An effective heuristic with evolutionary algorithm for the coordinated capacitated dynamic lot-size and delivery problem

This study considers an extensive coordinated capacitated dynamic lot-size problem that contains a delivery scheduling problem. The coordinated capacitated dynamic lot-size and delivery problem (CCLSDP) assumes that a family of products is subject to capacity constraints, and the demand without backorders is dynamic or time-varying but deterministic. The objective is to determine a replenishment and delivery schedule that minimizes the total system costs, including the family and minor setup costs, delivery costs, and inventory holding costs. We develop a six-phase heuristic (SPH) to solve the CCLSDP. The SPH contains six phases that can efficiently reschedule replenishment and delivery planning. Each phase of the SPH evaluates the cost and feasibility of the shifting operations, and determines whether to reschedule the system planning through a series of sequential shifting operations. To further enhance the overall performance, we embed SPH as a lifting procedure into evolutionary algorithms contributing to explore the search space and escapes the local optimum. Experimental results of randomly generated instances that were over a wide range of parameters indicate that the proposed heuristics could efficiently and effectively solve the complicated CCLSDP.