Liangze Yang, S. Wong, H. Ho, Mengping Zhang, Chi-Wang Shu
Recent decades have seen increasing concerns regarding air quality in housing locations. This study proposes a predictive continuum dynamic user-optimal model with combined choice of housing location, destination, route, and departure time. A traveler’s choice of housing location is modeled by a logit-type demand distribution function based on air quality, housing rent, and perceived travel costs. Air quality, or air pollutants, within the modeling region are governed by the vehicle-emission model and the advection-diffusion equation for dispersion. In this study, the housing-location problem is formulated as a fixed-point problem and the predictive continuum dynamic user-optimal model with departure-time consideration is formulated as a variational inequality problem. The Lax-Friedrichs scheme, the fast-sweeping method, the Goldstein-Levitin-Polyak projection algorithm, and self-adaptive successive averages are adopted to discretize and solve these problems. A numerical example is given to demonstrate the characteristics of the proposed housing-location choice problem with consideration of air quality and to demonstrate the effectiveness of the solution algorithms.
{"title":"Effects of Air Quality on Housing Location: A Predictive Dynamic Continuum User-Optimal Approach","authors":"Liangze Yang, S. Wong, H. Ho, Mengping Zhang, Chi-Wang Shu","doi":"10.1287/trsc.2021.1116","DOIUrl":"https://doi.org/10.1287/trsc.2021.1116","url":null,"abstract":"Recent decades have seen increasing concerns regarding air quality in housing locations. This study proposes a predictive continuum dynamic user-optimal model with combined choice of housing location, destination, route, and departure time. A traveler’s choice of housing location is modeled by a logit-type demand distribution function based on air quality, housing rent, and perceived travel costs. Air quality, or air pollutants, within the modeling region are governed by the vehicle-emission model and the advection-diffusion equation for dispersion. In this study, the housing-location problem is formulated as a fixed-point problem and the predictive continuum dynamic user-optimal model with departure-time consideration is formulated as a variational inequality problem. The Lax-Friedrichs scheme, the fast-sweeping method, the Goldstein-Levitin-Polyak projection algorithm, and self-adaptive successive averages are adopted to discretize and solve these problems. A numerical example is given to demonstrate the characteristics of the proposed housing-location choice problem with consideration of air quality and to demonstrate the effectiveness of the solution algorithms.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"140 1","pages":"1111-1134"},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86739972","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}
Traditionally, individuals and firms have bought and owned resources. For instance, owning a car was a status symbol for people. In recent years, this focus has changed. Young people just want to have some convenient means of transportation available. Sharing cars, bikes, or rides is also acceptable and has the advantage of increased sustainability compared with everyone owning such a device. A similar development can also be observed in industry. Already for decades, smaller farmers in rural areas have shared some expensive harvesting machines, via cooperatives. In recent years, cost pressures have incentivized all firms to use their equipment more efficiently and to avoid idle capacities. This has led, among other things, to the exchange of transportation requests between smaller carriers, when one of them faces an overload situation and another has idle resources. Similarly, some production firms share storage space and logistics service providers operate this storage jointly with greater efficiency than each firm could do separately. All these tendencies have led to the development of a new paradigm, the “Sharing Economy.” In transportation, these resources are the vehicles used to deliver goods or move passengers. Given an increasing pressure to act economically and ecologically sustainable, efficient mechanisms that help to benefit from idle capacities are on the rise. They are typically organized through digital platforms that facilitate the efficient exchange of goods or services and help cope with data privacy issues. Both transportation companies and heavy users of transportation services need to learn how to play in a world of shared idle capacities. “Transportation in the Sharing Economy” was the theme of the 2019 Transportation Science and Logistics Workshop held in Vienna, and it is therefore also the focus of this associated special issue. The papers included cover several forms of shared resources in transportation, where the first part deals with freight, whereas in the second part, the focus lies on passenger transportation. Each study highlights the benefits of sharing resources. We received 40 submissions, of which 10 papers were accepted for this special issue after the reviewing process.
{"title":"Transportation in the Sharing Economy","authors":"M. Gansterer, R. Hartl, M. Tzur","doi":"10.1287/trsc.2022.1143","DOIUrl":"https://doi.org/10.1287/trsc.2022.1143","url":null,"abstract":"Traditionally, individuals and firms have bought and owned resources. For instance, owning a car was a status symbol for people. In recent years, this focus has changed. Young people just want to have some convenient means of transportation available. Sharing cars, bikes, or rides is also acceptable and has the advantage of increased sustainability compared with everyone owning such a device. A similar development can also be observed in industry. Already for decades, smaller farmers in rural areas have shared some expensive harvesting machines, via cooperatives. In recent years, cost pressures have incentivized all firms to use their equipment more efficiently and to avoid idle capacities. This has led, among other things, to the exchange of transportation requests between smaller carriers, when one of them faces an overload situation and another has idle resources. Similarly, some production firms share storage space and logistics service providers operate this storage jointly with greater efficiency than each firm could do separately. All these tendencies have led to the development of a new paradigm, the “Sharing Economy.” In transportation, these resources are the vehicles used to deliver goods or move passengers. Given an increasing pressure to act economically and ecologically sustainable, efficient mechanisms that help to benefit from idle capacities are on the rise. They are typically organized through digital platforms that facilitate the efficient exchange of goods or services and help cope with data privacy issues. Both transportation companies and heavy users of transportation services need to learn how to play in a world of shared idle capacities. “Transportation in the Sharing Economy” was the theme of the 2019 Transportation Science and Logistics Workshop held in Vienna, and it is therefore also the focus of this associated special issue. The papers included cover several forms of shared resources in transportation, where the first part deals with freight, whereas in the second part, the focus lies on passenger transportation. Each study highlights the benefits of sharing resources. We received 40 submissions, of which 10 papers were accepted for this special issue after the reviewing process.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"10 1","pages":"567-570"},"PeriodicalIF":0.0,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75373473","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}
When scheduling the movement of individual vehicles on a traffic network, one must ensure that they never get too close to one another. This is normally modelled by segmenting the network and forbidding two vehicles to occupy the same segment at the same time. This approximation is often insufficient or too restraining. This study develops and systematises the use of conflict regions to model spatial proximity constraints. By extending the classical disjunctive programming approach to job-shop scheduling problems, we demonstrate how conflict regions can be exploited to efficiently schedule the collective movements of a set of vehicles, in this case aircraft moving on an airport ground network. We also show how conflict regions can be used in the short-term control of vehicle speeds to avoid collisions and deadlocks. The overall approach was implemented in a software system for air traffic management at airports and successfully evaluated for scheduling and guiding airplanes during an extensive human in the loop simulation exercise for the Budapest airport. Through simulations, we also provide numerical results to assess the computational efficiency of our scheduling algorithm.
{"title":"Scheduling Vehicles with Spatial Conflicts","authors":"O. Kloster, C. Mannino, A. Riise, P. Schittekat","doi":"10.1287/trsc.2021.1119","DOIUrl":"https://doi.org/10.1287/trsc.2021.1119","url":null,"abstract":"When scheduling the movement of individual vehicles on a traffic network, one must ensure that they never get too close to one another. This is normally modelled by segmenting the network and forbidding two vehicles to occupy the same segment at the same time. This approximation is often insufficient or too restraining. This study develops and systematises the use of conflict regions to model spatial proximity constraints. By extending the classical disjunctive programming approach to job-shop scheduling problems, we demonstrate how conflict regions can be exploited to efficiently schedule the collective movements of a set of vehicles, in this case aircraft moving on an airport ground network. We also show how conflict regions can be used in the short-term control of vehicle speeds to avoid collisions and deadlocks. The overall approach was implemented in a software system for air traffic management at airports and successfully evaluated for scheduling and guiding airplanes during an extensive human in the loop simulation exercise for the Budapest airport. Through simulations, we also provide numerical results to assess the computational efficiency of our scheduling algorithm.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"99 1","pages":"1165-1178"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91172731","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}
Since the start of the COVID-19 pandemic, disruptions have been experienced in many supply chains, particularly in personal protective equipment, testing kits, and even essential household goods. Effective vaccines to protect against COVID-19 were approved for emergency use in the United States in late 2020, which led to one of the most extensive vaccination campaigns in history. We continuously collect data on vaccine allocation, shipment and distribution, administration, and inventory in the United States, covering the entire vaccination campaign. In this article, we describe some data sets that we collaborated to obtain. We are publishing the data and making them freely available to researchers, media organizations, and other stakeholders so that others may use the data to develop insights about the distribution and wastage of vaccines during the current pandemic or to provide an informed future pandemic response. This article gives an overview of vaccine distribution logistics in the United States, describes the data we obtain, outlines how they may be accessed and used by others, and describes some high-level analyses demonstrating some aspects of the data (for data collected during January 1, 2021–March 31, 2021). This article also provides directions for future research using the collected data. Our goal is two-fold: (i) We would like the data to be used in many creative ways to inform the current and future pandemic response. (ii) We also want to inspire other researchers to make their data publicly available in a timely manner.
{"title":"Using COVID-19 Data on Vaccine Shipments and Wastage to Inform Modeling and Decision-Making","authors":"L. Hajibabai, Ali Hajbabaie, J. Swann, D. Vergano","doi":"10.1287/trsc.2022.1134","DOIUrl":"https://doi.org/10.1287/trsc.2022.1134","url":null,"abstract":"Since the start of the COVID-19 pandemic, disruptions have been experienced in many supply chains, particularly in personal protective equipment, testing kits, and even essential household goods. Effective vaccines to protect against COVID-19 were approved for emergency use in the United States in late 2020, which led to one of the most extensive vaccination campaigns in history. We continuously collect data on vaccine allocation, shipment and distribution, administration, and inventory in the United States, covering the entire vaccination campaign. In this article, we describe some data sets that we collaborated to obtain. We are publishing the data and making them freely available to researchers, media organizations, and other stakeholders so that others may use the data to develop insights about the distribution and wastage of vaccines during the current pandemic or to provide an informed future pandemic response. This article gives an overview of vaccine distribution logistics in the United States, describes the data we obtain, outlines how they may be accessed and used by others, and describes some high-level analyses demonstrating some aspects of the data (for data collected during January 1, 2021–March 31, 2021). This article also provides directions for future research using the collected data. Our goal is two-fold: (i) We would like the data to be used in many creative ways to inform the current and future pandemic response. (ii) We also want to inspire other researchers to make their data publicly available in a timely manner.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"45 1","pages":"1135-1147"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91246368","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}
Matthias Soppert, Claudius Steinhardt, C. Müller, Jochen Gönsch
Over the last decades, shared mobility systems have become an integral part of inner-city mobility. Modern systems allow one-way rentals, that is, customers can drop off the vehicle at a different location to where they began their trip. A prominent example is car sharing. Indeed, this work was motivated by the insight we gained in collaborating closely with Europe’s largest car sharing provider, Share Now. In car sharing, as well as in shared mobility systems in general, pricing optimization has turned out to be a promising means of increasing profit while challenged by limited vehicle supply and asymmetric demand across time and space. Thus, in practice, providers increasingly use minute pricing that is differentiated according to where a rental originates, that is, considering its location and the time of day. In research, however, such approaches have not been considered yet. In this paper, we therefore introduce the corresponding origin-based differentiated, profit-maximizing pricing problem for shared mobility systems. The problem is to determine spatially and temporally differentiated minute prices, taking network effects on the supply side and several practice relevant aspects into account. Based on a deterministic network flow model, we formulate the problem as a mixed-integer linear program and prove that it is NP-hard. For its solution, we propose a temporal decomposition approach based on approximate dynamic programming. The approach integrates a value function approximation to incorporate future profits and account for network effects. Extensive computational experiments demonstrate the benefits of capturing such effects in pricing generally, as well as showing our value function approximation’s ability to anticipate them precisely. Furthermore, in a case study based on Share Now data from Florence in Italy, we observe profit increases of around 9% compared with constant uniform minute prices, which are still the de facto industry standard.
{"title":"Differentiated Pricing of Shared Mobility Systems Considering Network Effects","authors":"Matthias Soppert, Claudius Steinhardt, C. Müller, Jochen Gönsch","doi":"10.1287/trsc.2022.1131","DOIUrl":"https://doi.org/10.1287/trsc.2022.1131","url":null,"abstract":"Over the last decades, shared mobility systems have become an integral part of inner-city mobility. Modern systems allow one-way rentals, that is, customers can drop off the vehicle at a different location to where they began their trip. A prominent example is car sharing. Indeed, this work was motivated by the insight we gained in collaborating closely with Europe’s largest car sharing provider, Share Now. In car sharing, as well as in shared mobility systems in general, pricing optimization has turned out to be a promising means of increasing profit while challenged by limited vehicle supply and asymmetric demand across time and space. Thus, in practice, providers increasingly use minute pricing that is differentiated according to where a rental originates, that is, considering its location and the time of day. In research, however, such approaches have not been considered yet. In this paper, we therefore introduce the corresponding origin-based differentiated, profit-maximizing pricing problem for shared mobility systems. The problem is to determine spatially and temporally differentiated minute prices, taking network effects on the supply side and several practice relevant aspects into account. Based on a deterministic network flow model, we formulate the problem as a mixed-integer linear program and prove that it is NP-hard. For its solution, we propose a temporal decomposition approach based on approximate dynamic programming. The approach integrates a value function approximation to incorporate future profits and account for network effects. Extensive computational experiments demonstrate the benefits of capturing such effects in pricing generally, as well as showing our value function approximation’s ability to anticipate them precisely. Furthermore, in a case study based on Share Now data from Florence in Italy, we observe profit increases of around 9% compared with constant uniform minute prices, which are still the de facto industry standard.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"1 1","pages":"1279-1303"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83560615","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 the future, vehicle sharing platforms for passenger transport will be unmanned, autonomous, and electric. These platforms must decide which vehicle should pick up which type of customer based on the vehicle’s battery level and customer’s travel distance. We design dynamic vehicle allocation policies for matching appropriate vehicles to customers using a Markov decision process model. To obtain the model parameters, we first model the system as a semi-open queuing network (SOQN) with multiple synchronization stations. At these stations, customers with varied battery demands are matched with semi-shared vehicles that hold sufficient remaining battery levels. If a vehicle’s battery level drops below a threshold, it is routed probabilistically to a nearby charging station for charging. We solve the analytical model of the SOQN and obtain approximate system performance measures, which are validated using simulation. With inputs from the SOQN model, the Markov decision process minimizes both customer waiting cost and lost demand and finds a good heuristic vehicle allocation policy. The experiments show that the heuristic policy is near optimal in small-scale networks and outperforms benchmark policies in large-scale realistic scenarios. An interesting finding is that reserving idle vehicles to wait for future short-distance customer arrivals can be beneficial even when long-distance customers are waiting.
{"title":"Dynamic Vehicle Allocation Policies for Shared Autonomous Electric Fleets","authors":"Yuxuan Dong, R. Koster, D. Roy, Yugang Yu","doi":"10.1287/trsc.2021.1115","DOIUrl":"https://doi.org/10.1287/trsc.2021.1115","url":null,"abstract":"In the future, vehicle sharing platforms for passenger transport will be unmanned, autonomous, and electric. These platforms must decide which vehicle should pick up which type of customer based on the vehicle’s battery level and customer’s travel distance. We design dynamic vehicle allocation policies for matching appropriate vehicles to customers using a Markov decision process model. To obtain the model parameters, we first model the system as a semi-open queuing network (SOQN) with multiple synchronization stations. At these stations, customers with varied battery demands are matched with semi-shared vehicles that hold sufficient remaining battery levels. If a vehicle’s battery level drops below a threshold, it is routed probabilistically to a nearby charging station for charging. We solve the analytical model of the SOQN and obtain approximate system performance measures, which are validated using simulation. With inputs from the SOQN model, the Markov decision process minimizes both customer waiting cost and lost demand and finds a good heuristic vehicle allocation policy. The experiments show that the heuristic policy is near optimal in small-scale networks and outperforms benchmark policies in large-scale realistic scenarios. An interesting finding is that reserving idle vehicles to wait for future short-distance customer arrivals can be beneficial even when long-distance customers are waiting.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"16 1","pages":"1238-1258"},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87304075","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}
Peer-to-peer transportation platforms dynamically match requests (e.g., a ride, a delivery) to independent suppliers who are not employed nor controlled by the platform. Thus, the platform cannot be certain that a supplier will accept an offered request. To mitigate this selection uncertainty, a platform can offer each supplier a menu of requests to choose from. Such menus need to be created carefully because there is a trade-off between selection probability and duplicate selections. In addition to a complex decision space, supplier selection decisions are vast and have systematic implications, impacting the platform’s revenue, other suppliers’ experiences (in the form of duplicate selections), and the request waiting times. Thus, we present a multiple scenario approach, repeatedly sampling potential supplier selections, solving the corresponding two-stage decision problems, and combining the multiple different solutions through a consensus algorithm. Extensive computational results using the Chicago Region as a case study illustrate that our method outperforms a set of benchmark policies. We quantify the value of anticipating supplier selection, offering menus to suppliers, offering requests to multiple suppliers at once, and holistically generating menus with the entire system in mind. Our method leads to more balanced assignments by sacrificing some “easy wins” toward better system performance over time and for all stakeholders involved, including increased revenue for the platform, and decreased match waiting times for suppliers and requests.
{"title":"Supplier Menus for Dynamic Matching in Peer-to-Peer Transportation Platforms","authors":"Rosemonde Ausseil, Jennifer A. Pazour, M. Ulmer","doi":"10.1287/trsc.2022.1133","DOIUrl":"https://doi.org/10.1287/trsc.2022.1133","url":null,"abstract":"Peer-to-peer transportation platforms dynamically match requests (e.g., a ride, a delivery) to independent suppliers who are not employed nor controlled by the platform. Thus, the platform cannot be certain that a supplier will accept an offered request. To mitigate this selection uncertainty, a platform can offer each supplier a menu of requests to choose from. Such menus need to be created carefully because there is a trade-off between selection probability and duplicate selections. In addition to a complex decision space, supplier selection decisions are vast and have systematic implications, impacting the platform’s revenue, other suppliers’ experiences (in the form of duplicate selections), and the request waiting times. Thus, we present a multiple scenario approach, repeatedly sampling potential supplier selections, solving the corresponding two-stage decision problems, and combining the multiple different solutions through a consensus algorithm. Extensive computational results using the Chicago Region as a case study illustrate that our method outperforms a set of benchmark policies. We quantify the value of anticipating supplier selection, offering menus to suppliers, offering requests to multiple suppliers at once, and holistically generating menus with the entire system in mind. Our method leads to more balanced assignments by sacrificing some “easy wins” toward better system performance over time and for all stakeholders involved, including increased revenue for the platform, and decreased match waiting times for suppliers and requests.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"1 1","pages":"1304-1326"},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90360166","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}
Yanguo Wang, Lei Zhao, M. Savelsbergh, Shengnan Wu
In the daily dispatching of last-mile urban delivery, a delivery manager has to consider workload balance among couriers to maintain workforce morale. We consider two types of workload: incentive workload, which relates to the delivery quantity and affects a courier’s income, and effort workload, which relates to the delivery time and affects a courier’s health. Incentive workload has to be balanced over a relatively long period of time (a payroll cycle—a week or a month), whereas effort workload has to be balanced over a relatively short period of time (a shift or a day). We introduce a multi-period workload balancing problem under stochastic demand and dynamic daily dispatching, formulate it as a Markov decision process (MDP), and derive a lower bound on the optimal value of the MDP model. We propose a balanced penalty policy based on cost function approximation and use a hybrid algorithm combining the modified nested partitions method and the KN++ procedure to search for an optimal policy parameter. A comprehensive numerical study demonstrates that the proposed balanced penalty policy performs close to optimal on small instances and outperforms four benchmark policies on large instances, and provides insight into the impact of demand variation and a manager’s importance weighting of operating cost and workload balance.
{"title":"Multi-Period Workload Balancing in Last-Mile Urban Delivery","authors":"Yanguo Wang, Lei Zhao, M. Savelsbergh, Shengnan Wu","doi":"10.1287/trsc.2022.1132","DOIUrl":"https://doi.org/10.1287/trsc.2022.1132","url":null,"abstract":"In the daily dispatching of last-mile urban delivery, a delivery manager has to consider workload balance among couriers to maintain workforce morale. We consider two types of workload: incentive workload, which relates to the delivery quantity and affects a courier’s income, and effort workload, which relates to the delivery time and affects a courier’s health. Incentive workload has to be balanced over a relatively long period of time (a payroll cycle—a week or a month), whereas effort workload has to be balanced over a relatively short period of time (a shift or a day). We introduce a multi-period workload balancing problem under stochastic demand and dynamic daily dispatching, formulate it as a Markov decision process (MDP), and derive a lower bound on the optimal value of the MDP model. We propose a balanced penalty policy based on cost function approximation and use a hybrid algorithm combining the modified nested partitions method and the KN++ procedure to search for an optimal policy parameter. A comprehensive numerical study demonstrates that the proposed balanced penalty policy performs close to optimal on small instances and outperforms four benchmark policies on large instances, and provides insight into the impact of demand variation and a manager’s importance weighting of operating cost and workload balance.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"2 1","pages":"1348-1368"},"PeriodicalIF":0.0,"publicationDate":"2022-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83467981","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}
We study the linked tactical design problems of fleet sizing and partitioning a service region into vehicle routing zones for same-day delivery (SDD) systems. Existing SDD studies focus primarily on operational dispatch problems and do not consider system design questions. Prior work on SDD system design has not considered the fleet sizing decision when a service region may be partitioned into zones dedicated to individual vehicles; such designs have been shown to improve system efficiency in related vehicle routing settings. Using continuous approximations to capture average-case operational behavior, we consider first the problem of independently maximizing the area of a single-vehicle delivery zone. We characterize area-maximizing dispatching policies and leverage these results to develop a procedure for calculating optimal areas as a function of a zone’s distance from the depot, given a maximum number of daily dispatches per vehicle. We then demonstrate how to derive fleet sizes from optimal area functions and propose an associated Voronoi approach to partition the service region into single-vehicle zones. We test the fleet sizing and partitioning approach in a computational study that considers two different service regions and demonstrate its pragmatism and effectiveness via an operational simulation. Using minimal computation, the approach specifies fleet sizes and builds vehicle delivery zones that meet operational requirements, verified by simulation results.
{"title":"Fleet Sizing and Service Region Partitioning for Same-Day Delivery Systems","authors":"Dipayan Banerjee, A. Erera, A. Toriello","doi":"10.1287/trsc.2022.1125","DOIUrl":"https://doi.org/10.1287/trsc.2022.1125","url":null,"abstract":"We study the linked tactical design problems of fleet sizing and partitioning a service region into vehicle routing zones for same-day delivery (SDD) systems. Existing SDD studies focus primarily on operational dispatch problems and do not consider system design questions. Prior work on SDD system design has not considered the fleet sizing decision when a service region may be partitioned into zones dedicated to individual vehicles; such designs have been shown to improve system efficiency in related vehicle routing settings. Using continuous approximations to capture average-case operational behavior, we consider first the problem of independently maximizing the area of a single-vehicle delivery zone. We characterize area-maximizing dispatching policies and leverage these results to develop a procedure for calculating optimal areas as a function of a zone’s distance from the depot, given a maximum number of daily dispatches per vehicle. We then demonstrate how to derive fleet sizes from optimal area functions and propose an associated Voronoi approach to partition the service region into single-vehicle zones. We test the fleet sizing and partitioning approach in a computational study that considers two different service regions and demonstrate its pragmatism and effectiveness via an operational simulation. Using minimal computation, the approach specifies fleet sizes and builds vehicle delivery zones that meet operational requirements, verified by simulation results.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"106 1","pages":"1327-1347"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87888094","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}
Before each flight departs, baggage has to be loaded into containers, which are then forwarded to the airplane. Planning the loading process consists of setting the start times for the loading process and depletion of the baggage storage as well as assigning handling facilities and workers. Flight delays and uncertain arrival times of passengers at the check-in counters require plans that are adjusted dynamically every few minutes and, hence, an efficient planning procedure. We propose a model formulation and a solution procedure that utilize historical flight data to generate reliable plans in a rolling planning fashion, allowing problem parameters to be updated in each reoptimization. To increase the tractability of the problem, we employ a column generation–based heuristic in which new schedules and work profiles are generated in subproblems, which are solved as dynamic programs. In a computational study, we demonstrate the robust performance of the proposed procedure based on real-world data from a major European airport. The results show that (i) the procedure outperforms both a constructive heuristic that mimics human decision making and a meta heuristic (tabu search) and (ii) being able to dynamically (re)allocate baggage handlers leads to improved solutions with considerably fewer left bags.
{"title":"A Data-Driven Approach for Baggage Handling Operations at Airports","authors":"C. Ruf, S. Schiffels, R. Kolisch, M. Frey","doi":"10.1287/trsc.2022.1127","DOIUrl":"https://doi.org/10.1287/trsc.2022.1127","url":null,"abstract":"Before each flight departs, baggage has to be loaded into containers, which are then forwarded to the airplane. Planning the loading process consists of setting the start times for the loading process and depletion of the baggage storage as well as assigning handling facilities and workers. Flight delays and uncertain arrival times of passengers at the check-in counters require plans that are adjusted dynamically every few minutes and, hence, an efficient planning procedure. We propose a model formulation and a solution procedure that utilize historical flight data to generate reliable plans in a rolling planning fashion, allowing problem parameters to be updated in each reoptimization. To increase the tractability of the problem, we employ a column generation–based heuristic in which new schedules and work profiles are generated in subproblems, which are solved as dynamic programs. In a computational study, we demonstrate the robust performance of the proposed procedure based on real-world data from a major European airport. The results show that (i) the procedure outperforms both a constructive heuristic that mimics human decision making and a meta heuristic (tabu search) and (ii) being able to dynamically (re)allocate baggage handlers leads to improved solutions with considerably fewer left bags.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"21 1","pages":"1179-1195"},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88952663","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：