Pub Date : 2024-11-30DOI: 10.1016/j.tre.2024.103877
Shakoor Barzanjeh, Fardin Ahmadizar, Jamal Arkat
The integration of trucks and drones in last-mile delivery has introduced new capabilities to the transportation industry. These two vehicles simultaneously offer unique features, which have improved performance and efficiency in the process of delivering products. This paper investigates a robust parallel drone scheduling traveling salesman problem with supporting drone, where drone travel times are uncertain and products gradually arrive at a depot over time. In this problem, a truck, a supporting drone, and service drones are located in the depot to deliver products with the goal of minimizing the total completion time. A mathematical model is proposed which is improved using the earliest release dates rule, followed by the development of an exact logic-based benders decomposition algorithm to solve the problem. In this algorithm, customers are initially assigned to the service drones or the truck in a master problem, and subsequent auxiliary problems are addressed utilizing the earliest release dates rule and a dynamic programming algorithm. Finally, various cuts are enhanced through strengthening techniques and sequentially added into the master problem. Numerical experiments demonstrate the efficiency of the improved mathematical model and the proposed algorithm. Furthermore, sensitivity analysis has provided several managerial recommendations for enhancing the delivery system performance.
{"title":"Logic-based benders decomposition algorithm for robust parallel drone scheduling problem considering uncertain travel times for drones","authors":"Shakoor Barzanjeh, Fardin Ahmadizar, Jamal Arkat","doi":"10.1016/j.tre.2024.103877","DOIUrl":"10.1016/j.tre.2024.103877","url":null,"abstract":"<div><div>The integration of trucks and drones in last-mile delivery has introduced new capabilities to the transportation industry. These two vehicles simultaneously offer unique features, which have improved performance and efficiency in the process of delivering products. This paper investigates a robust parallel drone scheduling traveling salesman problem with supporting drone, where drone travel times are uncertain and products gradually arrive at a depot over time. In this problem, a truck, a supporting drone, and service drones are located in the depot to deliver products with the goal of minimizing the total completion time. A mathematical model is proposed which is improved using the earliest release dates rule, followed by the development of an exact logic-based benders decomposition algorithm to solve the problem. In this algorithm, customers are initially assigned to the service drones or the truck in a master problem, and subsequent auxiliary problems are addressed utilizing the earliest release dates rule and a dynamic programming algorithm. Finally, various cuts are enhanced through strengthening techniques and sequentially added into the master problem. Numerical experiments demonstrate the efficiency of the improved mathematical model and the proposed algorithm. Furthermore, sensitivity analysis has provided several managerial recommendations for enhancing the delivery system performance.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"193 ","pages":"Article 103877"},"PeriodicalIF":8.3,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743943","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}
Pub Date : 2024-11-29DOI: 10.1016/j.tre.2024.103865
Shichang Li , Jie Wu , Jingyan Li , Fangkezi Zhou
This paper develops a game-theoretical model to investigate the adoption of personalized pricing in a supply chain with a common retailer and multiple suppliers. We first analyze the optimal personalized pricing strategy for the retailer and then examine its economic impacts on both suppliers and the entire supply chain, aiming to provide insights for supply chain managers regarding the adoption of personalized pricing strategy. We find that the personalized pricing strategy for the retailer depends critically on the degree of product differentiation between two suppliers. If the differentiation level is high, the retailer adopts personalized pricing for both products, which enhances suppliers’ profits and also enables the entire supply chain to achieve its maximum profit. If the differentiation level is moderate, the retailer adopts personalized pricing for only one product, even if both products are ex-ante symmetric. This pricing strategy benefits (may harm) the supplier whose products are priced differently (uniformly), deviating from the strategy preferred by the entire supply chain and therefore warranting special attention. If the differentiation level is low, the retailer again adopts personalized pricing for both products. However, this pricing strategy may harm upstream suppliers by intensifying upstream competition, although it benefits the retailer and the entire channel. Consequently, channel members may have inconsistent preferences for personalized pricing. Our work underscores that the downstream personalized pricing strategies are not always optimal for upstream suppliers and the entire supply chain, thereby holding significance for supply chain managers aiming to adopt personalized pricing for greater profits.
{"title":"The adoption of personalized pricing in a supply chain with a common retailer","authors":"Shichang Li , Jie Wu , Jingyan Li , Fangkezi Zhou","doi":"10.1016/j.tre.2024.103865","DOIUrl":"10.1016/j.tre.2024.103865","url":null,"abstract":"<div><div>This paper develops a game-theoretical model to investigate the adoption of personalized pricing in a supply chain with a common retailer and multiple suppliers. We first analyze the optimal personalized pricing strategy for the retailer and then examine its economic impacts on both suppliers and the entire supply chain, aiming to provide insights for supply chain managers regarding the adoption of personalized pricing strategy. We find that the personalized pricing strategy for the retailer depends critically on the degree of product differentiation between two suppliers. If the differentiation level is high, the retailer adopts personalized pricing for both products, which enhances suppliers’ profits and also enables the entire supply chain to achieve its maximum profit. If the differentiation level is moderate, the retailer adopts personalized pricing for only one product, even if both products are ex-ante symmetric. This pricing strategy benefits (may harm) the supplier whose products are priced differently (uniformly), deviating from the strategy preferred by the entire supply chain and therefore warranting special attention. If the differentiation level is low, the retailer again adopts personalized pricing for both products. However, this pricing strategy may harm upstream suppliers by intensifying upstream competition, although it benefits the retailer and the entire channel. Consequently, channel members may have inconsistent preferences for personalized pricing. Our work underscores that the downstream personalized pricing strategies are not always optimal for upstream suppliers and the entire supply chain, thereby holding significance for supply chain managers aiming to adopt personalized pricing for greater profits.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"193 ","pages":"Article 103865"},"PeriodicalIF":8.3,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743942","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}
A two-dimensional vector packing problem with conflicts and time windows (2DVPPCTW) is investigated in this study. It consists of packing items into the minimum number of bins, and items are characterized by different weights, volumes, and time windows. Items also have conflicts and cannot be packed in the same bin. We formulate the 2DVPPCTW as an integer programming model and reformulate it to the master problem and the subproblem based on the Danzig–Wolfe decomposition. An exact algorithm, contextual bandits learning-based branch-and-price-and-cut algorithm (CBL-BPC), is proposed for the 2DVPPCTW with reinforcement learning technique. In particular, we provide a CBL framework for the subproblem, which usually poses considerable computational challenges. Five heuristic algorithms, namely, adaptive large neighborhood search (ALNS), ant colony optimization heuristic (ACO), heuristic dynamic programming (DP), a combination of ALNS and heuristic DP, and a combination of ACO and heuristic DP, are developed as bandit arms in the CBL framework. The CBL framework adaptively chooses one of five heuristics algorithms to solve the subproblem by learning from previous experiences. An exact dynamic programming algorithm is invoked to guarantee optimality once the CBL fails to find a better solution to the subproblem. Rounded capacity inequalities and accelerating strategies are introduced to accelerate the solution. An extensive computational study shows that the CBL-BPC can solve all 800 instances optimally within a reasonable time frame and is highly competitive with state-of-the-art exact and heuristics methods.
{"title":"Contextual bandits learning-based branch-and-price-and-cut algorithm for the two-dimensional vector packing problem with conflicts and time windows","authors":"Yanru Chen , Mujin Gao , Zongcheng Zhang , Junheng Li , M.I.M. Wahab , Yangsheng Jiang","doi":"10.1016/j.tre.2024.103866","DOIUrl":"10.1016/j.tre.2024.103866","url":null,"abstract":"<div><div>A two-dimensional vector packing problem with conflicts and time windows (2DVPPCTW) is investigated in this study. It consists of packing items into the minimum number of bins, and items are characterized by different weights, volumes, and time windows. Items also have conflicts and cannot be packed in the same bin. We formulate the 2DVPPCTW as an integer programming model and reformulate it to the master problem and the subproblem based on the Danzig–Wolfe decomposition. An exact algorithm, contextual bandits learning-based branch-and-price-and-cut algorithm (CBL-BPC), is proposed for the 2DVPPCTW with reinforcement learning technique. In particular, we provide a CBL framework for the subproblem, which usually poses considerable computational challenges. Five heuristic algorithms, namely, adaptive large neighborhood search (ALNS), ant colony optimization heuristic (ACO), heuristic dynamic programming (DP), a combination of ALNS and heuristic DP, and a combination of ACO and heuristic DP, are developed as bandit arms in the CBL framework. The CBL framework adaptively chooses one of five heuristics algorithms to solve the subproblem by learning from previous experiences. An exact dynamic programming algorithm is invoked to guarantee optimality once the CBL fails to find a better solution to the subproblem. Rounded capacity inequalities and accelerating strategies are introduced to accelerate the solution. An extensive computational study shows that the CBL-BPC can solve all 800 instances optimally within a reasonable time frame and is highly competitive with state-of-the-art exact and heuristics methods.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"193 ","pages":"Article 103866"},"PeriodicalIF":8.3,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743947","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}
Pub Date : 2024-11-29DOI: 10.1016/j.tre.2024.103862
Sihan Wang , Wei Sun , Roberto Baldacci , Adel Elomri
This paper investigates a location–routing problem with heterogeneous fleet and weight-based carbon emissions, aiming to minimize total operating costs, including depot and vehicle fixed costs and carbon emissions. The problem is formulated using an extended formulation, from which a relaxation is derived. The relaxation is further strengthened with additional cuts. For this problem, we devise an exact algorithm that combines a branch-price-and-cut algorithm with state-of-the-art technologies. The exact algorithm relies on a novel bidirectional labeling algorithm; it merges labels through cost compensation, leading to an effective solution. We conduct comprehensive computational experiments to validate the proposed algorithm, evaluating key factors related to carbon emissions. Our findings confirm the algorithm’s efficacy, highlighting its potential as a useful tool for addressing the location routing problem in heterogeneous fleet scenarios.
{"title":"Exact solution of location–routing problems with heterogeneous fleet and weight-based carbon emissions","authors":"Sihan Wang , Wei Sun , Roberto Baldacci , Adel Elomri","doi":"10.1016/j.tre.2024.103862","DOIUrl":"10.1016/j.tre.2024.103862","url":null,"abstract":"<div><div>This paper investigates a location–routing problem with heterogeneous fleet and weight-based carbon emissions, aiming to minimize total operating costs, including depot and vehicle fixed costs and carbon emissions. The problem is formulated using an extended formulation, from which a relaxation is derived. The relaxation is further strengthened with additional cuts. For this problem, we devise an exact algorithm that combines a branch-price-and-cut algorithm with state-of-the-art technologies. The exact algorithm relies on a novel bidirectional labeling algorithm; it merges labels through <em>cost compensation</em>, leading to an effective solution. We conduct comprehensive computational experiments to validate the proposed algorithm, evaluating key factors related to carbon emissions. Our findings confirm the algorithm’s efficacy, highlighting its potential as a useful tool for addressing the location routing problem in heterogeneous fleet scenarios.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"193 ","pages":"Article 103862"},"PeriodicalIF":8.3,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743946","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}
Pub Date : 2024-11-29DOI: 10.1016/j.tre.2024.103876
Xiaoyang Wei , Hoong Chuin Lau , Zhe Xiao , Xiuju Fu , Xiaocai Zhang , Zheng Qin
With the growing emphasis on green shipping to reduce the environmental impact of maritime transportation, optimizing fuel consumption with maintaining high service quality has become critical in port operations. Ports are essential nodes in global supply chains, where tugboats play a pivotal role in the safe and efficient maneuvering of ships within constrained environments. However, existing literature lacks approaches that address tugboat scheduling under realistic operational conditions. To fill the research gap, this is the first work to propose the bi-objective dynamic tugboat scheduling problem that optimizes speed under stochastic and time-varying demands, aiming to minimize fuel consumption and manage service punctuality across a heterogeneous fleet. For the first time, we develop an extended Markov decision process framework that integrates both reactive task assignments and proactive waiting decisions, considering the dual objectives. Subsequently, an initial schedule for known requests is established using a mixed-integer linear programming model, and an anticipatory approximate dynamic programming method dynamically incorporates emerging demands through task assignments and waiting plans. This approach is further enhanced by an improved rollout algorithm to anticipate future scenarios and make decisions efficiently. Applied to the Singapore port, our methodology achieves a 12.8% reduction in the total sail cost compared to the tugboat company’s scheduling practices, resulting in significant daily savings. The results with benchmarking against three methods demonstrate improvements in cost efficiency and service punctuality, meanwhile, extensive sensitivity analysis provides managerial insights for operational practice.
{"title":"Bi-objective dynamic tugboat scheduling with speed optimization under stochastic and time-varying service demands","authors":"Xiaoyang Wei , Hoong Chuin Lau , Zhe Xiao , Xiuju Fu , Xiaocai Zhang , Zheng Qin","doi":"10.1016/j.tre.2024.103876","DOIUrl":"10.1016/j.tre.2024.103876","url":null,"abstract":"<div><div>With the growing emphasis on green shipping to reduce the environmental impact of maritime transportation, optimizing fuel consumption with maintaining high service quality has become critical in port operations. Ports are essential nodes in global supply chains, where tugboats play a pivotal role in the safe and efficient maneuvering of ships within constrained environments. However, existing literature lacks approaches that address tugboat scheduling under realistic operational conditions. To fill the research gap, this is the first work to propose the bi-objective dynamic tugboat scheduling problem that optimizes speed under stochastic and time-varying demands, aiming to minimize fuel consumption and manage service punctuality across a heterogeneous fleet. For the first time, we develop an extended Markov decision process framework that integrates both reactive task assignments and proactive waiting decisions, considering the dual objectives. Subsequently, an initial schedule for known requests is established using a mixed-integer linear programming model, and an anticipatory approximate dynamic programming method dynamically incorporates emerging demands through task assignments and waiting plans. This approach is further enhanced by an improved rollout algorithm to anticipate future scenarios and make decisions efficiently. Applied to the Singapore port, our methodology achieves a 12.8% reduction in the total sail cost compared to the tugboat company’s scheduling practices, resulting in significant daily savings. The results with benchmarking against three methods demonstrate improvements in cost efficiency and service punctuality, meanwhile, extensive sensitivity analysis provides managerial insights for operational practice.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"193 ","pages":"Article 103876"},"PeriodicalIF":8.3,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743941","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}
Pub Date : 2024-11-28DOI: 10.1016/j.tre.2024.103868
Lucas Clement , Stefan Spinler
As e-commerce grows, tackling its environmental impacts, including those of packaging, becomes increasingly challenging. This study explores the benefits of reusable industrial packaging systems in the e-commerce industry, addressing ecological and financial perspectives. We contribute by formulating a generalized balancing problem for returnable transport items (RTIs) and developing a discrete-event simulation model and a heuristic to solve the problem. One methodological novelty is our modeling of network nodes that are simultaneously senders and recipients of RTIs. By applying the methodology to a case study of a large European e-commerce retailer, we address the need for industry-specific empirical studies comparing single-use and reusable packaging and RTI management in e-commerce.
The case study reveals that reusing packaging and balancing its inventory across the network, even with packaging materials designed for single use, coupled with harmonizing packaging solutions, may produce substantial financial and ecological benefits. Furthermore, ours is the first study to include a CO2 price in optimizing RTI management. We find that while this does not have a positive effect on operational decision-making, it does steer strategic decisions towards more ecologically sustainable logistics systems. Our results indicate a savings potential of 30 to 75% of industrial packaging costs and an industry-wide CO2 reduction potential of 200,000 to 600,000 tons annually in Europe alone. Our research contributes to theoretical and practical understanding of sustainable packaging in e-commerce, offering a model and strategies for industry adaptation amidst growing environmental and regulatory pressures.
{"title":"Advancing sustainability in e-commerce packaging: A simulation-based study for managing returnable transport items","authors":"Lucas Clement , Stefan Spinler","doi":"10.1016/j.tre.2024.103868","DOIUrl":"10.1016/j.tre.2024.103868","url":null,"abstract":"<div><div>As e-commerce grows, tackling its environmental impacts, including those of packaging, becomes increasingly challenging. This study explores the benefits of reusable industrial packaging systems in the e-commerce industry, addressing ecological and financial perspectives. We contribute by formulating a generalized balancing problem for returnable transport items (RTIs) and developing a discrete-event simulation model and a heuristic to solve the problem. One methodological novelty is our modeling of network nodes that are simultaneously senders and recipients of RTIs. By applying the methodology to a case study of a large European e-commerce retailer, we address the need for industry-specific empirical studies comparing single-use and reusable packaging and RTI management in e-commerce.</div><div>The case study reveals that reusing packaging and balancing its inventory across the network, even with packaging materials designed for single use, coupled with harmonizing packaging solutions, may produce substantial financial and ecological benefits. Furthermore, ours is the first study to include a CO<sub>2</sub> price in optimizing RTI management. We find that while this does not have a positive effect on operational decision-making, it does steer strategic decisions towards more ecologically sustainable logistics systems. Our results indicate a savings potential of 30 to 75% of industrial packaging costs and an industry-wide CO<sub>2</sub> reduction potential of 200,000 to 600,000 tons annually in Europe alone. Our research contributes to theoretical and practical understanding of sustainable packaging in e-commerce, offering a model and strategies for industry adaptation amidst growing environmental and regulatory pressures.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"193 ","pages":"Article 103868"},"PeriodicalIF":8.3,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743944","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}
Pub Date : 2024-11-28DOI: 10.1016/j.tre.2024.103878
Meng Li, Kaiquan Cai, Peng Zhao
The advent of same-day delivery services has achieved immense popularity, driven by escalating customer expectations on fast shipping and the need for market competitiveness. To optimize such services, the use of heterogeneous fleets with vehicles and drones has proven effective in reducing the resource requirements needed for delivery. This paper focuses on investigating the same-day delivery dispatching and routing problem with a fleet of multiple vehicles and drones. In this problem, stochastic and dynamic requests, coupled with their stringent time constraints, require dispatchers to make real-time decisions about optimally assigning vehicles and drones, ensuring both efficiency and effectiveness in delivery operations while taking into account the routing. To tackle this complex problem, we model it with a route-based Markov decision process and develop a novel hierarchical decision approach based on deep reinforcement learning (HDDRL). The first level of the hierarchy is tasked with determining the departure times of vehicles, balancing the trade-offs between the delivery frequency and efficiency. The second level of the hierarchy is dedicated to determining the most suitable delivery mode for each request, whether by vehicles or drones. The third level is responsible for planning routes for vehicles and drones, thereby enhancing route efficiency. These three levels in the hierarchical framework collaborate to solve the problem in a synchronized manner, with the objective of maximizing the service requests within a day. Empirical results from computational experiments highlight the superiority of the HDDRL over benchmark methods, demonstrating not only its enhanced efficacy but also its robust generalization across diverse data distributions and fleet sizes. This underscores the HDDRL’s potential as a powerful tool for enhancing operational efficiency in same-day delivery services.
{"title":"Optimizing same-day delivery with vehicles and drones: A hierarchical deep reinforcement learning approach","authors":"Meng Li, Kaiquan Cai, Peng Zhao","doi":"10.1016/j.tre.2024.103878","DOIUrl":"10.1016/j.tre.2024.103878","url":null,"abstract":"<div><div>The advent of same-day delivery services has achieved immense popularity, driven by escalating customer expectations on fast shipping and the need for market competitiveness. To optimize such services, the use of heterogeneous fleets with vehicles and drones has proven effective in reducing the resource requirements needed for delivery. This paper focuses on investigating the same-day delivery dispatching and routing problem with a fleet of multiple vehicles and drones. In this problem, stochastic and dynamic requests, coupled with their stringent time constraints, require dispatchers to make real-time decisions about optimally assigning vehicles and drones, ensuring both efficiency and effectiveness in delivery operations while taking into account the routing. To tackle this complex problem, we model it with a route-based Markov decision process and develop a novel hierarchical decision approach based on deep reinforcement learning (HDDRL). The first level of the hierarchy is tasked with determining the departure times of vehicles, balancing the trade-offs between the delivery frequency and efficiency. The second level of the hierarchy is dedicated to determining the most suitable delivery mode for each request, whether by vehicles or drones. The third level is responsible for planning routes for vehicles and drones, thereby enhancing route efficiency. These three levels in the hierarchical framework collaborate to solve the problem in a synchronized manner, with the objective of maximizing the service requests within a day. Empirical results from computational experiments highlight the superiority of the HDDRL over benchmark methods, demonstrating not only its enhanced efficacy but also its robust generalization across diverse data distributions and fleet sizes. This underscores the HDDRL’s potential as a powerful tool for enhancing operational efficiency in same-day delivery services.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"193 ","pages":"Article 103878"},"PeriodicalIF":8.3,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743945","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}
Pub Date : 2024-11-27DOI: 10.1016/j.tre.2024.103859
Xinyu He , Lishuai Li , Yanfang Mo , Zhankun Sun , S. Joe Qin
Urban drone delivery, a rapidly evolving sector, holds the potential to enhance accessibility, address last-mile delivery issues, and alleviate ground traffic congestion in cities. Effective Unmanned Aircraft System Traffic Management (UTM) is essential to scale drone delivery. A critical aspect of UTM involves planning a city-wide network with spatially-separated air corridors (air routes). Most existing works have focused on routing problems or air traffic management. Compared to these problems, the air corridor planning problem requires much higher spatial and temporal resolutions and presents computational challenges due to the scale, complexity, and density of urban airspace, along with the coupling issues of multi-path planning. Therefore, we conducted this research to understand the complexity and computational resources required to optimally solve the air corridor planning problem. In this paper, we use a minimum-cost Multi-Commodity Network Flow (MCNF) model, a mathematical model, to model the problem and demonstrate the complexity of air corridor planning through the complexity of MCNF. We then apply Gurobi’s and GLPK’s integer programming (IP) solvers to find optimal solutions. Additionally, we present two existing multi-path graph search algorithms, the Sequential Route Network Planning (SRP) algorithm and the Distributed Route Network Planning (DRP) algorithm, to address this corridor planning problem. Numerical experiments conducted at various scales and settings using IP solvers and graph search algorithms indicate that finding an optimal solution requires significant computational resources and yields only a slight improvement in optimality compared to graph search algorithms. Thus, air corridor planning is complex both theoretically and numerically, and graph search algorithms can provide a feasible solution with good enough optimality for corridor planning in real-world scenarios. Moreover, the multi-path graph search algorithms can easily incorporate side constraints that are known to be impossible to solve with polynomial algorithms, making it more practical for real-world applications. Finally, we demonstrate the application of SRP and DRP in real-world 3D urban scenarios.
{"title":"Air Corridor Planning for Urban Drone Delivery: Complexity Analysis and Comparison via Multi-Commodity Network Flow and Graph Search","authors":"Xinyu He , Lishuai Li , Yanfang Mo , Zhankun Sun , S. Joe Qin","doi":"10.1016/j.tre.2024.103859","DOIUrl":"10.1016/j.tre.2024.103859","url":null,"abstract":"<div><div>Urban drone delivery, a rapidly evolving sector, holds the potential to enhance accessibility, address last-mile delivery issues, and alleviate ground traffic congestion in cities. Effective Unmanned Aircraft System Traffic Management (UTM) is essential to scale drone delivery. A critical aspect of UTM involves planning a city-wide network with spatially-separated air corridors (air routes). Most existing works have focused on routing problems or air traffic management. Compared to these problems, the air corridor planning problem requires much higher spatial and temporal resolutions and presents computational challenges due to the scale, complexity, and density of urban airspace, along with the coupling issues of multi-path planning. Therefore, we conducted this research to understand the complexity and computational resources required to optimally solve the air corridor planning problem. In this paper, we use a minimum-cost Multi-Commodity Network Flow (MCNF) model, a mathematical model, to model the problem and demonstrate the complexity of air corridor planning through the complexity of MCNF. We then apply Gurobi’s and GLPK’s integer programming (IP) solvers to find optimal solutions. Additionally, we present two existing multi-path graph search algorithms, the Sequential Route Network Planning (SRP) algorithm and the Distributed Route Network Planning (DRP) algorithm, to address this corridor planning problem. Numerical experiments conducted at various scales and settings using IP solvers and graph search algorithms indicate that finding an optimal solution requires significant computational resources and yields only a slight improvement in optimality compared to graph search algorithms. Thus, air corridor planning is complex both theoretically and numerically, and graph search algorithms can provide a feasible solution with good enough optimality for corridor planning in real-world scenarios. Moreover, the multi-path graph search algorithms can easily incorporate side constraints that are known to be impossible to solve with polynomial algorithms, making it more practical for real-world applications. Finally, we demonstrate the application of SRP and DRP in real-world 3D urban scenarios.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"193 ","pages":"Article 103859"},"PeriodicalIF":8.3,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723715","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}
Pub Date : 2024-11-27DOI: 10.1016/j.tre.2024.103870
Wenbo Lu , Yong Zhang , Jinhua Xu , Zheng Yuan , Peikun Li , Mingye Zhang , Hai L. Vu
To enhance the efficiency and sustainability of urban freight operations, China has initiated the Urban Green Freight Delivery (UGFD) project, which involves optimizing access control policies and introducing new energy vehicles. Identifying the parking trips of new energy vehicles and exploring the spatiotemporal patterns is crucial to actively promoting the optimal layout of temporary stops and the formulation of parking policies in the UGFD project. In this study, we aim to comprehend the spatiotemporal heterogeneity of parking for new energy vehicles both on roads (on-street) and within urban communities (off-street) for promoting the UGFD project. Its specific content includes: (1) proposing a method for identifying valid parking trips for the loading and unloading of goods based on trajectory data of UGFD new energy vehicles; and (2) mapping the identification results of valid parking trips onto communities and roads to analyze the spatiotemporal heterogeneity. Taking Suzhou, Jiangsu Province, China as an example, the identification results show that the established valid parking trips identification method can outperform state-of-the-art methods. The accuracy, precision, recall, and F1 value were found to be 0.957, 0.908, 0.937, and 0.922, respectively. Further examination of parking patterns indicates a bimodal temporal distribution of delivery demand, with peak activity occurring between 08:00–09:00 and between 14:00–17:00, with a higher delivery demand in the morning. Spatially, delivery demand was aggregated, while the parking time distribution of most delivery activities was normal. Additionally, the parking characteristics of communities and roads conformed to the ‘Rank–size rule’, suggesting that most delivery parking activities were concentrated in a few communities and roads. These findings can also be used in UGFD stop station utilization, travel time, arrival time prediction, and other related fields, all of which can further support relevant management departments in discovering abnormal delivery behaviors and reduce their negative impacts.
{"title":"Exploring spatiotemporal heterogeneity of urban green freight delivery parking based on new energy vehicle GPS data","authors":"Wenbo Lu , Yong Zhang , Jinhua Xu , Zheng Yuan , Peikun Li , Mingye Zhang , Hai L. Vu","doi":"10.1016/j.tre.2024.103870","DOIUrl":"10.1016/j.tre.2024.103870","url":null,"abstract":"<div><div>To enhance the efficiency and sustainability of urban freight operations, China has initiated the Urban Green Freight Delivery (UGFD) project, which involves optimizing access control policies and introducing new energy vehicles. Identifying the parking trips of new energy vehicles and exploring the spatiotemporal patterns is crucial to actively promoting the optimal layout of temporary stops and the formulation of parking policies in the UGFD project. In this study, we aim to comprehend the spatiotemporal heterogeneity of parking for new energy vehicles both on roads (on-street) and within urban communities (off-street) for promoting the UGFD project. Its specific content includes: (1) proposing a method for identifying valid parking trips for the loading and unloading of goods based on trajectory data of UGFD new energy vehicles; and (2) mapping the identification results of valid parking trips onto communities and roads to analyze the spatiotemporal heterogeneity. Taking Suzhou, Jiangsu Province, China as an example, the identification results show that the established valid parking trips identification method can outperform state-of-the-art methods. The <em>accuracy</em>, <em>precision</em>, <em>recall</em>, and <em>F1</em> value were found to be 0.957, 0.908, 0.937, and 0.922, respectively. Further examination of parking patterns indicates a bimodal temporal distribution of delivery demand, with peak activity occurring between 08:00–09:00 and between 14:00–17:00, with a higher delivery demand in the morning. Spatially, delivery demand was aggregated, while the parking time distribution of most delivery activities was normal. Additionally, the parking characteristics of communities and roads conformed to the ‘Rank–size rule’, suggesting that most delivery parking activities were concentrated in a few communities and roads. These findings can also be used in UGFD stop station utilization, travel time, arrival time prediction, and other related fields, all of which can further support relevant management departments in discovering abnormal delivery behaviors and reduce their negative impacts.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"193 ","pages":"Article 103870"},"PeriodicalIF":8.3,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723161","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}
Pub Date : 2024-11-27DOI: 10.1016/j.tre.2024.103852
Kechen Ouyang, David Z.W. Wang
Unlike traditional recharging at fixed stations, wireless charging lanes equipped on the road network enable electric vehicles (EVs) to recharge while in motion, reducing the time required for EV recharging. Indeed, the availability of wireless charging lanes allows the urban freight transport service operators to consider adjusting EVs routing plan to do EV recharging in transit so that the total operational costs can be further reduced. Envisioning that wireless charging lanes would be practically constructed on the road network, this study aims to investigate how the optimal EV routing plan can be determined in a pickup and delivery problem in the future electrified mobility system. The problem is formulated into a mixed-integer programming model, and we propose a branch-and-price algorithm to solve it. Additionally, a large neighborhood search heuristic is incorporated within the column generation framework to effectively tackle the sub-problems. Computational experiments on test instances highlight the effectiveness of our proposed algorithm and demonstrate the potential of wireless charging lanes to reduce total operational costs.
{"title":"Optimal operation strategies for freight transport with electric vehicles considering wireless charging lanes","authors":"Kechen Ouyang, David Z.W. Wang","doi":"10.1016/j.tre.2024.103852","DOIUrl":"10.1016/j.tre.2024.103852","url":null,"abstract":"<div><div>Unlike traditional recharging at fixed stations, wireless charging lanes equipped on the road network enable electric vehicles (EVs) to recharge while in motion, reducing the time required for EV recharging. Indeed, the availability of wireless charging lanes allows the urban freight transport service operators to consider adjusting EVs routing plan to do EV recharging in transit so that the total operational costs can be further reduced. Envisioning that wireless charging lanes would be practically constructed on the road network, this study aims to investigate how the optimal EV routing plan can be determined in a pickup and delivery problem in the future electrified mobility system. The problem is formulated into a mixed-integer programming model, and we propose a branch-and-price algorithm to solve it. Additionally, a large neighborhood search heuristic is incorporated within the column generation framework to effectively tackle the sub-problems. Computational experiments on test instances highlight the effectiveness of our proposed algorithm and demonstrate the potential of wireless charging lanes to reduce total operational costs.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"193 ","pages":"Article 103852"},"PeriodicalIF":8.3,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723160","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}
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