A new hybrid method for buffer sizing and machine allocation in unreliable production and assembly lines with general distribution time-dependent parameters

Abstract

This paper proposes a multi-objective mathematical formulation and a hybrid approach to solve buffer sizing and machine allocation problems simultaneously in unreliable production and assembly lines. This paper unlike prior researches assumes that time-dependent parameters of production systems are generally distributed (e.g., uniform, normal, gamma, etc.) and not only deterministic or exponential. This paper proposes a multi-objective mixed binary integer non-linear mathematical model to solve the problem of buffer sizing and machine allocation. The proposed mathematical model is capable of purchasing new machines (candidate) and also selling old machines (current available). In other words, this model compares the candidate machines to current available machines in each station based on different aspects and is capable to replace the current machines with candidate machines or to sell some of the current machines without replacement. To solve the mentioned problem, a new formulation for dealing with multi-objectiveness of the problem is proposed. This formulation generates a series of non-dominated solutions, and also, it is capable of generating a non-dominated solution between two adjacent non-dominated solutions determined by decision maker. A hybrid genetic algorithm (HGA) with a new dynamic mutation probability is proposed to solve the model. Since the proposed mathematical model and the proposed solution method are novel, the proposed HGA is compared to simple genetic algorithm and non-dominated sorting genetic algorithm (NSGA-II). The computational results indicate the effectiveness of the proposed HGA.