Design of an Efficient Genetic Algorithm to Solve the Industrial Car Sequencing Problem

Abstract

In many industrial sectors, decision makers are faced with large and complex problems that are often multi-objective. Many of these problems may be expressed as a combinatorial optimization problem in which we define one or more objective functions that we are trying to optimize. Thus, the car sequencing problem in an assembly line is a well known combinatorial optimization problem that cars manufacturers face. This problem involves scheduling cars along an assembly line composed of three consecutive shops: body welding and construction, painting and assembly. In the literature, this problem is most often treated as a single objective problem and only the capacity constraints of the assembly shop are considered (Dincbas et al., 1988). In this workshop, each car is characterized by a set of different options and the workstations where each option is installed are designed to handle a certain percentage of cars requiring the same options. To smooth the workload at the critical assembly workstations, cars requiring high work content must be dispersed throughout the production sequence. Industrial car sequencing formulation subdivides the capacity constraints into two categories, that are the capacity constraints linked to the highpriority options and the capacity constraints linked to the low-priority options. However, the reality of industrial production does not only take into account the assembly shop requirements. The industrial formulation proposed by French automobile manufacturer Renault, in the context of the ROADEF 2005 Challenge, also takes into account the paint shop requirements. In this workshop, the minimization of the amount of solvent used to purge the painting nozzles for colour changeovers, or when a known maximum number of vehicle bodies of the same colour have been painted, is an important objective to consider. Indeed, long sequences of cars of the same colour tend to render visual quality controls inaccurate. To ensure this quality control, the number of cars of the same colour must not exceed an upper limit. The industrial car sequencing problem (ICSP) is thus a multi-objective problem in nature, with three conflicting objectives to minimize. In the assembly shop, one tries to minimize the number of violations of capacity constraints related to high-priority options (HPO) and to low-priority options (LPO). In the paint shop, one tries to minimize the number of colour changes (COLOUR). In the 2005 ROADEF Challenge, the Renault automobile manufacturer proposes to tackle the problem by treating the three objectives lexicographically. O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m