An adaptive GLNPSO method for inventory replenishment supply chain problem with multiple-warehouse policy and budget consideration

Abstract

This paper presents an efficient computation tool for a multiple-warehouse inventory replenishment lot-sizing problem with supplier selection, quantity discounts, and budget constraint. The cost model of the considered inventory replenishment issue is formulated as a mixed-integer nonlinear programming (MINLP) problem. With the proposed MINLP problem being an NP-hard problem, the MINLP cost model was optimized by using a novel global–local neighbor particle swarm optimization algorithm (TPAGLNPSO), inspired by two-phase self-adaptive inertia weight and improved constraint-handling technique. Apart from introducing the velocity index to switch the search scheme from exploration to exploitation, the constraint-handling technique was utilized to guide and guarantee the particle search toward undiscovered regions in the solution space. The stability analysis for the proposed novel TPAGLNPSO was also conducted using the stochastic process theorem. The corresponding parameter selection ranges were obtained based on the convergent conditions. Finally, eight industry cases were provided as examples to evaluate the performance of the proposed TPAGLNPSO method, after which a sensitivity analysis for the MINLP cost model was performed. The results demonstrate the competitive performance of the proposed TPAGLNPSO optimizer in terms of solution quality (with near-optimal results) than the optimal solutions obtained from Lingo software and computational effort (CPU time). Such findings can provide practitioners marginal insights into the effective management of the supply chain inventory replenishment problem.