Transportation Research Part E-Logistics and Transportation Review最新文献

{"title":"Distributionally robust optimization for minimizing price fluctuations in quota system","authors":"","doi":"10.1016/j.tre.2024.103812","DOIUrl":"10.1016/j.tre.2024.103812","url":null,"abstract":"<div><div>Quota systems play a crucial role in regulating public-interest goods and controlling negative externalities, with a primary focus on social impacts rather than economic benefits. This paper examines the decision-making process for quota release, aiming to control growth rates and ensure price stability over time. We first develop a chance-constrained problem for quota systems, solving it using sample average approximation. Due to computational demands, alternative approximation methods are explored. We consider two types of quota systems: mature systems with known distributions and newly established systems with distributional ambiguity. For mature systems, Conditional Value-at-Risk (CVaR) is used to approximate the chance constraint, while for newly established systems, worst-case CVaR is employed within a robust optimization framework and the binary search algorithm is derived to efficiently solve the problem. The proposed models’ effectiveness is validated through computational studies using data from Singapore’s Vehicle Quota System. With known distributions, our CVaR sample average approximation (CVaR-SAA) model outperforms traditional models, reducing violation probability by more than 56.32%. With distributional ambiguity, worst-case CVaR approximation robust optimization (WCVaR-RO) model provides superior solutions, particularly in maximum violation probability (MVP). In the most notable case, WCVaR-RO reduces the MVP by over 53.37%. This research offers valuable insights into the management of quota systems.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":null,"pages":null},"PeriodicalIF":8.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic forecast-based procurement in seaborne forward freight markets under demand and price uncertainty","authors":"","doi":"10.1016/j.tre.2024.103830","DOIUrl":"10.1016/j.tre.2024.103830","url":null,"abstract":"<div><div>Volatility in freight rates and shipping demand poses financial risks for charterers and ship owners. Freight forward agreements (FFAs) are popular hedging tools for fixing freight rates in advance by specifying the amount of cargo to be transported at the maturity period of the agreement. Procurement decisions with FFAs require assessing future freight rates and shipping demand. Accepting an FFA price offer higher than future FFA and spot prices or procuring a larger amount than the actual demand constitutes risks for charterers. We consider the freight procurement problem of a charterer, minimizing the total expected cost using FFA and spot markets under price and demand uncertainty. We show that a state-dependent base-stock policy is optimal with non-decreasing base-stock levels as the demand period approaches when price and demand forecasts are not updated. To determine base-stock levels, we propose probabilistic forecast-based policies with updated forecasts and an increasing base-stock level policy (IBP) adjusting base-stock levels based on the number of periods left until the demand period. The proposed methods are compared with benchmark methods using synthetic data covering different market conditions and real data from 14 bulk and tanker routes. The evaluation period covers pre-crisis (2016–2019) and during-crisis periods (2020–2023), considering major events after 2019, such as the COVID-19 pandemic and the Russia–Ukraine conflict, which led to high market volatility. Numerical evaluations show that policies based on probabilistic forecasts outperform those based on point forecasts. Utilizing probabilistic demand forecasts results in lower costs than probabilistic price forecasts. Experiments on the market data show that IBP results in the lowest cost on average while avoiding excessive procurement due to being in line with the optimal procurement policy. IBP outperforms probabilistic forecast-based policies due to forecast biases in the volatile freight market.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":null,"pages":null},"PeriodicalIF":8.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effect of geographic risk factors on disaster mass evacuation strategies: A smart hybrid optimization","authors":"","doi":"10.1016/j.tre.2024.103825","DOIUrl":"10.1016/j.tre.2024.103825","url":null,"abstract":"<div><div>This paper investigates an urban Emergency Evacuation Network Design (EEND) problem on a large scale when geographical risk in different areas varies. The decisions to make are (i) determining active shelters, (ii) selecting evacuation routes, and (iii) managing the supply of relief commodities from distribution centers to shelters. A region prone to floods and hurricanes is divided into zones, each with a specific vulnerability risk. For each zone, a risk measure is calculated by combining the risk factors –transporting people and relief commodities and the placement of temporary shelters. The objective is to minimize the maximum risk across the network, ensuring a balanced distribution of risk. A combinatorial scenario planning approach is developed to manage the uncertainty in disaster severity and the evacuee numbers. To incorporate varied geographical risks, a smart hybrid optimization approach as a new solution technique is developed, tuned, and validated to solve the EEND problem. The proposed approach uses directed local search structures designed for the EEND problem and an AI-based self-parameter tuning module, enhancing performance. To extract insights, Rennes, France, is considered a case study. The results indicate a reduction in casualties using a min–max formulation compared to traditional sum-risk objectives. Further, a detailed evacuation plan that increases the number of city regions enhances EEND performance. Practical insights suggest minimizing the number of shelters to the essential capacity needed to host all evacuees, as additional shelters may lead to increased evacuation and supply routes, potentially in areas with higher risk.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":null,"pages":null},"PeriodicalIF":8.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resilience enhancement of multi-modal public transportation system via electric bus network redesign","authors":"","doi":"10.1016/j.tre.2024.103810","DOIUrl":"10.1016/j.tre.2024.103810","url":null,"abstract":"<div><div>The multi-modal public transportation system incorporating the electric bus network and the metro network plays a crucial role in meeting the daily transportation demands in urban areas. However, the inadequate connectivity between the electric bus network and the metro network has resulted in poor resilience of the multi-modal public transportation system. When disruptions occur at metro links or metro stations, stranded passengers cannot be rapidly evacuated through bus lines. Therefore, operators need to redesign the multi-modal public transportation system to enhance the integration between the electric bus network and the metro network. Since it is challenging to modify the fixed metro network, we thus focus on introducing redesign plans for the electric bus network, enabling the multi-modal public transportation system to exhibit the desired resilience in scenarios of disruptions. This paper proposes a two-level framework integrating electric bus network redesign at the tactical level and resource deployment at the planning level. For the redesign problem at the tactical level, this paper designs a tailored branch-and-price algorithm to generate high-quality redesign solutions for the electric bus network. For the resource deployment problem at the planning level, this paper determines the locations of charging facilities and the number of electric buses based on the redesigned electric bus network, considering uncertain passenger demands and metro capacities. We propose a two-layer robust optimization model for the resource deployment problem and develop a tailored column-and-constraint generation algorithm to solve it. Finally, this paper tests the performance of the developed models and algorithms on a set of instances in Beijing. The impact of the uncertainty budget, the number of electric buses, bus capacity, and charging time of electric buses on the system performance is discussed.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":null,"pages":null},"PeriodicalIF":8.3,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of drone technology and application of IoT on vaccine supply chain during a pandemic under uncertain Environment: A real case study of COVID-19 in Iran","authors":"","doi":"10.1016/j.tre.2024.103831","DOIUrl":"10.1016/j.tre.2024.103831","url":null,"abstract":"<div><div>Vaccination is a crucial way to combat the pandemic; in other words, vaccines play an important role in controlling the spread of the virus and ultimately ending the pandemic. This study presents a multi-objective mixed integer linear programming model for the vaccine supply chain considering uncertain cost, vaccine purchase, and lead time. Through the utilization of Internet of Things technology, data about various groups is collected. Upon identification of individuals with good health, the specific needs of each area are ascertained during each period. Subsequently, a mathematical model for the vaccine supply chain is presented, encompassing four distinct levels; manufacturers, distribution centers, health centers, and immunization centers. Furthermore, this model incorporates the utilization of drones to deliver vaccines from distribution centers to health centers because of the significant distance between these two levels. The proposed framework encompasses two main goals; minimizing the total cost and the waiting time for people in the queue. A novel fuzzy approach has been employed to deal with the uncertain parameters. The model’s validation is accomplished through the implementation of a real case study of COVID-19 in Iran. The findings indicate that the lack of Internet of Things technology implementation results in a higher number of individuals being directed to immunization centers, thereby elevating the likelihood of infection, and, this scenario leads to the unnecessary administration of vaccines, leading to resource wastage. Additionally, without using drones, vaccines cannot be delivered and injected into people on time. Ultimately, the proposed framework and methodology can be applied in almost larger dimensions and the results demonstrate the model and methods’ efficiency and effectiveness. Since this study is applied to the case study of COVID-19, the findings can be applied in the conditions of similar pandemics.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":null,"pages":null},"PeriodicalIF":8.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A meta-auction for on-demand transportation procurement in industry 5.0","authors":"","doi":"10.1016/j.tre.2024.103842","DOIUrl":"10.1016/j.tre.2024.103842","url":null,"abstract":"<div><div>The Cyber-Physical Internet (CPI) is a cutting-edge concept that brings together physical systems and cyber technologies to enable seamless interaction between the physical and virtual worlds. This innovative approach is revolutionizing the transportation industry by paving the way for a new era of logistics and transport networks. Introducing CPI into the procurement of transport services is leading to a re-evaluation of fundamental issues such as routing, mode choice and real-time pricing. This paper provides an in-depth discussion on the application of transport services procurement auctions in a CPI environment, with the aim of establishing a novel CPI-based trading platform for transport services using CPI technology, and calls the methodology proposed in this paper a <em>meta</em>-auction. The transport route allocation quandary is simplified into an auction model, where carriers truthfully submit unit route costs, and the winning carrier and pricing are determined using the single-unit Vickrey-Clark-Groves (VCG) method. To address scenarios with multiple carriers per network segment node, this paper proposes the multi-unit VCG auction method. Furthermore, the weighted affine VCG auction method is introduced, considering the weight of each network segment route. All three mechanisms are generalized VCG auctions, ensuring incentive compatibility, budget balance, allocation efficiency, and individual rationality. Case studies validate the effectiveness of the proposed methods, offering managerial insights based on key findings that are valuable for industry professionals and researchers in the CPI domain. This study highlights the transformative potential of CPI to revolutionize auctions for the procurement of transport services and underlines the benefits of combining physical and cyber technologies in auction design.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":null,"pages":null},"PeriodicalIF":8.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and selection of recycling strategy considering consumer preference","authors":"","doi":"10.1016/j.tre.2024.103824","DOIUrl":"10.1016/j.tre.2024.103824","url":null,"abstract":"<div><div>With the escalating issue of plastic pollution, exploring suitable post-consumer plastic waste (PCPW) recycling holds significant importance for global environmental protection. To address this issue, this paper proposes a novel PCPW recycling platform (PRP) that offers two recycling strategies to improve the recycling level of PCPW. Then, we construct a Stackelberg game model to analyze and compare three recycling strategies, <em>i.e.</em>, traditional recycling strategies, only trade-in for cash, and trade-in for cash and for new. Meantime, we consider the impact of consumer preference and government subsidies on recycling strategies. Research finding: (1) The operation of PRP increases the value of PCPW, particularly PRP’s only trade-in for cash has the highest recycling price. (2) PRP’s trade-in for cash and for new can attract more consumers to participate in PCPW recycling under certain conditions. (3) PRP’s only trade-in for cash enables recyclers and remanufacturers to reap greater benefits. To ensure the robustness of our research results, we conduct further analysis to explore the impact of corporate social responsibility (CRS) and the hassle cost. The study also provides management implications for promoting PCPW recycling.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":null,"pages":null},"PeriodicalIF":8.3,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transport behavior and government interventions in pandemics: A hybrid explainable machine learning for road safety","authors":"","doi":"10.1016/j.tre.2024.103841","DOIUrl":"10.1016/j.tre.2024.103841","url":null,"abstract":"<div><div>During a pandemic, transportation authorities and policymakers face significant challenges in identifying and validating new travel behavior and how it affects traffic crash patterns to develop effective safety strategies. A timely assessment of an emergency incident’s long-term impact and the development of appropriate response strategies are critical for managing future occurrences. This study investigates to answer these research questions (RQs):</div><div>RQ1: How do various spatio-temporal risk factors influence traffic crash injury severity during the different phases of the COVID-19 pandemic?</div><div>RQ2: What are the key risk factors influencing injury severity in automobile crashes during the pre-pandemic, early pandemic, between the first and second waves of the pandemic, and the post-pandemic era?</div><div>RQ3: How do the implemented government policies and interventions during the pandemic affect transport behavior and road safety?</div><div>This study presents a hybrid explainable machine learning approach based on eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanation (SHAP) to identify influential traffic crash-related risk factors for injury severity. Additionally, we propose a statistical learning approach using a nonlinear multinomial logit model to jointly analyze the count of automobile traffic crashes by injury severity and assess the impact of the COVID-19 pandemic across different phases. Our findings include a detailed analysis of system-level taxonomies across feature components, as well as the use of aggregate SHAP scores to classify crash data into high-level contributing variables during the pre-pandemic, intra-pandemic, and post-pandemic phases. The expected outcomes include insights such as identifying the best times to implement travel restrictions to reduce traffic accidents, understanding shifts in traffic flow patterns across pandemic phases, and determining effective public health interventions that can reduce both traffic accidents and congestion. Furthermore, the study reveals that the initial pandemic phase saw a significant decrease in traffic volume and accident rates. In contrast, the subsequent pandemic and post-pandemic phases saw an increase in severe accidents due to risky driving behaviors, emphasizing the importance of adaptive safety measures.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":null,"pages":null},"PeriodicalIF":8.3,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inventory placement with carbon cap-and-trade in guaranteed service supply chains","authors":"","doi":"10.1016/j.tre.2024.103813","DOIUrl":"10.1016/j.tre.2024.103813","url":null,"abstract":"<div><div>In this paper, we explore the impact of carbon trading on the safety stock placement optimization in multi-echelon supply chains. The carbon emission in each stage of the supply chain is measured through a function of the service time quoted by the stage as a key variable. Adopting the guaranteed service time modelling framework, we develop a safety stock placement model under the carbon cap-and-trade policy to study the complex trade-off among the carbon cap, the carbon price, and the service time. Based on the different relationship between the unit purchasing and selling carbon prices, we derive the tractable formulations of the proposed model using successive mixed-integer programming approximations. A series of observations through the model implementation on a real-world chain data from Willems (2008) are summarized to understand how the carbon cap and price can affect a firm’s safety stock placement and carbon emissions.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":null,"pages":null},"PeriodicalIF":8.3,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A simulation-based optimization approach for the recharging scheduling problem of electric buses","authors":"","doi":"10.1016/j.tre.2024.103835","DOIUrl":"10.1016/j.tre.2024.103835","url":null,"abstract":"<div><div>This study proposes a simulation-based optimization approach to address the recharging scheduling problem of electric buses to minimize charging waiting time. Poor scheduling could lead to longer waiting times and potentially affect operation schedules regarding time and service quality. This study addresses a simulation-based optimization framework to evaluate various performance metrics during electric bus service, including waiting times, charging costs, and the utilization of charging piles. In this study, we propose a hybrid approach, simplified swarm optimization (SSO), which is an evolutionary algorithm with a backtracking (BT) mechanism and dynamic charging in a simulation framework. Based on the dynamic charging, SSO is used to determine the additional charging in terms of battery capacities, and a BT mechanism is employed to enhance algorithm efficiency and achieve breakthroughs in solution quality. A case study from Taiwan with 43 generated datasets was conducted in deterministic and stochastic situations to compare the effectiveness and efficiency among three charging rules (i.e., full charging rule, flexible charging rule, dynamic charging rule) and two algorithms (i.e., particle swarm optimization and SSO<u>)</u> The results indicate the superior performance in all scenarios by using a statistical test, which offers effective decision support for bus operators’ electric bus recharging scheduling.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":null,"pages":null},"PeriodicalIF":8.3,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}