Reliable design of an integrated supply chain with expedited shipments under disruption risks

Abstract

This paper proposes a mathematical model for the design of a two-echelon supply chain where a set of suppliers serve a set of terminal facilities that receive uncertain customer demands. This model integrates a number of system decisions in both the planning and operational stages, including facility location, multi-level service assignments, multi-modal transportation configuration, and inventory management. In particular, we consider probabilistic supplier disruptions that may halt product supply from certain suppliers from time to time. To mitigate the impact from supplier disruptions, a terminal facility may be assigned to multiple supplies that back up each other with different service priorities. With such multi-level service assignments, even when some suppliers are disrupted, a facility may still be supplied by the remaining functioning suppliers. Expensive expedited shipments yet with assured fast delivery may be used in complement to less expensive regular shipments yet with uncertain long lead times. Combining these two shipment modes can better leverage the inventory management against uncertain demands. We formulate this problem into a mix-integer nonlinear program that simultaneously determines all these interdependent system decisions to minimize the expected system cost under uncertainties from both suppliers and demands. A customized solution algorithm based on the Lagrangian relaxation is developed to efficiently solve this model. Several numerical examples are conduced to test the proposed model and draw managerial insights into how the key parameters affect the optimal system design.

Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case

“…Dynamic simulation models are recognized as a suitable tool to observe and predict SC behaviors over time. Simulation studies allow adding additional, dynamic features to the optimization techniques which are widely used in SC risk analysis , Sadghiani et al, 2015, Cui et al, 2016, Ivanov et al, 2016 along with heuristic approaches (Meena and Sarmah, 2013, Zhang et al, 2015, Hasani and Khosrojerdi, 2016. Most of the existing studies utilize discrete-event simulation approach (Schmitt and Singh, 2012, Ivanov, 2017a, 2017b, Schmitt et al, 2017, Ivanov and Rozhkov, 2017, Macdonald et al, 2018, Ivanov, 2019, Tan et al, 2020 while some studies use agent-based (Li and Chan, 2013, Hou et al, 2018 and system dynamics (Wilson, 2007, Aboah et al, 2019 methods, too.…”

Substantiating the Logistics Chain Structure While Servicing the Flow of Requests for Road Transport Deliveries

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Ridesharing Problem with Flexible Pickup and Delivery Locations for App-Based Transportation Service: Mathematical Modeling and Decomposition Methods

“…It is actually a typical procedure that has been widely used in many previous studies for updating Lagrangian multipliers, such as Li [35], Zheng [36], Cui et al [37], and Yin et al [38]. By this, the optimality gap can be iteratively narrowed to eventually obtain a near-optimum solution with an acceptable tolerance or even zero in ideal cases, when the optimal solution is also achieved.…”