Production planning in emerging integrated assembly-delivery systems for customised products under uncertainty: a two-stage stochastic framework
Year: 2026
Type: article
Abstract: This paper investigates a production planning problem arising in an emerging integrated assembly-delivery system operating under a mass customisation environment. The system operator must jointly optimise order allocation and vehicle routing planning while accounting for assembly lead times and system uncertainties to minimise total distribution cost and mitigate late-delivery risk. To address the problem, we propose a two-stage stochastic location-routing planning framework that explicitly captures multi-depot operations, assembly lead times, the coupling between assembly and delivery, and stochastic travel and service times. Given the computational complexity of the problem, a problem-specific improved adaptive large neighbourhood search (IALNS) algorithm is developed, integrating a Latin hypercube sampling procedure, four problem-specific operators, and a principal component analysis-based k-means supported by several structural propositions. Five practice-oriented evaluation metrics, including runtime, total cost, route cost, recourse cost, and late delivery numbers, are designed to assess algorithm performance. Comparative results with Gurobi and four baseline heuristics validate the effectiveness of the problem-specific IALNS algorithm. Finally, sensitivity analyses on six key parameters provide practical managerial insights into assembly time management, recourse cost calibration, and robustness-cost trade-off strategies for operators operating under different risk attitudes.
Source: Figshare
Authors: Mengrui Liu, Nengmin Wang, Meng Zhang
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7167512171