Date of Publication :8th August 2024

Abstract:On time delivery is a very important issue for customers in supply chain management. The customer’s order includes more than one item at all times. How to finish the order on time, so that all items in the same order will be ready for delivery, is an important task. Therefore, this paper considers the dynamic demand lot-sizing problem (DLSP) and the customer-ordering problem (COP) together, namely DLSCOP, dynamic lot sizing problem with customer order considering. DLSP focuses on the deterministic time-varying batch ordering lot-sizing problem with backorders. The COP consists of a set of items that must be shipped as one batch at the same time. This work applies a modified particle swarm optimization (mPSO) to solve the problem. Two popular algorithms, Silver-Meal (SM) algorithm and Wagner-Whitin (WW) algorithm, for benchmarking are modified and two heuristics MSM, MWW are developed for solving DLSCOP. The genetic algorithm (GA) will be included in the simulation experiment for comparing. The simulation test considers 128 scenarios and 100 repetitions. In the statistical analysis, the mPSO performance is better than GA, MSM and MWW. The decision based on MPSO saves more than 10-50% cost, especially in those scenarios with long term, multiple items, and high expense rate (ordering cost and holding cost).

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