfortunately, the CU failed to improve the solution and had worse performance than that of Lingo, including all proposed methods.

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A hybrid firefly and particle swarm optimization algorithm with local search for the problem of municipal solid waste collection: a real-life example

Kaya

2022

Neural Comput & Applic

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Variable neighborhood strategy adaptive search for solving green 2-echelon location routing problem

Cited by 33 publications

References 27 publications

Variable Neighborhood Strategy Adaptive Search for Optimal Parameters of SSM-ADC 12 Aluminum Friction Stir Welding

Variable Neighborhood Strategy Adaptive Search for Optimal Parameters of SSM-ADC 12 Aluminum Friction Stir Welding

Variable Neighborhood Strategy Adaptive Search to Solve Parallel-Machine Scheduling to Minimize Energy Consumption While Considering Job Priority and Control Makespan

A hybrid firefly and particle swarm optimization algorithm with local search for the problem of municipal solid waste collection: a real-life example

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