Modular and Mobile Capacity Planning for Hyperconnected Supply Chain Networks
Xiaoyue Liu, Walid Klibi, Benoit Montreuil
TL;DR
The paper introduces DSMMCP, a new tactical optimization problem for hyperconnected supply chains that leverages short-term leasing of modular facilities and dynamic relocation of capacity modules under demand and supply uncertainty. It formalizes DSMMCP as a partially adaptive multi-stage stochastic program (PAMSSP) and develops an enhanced stochastic dual dynamic integer programming (SDDiP) algorithm with strengthened cuts, an alternating cut strategy, and parallelization. Theoretical results establish the monotone value of partial adaptivity and dominance properties of the strengthened cuts, while computational experiments on a real modular-construction dataset show about 15% cost savings over static planning and improved resilience, with robust performance on synthetic instances. The study demonstrates significant value from partial adaptivity, dynamic stochastic modeling, and modular/mobile capacity, highlighting practical implications for Physical Internet-inspired networks and flexible, sustainable supply chains. The work also points to future directions in multi-objective extensions, rolling-horizon integrations, and disaster-response deployments.
Abstract
The increased volatility of markets and the pressing need for resource sustainability are driving supply chains towards more agile, distributed, and dynamic designs. Motivated by the Physical Internet initiative, we introduce the Dynamic Stochastic Modular and Mobile Capacity Planning (DSMMCP) problem, which fosters hyperconnectivity through a network-of-networks architecture with modular and mobile capacities. The problem addresses both demand and supply uncertainties by incorporating short-term leasing of modular facilities and dynamic relocation of resources. We formulate DSMMCP as a partially adaptive multi-stage stochastic program that minimizes the expected multi-period costs under uncertainty. To tackle the inherent NP-hardness, we develop an enhanced stochastic dual dynamic integer programming (SDDiP) algorithm, which integrates strengthened cut generation, a tailored alternating cut strategy, and an efficient parallelization framework, and we establish structural dominance and monotonicity properties that formalize the value of the strengthened cuts and partial adaptivity. Numerical experiments inspired by a real case study of a large U.S. construction company demonstrate that the DSMMCP framework achieves approximately 15% cost savings over static planning while improving resilience, reducing outsourcing costs, and supporting sustainability. Complementary experiments on synthetic instances confirm the effectiveness of the proposed SDDiP algorithm in terms of solution quality and runtime, as well as the scalability and robustness of the partially adaptive stochastic modeling framework across different network sizes and uncertainty levels.