Nexsupply: A Multi-Objective Sequential Decision Framework for Spare-Part Inventory Optimization Under Demand and Lifecycle Uncertainty

Authors

  • Rong-Guei Tsai College of Computer Science, Guangdong University of Science and Technology, Guangdong, China
  • Yuanlin Zheng College of Engineering, Fuzhou Technology and Business University, Fujian, China
  • Wen Wang College of Computer Science, Guangdong University of Science and Technology, Guangdong, China
  • Yanhong Peng College of Computer Science, Guangdong University of Science and Technology, Guangdong, China
  • Xiaolan Chen College of Information and Media, Zhangzhou College of Science and Technology, Fujian, China
  • Shanna He College of Information and Media, Zhangzhou College of Science and Technology, Fujian, China
  • Lin Lin College of Information Engineering, Quanzhou Ocean Vocational College, Fujian, China
  • Delin Luo College of Information Engineering, Quanzhou Ocean Vocational College, Fujian, China
  • Wulin Yin College of Computer Science, Guangdong University of Science and Technology, Guangdong, China
  • Ting Yao College of Computer Science, Guangdong University of Science and Technology, Guangdong, China

DOI:

https://doi.org/10.23055/ijietap.2026.33.5.11693

Keywords:

Inventory optimization, Dynamic programming, Intelligent decision-making, Multi-objective cost model, Urban water supply systems

Abstract

Inventory management of maintenance spare parts is essential for the reliability and cost efficiency of urban water supply systems where heterogeneous components experience intermittent failure and demand. Conventional replenishment policies minimize procurement cost alone and overlook the operational overhead of frequent ordering. In this paper, we propose NexSupply, a finite-horizon sequential decision framework for spare-part inventory optimization under demand and equipment-lifecycle uncertainty. NexSupply formulates replenishment as an exact dynamic program over a per-material (period, inventory) state space and internalizes the ordering-frequency objective directly into the state-transition cost by weighted-sum scalarization. The frequency-penalty weight lambda trades off operational cost against the number of replenishment actions. Lambda equal to zero recovers the cost-only policy exactly. Experiments on synthetic instances and real-world cases against cost-priority, management-priority, EOQ, genetic-algorithm, and single-objective dynamic-programming baselines show that NexSupply achieves near cost-optimal total cost (which is indistinguishable from the cost-only optimum) while reducing ordering days by roughly 90% compared to a cost-only policy (which is less than 1% more cost). The dynamic program scales linearly in the planning horizon and number of materials, solving realistic cases in milliseconds. NexSupply provides a practical and tunable decision-support tool that balances economic efficiency and managerial overhead for uncertainty-stacked decision-support tools.

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Published

2026-10-13

How to Cite

Tsai, R.-G., Zheng, Y., Wang, W., Peng, Y., Chen, X., He, S., Lin, L., Luo, D., Yin, W., & Yao, T. (2026). Nexsupply: A Multi-Objective Sequential Decision Framework for Spare-Part Inventory Optimization Under Demand and Lifecycle Uncertainty. International Journal of Industrial Engineering: Theory, Applications and Practice, 33(5). https://doi.org/10.23055/ijietap.2026.33.5.11693

Issue

Vol. 33 No. 5 (2026)

Section

Supply Chain Management

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