Supply chain under demand uncertainty has been a challenging problem due to increased competition and market volatility in modern markets. Flexibility in planning decisions makes modular manufacturing a promising way to address this problem. In this work, the problem of multiperiod process and supply chain network design is considered under demand uncertainty. A mixed integer two-stage stochastic programming problem is formulated with integer variables indicating the process design and continuous variables to represent the material flow in the supply chain. The problem is solved using a rolling horizon approach. Benders decomposition is used to reduce the computational complexity of the optimization problem. To promote riskaverse decisions, a downside risk measure is incorporated in the model. The results demonstrate the several advantages of modular designs in meeting product demands.A pareto-optimal curve for minimizing the objectives of expected cost and downside risk is obtained.

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Supply chain (SC) networks have become more prominent, complex, and challenging to manage, especially considering the multitude of risks and uncertainty that may manifest. Studies have shown two basic approaches to hedge against the negative impact of SC disruptions: proactive and reactive. While the former methods suggest different approaches to generating robust and resilient structures, the latter approach ensures that the SC recovers effectively. A general shortcoming of existing work is not considering SC dynamics. Consequently, disruptions are considered static events without including the durations and recovery policies. In this work, we develop a SC model that aids decision-making in addressing disruptions by considering proactive and reactive strategies. We adopted a discrete time-expanded model to solve the SC problem and consider the disruption dynamics using the rolling horizon framework. In the proposed SC model, a graph network represents the SC, where the nodes consisting of suppliers, manufacturing sites, warehouses, and customers interact using the arcs. The arcs determine the flow of materials between nodes. Independent disruptions can occur at the nodes and/or arcs, and the time of disruption is quantified using the geometric distribution. In the advent of disruption, we have adopted adjusting routing plans, inventory levels, capacity flexibility, and other tactical and operational decisions to hedge against disruption. To illustrate the proposed approach, we used a small problem to illustrate the effect of arcs and node disruption in decision-making and a realistic case study to demonstrate the proposed framework’s computational complexity. The results suggested that the effect of node disruption is more predominant because the initial network configuration limits the flexibility at the nodes. Furthermore, it was shown that the SC operated efficiently, as the solution offers a balance between the service level and the total cost of operating the SC.

In this work, we proposed a two-stage stochastic programming model for a fourechelon supply chain problem considering possible disruptions at the nodes (supplier and facilities) as well as the connecting transportation modes and operational uncertainties in form of uncertain demands. The first stage decisions are supplier choice, capacity levels for manufacturing sites and warehouses, inventory levels, transportation modes selection, and shipment decisions for the certain periods, and the second stage anticipates the cost of meeting future demands subject to the first stage decision. Comparing the solution obtained for the two-stage stochastic model with a multi-period deterministic model shows that the stochastic model makes a better first stage decision to hedge against the future demand. This study demonstrates the managerial viability of the proposed model in decision making for supply chain network in which both disruption and operational uncertainties are accounted for.

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In this work, we proposed a two-stage stochastic programming model for a four-echelon supply chain problem considering possible disruptions at the nodes (supplier and facilities) as well as the connecting transportation modes and operational uncertainties in form of uncertain demands. The first stage decisions are supplier choice, capacity levels for manufacturing sites and warehouses, inventory levels, transportation modes selection, and shipment decisions for the certain periods, and the second stage anticipates the cost of meeting future demands subject to the first stage decision. Comparing the solution obtained for the two-stage stochastic model with a multi-period deterministic model shows that the stochastic model makes a better first stage decision to hedge against the future demand. This study demonstrates the managerial viability of the proposed model in decision making for supply chain network in which both disruption and operational uncertainties are accounted for.