Ordinal optimization based metaheuristic algorithm for optimal inventory policy of assemble-to-order systems

Abstract

Assemble-to-order (ATO) systems refer to a manufacturing process in which a customer must first place an order before the ordered item is manufactured. An ATO system that operates under a continuous-review base-stock policy can be formulated as a stochastic simulation optimization problem (SSOP) with a huge search space, which is known as NP-hard. This work develops an ordinal optimization (OO) based metaheuristic algorithm, abbreviated to OOMH, to determine a near-optimal design (target inventory level) in ATO systems. The proposed approach covers three main modules, which are meta-modeling, exploration, and exploitation. In the meta-modeling module, the extreme learning machine (ELM) is used as a meta-model to estimate the approximate objective value of a design. In the exploration module, the elite teaching-learning-based optimization (TLBO) approach is utilized to select N candidate designs from the entire search space, where the fitness of a design is evaluated using the ELM. In the exploitation module, the sequential ranking-and-selection (R&S) scheme is used to optimally allocate the computing resource and budget for effective selecting the critical designs from the N candidate designs. Finally, the proposed algorithm is applied to two general ATO systems. The large ATO system comprises 12 items on eight products and the moderately sized ATO system is composed of eight items on five products. Test results that are obtained using the OOMH approach are compared with those obtained using three heuristic methods and a discrete optimization-via-simulation (DOvS) algorithm. Analytical results reveal that the proposed method yields solutions of much higher quality with a much higher computational efficiency than the three heuristic methods and the DOvS algorithm.