https://www.cirrelt.ca/DocumentsTravail/CIRRELT-2008-36.pdf

The Design of Robust Value-Creating Supply Chain Networks: A Critical Review †

A wide range of relatively short-term disruptive events such as partial flooding, visibility reductions, traction hazards due to weather, and pavement deterioration occur on transportation networks on a daily basis. Despite being relatively minor when compared to catastrophes, these events still have profound impacts on traffic flow. To date there has been very little distinction drawn between different types of network-disruption studies and how the methodological approaches used in those studies differ depending on the specific research objectives and on the disruption scenarios being modeled. In this paper, we advance a methodological approach that employs different link-based capacity-disruption values for identifying and ranking the most critical links and quantifying network robustness in a transportation network. We demonstrate how an ideal capacity-disruption range can be objectively determined for a particular network and introduce a scalable system-wide performance measure, called the Network Trip Robustness (NTR) that can be used to directly compare networks of different sizes, topologies, and connectivity levels. Our approach yields results that are independent of the degree of connectivity and can be used to evaluate robustness on networks with isolating links. We show that system-wide travel-times and the rank-ordering of the most critical links in a network can vary dramatically based on both the capacity-disruption level and on the overall connectivity of the network. We further show that the relationships between network robustness, the capacity-disruption level used for modeling, and network connectivity are non-linear and not necessarily intuitive. We discuss our findings with respect to Braess' Paradox.

Identifying Critical Road Segments and Measuring System-Wide Robustness in Transportation Networks with Isolating Links: A Link-Based Capacity-Reduction Approach

A framework for designing robust food supply chains

The agricultural supply chains (ASCs) are exposed to unprecedented risks following COVID-19. It is necessary to investigate the impact of risks and to create resilient ASC organisations. In this st...

https://www.tandfonline.com/doi/pdf/10.1080/13675567.2020.1830049

Agriculture supply chain risks and COVID-19: mitigation strategies and implications for the practitioners

The new supply chain's frontier: Information management

As an event-driven system, a supply chain network will face not only the uncertainties in the supply chain, but also the unexpected events outside the supply chain network such as contingency, disruption, disaster and terrorism. These uncertainties and unexpected events have negative impacts on the survival and performance of a supply chain network. Robustness of supply chain networks is an important research issue as a vulnerable supply chain network may not be able to operate at all. While there has been extensive work on the measurement of supply chain performance, little of this work has focused on measuring a supply chain's robustness, that is, the ability to cope with deviation and disruption. This paper presents a system-wide approach to quantifying robustness index of supply chain networks. This approach considers both network structural robustness and network functional robustness. A hypothetical supply chain network is employed to demonstrate the proposed approach.

Development of supply chain network robustness index

In this paper, the Inoperability Input-Output Model (IIM) is deployed for assessing the impacts of disruptive events on supply chain networks under an unsafe environment. The IIM for supply chain networks has been proposed, based on the original IIM used in macro-economics. A new method called the OWA (Ordered Weighted Averaging) Operator has been formulated to evaluate the interdependency matrix, a key component of the IIM. Similar to the capability of the basic IIM to describe the propagation impacts of disruptions to macroeconomic systems, the IIM for supply chain networks is capable of describing the propagation effects of disruptions to interdependent supply chain components. IIM assesses the impacts of disruptions to supply chain networks with “inoperability” and “economic losses” metrics, which can provide helpful insights for risk management of supply chain networks. An example supply chain network is used to illustrate the application of IIM for systemic risk assessment of supply chains under an unsafe environment. A risk mitigation strategy by increasing the number of suppliers has been considered to reduce the adverse effects caused by the supply reduction of the single secondary supplier in the example supply chain network. In addition, a simulation model integrated with Monte Carlo simulation method has been developed to validate the accuracy of the IIM for supply chain. The example shows that the application of IIM can provide useful guidance toward supply chain risk management or mitigation strategies and it could also be used to test the effectiveness of these strategies. © 2009 Wiley Periodicals, Inc. Syst Eng

Inoperability input-output modeling (IIM) of disruptions to supply chain networks

Continuum modeling of supply chain networks using discontinuous Galerkin methods

With the changing technology, market requirements vary quickly. Manufacturing facilities must be able to exhibit high levels of flexibility and robustness despite significant changes in their operating requirements. This paper discusses the facility layout problem with stochastic product demand and logistic flows. The shape of the department is fixed, but the rotation is allowed. Criteria considered in this paper include not only the traditional material handling cost but also the shape ratio factor and the operational performance such as WIP value. Material handling cost is formulated by the mean value and the standard deviation. A hybrid algorithm based on Tabu search and simulated annealing is constructed to solve this problem. Experimental results illustrate the effectiveness of the proposed algorithm.

Facility Layout Optimization with Stochastic Logistic Flows

Options are introduced into supply chain management to improve the capability of handling demand uncertainty and hence seek better performance of the participants. An option model based on the newsvendor problem is presented to quantify and price a trading contract in a supply chain. With trading options, buyers (or retailers) can either order products from suppliers or purchase options from other retailers, and decide whether to buy or sell their remaining options in the second period after demand is realised in the first period. This paper examines how trading options work in a supply chain consisting of one supplier and a set of retailers in both competitive and cooperative scenarios. Using the concept of best response in game theory, the outcomes of option trading with interdependent demands are analysed. Depending on the current inventory, options in hand and demand information of the second period, the optimal trading quantity in the non-interdependent demand model could be found, where trading quantity is irrelevant to options price.

http://web.kaust.edu.sa/faculty/ShuyuSun/pub/tradingOptionSC2009.pdf

Ming Dong

Papers

Development of supply chain network robustness index

Inoperability input-output modeling (IIM) of disruptions to supply chain networks

Continuum modeling of supply chain networks using discontinuous Galerkin methods

Facility Layout Optimization with Stochastic Logistic Flows

Modelling and analysis of newsvendor-based trading options in supply chains