A novel variable neighborhood strategy adaptive search for SALBP-2 problem with a limit on the number of machine’s types

Pitakaso, Rapeepan; Sethanan, Kanchana; Jirasirilerd, Ganokgarn; Golińska-Dawson, Paulina

doi:10.1007/s10479-021-04015-1

Cited by 9 publications

(8 citation statements)

References 29 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%

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%

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%

“…In a manufacturing case study regarding the garment industry, the VaNSAS proposed by Jirasirilerd et al [22] presented a better solution and less computation time in order to minimize cycle time for a simple assembly line, balancing the Type 2 problem while considering the number and types of machines operated in each workstation. Recently, Pitakaso et al [38] applied VaNSAS to minimize the cycle time while considering the limited number of machine types in a particular workstation for the special case of the simple assembly line balancing Type 2 problems, where multi-skilled workers have a set of competencies that allow them to work on more than one machine in a workstation. Results showed that VaNSAS was able to reduce the cycle time and increase assembly line effectiveness.…”

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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“…For each IB, q is a predefined parameter that is set to 100 iterations [59]. The Pareto front was used to keep the nondominated solution.…”

Section: Perform the Wp Execution Processmentioning

confidence: 99%

A Novel Artificial Multiple Intelligence System (AMIS) for Agricultural Product Transborder Logistics Network Design in the Greater Mekong Subregion (GMS)

et al. 2022

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In recent years, agriculture products have contributed to 28.75% of Thailand’s GDP. China, Vietnam, Myanmar, Cambodia, Laos and Vietnam are the main markets for agricultural products. The annual export volume exceeds 119,222 million THB. The majority of them are shipped over Thailand’s land borders to its neighbors. Small and medium-sized farmers make up more than 85% of those who produce agricultural items. Numerous scholars have studied the transportation methods used by the Greater Mekong Subregion (GMS) nations along the economic corridor, but the majority of them have concentrated on import–export operations involving sizable firms, which are not applicable to the transportation of agricultural products, particularly when attention is paid to small and medium-sized farmers. In this study, mixed-integer programming (MIP) is presented to design an agricultural product logistics network. In order to prolong the lifespan of the container used, the MIP’s primary goal is to maximize the total chain profit while maintaining the lowest container usage possible. The approach was developed to increase small and medium-sized farmers’ ability to compete. Small and medium-sized farmers bring their products to an agricultural product collecting center, also known as a container loading facility. After that, skilled logistics companies distribute the goods. In order to convey the goods to the final clients in neighboring nations, the proper locations of the containing loading centers, the correct transportation option and the borders must be decided. The issue was identified as multi-echelon location–allocation sizing (MELLS), an NP-hard problem that cannot be handled in an efficient manner. To solve a real-world problem, however, efficient techniques must be supplied. AMIS, an artificial multiple intelligence system, was created to address the suggested issue. AMIS was developed with the goal of leveraging a variety of methods for local search and development. There are several well-known heuristics techniques employed in the literature, including the genetic algorithm (GA) and the differential evolution algorithm (DE). With respect to the improved solutions obtained, the computational results show that AMIS exceeds the present heuristics, outperforming DE and GA by 9.34% and 10.95%, respectively. Additionally, the system’s farmers made a total of 15,236,832 THB in profit, with an average profit per container of 317,434 THB and an average profit per farmer of 92,344.44 THB per crop. The container loading center uses 48 containers, with a 5.33 container average per container loading center (CLC). The farmers’ annual revenues were previously less than 88,402 THB per family per year, so we can predict that the new network may increase customers’ annual income by 4.459% for each crop.

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A novel variable neighborhood strategy adaptive search for SALBP-2 problem with a limit on the number of machine’s types

Cited by 9 publications

References 29 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 Novel Artificial Multiple Intelligence System (AMIS) for Agricultural Product Transborder Logistics Network Design in the Greater Mekong Subregion (GMS)

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