Buffer allocation problem and preventive maintenance planning in non-homogenous unreliable production lines

Abstract

In the current paper, a special class of buffer allocation problem (BAP)—buffer and preventive maintenance periods allocation problems—is studied. Three objective functions including the maximization of production rate, the minimization of total buffer size, and the total number of defective units are examined in order to solve the proposed model. In addition, an integrated simulation and meta-heuristic algorithm method is used for the same purpose. The proposed model and solution approach are applied to a case study, i.e., water heater production line in Polar Saunier Duval Company. Furthermore, the model and solutions will be assessed. In this integrated approach, genetic algorithm (GA) and particle swarm optimization (PSO) algorithms are used as meta-heuristic algorithms. These algorithms are compared in terms of quality of solutions, computational time, and relative deviation of final solutions in 0.95 confidence level. Numerical results reveal that GA is superior to PSO in terms of the quality of obtained solutions and relative deviation from optimal solutions (RPD). Furthermore, PSO converges in shorter computational time. Finally, based on outputs of the integrated solution approach, some important points about the parameters of the production line are presented. Computational results show that production rate is the most influential objective function in optimal solutions. However, drawing from the characteristics of production line, the number of defective products has less impact on optimal solutions in comparison with other objective functions. It is also concluded that there is a direct relationship between the number of defective products and time periods between preventive maintenance operations; in other words, if the time periods between preventive maintenance operations increase, the number of defective products grow.