A developed genetic algorithm for solving the multi-objective supply chain scheduling problem

Purpose - Proper management of supplies and their delivery greatly affects the competitiveness of companies. This paper aims to propose an integrated decision-making approach for integrated transportation and production scheduling problem in a two-stage supply chain. The objective functions are minimizing the total delivery tardiness, production cost and the emission by suppliers and vehicles and maximizing the production quality. Design/methodology/approach - First, the mathematical model of the problem is presented. Consequently, a new algorithm based on a combination of the genetic algorithm (GA) and the VIKOR method in multi-criteria decision-making, named GA-VIKOR, is introduced. To evaluate the efficiency of GA-VIKOR, it is implemented in a pharmaceutical distribution company located in Iran and the results are compared with those obtained by the previous decision-making process. The results are also compared with a similar algorithm which does not use the VIKOR method and other algorithm mentioned in the literature. Finally, the results are compared with the optimized solutions for small-sized problems. Findings - Results indicate the high efficiency of GA-VIKOR in making decisions regarding integrated production supply chain and transportation scheduling. Research limitations/implications - This research aids the manufacturers to minimize their total delivery tardiness and production cost and at the same time maximize their production quality. These improve the customer satisfaction as a part of social and manufacturer's power of competitiveness. Furthermore, the emission minimizing objective functions directly provides benefits to the environment and the society. Originality/value - This paper investigates a new supply chain scheduling the problems and presents its mathematical formulation. Moreover, a new algorithm is introduced to solve the multi-objective problems.