The Principles and Methods of Locating Logistics Centers in Transport Networks

Macioszek, Elżbieta

doi:10.1007/978-3-030-71771-1_10

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Over recent decades, the coupling and correlation among risk factors involved in engineering risk scenarios has been a great concern of scholars (Ledwoch et al., 2016), and graph theory is gaining an increasing amount of recognition due to the ability to deal with such problems very naturally (Chapela et al., 2015). Typical applications of graph theory scenarios associated with risk analysis mainly include information networks (Boroojerdi et al., 2008), biological networks (Girvan & Newman, 2002), social networks (Benitez‐Andrades et al., 2020), and transport networks (Macioszek, 2021). Successful transplantation of graph theory in risk analysis has prompted the emergence of some typical derivative methods based on graph theory (Bonacich, 1972; Macioszek, 2021), such as CN, Bayesian networks (Ma et al., 2020), fault tree analysis (FTA) (Qiao et al., 2020b), event tree analysis (ETA) (Ferdous et al., 2011), failure modes and effects analysis (FMEA) (Wu et al., 2010) and the bow‐tie model (Logrosa et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Typical applications of graph theory scenarios associated with risk analysis mainly include information networks (Boroojerdi et al., 2008), biological networks (Girvan & Newman, 2002), social networks (Benitez‐Andrades et al., 2020), and transport networks (Macioszek, 2021). Successful transplantation of graph theory in risk analysis has prompted the emergence of some typical derivative methods based on graph theory (Bonacich, 1972; Macioszek, 2021), such as CN, Bayesian networks (Ma et al., 2020), fault tree analysis (FTA) (Qiao et al., 2020b), event tree analysis (ETA) (Ferdous et al., 2011), failure modes and effects analysis (FMEA) (Wu et al., 2010) and the bow‐tie model (Logrosa et al., 2021). In addition, the techniques originated from system theory are also frequently applied to assess the risks, such as human factors analysis and classification system (HFACS) (Salmon et al., 2012), STAMP (Salmon et al., 2012), functional resonance analysis method (FRAM) (Alboghobeish & Shirali, 2021), systems theoretic process analysis (STPA) (Plioutsias, Karanikas, and Chatzimihailidou, 2017).…”

Section: Introductionmentioning

confidence: 99%

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A novel methodology concentrating on risk propagation to conduct a risk analysis based on a directed complex network

Deng

Qiao

et al. 2022

Risk Analysis

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A novel methodology is proposed in the present study to describe the risk propagation process by quantitatively evaluating the criticality and sensitivity of risk events according to complex network theory, based on which risk matrices are developed to interrupt the risk propagation process by setting up safety barriers. The applicability and accuracy of the improved k-shell decomposition algorithm and risk flow model for calculating the criticality proposed in this study are verified by the susceptible-infectedrecovered (SIR) simulation, which is widely regarded as a benchmark for complex networks (CN) issues. The results confirm the advantages of the proposed methodologies considering comprehensively various comparison indicators. The sensitivity of the nodes is quantified by running an SIR simulation with a variable infection rate and recovery rate. Finally, the criticality and sensitivity of risk events contribute to the development of risk matrices with three different risk scenarios, based on which the applicability and effectiveness of safety barriers are qualitatively analyzed to interrupt the risk propagation process. The framework and methodologies proposed in this study could well present the risk propagation process within CNs and are proven to have a great potential for studies on safety barriers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel methodology concentrating on risk propagation to conduct a risk analysis based on a directed complex network

Deng

Qiao

et al. 2022

Risk Analysis

View full text Add to dashboard Cite

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“…Traditional hub location models have been studied in depth for more than 30 years due to their wide range of application [21], [22]. Macioszek et al [23] introduced methods of determining the locations of logistics centres in transport networks, and more complete reviews of hub location strategies have been presented by Alumur et al [24] and Campbel et al [25].…”

Section: Introductionmentioning

confidence: 99%

Robust Optimization of the Hub Location Problem for Fresh Agricultural Products With Uncertain Demand

Han

Liu

2022

IEEE Access

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Traditional hub location problems are usually based on deterministic circumstances. However, many uncertain factors can cause demand to vary in the long run, which increases the difficulty of strategic hub location planning. The hub location problem for fresh agricultural products is studied considering the perishability of the products and the uncertainty of customer demands. An uncertain demand variable is described by an affine function of the nominal mean and several independent uncertainty sources and is further adjusted by the deterioration rate. An uncapacitated robust hub location model for fresh agricultural products with uncertain demand is first established and solved using the Lagrangian relaxation approach. Then, a robust optimization model for the corresponding capacitated hub location problem is given. A numerical study based on the Australian Post data set (AP20) shows that the deterioration rate of fresh agricultural products, the uncertainty of demand and the degree of conservatism of decision makers all have significant impacts on the total transportation profit. Furthermore, the capacitated model yields more profit than the uncapacitated model because it allows the effects of the deterioration rate and uncertainty to be moderated through flow reallocation. The proposed models are useful for helping decision makers determine the locations and capacities of hubs for fresh agricultural products in accordance with different risk preferences.

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A Bi-Level Programming Approach to the Location-Routing Problem with Cargo Splitting under Low-Carbon Policies

2021

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To identify the impact of low-carbon policies on the location-routing problem (LRP) with cargo splitting (LRPCS), this paper first constructs the bi-level programming model of LRPCS. On this basis, the bi-level programming models of LRPCS under four low-carbon policies are constructed, respectively. The upper-level model takes the engineering construction department as the decision-maker to decide on the distribution center’s location. The lower-level model takes the logistics and distribution department as the decision-maker to make decisions on the vehicle distribution route’s scheme. Secondly, the hybrid algorithm of Ant Colony Optimization and Tabu Search (ACO-TS) is designed, and an example is introduced to verify the model’s and algorithm’s effectiveness. Finally, multiple sets of experiments are designed to explore the impact of various low-carbon policies on the decision-making of the LRPCS. The experimental results show that the influence of the carbon tax policy is the greatest, the carbon trading and carbon offset policy have a certain impact on the decision-making of the LRPCS, and the influence of the emission cap policy is the least. Based on this, we provide the relevant low-carbon policies advice and management implications.

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The Principles and Methods of Locating Logistics Centers in Transport Networks

Cited by 3 publications

References 14 publications

A novel methodology concentrating on risk propagation to conduct a risk analysis based on a directed complex network

A novel methodology concentrating on risk propagation to conduct a risk analysis based on a directed complex network

Robust Optimization of the Hub Location Problem for Fresh Agricultural Products With Uncertain Demand

A Bi-Level Programming Approach to the Location-Routing Problem with Cargo Splitting under Low-Carbon Policies

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