Affiliations: Information and Operations Management Department, University of North Carolina at Charlotte, Charlotte, NC 28223, USA email: [email protected] | Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA email: [email protected] | Charlotte-Mecklenburg Police Department, 601 E. Trade Street, Charlotte, NC 28202, USA email: [email protected]
Note: [] Address for correspondence: Information and Operations Management Department, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
Note: [] Address for correspondence: Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
Note: [] Address for correspondence: Charlotte-Mecklenburg Police Department, 601 E. Trade Street, Charlotte, NC 28202, USA.
Abstract: The economic lot and delivery scheduling problem (ELDSP) involves a supply chain consisting of a supplier and an assembly facility, where direct shipments are made from one to the other. The supplier produces multiple components on a single machine or a production line. The assembly facility uses these components at a constant rate. The supplier incurs a sequence-independent setup cost and setup time each time the production line is changed over from one component to another. On the other hand, setup costs and times for the assembly facility are negligible. There is also a fixed charge for each delivery. The problem is to find a “just-in-time” schedule in which one production run of each component and a subsequent delivery of these components to the assembly facility occur in each cycle. The objective is to find the best sequence and cycle duration that minimizes the average cost per unit time of transportation, inventory at both the supplier and the assembly facility, and setup costs at the supplier. In this paper we investigate the usefulness of an evolutionary algorithm for solving this economic lot and delivery scheduling problem.
Keywords: heuristic search, evolutionary algorithms, scheduling, economic lot scheduling and delivery
