A comparative study of Multi-Objective Ant Colony Optimization algorithms for the Time and Space Assembly Line Balancing Problem - Rada-Vilela, Juan; Chica, Manuel; Cordón, Óscar; Damas, Sergio (Applied soft computing, 2013)
[Display omitted] •Assembly line balancing (ALB) is a classical and complex industrial problem.•Time and space assembly line balancing (TSALBP) is a realistic model for ALB.•We consider a TSALBP variant based on multiobjective optimization of time and space.•Different MOACO proposals are designed to solve the problem and to be compared.•Experimentation shows best proposals and way of using heuristic information for the problem. Assembly lines for mass manufacturing incrementally build production items by performing tasks on them while flowing between workstations. The configuration of an assembly line consists of assigning tasks to different workstations in order to optimize its operation subject to certain constraints such as the precedence relationships between the tasks. The operation of an assembly line can be optimized by minimizing two conflicting objectives, namely the number of workstations and the physical area these require. This configuration problem is an instance of the TSALBP, which is commonly found in the automotive industry. It is a hard combinatorial optimization problem to which finding the optimum solution might be infeasible or even impossible, but finding a good solution is still of great value to managers configuring the line. We adapt eight different Multi-Objective Ant Colony Optimization (MOACO) algorithms and compare their performance on ten well-known problem instances to solve such a complex problem. Experiments under different modalities show that the commonly used heuristic functions deteriorate the performance of the algorithms in time-limited scenarios due to the added computational cost. Moreover, even neglecting such a cost, the algorithms achieve a better performance without such heuristic functions. The algorithms are ranked according to three multi-objective indicators and the differences between the top-4 are further reviewed using statistical significance tests. Additionally, these four best performing MOACO algorithms are favourably compared with the Infeasibility Driven Evolutionary Algorithm (IDEA) designed specifically for industrial optimization problems.