Simple Assembly Line Balancing Problem Type 2 By Variable Neighborhood Strategy Adaptive Search: A Case Study Garment Industry

Abstract

This article aims to minimize cycle time for a simple assembly line balancing problem type 2 by presenting a variable neighborhood strategy adaptive search method (VaNSAS) in a case study of the garment industry considering the number and types of machines used in each workstation in a simple assembly line balancing problem type 2 (SALBP-2M). The variable neighborhood strategy adaptive search method (VaNSAS) is a new method that includes five main steps, which are (1) generate a set of tracks, (2) make all tracks operate in a specified black box, (3)operate the black box, (4) update the track, and (5) repeat the second to fourth steps until the termination condition is met. The proposed methods have been tested with two groups of test instances, which are datasets of (1) SALBP-2 and (2) SALBP-2M. The computational results show that the proposed methods outperform the best existing solution found by the LINGO modeling program. Therefore, the VaNSAS method provides a better solution and features a much lower computational time.objective of this research is to develop an appropriate method for solving a simple assembly line balancing problem type 2, presenting a case study of the garment industry.Chesbrough [1] said that large organizations work together to drive innovation and create sustainable growth by using the concept of open innovation, which is considered to be related to industry that has adopted "technology, tools, or methods" for the industry in production systems, as well as making products according to the various needs of consumers. However, the industry still has to maintain production efficiency in order to meet the required quality. In addition, new technology, tools, and methods must be also studied and selected to be suitable for the production system and the current knowledge. Many industries still rely on human labor for production, especially the garment industry. When a problem occurs, it will be solved immediately without applying technology or any new methods to resolve it, which causes production efficiency to be at a low level. Therefore, to increase the capacity of the production system, in order to compete with other industries, it is necessary to use the concept of open innovation to help in production planning, reduce the time required for industry operations, and increase production efficiency.The assembly line balancing problem is a form of production planning used for task assignments [2] or to assign work to each station to let each work station operate with the same average production time and allow the process flow system to be flexible and eliminate delay or bottlenecks in order to be able to produce products correctly and eliminate mistakes during production. The precedence diagram or table of relationships determines the operation according to the workflow that is clearly specified in the production process. There are different objectives for problem solving, such as reducing production time, reducing the number of workstations, determining the efficiency of assembly line bal...

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

“…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.…”

“…Numerical results showed that, during the simulation, VaNSAS could find optimal solutions. 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.…”

“…Parallel machine scheduling is defined as sequencing and assigning jobs into machines when similar types of machines are available and jobs can be scheduled in these machines. A variety of sequencing and/or processing restrictions often exist when decision makers try to minimize some related objective functions [14][15][16][17][18][19][20][21][22][23][24]; however, most enterprises still use advanced machines running alongside outdated ones. In contrast to the old ones, modern machines are usually adjusted to work at a high speed and save energy.…”

Modified Differential Evolution Algorithm for U-Shaped Assembly Line Balancing Type 2

“…Assembly line balancing is a form of production planning used to assign appropriate tasks to each workstation in order to reduce cycle time or the number of workstations, increase flexibility in process flow, and eliminate delays or bottlenecks during production. There are two types of assembly line balancing problems: simple assembly line balancing problem (SALBP) and general assembly line balancing problem (GALBP) [1,2], as shown in Fig. 1.…”

“…Seyed-Alagheband, Fatemi Ghomi and Zandieh [20] applied the simulated annealing algorithms (SA) to solve sequence-dependent setup time problems with the objective of minimizing the cycle time for a given number of workstations and found that this algorithm was effective in terms of computation time and optimal solutions. Jirasirilerd et al [2] presented a variable neighborhood strategy adaptive search (VaNSAS) to minimize the cycle time for the SALBP-2 problem in the garment industry considering the number and type of machines used at each workstation and found that the algorithm provides a better solution and much less computation time compared to the program LINGO.…”