Data‐Driven Rolling Horizon Approach for Dynamic Design of Supply Chain Distribution Networks under Disruption and Demand Uncertainty

Mohammad Fattahi,Kannan Govindan

doi:10.1111/deci.12481

Abstract

ABSTRACTWe address the dynamic design of supply chain networks in which the moments of demand distribution function are uncertain and facilities’ availability is stochastic because of possible disruptions. To incorporate the existing stochasticity in our dynamic problem, we develop a multi‐stage stochastic program to specify the optimal location, capacity, inventory, and allocation decisions. Further, a data‐driven rolling horizon approach is developed to use observations of the random parameters in the stochastic optimization problem. In contrast to traditional stochastic programming approaches that are valid only for a limited number of scenarios, the rolling horizon approach makes the determined decisions by the stochastic program implementable in practice and evaluates them. The stochastic program is presented as a quadratic conic optimization, and to generate an efficient scenario tree, a forward scenario tree construction technique is employed. An extensive numerical study is carried out to investigate the applicability of the presented model and rolling horizon procedure, the efficiency of risk‐measurement policies, and the performance of the scenario tree construction technique. Several key practical and managerial insights related to the dynamic supply chain network design under uncertainty are gained based on the computational results.

Highlights

In supply chain (SC) management, a main planning problem is the SC design that includes long-term strategic decisions
This study addresses the dynamic supply chain network design (SCND) under stochastic demand and disruption events, which is scarcely addressed in the literature based on the existing surveys (e.g., Govindan, Fattahi, & Keyvanshokooh, 2017)
In period t, to evaluate the problem’s true objective value related to implementable decisions in this time period, the multi-stage stochastic programs (MSSP) should be solved in which period t is the first period, the implementable decisions are fixed based on their optimal value in period t, stochastic parameters in the period t are known according to path ω, and an updated scenario tree is employed

Summary

INTRODUCTION

In supply chain (SC) management, a main planning problem is the SC design that includes long-term strategic decisions. In addition to the mentioned drawbacks of the multi-stage stochastic programming, we lack answers to this key question in the practice: How can the dynamic design of a SC network be adjusted based on the realization of stochastic parameters, such as the target market’s changes and long-run demand rates, and the occurrence of disruption events over time to reduce SC loss performance and operational risks?. To address the second mentioned drawback of the multi-stage stochastic programming and the research question, we propose a data-driven rolling horizon procedure as an innovative way of using data that is realized as time progresses and of adjusting the decisions in practice for stochastic optimization problems By this approach, we use observations of the stochastic parameters over time as direct inputs to the dynamic SCND problem.

LITERATURE REVIEW

Literature Gaps

Objective function value

CONCLUSIONS