An improved preference-based variable neighborhood search algorithm with ar-dominance for assembly line balancing considering preventive maintenance scenarios

Abstract

For assembly line balancing problem considering preventive maintenance scenarios (ALBP-PM), the production managers’ preferences should be concerned and hence the derived Pareto-optimal solution set (POS) should be advanced towards the corresponding region-of-interest. Otherwise, production managers might select a final solution far from their interests, and then obviously reducing the quality and efficiency of decision-making. Thus, a preference-based multi-objective optimization problem is formulated to simultaneously minimize the cycle times under different scenarios and total task adjustments among scenarios. An improved variable neighborhood search algorithm (IVNS) with novel ar-dominance and three modifications is proposed to obtain a preferred POS. Specifically, ar-dominance is integrated into it to guide the advancement direction of the preferred POS. Enhanced neighborhood structures based on critical stations are proposed to generate better-quality neighbor solutions whose cycle times are mathematically proven smaller. On this basis, a local search strategy is designed to randomly select a neighbor solution to guarantee quality and decrease the computational cost. A problem-specific adaptive restart mechanism is developed to escape from the local optimum. Computational results suggest that the IVNS obtains a better preferred POS than other eight state-of-the-art algorithms in terms of convergence, distribution and closeness to the preferences, and furthermore, incorporating preferences into the ALBP-PM helps production managers make final decisions in a more precise and faster way.