Facility Disruptions Research Articles

Optimal Design of Resilient Carbon Capture, Utilization and Storage (CCUS) Supply Chain Networks under Facility Disruption

In recent years, various kinds of carbon dioxide capture, utilization and storage supply chain network design (CCUS SCND) problems have been extensively studied by scholars from the supply chain management community and other fields. The existing works mainly focus on the various deterministic or uncertainty problems; few works consider the CCUS SCND resilience problem in the context of utilization/storage facility disruptions due to unexpected natural disasters or other geological anomaly events. This paper aims to study the CCUS SCND resilience problem under utilization/storage facility capacity disruption risk. We propose a stochastic mixed-integer linear programming model for the considered problem. In the considered problem, the main decisions related to the following areas are taken into account: supply chain design and planning; facility disruption risk handling, including the optimal determination of facility locations and the matching of carbon dioxide emission sources and utilization/storage facilities; carbon dioxide normal transportation planning; and transshipment planning for various disruption scenarios. Finally, an experimental study comprising a case study from China is conducted to validate the effectiveness and performance of our proposed model. The obtained results show that the supply chain networks for the case study obtained by our proposed model are efficient, cost-effective and resilient in mitigating various kinds of utilization/storage facility disruption scenarios, showing the model can be applied to large-scale CCUS projects to help managers effectively deal with disruption risks. Future research should consider multiple disruption events and propose multiple effective resilience strategies.

A scenario-based robust approach for joint planning of multi-blood product logistics and multi-casualty type evacuation

This paper presents a two-stage hybrid robust programming model that integrates multiple blood products scheduling and multiple casualty types evacuation under facility disruptions risk and casualty number uncertainty. The model seeks to maximize the rescue efficiency and minimize the total operations cost. Under a set of disruption scenarios, we adopt a scenario-based robust method to address disruption risks at temporary medical centres and two robust uncertainty sets to deal with uncertain casualty numbers. We propose a proximal bundle algorithm to solve large-scale instances of the proposed hybrid robust model approximately. Extensive numerical experiments show that: (i) the trade-off between model robustness and solution robustness can help the decision-makers determine an appropriate risk-aversion weight; (ii) compared with the corresponding stochastic and scenario-based robust models, the hybrid robust model can obtain more robust solutions with a slight increase in cost; (iii) the proximal bundle algorithm can produce near-optimal solutions within reasonable computational times; (iv) some model parameters have significant impact on the total cost, which can help decision-makers set the appropriate parameters to achieve the desired outcome. Finally, we also use the real data of the 2008 Wenchuan County Earthquake in Sichuan Province, China, to illustrate the application of the model.

Robust optimization of a closed-loop supply chain network based on an improved genetic algorithm in an uncertain environment

Aiming at the network structure design problems of facility location, path planning, and flow distribution in an e-commerce closed-loop supply chain network under an uncertain environment, the uncertainties of both the forward and reverse logistics demands and the impact of facility disruptions are analyzed. A robust optimization model is established using the robust peer-to-peer optimization method. An algorithm is designed that combines the minimum spanning tree algorithm with a two-layer adaptive genetic algorithm (Prim-DMGA) to solve the model. The results show that the Prim-DMGA not only requires less computation time, but also generates solutions of higher quality. In addition, the application of the robust optimization model reduces the adverse effects of uncertain factors on the e-commerce closed-loop supply chain network, thereby improving its robustness.

Designing a resilient medical laboratory service network under disruption and uncertainty: A case study

Health service networks play a key role in ensuring the health of society. Disruptive and operational risks are constantly affecting the goals of healthcare supply chain and service networks. Therefore, adopting a suitable approach to deal with risks is critical in the mentioned networks. This study proposes a mathematical model for designing an integrated resilient medical laboratory service network and the supply chain of laboratory consumables under multiple facility disruptions to minimize the total network design costs. A robust possibilistic optimization approach is employed to deal with the uncertainty of input parameters. Then, a case study is presented to extract the results and evaluate the proposed model. Regarding the results, the integration of the laboratory service network and laboratory consumables supply chain significantly reduces costs and improves the service process in the long run. Also, adopting more efficient resiliency strategies will increase the cost savings of the entire network.

A two-stage chance constrained stochastic programming model for emergency supply distribution considering dynamic uncertainty

This paper presents a comprehensive approach to addressing the challenges of designing a reliable emergency logistics network under the dynamic uncertainty of natural disasters. The proposed multimodal transportation strategy based on level assignments can effectively mitigate the risk of facility disruptions and network uncertainty and enhance the adaptability of the system to diverse emergency supplies. The established two-stage chance constrained stochastic programming model and improved evolutionary algorithm are powerful tools for optimizing the network design and improving its resilience. Based on the real-world case of the Ya’an earthquake in China, the impacts of various practical constraints are analyzed. Several insights into the decision-making process of emergency logistics management are provided: managers should prioritize time utility over cost utility and use multiple aircraft and helicopters, supplies should be sorted into fewer and more important categories and placed in smaller packages for efficient transportation, multimodal transportation should be considered based on geographical and traffic conditions.

Distributionally robust location-allocation with demand and facility disruption uncertainties in emergency logistics

Emergency logistics is vital to disaster relief management. In this paper we develop a distributionally robust optimization model (DROM) for optimizing the locations of distribution centres and backup warehouses, and the distribution of disaster relief supplies in emergency logistic networks by minimizing the expected total cost and the total delivery time. Based on limited historical distribution information, the model considers uncertain demand and uncertain facility disruptions, and describes their distributions through ambiguity sets. Following the adaptability and tractability of the ambiguity sets, we show that the model can be equivalently re-formulated as a mixed-integer linear program. To solve the model, we propose an exact algorithm based on Benders decomposition (BD). We also introduce an in-out Benders cut generation strategy to improve the efficiency of the BD algorithm. Finally, we perform extensive numerical studies to test the performance of the BD algorithm, ascertain the benefits of the proposed DROM over the corresponding deterministic and stochastic models, and examine the impacts of the key model parameters to gain managerial insights.

Model and algorithm for augmenting logistics network resilience with hybrid facilities and robust strategies

This paper introduces a mixed-integer nonlinear programming (MINLP) model aimed at designing a dependable closed-loop supply chain that can function effectively despite disruptions. The model incorporates several strategies to reduce the risks associated with random facility disruptions. First, the concept of p-robustness criterion is employed, which sets upper bounds on the cost in various disruption scenarios to protect against disruptions. To accommodate for the varying levels of reliability of facilities, the network consists of two categories of infrastructure: reliable and defective hybrid infrastructure. Thirdly, a sharing strategy is proposed to mitigate for the capacity loss at unreliable facilities by permitting transportation of products from reliable facilities to unreliable facilities. Additionally, an improved genetic algorithm (IGA) is devised for the MINLP model efficiently. The efficacy of the model and the tailored algorithm is evaluated by means of computational experiments. Finally, a sensitivity analysis is ultimately performed to furnish managers with meaningful insights. For example, the sharing strategies demonstrate a significant decrease of total cost. This paper contributes to enhancing resilience in logistics networks, ensuring their reliable performance even in the face of disruptions.

Collaborative distribution network design with surging demand and facility disruptions

In this paper, we study a new approach to horizontal collaboration, the so-called coalition configuration among partnering companies through sharing distribution centers in a network design problem. Horizontal collaboration is established by making joint decisions and sharing resources among various parties at the same level of the supply chain which improves operational efficiency and competitiveness. In particular, the question we address is which coalition should be formed in each distribution center while the objective function seeks to minimize the total cost in the network. Beyond that, potential uncertainties, i.e., operational and disruption risks, are considered in the collaborative design phase. Thus, this paper studies a two-stage robust optimization model for collaborative distribution network design under surging demand and facility disruptions. A tailored column-and-constraint generation algorithm is utilized to solve the proposed robust model. Extensive numerical experiments have been conducted to analyze solutions obtained by the model in various situations, e.g., when the grand coalition forms, for decisions ranging from fully centralized to fully decentralized settings, for collaborative approaches versus non-collaborative approaches, and under different amounts of uncertainty budgets. The results demonstrate that the coalition formation mechanism proposes some competitive solutions with savings of the grand coalition. We also find that not only does collaboration always increase the flexibility and resilience of the network but also in collaborative approaches, the average price of robustness is comparatively lower than that of non-collaborative approaches.

A two-stage stochastic model for intermodal terminal location and freight distribution under facility disruptions

ABSTRACT A two-stage stochastic model is developed for intermodal facility location and freight distribution under random disruptions at shipper facilities and/or intermodal terminals (IMTs). The magnitude of the disruption and the impacted locations are uncertain parameters. A two-stage stochastic programming model is used to address supply uncertainty at shippers and throughput capacity uncertainty at IMTs. A level-method based decomposition approach and the L-shaped method are used to solve the model. The state of South Carolina in the U.S.A. is used as a case study with the goal of determining the set of IMT locations that minimise the total long-run network costs due to hurricane disruptions. A methodology is developed to generate realistic scenarios. The Freight Analysis Framework Version 4.5 data set is used to generate demands and supply, and k-means clustering is used with the Hurricane database (HURDAT2) to generate hurricane disruption scenarios. Sensitivity analyses are performed by varying the disruption probabilities, disruption duration, and direct shipping cost parameters. The results indicate that as disruptions increase, less disrupted intermodal facilities are opened. Also, as direct shipping costs increase, the long-term savings increase non-linearly for all magnitudes of disruptions.

Casualty scheduling optimisation with facility disruptions under grey information in early stage of post-earthquake relief

PurposeThe purpose of this paper is to propose a new casualty scheduling optimisation problem and to effectively treat casualties in the early stage of post-earthquake relief.Design/methodology/approachDifferent from previous studies, some new characteristics of this stage are considered, such as the grey uncertainty information of casualty numbers, the injury deterioration and the facility disruption scenarios. Considering these new characteristics, we propose a novel casualty scheduling optimisation model based on grey chance-constrained programming (GCCP). The model is formulated as a 0–1 mixed-integer nonlinear programming (MINP) model. An improved particle swarm optimisation (PSO) algorithm embedded in a grey simulation technique is proposed to solve the model.FindingsA case study of the Lushan earthquake in China is given to verify the effectiveness of the model and algorithm. The results show that (1) considering the facility disruption in advance can improve the system reliability, (2) the grey simulation technology is more suitable for dealing with the grey uncertain information with a wider fluctuation than the equal-weight whitening method and (3) the authors' proposed PSO is superior to the genetic algorithm and immune algorithm.Research limitations/implicationsThe casualty scheduling problem in the emergency recovery stage of post-earthquake relief could be integrated with our study to further enhance the research value of this paper.Practical implicationsConsidering the facility disruption in advance is beneficial to treat more patients. Considering the facility disruption in the design stage of the emergency logistics network can improve the reliability of the system.Originality/value(1) The authors propose a new casualty scheduling optimisation problem based on GCCP in the early stage of post-earthquake relief. The proposed problem considers many new characteristics in this stage. To the best of the authors' knowledge, the authors are the first to use the GCCP to study the casualty scheduling problem under the grey information. (2) A MINP model is established to formulate the proposed problem. (3) An improved integer-encoded particle swarm optimisation (PSO) algorithm embedded grey simulation technique is designed in this paper.

A meta heuristic approach for reliable capacitated facility joint inventory-location problem with round-trip transportation under imperfect information of disruption in a Fuzzy environment

In today’s systems and networks, disruption is inevitable. Designing a reliable system to overcome probable facility disruptions plays a crucial role in planning and management. This article proposes a reliable capacitated facility joint inventory-location problem where location-independent disruption may occur in facilities. The system tries to satisfy customer’s demands and considers penalty costs for unmet customer demand. The article aims to minimize total costs such as establishing inventory, uncovered demand’s penalty, and transportation costs. While many articles in this area only use exact methods to solve the problem, this article uses a metaheuristic algorithm, the red deer algorithm, and the exact methods. Various numerical examples have shown the outstanding performance of the red deer algorithm compared to exact methods. Sensitivity analyses show the impacts of various parameters on the objective function and the optimal facility layouts. Lastly, managerial insights will be proposed based on sensitivity analysis.

Achieving Sustainability While Managing Supply-Distribution Operations During Facility Disruptions

Achieving Sustainability While Managing Supply-Distribution Operations During Facility Disruptions

A General Model and Efficient Algorithms for Reliable Facility Location Problem Under Uncertain Disruptions

This paper studies the reliable uncapacitated facility location problem in which facilities are subject to uncertain disruptions. A two-stage distributionally robust model is formulated, which optimizes the facility location decisions so as to minimize the fixed facility location cost and the expected transportation cost of serving customers under the worst-case disruption distribution. The model is formulated in a general form, where the uncertain joint distribution of disruptions is partially characterized and is allowed to have any prespecified dependency structure. This model extends several related models in the literature, including the stochastic one with explicitly given disruption distribution and the robust one with moment information on disruptions. An efficient cutting plane algorithm is proposed to solve this model, where the separation problem is solved respectively by a polynomial-time algorithm in the stochastic case and by a column generation approach in the robust case. Extensive numerical study shows that the proposed cutting plane algorithm not only outperforms the best-known algorithm in the literature for the stochastic problem under independent disruptions but also efficiently solves the robust problem under correlated disruptions. The practical performance of the robust models is verified in a simulation based on historical typhoon data in China. The numerical results further indicate that the robust model with even a small amount of information on disruption correlation can mitigate the conservativeness and improve the location decision significantly. Summary of Contribution: In this paper, we study the reliable uncapacitated facility location problem under uncertain facility disruptions. The problem is formulated as a two-stage distributionally robust model, which generalizes several related models in the literature, including the stochastic one with explicitly given disruption distribution and the robust one with moment information on disruptions. To solve this generalized model, we propose a cutting plane algorithm, where the separation problem is solved respectively by a polynomial-time algorithm in the stochastic case and by a column generation approach in the robust case. The efficiency and effectiveness of the proposed algorithm are validated through extensive numerical experiments. We also conduct a data-driven simulation based on historical typhoon data in China to verify the practical performance of the proposed robust model. The numerical results further reveal insights into the value of information on disruption correlation in improving the robust location decisions.

Facility Disruptions in a Closed-loop Supply Chain Featuring Warranty Policy and Quality-based Segmentation of Returns

This paper focuses on a closed-loop supply chain that deals with disruptions in the distribution centers, and optimizes the network in two dimensions of sustainability: economic and environmental. Economically, the proposed network maximizes the profits of the customers, manufacturers and distributors. Three avenues for cost minimization are designed for the customer by adding the warranty periods, the reworking options, and the incentives for returning the used items. Non-dominated solutions via the Reservation Level-driven Tchebycheff procedure are found by appropriate choice of facility establishment and suitable allocation links considering the disruption in the distribution centers.Environmentally, the model adopts a zero-waste strategy by embedding various return-segmentation policies and a secondary chain. The backward flow depends on the customers' choice of reworking, the validity of the warranty contract, and the quality of the returns. The test results indicate that due to various revenue options, the manufacturing and distribution centers prefer returns with medium-range quality, while due to the incentives offered for the recyclable items, the customers benefit the most from returning the items with the lowest quality. The tests on the probability of disruptions indicate that establishing a minimum number of the manufacturing and/or distribution sites without disruption leads to better overall performance.

Two-Stage Stochastic Program for Supply Chain Network Design under Facility Disruptions

A supply chain disruption is an unanticipated event that disrupts the flow of materials in a supply chain. Any given supply chain disruption could have a significant negative impact on the entire supply chain. Supply chain network designs usually consider two stage of decision process in a business environment. The first stage deals with strategic levels, such as to determine facility locations and their capacity, while the second stage considers in a tactical level, such as production quantity, delivery routing. Each stage’s decision could affect the other stage’s result, and it could not be determined individual. However, supply chain network designs often fail to account for supply chain disruptions. In this paper, this paper proposed a two-stage stochastic programming model for a four-echelon global supply chain network design problem considering possible disruptions at facilities. A modified simulated annealing (SA) algorithm is developed to determine the strategic decision at the first stage. The comparison of traditional supply chain network decision framework shows that under disruption, the stochastic solutions outperform the traditional one. This study demonstrates the managerial viability of the proposed model in designing a supply chain network in which disruptive events are proactively accounted for.

Robust facility location under demand uncertainty and facility disruptions

Facility location decision is strategic: the construction of a new facility is typically costly and the impact of the decision is long-lasting. Environmental changes, such as population shift and natural disasters, may cause today’s optimal location decision to perform poorly in the future. Thus, it is important to consider potential uncertainties in the design phase, while explicitly taking into account the possible customer reassignments as recourse decisions in the execution phase. This paper studies a robust fixed-charge location problem under uncertain demand and facility disruptions. To model this problem, we adopt a two-stage robust optimization framework, where the first-stage location decision is made here-and-now and the second-stage allocation decision can be deferred until the uncertainty information is revealed. We develop a column-and-constraint generation (C&CG) algorithm to solve the models exactly and benchmark it with the other C&CG algorithm in the literature. We further extend our modeling and solution schemes to facility fortification problems under uncertainties, where investment decisions are made for already existing supply chain systems to protect facilities from disruptions and against uncertain demand. We conduct extensive numerical tests to study the differences in solutions produced by the three robust models and the impacts of uncertainties on solution configuration. Results show that our C&CG algorithm can solve more instances to optimality and consume less computing time on average, compared to the benchmark algorithm. Several managerial insights are also drawn from our numerical experiments.

Cross-evaluation based super efficiency DEA approach to designing disaster recovery center location-allocation-routing network schemes

PurposeFinding efficient disaster recovery center location-allocation-routing (DRCLAR) network schemes play a vital role in the disaster recovery logistics network (DRLN) design. The purpose of this paper is to propose and demonstrate how to design efficient DRCLAR network schemes under the risk of facility disruptions as a part of the disaster relief activities.Design/methodology/approachA goal programming (GP) model is formulated to consider four performance measures simultaneously for the DRCLAR design. The cross-evaluation based-super efficiency data envelopment analysis (DEA) approach is applied to better evaluate the DRCLAR network schemes generated by solving the GP model so that more efficient network schemes can be identified.FindingsThe proposed approach identifies more efficient DRCLAR network schemes consistently among various network schemes generated by GP. We find that combining these two methods compensates for each method's weaknesses and enhances the discriminating power of the DEA method for effectively identifying and ranking the network schemes.Originality/valueThis study presents how to generate balanced DRCLAR network schemes and how to evaluate various network schemes for identifying efficient ones. The proposed procedure of developing and evaluating them could be extended for designing some disaster recovery/relief supply chain systems with conflicting performance measures.

A two-stage stochastic mixed-integer program for reliable supply chain network design under uncertain disruptions and demand

Having a reliable supply chain network is one of the most effective ways to plan and deal with facility failures and demand uncertainty due to very limited options available in a facility location problem. This research addresses issues bordering on capacitated supply chain problems, specifically on how reliable supply chain networks can be designed in the face of random facility disruptions and uncertain demand with a combination of hardening selected facilities and product reassignment. The proposed multi-period capacitated facility location and allocation problem is modeled as a two-stage stochastic mixed-integer formulation that minimizes the total establishing and transportation cost. The L-shaped method of stochastic linear programming is applied by integrating with two types of optimality and feasibility cuts for solving the stochastic model. This research aims to improve the proposed algorithm in two different ways: replacing the single-cut approach with a multi-cut and showing relatively complete recourse in the stochastic model by reformulating the original model. Finally, to illustrate the applicability of the model and computational effectiveness of the improved algorithm, a case study with different scenarios is presented and the results are discussed. Our computational results show that the proposed solution algorithm is not only capable of solving large-scale problems, but also avoids long run times. It is also demonstrated that substantial improvements in reliability of the system can often be possible with minimal increases in facility cost.

Robust facility location with structural complexity and demand uncertainty

Building robust logistics networks against facility disruptions has been gaining more attention from both researchers and practitioners. However, in pursuit of high built-in network robustness, one usually has to increase the network redundancy and complexity, which adds to the difficulty of management and induces additional operating costs. In this paper, we aim to balance the trade-off between robustness and complexity in the logistics network design problem and propose a model that explicitly considers the demand uncertainty. Due to the non-convexity of our model, we present a linear reformulation method that transforms the model into a MILP. From our numerical studies, in some cases, we can simultaneously enhance robustness and reduce complexity. When the demand uncertainty increases, the network will become less robust. It will require more backup links to guarantee a given robustness requirement. Consequently, the network can exhibit higher complexity and incur additional costs. We also observe it might be possible to dramatically reduce the complexity at a reasonable cost. One can achieve a relatively simple and robust network by only maintaining backup links for customer zones with large demand volumes. Finally, we provide discussions on managerial implications.

A Risk-averse Inventory-based Supply Chain Protection Problem with Adapted Stochastic Measures under Intentional Facility Disruptions: Decomposition and Hybrid Algorithms

Owing to rising intentional events, supply chain disruptions have been considered by setting up a game between two players, namely, a designer and an interdictor contesting on minimizing and maximizing total cost, respectively. The previous studies have found the equilibrium solution by taking transportation, penalty and restoration cost into account. To contribute further, we examine how incorporation of inventory cost influences the players’ strategies. Assuming risk-averse feature of the designer and fully optimizing property of the interdictor with limited budget, the conditional-value-at-risk is employed to be involved in total cost. Using special order sets of type two and duality role, the linearized tri-level problem is solved by column-and-constraint generation and benders decomposition algorithms in terms of small-sized instances. In terms of larger-sized instances, we also contribute to prior studies by hybridizing corresponding algorithms with bio-geography based optimization method. Another non-trivial extension of our work is to define adapted stochastic measures based on the proposed mean-risk tri-level formulation. Borrowing instances from prior papers, the computational results indicate the managerial insights on players’ decisions, the model’s efficiency and performance of the algorithms.