SOLVING THE MULTI-MANNED ASSEMBLY LINE BALANCING PROBLEM WITH DEPENDENT TASK TIMES BY MEANS OF EMPIRICALLY ADJUSTED GREEDY HEURISTICS (EAGH)

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Abstract
Multi-manned assembly lines are widely used in manufacturing industries that process a high-volume of large-sized workpieces. At each workstation there are multiple workers simultaneously performing different tasks with the possibility of interfering with each other, leading to an increase in the processing task times. This paper studies this type of problem: the multi-manned assembly line balancing problem with dependent task times (MALBP-DTT). As discussed in recent literature, the HEUR_PART procedure presents the best behaviour for solving MALBP-DTT. In this paper, HEUR_PART is improved by using the Empirically Adjusted Greedy Heuristics (EAGH) procedure along with a new procedure (named “EAGH-CKTL”) that is presented in this paper and is based on using EAGH combined with the cocktail of heuristics concept. EAGH and EAGH-CKTL are used to design new priority rules for solving MALBP-DTT through the HEUR_PART steps. In particular, EAGH-CKTL is applied for building new priority rules that have good performance as part of a cocktail of heuristics. The computational experiments show the efficiency of using both EAGH and EAGH-CKTL in the process of designing efficient priority rules: one of the priority rules designed with EAGH presents better performance than any other rule proposed in the literature for HEUR_PART, while another rule designed with EAGH-CKTL evidences a remarkable improvement in the HEUR_PART results when added to its original cocktail of heuristics. Keywords: combinatorial optimisation; assembly line balancing; multi-manned workstations; dependent task times; EAGH; cocktail of heuristics.