LAUSR

Abstract Scheduling the supply chain in an integrated manner is a quite fundamental subject in supply chain management. To consider due-dates with production and distribution times at the same time could lead to reduced costs and, thereby, increased profitability. Integrating the problems dealing with due-date assignment, production/distribution times, and routing, while incorporating environmental considerations, could not only increase the cohesion among different decision-making levels and reduce costs in the long run, it would also contribute to the improvement of environmental conditions and benefit the world population. In this study, the integrated supply chain scheduling problem features assignment of due dates, batch delivery, assignment to multiple heterogeneous vehicles based on their capacity, and delivery of customer orders in time-windows. The objective is to minimize distribution cost, fixed and variable fuel costs, the carbon emitted by the vehicles, total delivery tardiness, and customer dissatisfaction. In this model, the customers are categorized into five clusters based on the length of their association, how recently they began interacting, how often they interact, and the monetary value of their interaction with the organization. The members of these clusters are called core customers, potential customers, new customers, lost customers, and resource-consumption customers. A mixed integer non-linear programming model is introduced for this problem which is solved using three multi-objective metaheuristic algorithms: Multi-Objective Particle Swarm Optimization, Non-dominated Sorting Genetic Algorithm II, and Multi Objective Ant Colony Optimization. A number of performance criteria and statistical tests are used to evaluate the algorithms.