A Multi-objective Mixed-integer Programming Approach for Supply Chain Disruption Response with Lead-Time Awareness

TL;DR

This paper presents a multi-objective mixed-integer programming model for supply chain disruption response that incorporates lead-time considerations and uses simulation for performance evaluation under uncertainty.

Contribution

It introduces a novel MIP model that integrates lead-time and disruption effects into supply chain routing and scheduling, enhancing decision-making during disruptions.

Findings

Lead time variability significantly impacts supply chain performance.

The proposed model effectively balances transportation, shortage, and lateness costs.

Simulation results demonstrate improved disruption response strategies.

Abstract

Supply chain (SC) risk management is influenced by both spatial and temporal attributes of different entities (suppliers, retailers, and customers). Each entity has given capacity and lead time for processing and transporting products to downstream entities. Under disruptive events, lead time and capacities may vary, which affects the overall SC performance. There have been many studies on SC disruption mitigation, but often without considering lead time and the magnitude of lateness. In this paper, we formulate a mixed-integer programming (MIP) model to optimize SC operations via a routing and scheduling approach, to model the delivery time of products at different entities as they flow throughout the SC network. We minimize a weighted sum of multiple objectives involving costs related to transportation, shortage, and delivery lateness. We also develop a discrete-event simulation framework to evaluate the performance of solutions to the MIP model under lead time uncertainty. Via extensive numerical studies, we show how the attributes of SC entities affect the performance, so that we can improve SC design and operations under various uncertainties.