Minimizing sum of the due date assignment costs, maximum tardiness and distribution costs in a supply chain scheduling problem

• The model of Integrated Due-date Assignment and Production and Outbound Distribution Scheduling is addressed.
• Two meta-heuristic algorithms including Adaptive Genetic Algorithm and Parallel Simulated Annealing and one heuristic algorithm are developed.
• In AGA, six new crossover and mutation operators and in PSA one new neighborhood generation are introduced.
• A full factorial experimental design is used to test the performance of meta-heuristic algorithms.
• Computational results show the better performance of AGA.

In production systems, manufacturers face important decisions that affect system profit. In this paper, three of these decisions are modelled simultaneously: due date assignment, production scheduling, and outbound distribution scheduling. These three decisions are made in the sales, production planning and transportation departments. Recently, many researchers have devoted attention to the problem of integrating due date assignment, production scheduling and outbound distribution scheduling. In the present paper, the problems of minimizing costs associated with maximum tardiness, due date assignment and delivery for a single machine are considered. Mixed Integer Non-Linear Programming (MINLP) and a Mixed Integer Programming (MIP) are used for the solution. This problem is NP-hard, so two meta-heuristic algorithms, an Adaptive Genetic Algorithm (AGA) and a Parallel Simulated Annealing algorithm (PSA), are used for solution of large-scale instances. The present paper is the first time that crossover and mutation operators in AGA and neighbourhood generation in PSA have been used in the structure of optimal solutions. We used the Taguchi method to set the parameters, design of experiments (DOE) to generate experiments, and analysis of variance, the Friedman, Aligned Friedman, and Quade tests to analyse the results. Also, the robustness of the algorithms was addressed. The computational results showed that AGA performed better than PSA.

Average deviation from optimal solution.Figure optionsDownload as PowerPoint slide