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

Abstract

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.

Optimisation of Simple Assembly Line Balancing Problem Type E: A Systematic Literature Review

“…In this situation, a SALBP-2 sequence must be solved and the best pair (𝑀, 𝐶 𝑇 ) that maximises 𝐸 is selected as the best SALBP-E solution. The performance of the proposed GA is evaluated on three different problems and the results are compared with those achieved by the Hybrid Reactive Greedy Randomised Adaptive Search procedure [50]. Based on the results, the authors of this study show that the proposed GA is efficient in solving SALBP-2 as a subproblem of SALBP-E and can achieve the same results as the efficient Hybrid Reactive GRASP.…”