Resilient transportation network design with disruption uncertainty and lead times

Abstract

Cost-efficient and reliable transports are needed to supply products competitively. Thus, particularly in increasingly complex and global supply chains, identifying the optimal transportation mode is a critical decision. Transportation modes, however, are prone to disruptions, such as hurricanes, low water levels, or port shutdowns, resulting in transportation stops and cost increases. To counteract these disruptions, different resilience strategies are studied to increase the capability of a network to withstand, adapt, and recover from disruptions. For a cost-optimal use, it is necessary to determine the optimal mix of strategic, tactical, and operational strategies. We provide a decision-support model that decides on the optimal mix of resilience strategies, such as multi-sourcing, inventory, or operational re-routing, for a supply chain with transportation disruption uncertainty to minimize total expected costs. The problem is formulated as a two-stage stochastic mixed-integer linear program that explicitly considers lead times. To handle large instances, we propose a Benders decomposition approach enhanced through lower-bound lifting and valid inequalities, branch-and-benders-cut, and a warm-start heuristic. Computational experiments show that large instances can be solved to near-optimality, whereas a commercial solver does not find feasible solutions. We present a case study for a company’s inbound supply chain design with recurring transportation cost uncertainty. Considering disruption and lead time effects, a mix of resilience strategies from strategic to operational level leads to cost improvements of up to 50%. Furthermore, we show that the ability to predict disruptions can further reduce resilience-related costs by 10% if sufficient operational re-routing capacities are available.

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