Resilient Network Design Research Articles

Resilient and sustainable semiconductor supply chain network design under trade credit and uncertainty of supply and demand

Supply Chain (SC) performance appraisal extends beyond economic evaluation to encompass environmental and social impacts, as well as resilience to supply and demand disruptions. Thus, SC network design decisions should simultaneously optimize all these performance metrics as a primary objective. However, existing studies optimized these objectives independently or in a limited scope, failing to comprehensively address the interconnected nature of these performance metrics. Therefore, this study proposes a mixed integer linear programming (MILP) model with four objectives to address a multi-tier resilient and sustainable semiconductor SC network design problem under trade credit, supply, and demand uncertainties. The model aims to determine the optimal number and location of various facilities, the amount to order from suppliers, economic production quantities, quantity allocation between SC entities, and ideal supplier selections. The proposed supply chain network contributes to the global effort to reduce carbon emissions and enhance the circular economy by considering carbon trading, recycling, and the proper disposal of end-of life products. Uncertainties in input parameters are addressed using a fuzzy programming approach, while lexicographic optimization and the ɛ-constraint method are used to solve the proposed model. This paper presents a numerical example to illustrate the applicability of the proposed model and provide managerial insights. The numerical analysis results show that provided an appropriate confidence level for the uncertain parameters, the sets of Pareto optimal solutions can be generated. And it also shows that the total SC cost increases with decreasing carbon emissions level and increasing job opportunities created. This research advances sustainable and resilient supply chain management practices while offering practical guidance for decision-makers in real-world contexts.

Ecological Robustness-Oriented Grid Network Design for Resilience Against Multiple Hazards

Power systems are critical infrastructure for reliable and secure electric energy delivery. Incidents are increasing, as unexpected multiple hazards ranging from natural disasters to cyberattacks threaten the security and functionality of society. Inspired by resilient ecosystems, this paper presents a resilient network design approach with an ecological robustness (R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ECO</sub> )-oriented optimization to improve power systems' ability to maintain a secure operating state throughout unknown hazards. The approach uses R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ECO</sub> , a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">surprisal</i> -based metric that captures key features of an ecosystem's resilient structure, as an objective to strategically design the electrical network. The approach enables solvability and practicality by introducing a stochastic-based candidate branch creation algorithm and a Taylor series expansion for relaxation of the R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ECO</sub> formulation. Finally, studies are conducted on the R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ECO</sub> -oriented approach using the IEEE 24 Bus RTS and the ACTIVSg200 systems. Results demonstrate improvement of the system's reliability under multiple hazards, network properties of robust structure and equally distributed power flows, and survivability against cascading failures. From the analysis, we observe that a more redundant network structure with equally distributed power flows benefits its resilience.

An integrated decision support framework for resilient vaccine supply chain network design

Designing resilient supply chain networks for vaccine development and distribution requires reliable and robust infrastructure. This stud develops a novel two-stage decision support framework for configuring multi-echelon Supply Chain Networks (SCNs), resilient supplier selection, and order allocation under uncertainty. Resilient supplier selection is done using a hybrid Multi-Criteria Decision-Making (MCDM) approach based on Best-Worst Method (BWM), Weighted Aggregated Sum Product Assessment (WASPAS), and Type-2 Neutrosophic Fuzzy Numbers (T2NN). A robust multi-objective optimization model is then built for order allocation considering resiliency scores, reliability of facilities, and uncertain supply and demand. The objectives are to minimize the total cost of SCN design, maximize the resiliency score, and maximize the reliability of SC, respectively. A Non-dominated Sorting Genetic Algorithm II (NSGA-II) is developed to tackle the problem on large scales, tuned by the Taguchi design technique. The NSGA-II solution is compared to the ε-constraint and Multi-objective Particle Swarm Optimization (MOPSO) solutions using test problems. We demonstrate the superiority of the suggested NSGA-II method over the two competing methods according to five performance metrics. A case study is then investigated to illustrate the applicability and effectiveness of the offered methodology for COVID-19 vaccine distribution in a developing country. It is revealed that the models and algorithms can treat the problem optimally, such that Germany is the main source (approximately 25.61%) while India does not contribute to the supply of vaccines.

Two-Stage Robust Design of Resilient Active Distribution Networks Considering Random Tie Line Outages and Outage Propagation

This paper proposes a two-stage method for the robust design of resilient active distribution networks (ADNs) against high-impact and low-probability (HILP) events. The line hardening and the deployment of remote-controlled switches (RCSs) are considered as two powerful measures for resilience enhancement. Especially, the hardening of tie lines and the deployment of bilateral tie switches are emphasized as part of the resilient design. A novel progressive detection mechanism (PDM) is devised to estimate the potential propagation of outages and identify surviving nodes outside of the minimum outage area after intentional islanding. The proposed PDM method considers potential RCS locations among regular lines and tie lines on outage to calculate the global optimal design scheme. The two-stage robust design model is formulated as a mixed-integer linear programming (MILP). The nested column-and-constraint generation (nested C&CG) algorithm is customized to solve the proposed model. Numerical results on a modified IEEE 33-node distribution system demonstrate the effectiveness and the superiority of the proposed resilience enhancement method.

Multi-tier supply chain network design: A key towards sustainability and resilience

The inescapable need for sustainable supply chains has fascinated controversial attention, in the last two decades, due to increasing governmental regulations and societal awareness. But, facilities and their linkages, within the supply chain network, are subject to perturbations due to natural or human-made events (e.g., strikes and floods). Therefore, managers are concerned about re-engineering a supply chain network that is simultaneously sustainable and resilient. Hence, there is an inevitable need to embrace sustainability and resilience at a strategic level for the supply chain management. Motivated by this necessity, this paper presents a new panacea methodology towards a sustainable and resilient (susilient, henceforth) two-tier supply chain network design (S-2TSCND). To this end, an extended exploration regarding the relationship and completeness between sustainability and resilience, in the supply chain context, was explored and discussed. It presents a new term for “susilient” supply chain due to the strong correlation found between these two paradigms. Then, a holistic framework was developed to identify dimensions, enablers, and criteria towards susilient development. Next, a fuzzy four-objective optimization model (FFOOM) was developed to (1) solve the susilient facility location problem; (2) assign the optimal order quantities among the selected facilities considering the susilience aspect; and (3) derive a trade-off between sustainability and resilience. The susilience values of nominated facilities were quantified via a hybrid AHP-OCRA method. These values were then merged into the FFOOM to consolidate susilience performance into the strategic and tactical decisions. A set of trade-offs was then derived via the ε-constraints method. Finally, each trade-off was examined via the global criterion approach to measure its distance from the ideal solution aiming to select the final supply chain network design. The validity of the developed methodology was proved on a re-configuration of an existing real 2TSCND.

A resilient closed-loop supply chain network design through integrated sourcing and pricing strategies

A resilient closed-loop supply chain network design through integrated sourcing and pricing strategies

Resilient closed-loop supply chain network design considering quality uncertainty: A case study of stone quarries

According to the important role of the mining industry in the economic growth of developing countries, this industry is taken into consideration significantly. However, few studies have been conducted on developing mining supply chains that take into account the unique conditions of this industry, such as disruptions and product quality. This paper proposes a multi-period, multi-product mixed-integer quadratic programming problem to optimize a closed-loop stone supply chain network design. Compared to previous studies, we consider resilience in the mining supply chain for the first time. Furthermore, the quality characteristics of extracted and final stones are considered in a developed reliable model, and two-stage stochastic programming is applied to handle the quality uncertainty in the mining supply chain. Additionally, a price-dependent demand is considered in this research to investigate the competition between local and imported stone prices. A real-world case study on Iranian stone mining is analyzed to demonstrate the efficiency and applicability of the proposed model. Finally, some managerial insights are presented as guidance to managers of the mentioned supply chain to help them make better decisions. The results show that the transportation costs impact profit more than the operational costs. Moreover, West Azerbaijan and Isfahan have great potential for being the poles of stone in Iran. West Azerbaijan is also appropriate for exporting stones to neighboring countries, geographically.

Urban Biomimicry for Flood Mitigation Using an Ecosystem Service Assessment Tool in Central Wellington, New Zealand

Many cities are vulnerable to flooding due to their high proportion of impervious surfaces and lack of vegetated land cover. This vulnerability will often be exacerbated by changing rainfall and storm patterns due to climate change. Using the principles of urban biomimicry, this study aims to show an ecosystem service-based approach to designing an urban green infrastructure network for stormwater management in densely built areas that more closely emulates natural hydrology processes. Nature Braid (next-generation LUCI) is an ecosystem services assessment tool that was used to simulate flood mitigation ecosystem services in a 13.7 km2 urban water catchment in Wellington, Aotearoa New Zealand. The simulation results revealed that 59% of the catchment does not contain or benefit from flood-mitigating land cover features. Adding 0.6 km2 (4% of the catchment) of green roofs alongside major stormwater flow paths resulted in a nearly three-fold decrease (11%) in the unmitigated flooding area. These results suggest that green roofs could help manage stormwater and mitigate flooding in the densely built areas of the catchment. Using ecosystem service assessment tools, like Nature Braid, can inform the design of more regenerative and resilient urban green infrastructure networks that help mitigate climate change impacts on urban residents.

A hybrid modeling approach for resilient agri-supply network design in emerging countries: Colombian coffee supply chain

Sustainability and resilience in Agri-Food Supply Chains is a challenging topic of current interest in the research community. Resilience for Agri-Food Supply Chain (AFSC) is the capability of the supply network to manage and mitigate disruptions due to global warming and natural phenomena such as landslides and floods of crops, among others caused by humans. A significant challenge is to design efficient and resilient AFSCs in emerging countries while perishability constraints are considered. A methodology to design an AFSC for emerging countries is addressed in this research. The phenomena that aid in identifying critical aspects of the AFSC affecting their resilience are identified. The former approach combines optimization and simulation schemes by considering resilience metrics related to availability and connectivity. Indeed, the solution approach addresses the uncertainty by using simulation of disruptive events and finding resilient designs using mathematical programming. The proposed framework has been evaluated in a Colombian coffee supply chain. The obtained results show the efficiency of the proposed scheme to design AFSCs and allow the practitioners to measure, predict, compare, and improve the level of resilience of their supply chains (SCs).

Optimization of survivable filterless optical networks exploiting digital subcarrier multiplexing

In aggregation networks, the traffic patterns resemble hub-and-spoke characteristics, with a few hub nodes connecting several leaf nodes to the outside of the networks. The use of traditional point-to-point transceivers in these applications results in many low-capacity devices at the hub nodes. Optical transceivers leveraging digital subcarrier multiplexing (DSCM) have recently been proposed to support point-to-multipoint transmission in the optical domain, allowing the use of fewer high-capacity devices at these nodes, thus significantly reducing both capital expenditure and operational expenditure. A high-capacity signal comprising multiple subcarriers is transmitted/received at the hub node, while each leaf node has only to transmit/receive the subset of subcarriers that are intended for it, enabling optimization of the type of transceiver used at each node. Broadcasting the optical signal using an optical tree to reach the different leaf nodes, coupled with the possibility of supporting failure survivability, requires developing algorithms to jointly optimize transceiver deployment and the underlying optical trees. This work proposes a novel integer linear programming model for optimizing the design of resilient metro-aggregation networks using DSCM-based coherent transceivers. Results obtained over two realistic mesh networks show that transceiver expenditures can be reduced by a figure between 23% and 44%.

Distributed Self-Healing for Resilient Network Design in Local Resource Allocation Control

Many infrastructure networks are considered the backbone of our society; however, increasing disasters and terrors cause serious damage to energy, water, communication, and transportation systems. In this study, we proposed a distributed self-healing method for the damaged networks whose original structure is extremely vulnerable and scale-free. For reconstructing a sustainable network, the key ideas of our method are ring formation and enhancing loops by adding the resource of healing links between low-degree nodes inspired from the state-of-the-art edge rewiring methods. In emulating a healing algorithm asynchronously, we showed that the reconstructed network has both higher robustness and efficiency than the ones using the conventional self-healing methods. Our distributed self-healing method will be useful as a basic framework for sustainable network reconstruction.

Resilient Transportation Network Design under Uncertain Link Capacity Using a Trilevel Optimization Model

This study addresses uncertainty in a transportation network by proposing a trilevel optimization model, which improves resiliency against uncertain disruptions. The goal is to minimize the total travel time by designing a resilient transportation network under uncertain disruptions and deterministic origin-destination demands. The trilevel optimization model has three levels. The lower level determines the network flow, and the middle level assesses the network’s resiliency by identifying the worst-case scenario disruptions that could lead to maximal travel time. The upper-level takes the system perspective to expand the existing transportation network to enhance resiliency. We also propose a formulation for the network flow problem to significantly reduce the number of variables and constraints. Two algorithms have been developed to solve the trilevel model. The results of solving the highway network in Iowa show that the trilevel optimization model improves the total travel time by an average of 41%.

A methodological framework for efficient and resilient supply network design

The growing competition and the destabilising effects of climate, disease and other external perils bring important challenges to companies worldwide. Supply chain (SC) networks become more and more complex with a growing exposure to a broad range of uncertainties, some of which may cause network disruptions. Neglecting these kinds of risks may lead to adverse consequences, such as negative financial effects, higher transportation costs, order delays, inventory shortages, and loss of market shares. Frequent disruption events that have been continuously increasing over recent years have clearly shown the key role of supply chain resilience. To hedge against SC disruptions, a well-designed and reliable supply network that performs efficiently in normal situations and resiliently during unstable conditions is a top priority. In this paper, starting from the discussion of the main design drivers for a resilient SC, a methodological framework is proposed to support both efficient and resilient supply network design. The procedure gets foundation from the recent literature, and it is directly derived from its application in a numerical example and an industrial case.

Resilient Closed-Loop Supply Chain Network Design Considering Quality Uncertainty: A Case Study of Stone Quarries

Resilient Closed-Loop Supply Chain Network Design Considering Quality Uncertainty: A Case Study of Stone Quarries

Robust optimization of risk-aware, resilient and sustainable closed-loop supply chain network design with Lagrange relaxation and fix-and-optimize

ABSTRACT This study explores a Robust, Risk-aware, Resilient, and Sustainable Closed-Loop Supply Chain Network Design (3RSCLSCND) to tackle demand fluctuation like COVID-19 pandemic. A two-stage robust stochastic multiobjective programming model serves to express the proposed problems in formulae. The objective functions include minimising costs, CO2 emissions, energy consumption, and maximising employment by applying Conditional Value at Risk (CVaR) to achieve reliability through risk reduction. The Entropic Value at Risk (EVaR) and Minimax method are used to compare with the proposed model. We utilise the Lp-Metric method to solve the multiobjective problem. Since this model is complex, the Lagrange relaxation and Fix-and-Optimise algorithm are applied to find lower and upper bounds in large-scale, respectively. The results confirm the superior power of the model offered in estimating costs, energy consumption, environmental pollution, and employment level. This model and algorithms are applicable for other CLSC problems.

Designing a Resilient and Sustainable Logistics Network under Epidemic Disruptions and Demand Uncertainty

To face the new challenges caused by modern industry, logistics operations managers need to focus more on integrating sustainability goals, adapt to unexpected disruptions and find new strategies and models for logistics management. The COVID-19 pandemic has proven that unforeseen fragilities, negatively affecting the supply chain performance, can arise rapidly, and logistics systems may confront unprecedented vulnerabilities regarding network structure disruption and high demand fluctuations. The existing studies on a resilient logistics network design did not sufficiently consider sustainability aspects. In fact, they mainly addressed the independent planning of decision-making problems with economic objectives. To fill this research gap, this paper concentrates on the design of resilient and sustainable logistics networks under epidemic disruption and demand uncertainty. A two-stage stochastic mixed integer programming model is proposed to integrate key decisions of location–allocation, inventory and routing planning. Moreover, epidemic disruptions and demand uncertainty are incorporated through plausible scenarios using a Monte Carlo simulation. In addition, two resiliency strategies, namely, capacity augmentation and logistics collaboration, are included into the basic model in order to improve the resilience and the sustainability of a logistics chain network. Finally, numerical examples are presented to validate the proposed approach, evaluate the performance of the different design models and provide managerial insights. The obtained results show that the integration of two design strategies improves resilience and sustainability.

Sustainable, resilient and responsive mixed supply chain network design under hybrid uncertainty with considering COVID-19 pandemic disruption.

The occurrence of the COVID-19 pandemic is a disruption that has adversely affected many supply chains (SCs) around the world and further proved the necessity of combination and interaction of resilience and sustainability. In This paper, a multi-objective mixed-integer linear programming model is developed for responsive, resilient and sustainable mixed open and closed-loop supply chain network design (SCND) problem. The uncertainty of the problem is handled with a hybrid robust-stochastic optimization approach. A Lagrangian relaxation (LR) method and a constructive heuristic (CH) algorithm are developed for overcoming problem complexity and solving large-scale instances. In order to assess the performance of the mathematical model and solution methods, some test instances are generated. The computations showed that the model and the solution methods are efficient and can obtain high-quality solutions in suitable CPU times. Other analyses and computations are done based on a real case study in the tire industry. The results demonstrate that resilient strategies are so effective and can improve economic, environmental and social dimensions substantially. Research findings suggest that the proposed model can be used as an efficient tool for designing sustainable and resilient SCs and the related decision-makings. Also, our findings prove that resilience is necessary for continued SC sustainability. It is concluded that using proposed resilience strategies simultaneously brings the best outcome for SC objectives. Based on the sensitivity analyses, the responsiveness level significantly affects SC objectives, and managers should consider the trade-off between responsiveness and their objectives.

Resilient protected area network enables species adaptation that mitigates the impact of a crash in food supply

With coastal wader populations exhibiting long-term declines globally, understanding how they respond to changes in their preferred prey is important for future predictions, especially given the potential for warming seas to affect invertebrate populations. The cockleCerastoderma edulepopulation in the Burry Inlet Special Protection Area (SPA) in south Wales, UK, declined from 1997-2004 before an abrupt ‘crash’ in stocks between 2004 and 2010. While there has been some recovery since, stocks of larger cockles are still very low. Using survey data from the UK Wetland Bird Survey and analyses of apparent survival and biometrics from ringing, we investigated how the Burry Inlet SPA’s wintering Eurasian oystercatcherHaematopus ostraleguspopulation responded to this crash. Our analysis showed that both body condition and apparent survival of wintering adult oystercatchers were reduced in the years following the cockle crash but both recovered. The number of birds using the Burry Inlet SPA decreased through the course of the cockle stock decline whilst numbers of birds in the adjacent Carmarthen Bay increased, indicating the importance of adjacent sites for buffering the effects of such changes, i.e. protected secondary habitats can be a vital component of a resilient site network. Our findings are useful in understanding how a predator copes with a serious decline in its preferred food stocks. This study has wide applicability in planning the management of coastal wetlands and shellfisheries as well as the design of resilient protected area networks in the light of environmental change.

Ensuring sustainability in the reverse supply chain in case of the ripple effect: A two-stage stochastic optimization model

The devastating impact of the ripple effect increases the importance of the reverse supply chain (RSC) design to ensure sustainability in the long-term. That being the case, in this study, a two-stage stochastic mixed-integer optimization model is proposed to design an RSC network under uncertainty sourcing from the ripple effect (i.e. external side of RSC) by considering the environmental and economic dimensions of sustainability. The environmental and economic disruptions of the ripple effect are represented by the increase in the carbon emission levels and the distance of roads, and the decrease in the capacity of facilities, respectively. Accordingly, a set of scenarios is considered based on the disruption levels (low- and high-impact) in case of the ripple effect. Furthermore, an α-reliability constraint is integrated into the model to further analyze the occurrence of scenarios. The model allows us to make integrated operational and strategic decisions by placing an emphasis on the carbon emission levels (i.e. environmental dimension) and the total cost (i.e. economic dimension). To obtain some remarkable insights, the proposed model is validated through computational experiments based on data extracted from a real case. The computational results show that the ripple effect increases the emission level and total cost up to 40%. For this reason, it suggested that the regulations regarding WEEE (Waste Electrical and Electronic Equipment) should be prepared by considering sustainability in the entire RSC network. Besides, it is realized that the centralized distribution strategy leads to a more resilient RSC network design.

Innovative strategy to design a mixed resilient-sustainable electricity supply chain network under uncertainty

Motivated by a real-world case, this paper deals with uncertainty issues in the resilient and sustainable electricity supply chain network design. Uncertainty is always found in electricity demand prediction and perfect foresight is not possible. The resilience of electricity networks in face of uncertainty can prevent catastrophic impacts. Besides, due to the growing concerns about social impacts, considering social measures along with the other measures is becoming critical when analyzing the network costs. In this paper, a multi-objective optimization model is developed, which consists of the total cost in the first objective, resiliency measures in the second objective, and some aspects of corporate social responsibility in the third objective. The resiliency measures minimize de-resiliency, including successive establishment, distributed generators inadequacy, congestion through electrical lines, and energy dissatisfaction level. A novel robust approach is also proposed to deal with the uncertainty of electricity demand based on the light robust programming and possibilistic theory in the fuzzy logic. By investigating a real case in Iran, the contributions of the considered measures, and the effects of uncertainty are analyzed. The analyses reveal that by applying the proposed model the decision-makers can enhance corporate social responsibility and resiliency by 50% and 20%, respectively, although it increases the total cost by 50%. Moreover, the results of applying the proposed fuzzy-robust approach show that how the decision-makers can effectively allocate the uncertainty budget and protection level to attain more robust solutions in terms of standard deviation and mean value of the total cost.