A hybrid Markov process-mathematical programming approach for joint location-inventory problem under supply disruptions

Abstract

This paper introduces a joint location-inventory problem, in which facilities become temporarily unavailable. A hybrid approach based on the Markov process and mathematical programming techniques is presented to design the distribution network of a supply chain in an integrated manner. In the first phase, the Markov process derives some performance features of inventory policy. In the second phase, using outputs of the Markov process, the location-inventory problem is formulated as a mixed-integer nonlinear programming model. Moreover, a robust possibilistic programming approach is utilized, which is able to provide a more stable supply chain structure under almost all possible values of imprecise parameters. Since the proposed problem is complicated to solve by means of exact methods, we develop a simulated annealing algorithm in order to find near-optimal solutions in reasonable computational times. The obtained computational results reveal the efficiency and effectiveness of the proposed solution approach. Finally, some insights are provided and the performance of the proposed robust optimization approach is compared to traditional possibilistic chance constrained method.

Optimizing a bi-objective location-allocation-inventory problem in a dual-channel supply chain network with stochastic demands

“…The developed model by them determines the re-order point, order size and location of warehouses and minimizes system costs simultaneously. Dehghani et al (2018) studied the problem of location-inventory by combining mathematical modeling and Markov process technique in a sustainable supply chain. Dehghan et al (2021) studied the location-routing problem with the aim of minimizing location and distribution costs under the risk of disruption.…”

A scenario-based optimization model for planning and redesigning the sale and after-sales services closed-loop supply chain

“…They implemented a hybrid robust stochastic optimization approach to tackle the uncertainties that are associated with different parameters. Dehghani et al [4] presented a mixed-integer nonlinear optimization model for the location-inventory problem in a distribution supply chain. A hybrid Markov process was proposed as the solution method.…”

Capacitated location routing problem with simultaneous pickup and delivery under the risk of disruption

“…The location-routing problem (LRP) integrates the strategic location and tactical routing decisions [3]. The LRP is often defined as a special case of VRP, in which the optimal number and location of depots along with distribution routes must be determined simultaneously [4], [5].…”

“…In most LRPs, one typical assumption is that depots are always available and can serve customers under any circumstances. However, this assumption is not valid in real settings when the depots are exposed to uncertainty risk [3]. The uncertainty in SCM can be generally categorized into operational uncertainty (risk) and disruption.…”

“…However, disruption risks can either completely or partly interrupt the functionality of supply chains' elements, typically for an uncertain amount of time [9]. Such disruptions are caused by nature or disasters such as earthquakes, hurricanes, terrorist attacks, economic and financial crises, labor strikes, or machine breakdowns [2], [3]. Disruptions are mostly discrete and significant, whereas the uncertainties from operational sources are usually continuous and minor [10].…”