E. Fernández, Markus Leitner, I. Ljubić, Mario Ruthmair
Concerns about greenhouse gas emissions and government regulations foster the use of electric vehicles. Several recently published articles study the use of electric vehicles (EVs) in node-routing problems. In contrast, this article considers EVs in the context of arc routing while also addressing practically relevant aspects that have not been addressed sufficiently so far. These include dynamic charging of EVs while driving, speed-dependent energy consumption, and nonlinear charging functions that depend on the battery’s state of charge and the charging time. A generic way of dealing with these aspects is introduced through the concept of an energy-indexed graph, which is used to derive an integer linear programming formulation and a solution framework based on branch and cut. Efficient construction heuristics and a local search for approximately solving large-scale instances are proposed. A computational study is performed on realistic problem instances. Besides analyzing the performance of all proposed methods, the obtained results also provide insights into strategic decisions related to the battery size and the amount of charging facilities.
{"title":"Arc Routing with Electric Vehicles: Dynamic Charging and Speed-Dependent Energy Consumption","authors":"E. Fernández, Markus Leitner, I. Ljubić, Mario Ruthmair","doi":"10.1287/trsc.2022.1126","DOIUrl":"https://doi.org/10.1287/trsc.2022.1126","url":null,"abstract":"Concerns about greenhouse gas emissions and government regulations foster the use of electric vehicles. Several recently published articles study the use of electric vehicles (EVs) in node-routing problems. In contrast, this article considers EVs in the context of arc routing while also addressing practically relevant aspects that have not been addressed sufficiently so far. These include dynamic charging of EVs while driving, speed-dependent energy consumption, and nonlinear charging functions that depend on the battery’s state of charge and the charging time. A generic way of dealing with these aspects is introduced through the concept of an energy-indexed graph, which is used to derive an integer linear programming formulation and a solution framework based on branch and cut. Efficient construction heuristics and a local search for approximately solving large-scale instances are proposed. A computational study is performed on realistic problem instances. Besides analyzing the performance of all proposed methods, the obtained results also provide insights into strategic decisions related to the battery size and the amount of charging facilities.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"89 1","pages":"1219-1237"},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78386479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Metro systems play an important role in the public transportation system, and metro safety and passenger satisfaction are of great concern to urbanized societies. Identifying critical platforms and tracks is a fundamental and significant step to improving a metro system's safety and passenger satisfaction, which has never been examined in the literature. Typical critical link analysis for road networks cannot be applied to metro network systems because of the different characteristics of these two transportation modes, such as vehicle operation. In addition, the existing studies on critical stations for metro networks cannot reveal the different importance of platforms involved in one station. This study proposes a novel framework to identify critical platforms and tracks for a metro system with consideration of its spatial characteristics and temporal dynamics using smart card data. We first develop an entirely directed model to describe a metro system where nodes and arcs represent platforms and tracks, respectively. Critical platforms and tracks are then defined and assessed based on dynamic waiting time and onboard crowdedness. The proposed approach is validated by historical smart card data of the Shenzhen Metro system, and the results show time-variant rankings of critical platforms and tracks over the time of the day and the day of the week.
{"title":"On Time-Dependent Critical Platforms and Tracks in Metro Systems","authors":"Junwei Wang, Yue Gao, Yao-Feng Cheng","doi":"10.1287/trsc.2022.1124","DOIUrl":"https://doi.org/10.1287/trsc.2022.1124","url":null,"abstract":"Metro systems play an important role in the public transportation system, and metro safety and passenger satisfaction are of great concern to urbanized societies. Identifying critical platforms and tracks is a fundamental and significant step to improving a metro system's safety and passenger satisfaction, which has never been examined in the literature. Typical critical link analysis for road networks cannot be applied to metro network systems because of the different characteristics of these two transportation modes, such as vehicle operation. In addition, the existing studies on critical stations for metro networks cannot reveal the different importance of platforms involved in one station. This study proposes a novel framework to identify critical platforms and tracks for a metro system with consideration of its spatial characteristics and temporal dynamics using smart card data. We first develop an entirely directed model to describe a metro system where nodes and arcs represent platforms and tracks, respectively. Critical platforms and tracks are then defined and assessed based on dynamic waiting time and onboard crowdedness. The proposed approach is validated by historical smart card data of the Shenzhen Metro system, and the results show time-variant rankings of critical platforms and tracks over the time of the day and the day of the week.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"13 1","pages":"953-971"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87483322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Our study provides an experimental benchmark for state-of-the-art solution algorithms with hub location problems. Such problems are fundamental optimization problems in location science with widespread application areas, such as transportation, telecommunications, economics, and geography. Given they combine aspects of facility location and quadratic assignment problems, the majority of hub location problems are NP-hard and, accordingly, several solution techniques have been proposed for solving these problems. In this study, we report on the results of a large benchmark and reproduction effort to investigate 12 fundamental hub location problems that combine single or multiple allocation, a p-hub median objective or fixed hub set-up costs, capacitated or uncapacitated hubs, and complete or incomplete networks. We implemented four standard exact algorithms on these 12 problems as proposed in the literature. Algorithms are evaluated on subsets of three standard data sets in the field (CAB, TR, and AP); we computed more than 5,000 optimal solutions for these data sets. We report comparisons of solution techniques regarding wall clock time, convergence speed, memory use, and the impact of data features. In addition, we identify patterns in optimal solutions across these 12 problems, extracting insights regarding solution similarity, hub set candidates, and economies of scale. All results and programs are being made available to the public for free academic use.
{"title":"Toward a Reference Experimental Benchmark for Solving Hub Location Problems","authors":"S. Wandelt, Weibin Dai, Jun Zhang, Xiaoqian Sun","doi":"10.1287/trsc.2021.1094","DOIUrl":"https://doi.org/10.1287/trsc.2021.1094","url":null,"abstract":"Our study provides an experimental benchmark for state-of-the-art solution algorithms with hub location problems. Such problems are fundamental optimization problems in location science with widespread application areas, such as transportation, telecommunications, economics, and geography. Given they combine aspects of facility location and quadratic assignment problems, the majority of hub location problems are NP-hard and, accordingly, several solution techniques have been proposed for solving these problems. In this study, we report on the results of a large benchmark and reproduction effort to investigate 12 fundamental hub location problems that combine single or multiple allocation, a p-hub median objective or fixed hub set-up costs, capacitated or uncapacitated hubs, and complete or incomplete networks. We implemented four standard exact algorithms on these 12 problems as proposed in the literature. Algorithms are evaluated on subsets of three standard data sets in the field (CAB, TR, and AP); we computed more than 5,000 optimal solutions for these data sets. We report comparisons of solution techniques regarding wall clock time, convergence speed, memory use, and the impact of data features. In addition, we identify patterns in optimal solutions across these 12 problems, extracting insights regarding solution similarity, hub set candidates, and economies of scale. All results and programs are being made available to the public for free academic use.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"2 1","pages":"543-564"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82790435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study considers the multivehicle inventory routing problem in which a supplier has to build a distribution plan over a discrete time horizon to replenish a set of customers that faces a given demand. Transportation costs as well as inventory costs at the supplier and at the customers are to be minimized. A matheuristic algorithm is proposed that is based on sequentially solving different mixed integer linear programs. The algorithm merges the advantage of being easy to design and implement, as it is mainly based on the problem formulation, with the benefit of providing high-quality solutions. A computational study is performed on benchmark test instances by comparing the results with the ones obtained from previous algorithms proposed in the literature. The results show that the matheuristic algorithm outperforms the existing heuristic algorithms and finds a significant number of new best solutions in both small and large instances.
{"title":"An Effective Matheuristic for the Multivehicle Inventory Routing Problem","authors":"O. Solyalı, Haldun Süral","doi":"10.1287/trsc.2021.1123","DOIUrl":"https://doi.org/10.1287/trsc.2021.1123","url":null,"abstract":"This study considers the multivehicle inventory routing problem in which a supplier has to build a distribution plan over a discrete time horizon to replenish a set of customers that faces a given demand. Transportation costs as well as inventory costs at the supplier and at the customers are to be minimized. A matheuristic algorithm is proposed that is based on sequentially solving different mixed integer linear programs. The algorithm merges the advantage of being easy to design and implement, as it is mainly based on the problem formulation, with the benefit of providing high-quality solutions. A computational study is performed on benchmark test instances by comparing the results with the ones obtained from previous algorithms proposed in the literature. The results show that the matheuristic algorithm outperforms the existing heuristic algorithms and finds a significant number of new best solutions in both small and large instances.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"8 1","pages":"1044-1057"},"PeriodicalIF":0.0,"publicationDate":"2022-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84237600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In bike-sharing systems, the spatiotemporal imbalance of bike flows leads to shortages of bikes at some locations and overages at some others, depending on the time of the day, resulting in user dissatisfaction. Repositioning needs to be performed timely to deal with the spatiotemporal imbalance and to meet user demand in time. In this paper, we study the dynamic intra-cell repositioning of bikes by a single mover in free-floating bike-sharing systems. Considering that users can drop off bikes almost anywhere in free-floating systems, we study the simultaneous reposition of bikes among gathering points and collection of bikes scattered along the paths between gathering points under stochastic demands at both the gathering points and along the paths. We formulate the problem as a Markov decision process (MDP), design a policy function approximation (PFA) algorithm, and apply the optimal computing budget allocation method (OCBA) to search for the optimal policy parameters. We perform a comprehensive numerical study using test instances constructed based on the real data set of a major free-floating bike-sharing company in China, which demonstrates the outperformance of the proposed PFA policy against the benchmark policies and the practical implications on the value of repositioning and the impact of bike scatteredness.
{"title":"Dynamic Intra-Cell Repositioning in Free-Floating Bike-Sharing Systems Using Approximate Dynamic Programming","authors":"Xue Luo, Li Li, Lei Zhao, Jia-Jiang Lin","doi":"10.1287/trsc.2021.1122","DOIUrl":"https://doi.org/10.1287/trsc.2021.1122","url":null,"abstract":"In bike-sharing systems, the spatiotemporal imbalance of bike flows leads to shortages of bikes at some locations and overages at some others, depending on the time of the day, resulting in user dissatisfaction. Repositioning needs to be performed timely to deal with the spatiotemporal imbalance and to meet user demand in time. In this paper, we study the dynamic intra-cell repositioning of bikes by a single mover in free-floating bike-sharing systems. Considering that users can drop off bikes almost anywhere in free-floating systems, we study the simultaneous reposition of bikes among gathering points and collection of bikes scattered along the paths between gathering points under stochastic demands at both the gathering points and along the paths. We formulate the problem as a Markov decision process (MDP), design a policy function approximation (PFA) algorithm, and apply the optimal computing budget allocation method (OCBA) to search for the optimal policy parameters. We perform a comprehensive numerical study using test instances constructed based on the real data set of a major free-floating bike-sharing company in China, which demonstrates the outperformance of the proposed PFA policy against the benchmark policies and the practical implications on the value of repositioning and the impact of bike scatteredness.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"8 1","pages":"799-826"},"PeriodicalIF":0.0,"publicationDate":"2022-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81352144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper examines the morning commute problem when both household commuters and individual commuters are considered in a Y-shaped network with two upstream links and a single downstream link. The household parents daily pass through an upstream bottleneck with a limited capacity before a school and drop off their children. Then, they traverse the downstream bottleneck common to both household and individual commuters and arrive at the workplace. We explore the effects of staggering policy, that is, staggering work and school start times, on the distribution of traffic congestion and social welfare. We analytically solve all the equilibrium cases and reveal all the traffic congestion patterns. The results reveal that the staggering policy may be harmful in certain cases. When the demand of individuals is relatively low, the staggering policy may not improve social welfare. When the demand of individuals is high, social welfare can be significantly improved if the schedule gap between the work start time and school start time is optimized. The effects of the staggering policy on system performance are examined. We derive a Pareto frontier, which provides a good candidate set for policymakers when the two system performance measures, that is, the total system cost and the total congestion cost, are considered. Our results show that the optimal staggering policy on system performance depends on the demand distribution of the two groups. When the demand of individuals is high, there exists a unique optimal staggering policy that optimizes system performance. However, when the demand of individuals is low, the optimal staggering policy should be selected from the Pareto frontier. Furthermore, we re-examine the capacity expansion paradox under the staggering policy. Our study shows the capacity expansion at the downstream bottleneck can always reduce the total system cost. However, the paradoxical phenomenon may arise when the capacity of the upstream bottleneck is expanded, but it can be eliminated if the schedule gap is properly designed.
{"title":"On the Morning Commute Problem in a Y-shaped Network with Individual and Household Travelers","authors":"Dongdong He, Yang Liu, Qiuyan Zhong, D. Wang","doi":"10.2139/ssrn.3881217","DOIUrl":"https://doi.org/10.2139/ssrn.3881217","url":null,"abstract":"This paper examines the morning commute problem when both household commuters and individual commuters are considered in a Y-shaped network with two upstream links and a single downstream link. The household parents daily pass through an upstream bottleneck with a limited capacity before a school and drop off their children. Then, they traverse the downstream bottleneck common to both household and individual commuters and arrive at the workplace. We explore the effects of staggering policy, that is, staggering work and school start times, on the distribution of traffic congestion and social welfare. We analytically solve all the equilibrium cases and reveal all the traffic congestion patterns. The results reveal that the staggering policy may be harmful in certain cases. When the demand of individuals is relatively low, the staggering policy may not improve social welfare. When the demand of individuals is high, social welfare can be significantly improved if the schedule gap between the work start time and school start time is optimized. The effects of the staggering policy on system performance are examined. We derive a Pareto frontier, which provides a good candidate set for policymakers when the two system performance measures, that is, the total system cost and the total congestion cost, are considered. Our results show that the optimal staggering policy on system performance depends on the demand distribution of the two groups. When the demand of individuals is high, there exists a unique optimal staggering policy that optimizes system performance. However, when the demand of individuals is low, the optimal staggering policy should be selected from the Pareto frontier. Furthermore, we re-examine the capacity expansion paradox under the staggering policy. Our study shows the capacity expansion at the downstream bottleneck can always reduce the total system cost. However, the paradoxical phenomenon may arise when the capacity of the upstream bottleneck is expanded, but it can be eliminated if the schedule gap is properly designed.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"21 1","pages":"848-876"},"PeriodicalIF":0.0,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82707963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The efficient management of metro lines is a major concern for public transport operators. Traditionally, metro lines are operated through regular timetables, that is, timetables where trains have a constant headway between all stations. In this paper, we propose a demand-driven metro timetabling strategy and elaborate exact solution methods for the case of a two-directional metro corridor. In doing so, we avoid imposing any predetermined structure to the timetable, and instead control the trains individually to best match passenger demand. We consider that trains may short turn, that is, trains that are not required to serve the line from terminal to terminal, but instead may reverse direction before reaching the terminal. We present a mixed integer linear programming formulation for the demand-driven timetabling problem of a two-directional metro corridor with short turning. Furthermore, we develop an efficient exact algorithm using cut generation for an alternative formulation with an exponential number of constraints, and derive two classes of valid inequalities. We evaluate the proposed formulation and algorithm considering seven possible cut generation strategies on a number of test instances from artificially generated lines and on two test beds derived from real-world lines. Through the computational experiments, we demonstrate the effectiveness of the developed algorithm and the added value of the proposed strategy in terms of passengers’ waiting time.
{"title":"Demand-Driven Timetabling for a Metro Corridor Using a Short-Turning Acceleration Strategy","authors":"Tommaso Schettini, O. Jabali, F. Malucelli","doi":"10.1287/trsc.2021.1118","DOIUrl":"https://doi.org/10.1287/trsc.2021.1118","url":null,"abstract":"The efficient management of metro lines is a major concern for public transport operators. Traditionally, metro lines are operated through regular timetables, that is, timetables where trains have a constant headway between all stations. In this paper, we propose a demand-driven metro timetabling strategy and elaborate exact solution methods for the case of a two-directional metro corridor. In doing so, we avoid imposing any predetermined structure to the timetable, and instead control the trains individually to best match passenger demand. We consider that trains may short turn, that is, trains that are not required to serve the line from terminal to terminal, but instead may reverse direction before reaching the terminal. We present a mixed integer linear programming formulation for the demand-driven timetabling problem of a two-directional metro corridor with short turning. Furthermore, we develop an efficient exact algorithm using cut generation for an alternative formulation with an exponential number of constraints, and derive two classes of valid inequalities. We evaluate the proposed formulation and algorithm considering seven possible cut generation strategies on a number of test instances from artificially generated lines and on two test beds derived from real-world lines. Through the computational experiments, we demonstrate the effectiveness of the developed algorithm and the added value of the proposed strategy in terms of passengers’ waiting time.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"10 1","pages":"919-937"},"PeriodicalIF":0.0,"publicationDate":"2022-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79071285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amine Mohamed Falek, C. Pelsser, S. Julien, Fabrice Théoleyre
Many algorithms compute shortest-path queries in mere microseconds on continental-scale networks. Most solutions are, however, tailored to either road or public transit networks in isolation. To fully exploit the transportation infrastructure, multimodal algorithms are sought to compute shortest paths combining various modes of transportation. Nonetheless, current solutions still lack performance to efficiently handle interactive queries under realistic network conditions where traffic jams, public transit cancelations, or delays often occur. We present a multimodal separators–based algorithm (MUSE), a new multimodal algorithm based on graph separators to compute shortest travel time paths. It partitions the network into independent, smaller regions, enabling fast and scalable preprocessing. The partition is common to all modes and independent of traffic conditions so that the preprocessing is only executed once. MUSE relies on a state automaton that describes the sequence of modes to constrain the shortest path during the preprocessing and the online phase. The support of new sequences of mobility modes only requires the preprocessing of the cliques, independently for each partition. We also augment our algorithm with heuristics during the query phase to achieve further speedups with minimal effect on correctness. We provide experimental results on France’s multimodal network containing the pedestrian, road, bicycle, and public transit networks.
{"title":"MUSE: Multimodal Separators for Efficient Route Planning in Transportation Networks","authors":"Amine Mohamed Falek, C. Pelsser, S. Julien, Fabrice Théoleyre","doi":"10.1287/trsc.2021.1104","DOIUrl":"https://doi.org/10.1287/trsc.2021.1104","url":null,"abstract":"Many algorithms compute shortest-path queries in mere microseconds on continental-scale networks. Most solutions are, however, tailored to either road or public transit networks in isolation. To fully exploit the transportation infrastructure, multimodal algorithms are sought to compute shortest paths combining various modes of transportation. Nonetheless, current solutions still lack performance to efficiently handle interactive queries under realistic network conditions where traffic jams, public transit cancelations, or delays often occur. We present a multimodal separators–based algorithm (MUSE), a new multimodal algorithm based on graph separators to compute shortest travel time paths. It partitions the network into independent, smaller regions, enabling fast and scalable preprocessing. The partition is common to all modes and independent of traffic conditions so that the preprocessing is only executed once. MUSE relies on a state automaton that describes the sequence of modes to constrain the shortest path during the preprocessing and the online phase. The support of new sequences of mobility modes only requires the preprocessing of the cliques, independently for each partition. We also augment our algorithm with heuristics during the query phase to achieve further speedups with minimal effect on correctness. We provide experimental results on France’s multimodal network containing the pedestrian, road, bicycle, and public transit networks.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"21 1","pages":"436-459"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82921301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Strategy-based equilibrium traffic assignment (SETA) problems define travel choice broadly as a strategy rather than a simple path. Travelers navigating through a network based on a strategy end up following a hyperpath. SETA is well suited to represent a rich set of travel choices that take place en route at nodes, such as transit passengers’ transfer decisions, truckers’ bidding decisions, and taxi drivers’ reposition decisions. This paper recognizes and highlights the commonalities among classical and emerging SETA problems and proposes to unify them within the same modeling framework, built on the concept of a hypergraph. A generic hyperbush algorithm (HBA) is developed by decomposing a hypergraph into destination-based hyperbushes. By constructing hyperbushes and limiting traffic assignments to them, HBA promises to obtain more precise solutions to larger instances of SETA problems at a lower computational cost, both in terms of CPU time and memory consumption. To demonstrate its generality and efficiency, we tailor HBA to solve two SETA problems. The results confirm that HBA consistently outperforms the benchmark algorithms in the literature, including two state-of-the-art hyperpath-based algorithms. To obtain high-quality equilibrium solutions for SETA instances of practical size, HBA runs up to five times faster than the best competitor with a fraction of its memory consumption.
{"title":"Hyperbush Algorithm for Strategy-Based Equilibrium Traffic Assignment Problems","authors":"Zhandong Xu, Jun Xie, Xiaobo Liu, Y. Nie","doi":"10.1287/trsc.2021.1113","DOIUrl":"https://doi.org/10.1287/trsc.2021.1113","url":null,"abstract":"Strategy-based equilibrium traffic assignment (SETA) problems define travel choice broadly as a strategy rather than a simple path. Travelers navigating through a network based on a strategy end up following a hyperpath. SETA is well suited to represent a rich set of travel choices that take place en route at nodes, such as transit passengers’ transfer decisions, truckers’ bidding decisions, and taxi drivers’ reposition decisions. This paper recognizes and highlights the commonalities among classical and emerging SETA problems and proposes to unify them within the same modeling framework, built on the concept of a hypergraph. A generic hyperbush algorithm (HBA) is developed by decomposing a hypergraph into destination-based hyperbushes. By constructing hyperbushes and limiting traffic assignments to them, HBA promises to obtain more precise solutions to larger instances of SETA problems at a lower computational cost, both in terms of CPU time and memory consumption. To demonstrate its generality and efficiency, we tailor HBA to solve two SETA problems. The results confirm that HBA consistently outperforms the benchmark algorithms in the literature, including two state-of-the-art hyperpath-based algorithms. To obtain high-quality equilibrium solutions for SETA instances of practical size, HBA runs up to five times faster than the best competitor with a fraction of its memory consumption.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"56 1","pages":"877-903"},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74919457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shopping malls are densely located in major cities such as Singapore and Hong Kong. Tenants in these shopping malls generate a large number of freight orders to their contracted logistics service providers, who independently plan their own delivery schedules. These uncoordinated deliveries and limited docking capacity jointly cause congestion at the shopping malls. A delivery coordination platform centrally plans the vehicle routes for the logistics service providers and simultaneously schedules the dock time slots at the shopping malls for the delivery orders. Vehicle routing and dock scheduling decisions need to be made jointly against the backdrop of travel time and service time uncertainty and subject to practical operations rules. We model this problem as a two-stage stochastic mixed integer program, develop an adaptive large neighborhood search algorithm that approximates the second stage recourse function using various sample sizes, and examine the associated in-sample and out-of-sample stability. Our numerical study on a testbed of instances based on real data in Singapore demonstrates the value of coordination and the value of stochastic solutions.
{"title":"Coordinated Delivery to Shopping Malls with Limited Docking Capacity","authors":"Ruidian Song, H. Lau, Xue Luo, Lei Zhao","doi":"10.1287/trsc.2021.1109","DOIUrl":"https://doi.org/10.1287/trsc.2021.1109","url":null,"abstract":"Shopping malls are densely located in major cities such as Singapore and Hong Kong. Tenants in these shopping malls generate a large number of freight orders to their contracted logistics service providers, who independently plan their own delivery schedules. These uncoordinated deliveries and limited docking capacity jointly cause congestion at the shopping malls. A delivery coordination platform centrally plans the vehicle routes for the logistics service providers and simultaneously schedules the dock time slots at the shopping malls for the delivery orders. Vehicle routing and dock scheduling decisions need to be made jointly against the backdrop of travel time and service time uncertainty and subject to practical operations rules. We model this problem as a two-stage stochastic mixed integer program, develop an adaptive large neighborhood search algorithm that approximates the second stage recourse function using various sample sizes, and examine the associated in-sample and out-of-sample stability. Our numerical study on a testbed of instances based on real data in Singapore demonstrates the value of coordination and the value of stochastic solutions.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"290 1","pages":"501-527"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88969954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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