Mathematical Modelling of Mixed-Model Assembly Line Balancing Problem with Resources Constraints

Razali, Muhamad Magffierah; Rashid, Mohd Fadzil Faisae Ab; Make, Muhammad Razif Abdullah

doi:10.1088/1757-899x/160/1/012002

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…To evaluate the performance of selected metaheuristic algorithms, a problem modeling was constructed with the objective functions to minimize cycle time, minimize product rate variation (PRV) and resources used on the assembly line [9]. Mixed-model assembly line consists of more than one product which will be assembled on the same line.…”

Section: Problem Modelingmentioning

confidence: 99%

“…Second was to minimize product rate variation (PRV) while the third was to minimize resources used on the assembly line. These selected objective functions and their related constraints were formulated as below [9]:…”

Section: Objective Functions and Constraintsmentioning

confidence: 99%

“…This can be accomplished by distributing the fitness value with the maximum value for every objective function measured. After applying the weighted sum approach, the normalized fitness functions is represented in Equation (9). For this purpose, w1, w2, and w3 were set to 0.33 each to give equal weightage for all objective functions.…”

Section: Objective Functions and Constraintsmentioning

confidence: 99%

“…journal.ump.edu.my/jmmst ◄ to measure the studied metaheuristic algorithm performance . The most common objective functions being studied in existing literature are minimize cycle time [9], minimize number of workstation [10], minimize total idle cost [11], and balance workload [12].…”

Section: Introductionmentioning

confidence: 99%

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Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints

Razali¹,

Rashid²,

Make³

2020

JMMST

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Mixed-model assembly line balancing problem (MMALBP) is an NP-hard problem which requires an effective algorithm for solution. In this study, an assessment of metaheuristic algorithms to optimize MMALBP is conducted using four popular metaheuristics for this problem, namely Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Genetic Algorithm (GA). Three categories of test problem (small, medium, and large) were used ranging from 8 to 100 number of tasks. For computational experiment, MATLAB software is used in investigate the metaheuristic algorithms performances to optimize the designated objective functions. The results reveal that ACO algorithm performed better in term of finding the best fitness functions when dealing with a large number of tasks. Averagely, it has produces better solution quality than PSO by 5.82%, GA by 9.80%, and SA by 7.66%. However, PSO more superior in term of processing time compared to ACO by 29.25%, GA by 40.54%, and SA by 73.23%. Hence, future research directions such as using the actual manufacturing assembly line data to test the algorithm performances are likely to happen.

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Section: Problem Modelingmentioning

confidence: 99%

Section: Objective Functions and Constraintsmentioning

confidence: 99%

Section: Objective Functions and Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints

Razali¹,

Rashid²,

Make³

2020

JMMST

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“…The difference between models lies in the number of tasks, processing time of each task, precedence tasks relations and the amount of production [4]. The lot size of each model during the production is equal to one as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Grasp-genetic Algorithm for Mixed-model Assembly Line Balancing Problem Type 2

Belkharroubi¹,

Yahyaoui²

2021

IJC

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In manufacturing systems, mixed model assembly lines are used to produce different products to deal with the problem of customers’ demands variety, and minimizing the cycle time in such assembly line is a critical problem. This paper addresses the mixed model assembly line balancing problem type 2 that consists in finding the optimal cycle time for a given number of workstations. A hybrid Greedy randomized adaptive search procedure-Genetic algorithm is proposed to find the optimal assignment of tasks among workstations that minimize the cycle. A Ranked Positional Weight heuristic is used in the construction phase of the proposed GRASP, and in the local search phase, a neighborhood search procedure is used to ameliorate the constructed solutions in the construction phase. The GRASP is executed many times in order to seed the initial population of the proposed genetic algorithm, and the results of the executions are compared with the final solutions obtained by the hybrid GRASP-GA. In order to test the proposed approaches, a numerical example is used.

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Product Rate Variation (PRV) Problem and Its Variants

Rahul

Srinivasan

2022

Studies in Quantitative Decision Making

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Mathematical Modelling of Mixed-Model Assembly Line Balancing Problem with Resources Constraints

Cited by 7 publications

References 17 publications

Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints

Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints

A Hybrid Grasp-genetic Algorithm for Mixed-model Assembly Line Balancing Problem Type 2

Product Rate Variation (PRV) Problem and Its Variants

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