A nondominated sorting ant colony optimization algorithm for complex assembly line balancing problem incorporating incompatible task sets

Abstract

Normal 0 false false false TR X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Normal Tablo"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-ansi-language:TR;} Two-sided assembly lines are heavily used in automotive industry for producing large-sized products such as buses, trucks and automobiles. Mixed-model lines help manufacturers satisfy customized demands at a reasonable cost with desired quality. This paper addresses to mixed-model two-sided lines incorporating incompatible task groups and proposes a new method for minimizing two conflicting objectives, namely cycle time and the number of workstations, to maximize line efficiency. While such an approach yields to a so-called type-E problem in the line balancing domain, the proposed nondominated sorting ant colony optimization (NSACO) approach provides a set of solutions dominating others in terms of both objectives (pareto front solutions). The solution which has the highest line efficiency among pareto front solutions is then determined as the best solution. An additional performance criterion is also applied when two different solutions have the same values for both objectives. The solution which has the smoother workload distribution is favoured when both criteria are the same. NSACO is described and a numerical example is provided to exhibit its running mechanism. The performance of the algorithm is tested through test problems in two conditions, i.e. incompatible task sets are considered and not considered, and computational results are presented for the first time. The results indicate that NSACO has a promising solution capacity.