Managing Uncertainty in Supply Chain Operating Cost Using Genetic Algorithm

Supply chain uncertainty has been captured in various forms like supply uncertainty, production uncertainty and demand uncertainty. Uncertainty refers measuring the degree of differences between the models and the respective real systems values or between the estimation of variables and their true values [1]. The uncertainty can be caused by the errors associated with the model itself and the uncertainties of the model inputs. The significant part of managing uncertainty is identifying as many sources and factors of uncertainty as possible [10]. For production planning, one typically needs to determine the variable production costs, including manufacturing costs, Labour cost, materials cost, inventory holding costs, and any relevant resource acquisition costs [9]. The identification the various sources and factors of uncertainty in manufacturing/production environment has been done here [4]. The main purpose of this paper is to minimize production costs. The fundamental concern of manufacturing resources planning is to guarantee that the best promising quantity of the item is released at the lowest costs within some given constraints of the system like availability of the resource (s) in need [5]. A Genetic algorithm is proposed that uses a set of crossover and mutation operators for solving the problem.