Balancing stochastic two-sided assembly line with multiple constraints using hybrid teaching-learning-based optimization algorithm

Abstract

Two-sided assembly lines are usually found in the factories which produce large-sized products. In most literatures, the task times are assumed to be deterministic while these tasks may have varying operation times in real application, causing the reduction of performance or even the infeasibility of the schedule. Moreover, the ignorance of some specific constraints including positional constraints, zoning constraints and synchronism constraints will result in the invalidation of the schedule. To solve this stochastic two-sided assembly line balancing problem with multiple constraints, we propose a hybrid teaching-learning-based optimization (HTLBO) approach which combines both a novel teaching-learning-based optimization algorithm for global search and a variable neighborhood search with seven neighborhood operators for local search. Especially, a new priority-based decoding approach is developed to ensure that the selected tasks satisfy most of the constraints identified by multiple thresholds of the priority value and to reduce the idle times related to sequence-dependence among tasks. Experimental results on benchmark problems demonstrate both remarkable efficiency and universality of the developed decoding approach, and the comparison among 11 algorithms shows the effectiveness of the proposed HTLBO.