Multiobjective memetic algorithms for time and space assembly line balancing

This paper presents three proposals of multiobjective memetic algorithms to solve a more realistic extension of a classical industrial problem: time and space assembly line balancing. These three proposals are, respectively, based on evolutionary computation, ant colony optimisation, and greedy randomised search procedure. Different variants of these memetic algorithms have been developed and compared in order to determine the most suitable intensification–diversification trade-off for the memetic search process. Once a preliminary study on nine well-known problem instances is accomplished with a very good performance, the proposed memetic algorithms are applied considering real-world data from a Nissan plant in Barcelona (Spain). Outstanding approximations to the pseudo-optimal non-dominated solution set were achieved for this industrial case study.

► 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.
► We present three multiobjective memetic proposals with problem-specific operators.
► The experimentation is performed on both classical and real-world problem instances.