Sergio Damas Arroyo

NTIGen: a software for generating Nissan based instances for time and space assembly line balancing

The time and space assembly line balancing problem (TSALBP) is a re- alistic multiobjective version of assembly line balancing industrial problems in- volving the joint optimization of conflicting criteria such as the cycle time, the number of stations, and the area of these stations. For this family of problems there is not any repository where researchers and practitioners can o bt ain realistic problem instances also containing information on mixed products plans. In this contribution we introduce a new TSALBP instance software generator that can produce problem instances having industrial real-like features. This generator is called NTIGen (Nissan TSALBP Instance GENerator) since it is developed from the information and real data of the assembly line and production planning of the Nissan plant of Bar ce lona. The NTIGen software as well as some benchmark in- stances are publicly available on Internet and could be used by researchers to carry out general TSALBP experiments and to also discriminate between di ff erent as- sembly line configurations when future demand conditions vary

A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand

Changes in demand when manufacturing different products require an optimization model that includes robustness in its definition and methods to deal with it. In this work we propose the r-TSALBP, a multiobjective model for assembly line balancing to search for the most robust line configurations when demand changes. The robust model definition considers a set of demand scenarios and presents temporal and spatial overloads of the stations in the assembly line of the products to be assembled. We present two multiobjective evolutionary algorithms to deal with one of the r-TSALBP variants. The first algorithm uses an additional objective to evaluate the robustness of the solutions. The second algorithm employs a novel adaptive method to evolve separate populations of robust and non-robust solutions during the search. Results show the improvements of using robustness information during the search and the outstanding behavior of the adaptive evolutionary algorithm for solving the problem. Finally, we analyze the managerial impacts of considering the r-TSALBP model for the different organization departments by exploiting the values of the robustness metrics.

Métricas sobre la robustez de soluciones en el problema TSALB ante la variación del mix de producción

Aplicacion de metricas de robustez a configuraciones de linea en TSALBP - Ejemplo prototipo.

A multiobjective genetic algorithm and robustness information function for time and space assembly line balancing

The time and space assembly line balancing problem (TSALBP) is a realistic multiobjective version of assembly line balancing industrial problems involving the joint optimization of conflicting criteria such as the cycle time, the number of stations, and the area of these stations. However, the existing problem formulation does not consider the industrial scenario where the demand of a set of mixed products is variable and uncertain. In this contribution we introduce a novel robustness function to measure the robustness of the line configuration when the production plans demand changes. The function is used as additional a posteriori information for the non-dominated solutions found by an advanced multiobjective genetic algorithm. Because of their independence, these robustness functions can be used in conjunction with any other multiobjective metaheuristic. Results show how the use of the robustness function can help the decision maker to select robust non-dominated solutions when future demand conditions vary in the assembly line configurations.