Designing a Simulink model for a mixed model stochastic assembly line : A case study using a water bottling plant

Kuriakose, R. B.; Vermaak, H.J.

doi:10.1080/09720529.2020.1741184

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…21,22 In general, there are two main types of ALBP: (1) simple assembly line balancing problems (SALBPs) in which assembly lines are arranged as serial lines in a sequential pattern with all input parameters, without considering uncertainty 23,24 ; (2) generalized assembly line balancing problems which include all issues not addressed in SALBPs and display more constraints than SALBPs. 25 In the recent years, the extensions of ALBP have been conducted by researchers including mixed-model ALBP, 26,27 stochastic assembly line balancing with learning effects, 28 type-1 U-shaped assembly line balancing under uncertain task time, 29 two-sided ALBP, 30 reconfigurable transfer line balancing problem, 31 robotic ALBP, 32 robust ALBPs, 33 simple type-1 ALBPs, 34 flexible multimanned ALBP, 35 cost-oriented resource-constrained multimodel ALBP, 36 multimanned ALPB with skilled workers, 37 and disassembly line balancing problem. 38 Published literature shows that the scope of the ALBP in research is indeed quite clear, with well-defined sets of assumptions, parameters, and objective functions.…”

Section: Assembly Line Balancingmentioning

confidence: 99%

A complex garment assembly line balancing using simulation‐based optimization

Bongomin

Mwasiagi

Nganyi

et al. 2020

Engineering Reports

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The nascent wave of disruptive competition in the current business environment brought about by the fourth industrial revolution (Fashion 4.0 or Apparel 4.0) is enormous. Therefore, it is important for the apparel industry to be flexible enough to respond quickly to the unstable customers' demand through continuous improvement of their process efficiency and productivity. This study proposed assembly line balancing problem (ALBP) for complex garment assembly line using simulation-based optimization under stochastic task times. The proposed ALBP solution approach aimed at minimizing the cycle time for a given number of workstations with consideration of constraints on number of resources, precedence relations, and resource types. The empirical study was conducted at Southern Range Nyanza Limited (NYTIL) garment facility and a complex trouser assembly line with 69 workstations was considered. The discrete event simulation of the trouser assembly line was developed using Arena simulation software. The local optimal solution was obtained from simulation experiments which was adopted for the optimization process. The OptQuest tool was used to solve a single objective optimization with discrete control values. The results showed that the average throughput increased by 30% for local optimal line balancing and 55% for global optimal line balancing. Consequently, the cycle time reduced by 23% and 36%, respectively.

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Section: Assembly Line Balancingmentioning

confidence: 99%

A complex garment assembly line balancing using simulation‐based optimization

Bongomin

Mwasiagi

Nganyi

et al. 2020

Engineering Reports

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“…The mixed-model assembly line has several advantages, such as quick response to customer demands, fewer expenses to produce the new models of a given base model by using the same facilities, and more production flexibility [12,13]. In other words, the mixed-model assembly line is an essential factor for today's manufacturers to overcome the recent challenges associated with operating in the Industry 4.0 environment, particularly regarding the diversity of products, as well as the rapid response to ever-changing customer expectations and demands [14]. Despite many advantages of mixed-model assembly lines, supplying these high-variant mixed-model lines with the required material is a challenging task.…”

Section: Introductionmentioning

confidence: 99%

“…Create the set of available cycles (i.e., the cycles that are not removed in the first stage) AC, according to Equation (14).…”

mentioning

confidence: 99%

Enabling Mass Customization and Manufacturing Sustainability in Industry 4.0 Context: A Novel Heuristic Algorithm for in-Plant Material Supply Optimization

Fathi

Ghobakhloo

2020

Sustainability

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The fourth industrial revolution and the digital transformation of consumer markets require contemporary manufacturers to rethink and reshape their business models to deal with the ever-changing customer demands and market turbulence. Manufacturers nowadays are inclined toward product differentiation strategies and more customer-focused approaches to stay competitive in the Industry 4.0 environment, and mass customization and product diversification are among the most commonly implemented business models. Under such circumstances, an economical material supply to assembly lines has become a significant concern for manufacturers. Consequently, the present study deals with optimizing the material supply to mixed-model assembly lines that contribute to the overall production cost efficiency, mainly via the reduction of both the material transportation and material holding costs across production lines, while satisfying certain constraints. Given the complexity of the problem, a novel two-stage heuristic algorithm is developed in this study to enable a cost-efficient delivery. To assess the efficiency and effectiveness of the proposed heuristic algorithm, a set of test problems are solved and compared against the best solution found by a commercial solver. The results of the comparison reveal that the suggested heuristic provides reasonable solutions, thus offering immense opportunities for production cost efficiency and manufacturing sustainability under the mass customization philosophy.

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