A bi-objective model for integrated scheduling of production and distribution in a supply chain with order release date restrictions. (July 2016)

• Record Type: Journal Article
• Authors: Jamili, Negin; Ranjbar, Mohammad; Salari, Majid
• Is Part Of: Journal of manufacturing systems. Volume 40(2016)Part 1
• Page Start: 105
• Page End: 118
• Publication Date: 2016-07
• Subjects: Supply chain management -- Scheduling -- Routing -- Bi-objective optimization; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2016.06.005
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: An integrated production and distribution scheduling model in a supply chain with production and transportations costs is studied. We analyze the problem in both single objective and bi-objective cases and formulate it as a linear integer programming model. We develop a set of heuristic and metaheuristic algorithms for both single objective and bi-objective cases. Using extensive computational results, we compare solutions approaches and show the efficiency of production and distribution integration. Abstract: We study an integrated production and distribution scheduling model in a supply chain. In this problem, there is a set of orders from several customers that has to be processed on a single machine located in a factory. Each order has a release date imposed by the corresponding supplier for delivering the order. In the factory, orders must be batched and routed to be delivered to the corresponding customers. We propose a bi-objective model in order to find a joint schedule of production and distribution to optimize the customers' service level, measured as the mean delivery time and the total transportation cost. An integer linear programming model, in which a weighted sum of both objectives is converted to a single objective, is developed and solved. In addition, two local search algorithms and a metaheuristic algorithm based on tabu search are developed to solve large-scale problems at reasonable times. Moreover, two bi-objective solution methods are developed to find the Pareto solutions for the bi-objective problem. Finally, the performances of all developed solution approaches are analyzed and compared using randomly generated test sets and managerial insights are drawn from multiple numerical experiments.