Exact methods and a variable neighborhood search for the robust capacitated p-median problem

• Published in: Computers & operations research 2025-01, Vol.173, p.106851, Article 106851
• Main Authors: Campos, Rafael A., Chagas, Guilherme O., Coelho, Leandro C., Munari, Pedro
• Format: Article
• Language: English
• Subjects: Branch-and-cut; Branch-and-price; Capacitated p-median problem; Demand uncertainty; Robust optimization; Variable neighborhood search
• ISSN: 0305-0548
• DOI: 10.1016/j.cor.2024.106851

The capacitated p-median problem (CPMP) involves placing p identical facilities in a network and assigning customer nodes to these facilities to satisfy all customer demands with minimal transportation costs. In practical applications, demand and distance parameters are often uncertain during the planning process, leading to infeasible or excessively costly solutions if these uncertainties are disregarded. This paper addresses the robust CPMP (RCPMP), which incorporates demand uncertainty into the problem using the robust optimization paradigm. We propose a general framework to model and solve the RCPMP, considering different polyhedral uncertainty sets, namely the cardinality-constrained and the knapsack sets. We develop exact approaches encompassing compact models, different families of valid inequalities, and branch-and-cut and branch-and-price algorithms, exploring both implemented uncertainty sets and problem structure. Furthermore, we implement an efficient Variable Neighborhood Search (VNS) heuristic to solve these robust variants, which incorporates state-of-the-art algorithms, parallelization techniques, and optimized data structures. Computational experiments using adapted benchmark instances with up to 400 nodes indicate the effectiveness of the proposed approaches. The results show that using parallelization and hash tables within the VNS heuristic promotes significant performance improvements and yields near-optimal solutions for most instances, as well as outperforming the exact approaches in several instances where the optimal solution was not found. Moreover, these results highlight the benefits of using robust solutions in practical settings, especially when considering different uncertainty sets to generate solutions with advantageous trade-offs between cost and risk. •Robust counterparts of the capacitated p-median problem under demand uncertainty.•We consider the cardinality-constrained and knapsack uncertainty sets.•Compact formulations, branch-and-cut, and branch-and-price algorithms for the problem.•Efficient variable neighborhood search with enhanced data structure and parallelization.•Robust solutions reduce risk significantly and provide interesting managerial insights.