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

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.

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.

Results indicate the high efficiency of GA-VIKOR in making decisions regarding integrated production supply chain and transportation scheduling.

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.

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.