Anna Livia Croella, Veronica Dal Sasso, Leonardo Lamorgese, C. Mannino, P. Ventura
When major disruptions occur in a rail network, the infrastructure manager and train operating companies may be forced to stop trains until the normal status is recovered. A crucial aspect is to identify, for each train, a location (a safe place) where the train can hold during the disruption, avoiding to disconnect the network and allowing a quick recovering of the plan, at restart. We give necessary and sufficient conditions for a safe place assignment to have the desired property. We then translate such conditions into constraints of a suitable binary formulation of the problem. Computational results on a set of instances provided by a class 1 U.S. railroad show how the approach can be used effectively in the real-life setting that motivates the study, by returning optimal assignments in a fraction of a second.
{"title":"Disruption Management in Railway Systems by Safe Place Assignment","authors":"Anna Livia Croella, Veronica Dal Sasso, Leonardo Lamorgese, C. Mannino, P. Ventura","doi":"10.1287/trsc.2021.1107","DOIUrl":"https://doi.org/10.1287/trsc.2021.1107","url":null,"abstract":"When major disruptions occur in a rail network, the infrastructure manager and train operating companies may be forced to stop trains until the normal status is recovered. A crucial aspect is to identify, for each train, a location (a safe place) where the train can hold during the disruption, avoiding to disconnect the network and allowing a quick recovering of the plan, at restart. We give necessary and sufficient conditions for a safe place assignment to have the desired property. We then translate such conditions into constraints of a suitable binary formulation of the problem. Computational results on a set of instances provided by a class 1 U.S. railroad show how the approach can be used effectively in the real-life setting that motivates the study, by returning optimal assignments in a fraction of a second.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"64 1","pages":"938-952"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91478648","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}
Traffic congestion has become a serious issue around the globe, partly owing to single-occupancy commuter trips. Ridesharing can present a suitable alternative for serving commuter trips. However, there are several important obstacles that impede ridesharing systems from becoming a viable mode of transportation, including the lack of a guarantee for a ride back home as well as the difficulty of obtaining a critical mass of participants. This paper addresses these obstacles by introducing a traveler incentive program (TIP) to promote community-based ridesharing with a ride back home guarantee among commuters. The TIP program allocates incentives to (1) directly subsidize a select set of ridesharing rides and (2) encourage a small, carefully selected set of travelers to change their travel behavior (i.e., departure or arrival times). We formulate the underlying ride-matching problem as a budget-constrained min-cost flow problem and present a Lagrangian relaxation-based algorithm with a worst-case optimality bound to solve large-scale instances of this problem in polynomial time. We further propose a polynomial-time, budget-balanced version of the problem. Numerical experiments suggest that allocating subsidies to change travel behavior is significantly more beneficial than directly subsidizing rides. Furthermore, using a flat tax rate as low as 1% can double the system’s social welfare in the budget-balanced variant of the incentive program.
{"title":"A Traveler Incentive Program for Promoting Community-Based Ridesharing","authors":"Amirmahdi Tafreshian, Neda Masoud","doi":"10.1287/trsc.2021.1121","DOIUrl":"https://doi.org/10.1287/trsc.2021.1121","url":null,"abstract":"Traffic congestion has become a serious issue around the globe, partly owing to single-occupancy commuter trips. Ridesharing can present a suitable alternative for serving commuter trips. However, there are several important obstacles that impede ridesharing systems from becoming a viable mode of transportation, including the lack of a guarantee for a ride back home as well as the difficulty of obtaining a critical mass of participants. This paper addresses these obstacles by introducing a traveler incentive program (TIP) to promote community-based ridesharing with a ride back home guarantee among commuters. The TIP program allocates incentives to (1) directly subsidize a select set of ridesharing rides and (2) encourage a small, carefully selected set of travelers to change their travel behavior (i.e., departure or arrival times). We formulate the underlying ride-matching problem as a budget-constrained min-cost flow problem and present a Lagrangian relaxation-based algorithm with a worst-case optimality bound to solve large-scale instances of this problem in polynomial time. We further propose a polynomial-time, budget-balanced version of the problem. Numerical experiments suggest that allocating subsidies to change travel behavior is significantly more beneficial than directly subsidizing rides. Furthermore, using a flat tax rate as low as 1% can double the system’s social welfare in the budget-balanced variant of the incentive program.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"25 1","pages":"827-847"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87427755","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}
Alexandre M. Florio, D. Feillet, M. Poggi, Thibaut Vidal
We consider the vehicle routing problem with stochastic demands (VRPSD), a problem in which customer demands are known in distribution at the route planning stage and revealed during route execution upon arrival at each customer. A long-standing open question on the VRPSD concerns the benefits of allowing, during route execution, partial reordering of the planned customer visits. Given the practical importance of this question and the growing interest on the VRPSD under optimal restocking, we study the VRPSD under a recourse policy known as the switch policy. The switch policy is a canonical reoptimization policy that permits only pairs of successive customers to be reordered. We consider this policy jointly with optimal preventive restocking and introduce a branch-cut-and-price algorithm to compute optimal a priori routing plans in this context. At its core, this algorithm features pricing routines where value functions represent the expected cost-to-go along planned routes for all possible states and reordering decisions. To ensure pricing tractability, we adopt a strategy that combines elementary pricing with completion bounds of varying complexity, and solve the pricing problem without relying on dominance rules. Our numerical experiments demonstrate the effectiveness of the algorithm for solving instances with up to 50 customers. Notably, they also give us new insights into the value of reoptimization. The switch policy enables significant cost savings over optimal restocking when the planned routes come from an algorithm built on a deterministic approximation of the data, an important scenario given the difficulty of finding optimal VRPSD solutions. The benefits are smaller when comparing optimal a priori VRPSD solutions obtained for both recourse policies. As it appears, further cost savings may require joint reordering and reassignment of customer visits among vehicles when the context permits.
{"title":"Vehicle Routing with Stochastic Demands and Partial Reoptimization","authors":"Alexandre M. Florio, D. Feillet, M. Poggi, Thibaut Vidal","doi":"10.1287/trsc.2022.1129","DOIUrl":"https://doi.org/10.1287/trsc.2022.1129","url":null,"abstract":"We consider the vehicle routing problem with stochastic demands (VRPSD), a problem in which customer demands are known in distribution at the route planning stage and revealed during route execution upon arrival at each customer. A long-standing open question on the VRPSD concerns the benefits of allowing, during route execution, partial reordering of the planned customer visits. Given the practical importance of this question and the growing interest on the VRPSD under optimal restocking, we study the VRPSD under a recourse policy known as the switch policy. The switch policy is a canonical reoptimization policy that permits only pairs of successive customers to be reordered. We consider this policy jointly with optimal preventive restocking and introduce a branch-cut-and-price algorithm to compute optimal a priori routing plans in this context. At its core, this algorithm features pricing routines where value functions represent the expected cost-to-go along planned routes for all possible states and reordering decisions. To ensure pricing tractability, we adopt a strategy that combines elementary pricing with completion bounds of varying complexity, and solve the pricing problem without relying on dominance rules. Our numerical experiments demonstrate the effectiveness of the algorithm for solving instances with up to 50 customers. Notably, they also give us new insights into the value of reoptimization. The switch policy enables significant cost savings over optimal restocking when the planned routes come from an algorithm built on a deterministic approximation of the data, an important scenario given the difficulty of finding optimal VRPSD solutions. The benefits are smaller when comparing optimal a priori VRPSD solutions obtained for both recourse policies. As it appears, further cost savings may require joint reordering and reassignment of customer visits among vehicles when the context permits.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"34 1","pages":"1393-1408"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79302717","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}
Split delivery routing problems are concerned with serving the demand of a set of customers with a fleet of capacitated vehicles at minimum cost, where a customer can be served by more than one vehicle if beneficial. They generalize traditional variants of routing problems and have applications in commercial and humanitarian logistics. Previously, formulations involving only commonly used arc-based variables have provided only relaxations for split delivery variants, as the possibility of visiting customers more than once introduces modeling challenges. The only known compact formulations are based on variables indexed by vehicle or by visit number and perform poorly when using general-purpose integer programming software. We present compact formulations that avoid the use of these types of variables and that can model split delivery routing problems with and without time windows. Computational experiments demonstrate their superior performance over existing compact formulations. We also develop a branch-and-cut algorithm that balances the efficiency derived from a relaxed formulation with the strength derived from one of the proposed formulations and demonstrate its efficacy on a large set of benchmark instances. The algorithm solves 95 instances to proven optimality for the first time and improves the best known lower and/or upper bound for many other instances.
{"title":"Compact Formulations for Split Delivery Routing Problems","authors":"P. Munari, M. Savelsbergh","doi":"10.1287/trsc.2021.1106","DOIUrl":"https://doi.org/10.1287/trsc.2021.1106","url":null,"abstract":"Split delivery routing problems are concerned with serving the demand of a set of customers with a fleet of capacitated vehicles at minimum cost, where a customer can be served by more than one vehicle if beneficial. They generalize traditional variants of routing problems and have applications in commercial and humanitarian logistics. Previously, formulations involving only commonly used arc-based variables have provided only relaxations for split delivery variants, as the possibility of visiting customers more than once introduces modeling challenges. The only known compact formulations are based on variables indexed by vehicle or by visit number and perform poorly when using general-purpose integer programming software. We present compact formulations that avoid the use of these types of variables and that can model split delivery routing problems with and without time windows. Computational experiments demonstrate their superior performance over existing compact formulations. We also develop a branch-and-cut algorithm that balances the efficiency derived from a relaxed formulation with the strength derived from one of the proposed formulations and demonstrate its efficacy on a large set of benchmark instances. The algorithm solves 95 instances to proven optimality for the first time and improves the best known lower and/or upper bound for many other instances.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"1 1","pages":"1022-1043"},"PeriodicalIF":0.0,"publicationDate":"2022-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80447957","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}
Wei Liu, Fangni Zhang, Xiaolei Wang, Chaoyi Shao, Hai Yang
This study examines the pricing strategy of a parking sharing platform that rents the daytime-usage rights of private parking spaces from parking owners and sells them to parking users. In an urban area with both shared parking and curbside parking, a choice equilibrium model is proposed to predict the number of shared parking users under any given pricing strategy of the platform. We analytically analyze how the pricing strategy of the platform (price charged on users and rent paid to parking owners or sharers) would affect the parking choice equilibrium and several system efficiency metrics. It is shown that the platform is profitable when some parking owners have a relatively small inconvenience cost from sharing their spaces, but its profit is always negative at minimum social cost. Numerical studies are conducted to illustrate the analytical results and provide further understanding.
{"title":"Unlock the Sharing Economy: The Case of the Parking Sector for Recurrent Commuting Trips","authors":"Wei Liu, Fangni Zhang, Xiaolei Wang, Chaoyi Shao, Hai Yang","doi":"10.1287/trsc.2021.1103","DOIUrl":"https://doi.org/10.1287/trsc.2021.1103","url":null,"abstract":"This study examines the pricing strategy of a parking sharing platform that rents the daytime-usage rights of private parking spaces from parking owners and sells them to parking users. In an urban area with both shared parking and curbside parking, a choice equilibrium model is proposed to predict the number of shared parking users under any given pricing strategy of the platform. We analytically analyze how the pricing strategy of the platform (price charged on users and rent paid to parking owners or sharers) would affect the parking choice equilibrium and several system efficiency metrics. It is shown that the platform is profitable when some parking owners have a relatively small inconvenience cost from sharing their spaces, but its profit is always negative at minimum social cost. Numerical studies are conducted to illustrate the analytical results and provide further understanding.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"73 1","pages":"338-357"},"PeriodicalIF":0.0,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84932963","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 scheduled service network design problem (SSNDP) can support planning the transportation operations of consolidation carriers given shipment-level service commitments regarding available and due times. These available and due times impact transportation costs by constraining potential consolidation opportunities. However, such available and due times may be changed, either because of negotiations with customers or redesigned internal operations to increase shipment consolidation and reduce transportation costs. As changing these times can lead to customer service and operational issues, we presume a carrier seeks to do so for a limited number of shipments. We propose a new variant of the SSNDP, the flexible scheduled service network design problem, that identifies the shipments for which these times should be changed to minimize total transportation and handling costs. We present a solution approach for this problem that outperforms a commercial optimization solver on instances derived from the operations of a U.S. less-than-truckload freight transportation carrier. With an extensive computational study, we study the savings potential of leveraging flexibility and the operational settings that are fertile ground for doing so.
{"title":"The Flexible Scheduled Service Network Design Problem","authors":"Mike Hewitt","doi":"10.1287/trsc.2021.1114","DOIUrl":"https://doi.org/10.1287/trsc.2021.1114","url":null,"abstract":"The scheduled service network design problem (SSNDP) can support planning the transportation operations of consolidation carriers given shipment-level service commitments regarding available and due times. These available and due times impact transportation costs by constraining potential consolidation opportunities. However, such available and due times may be changed, either because of negotiations with customers or redesigned internal operations to increase shipment consolidation and reduce transportation costs. As changing these times can lead to customer service and operational issues, we presume a carrier seeks to do so for a limited number of shipments. We propose a new variant of the SSNDP, the flexible scheduled service network design problem, that identifies the shipments for which these times should be changed to minimize total transportation and handling costs. We present a solution approach for this problem that outperforms a commercial optimization solver on instances derived from the operations of a U.S. less-than-truckload freight transportation carrier. With an extensive computational study, we study the savings potential of leveraging flexibility and the operational settings that are fertile ground for doing so.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"85 1","pages":"1000-1021"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88230432","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 time it takes passengers to board an airplane is known to influence the turn-around time of the aircraft and thus bears a significant cost-saving potential for airlines. Although minimizing boarding time therefore is the most important goal from an economic perspective, previous efforts to design efficient boarding strategies apparently never tackled this task directly. In this paper, we first rigorously define the problem and prove its NP-hardness. While this generally justifies the development of inexact solution methods, we show that all commonly discussed boarding strategies may in fact give solutions that are far from optimal. We complement these theoretical findings by a simple time-aware boarding strategy with guaranteed approximation quality (under reasonable assumptions) as well as a local improvement heuristic and an exact mixed-integer programming (MIP) formulation. Our numerical experiments with simulation data show that for several airplane cabin layouts, provably high-quality or even optimal solutions can be obtained within reasonable time in practice by means of our MIP approach. We also empirically assess the sensitivity of boarding strategies with respect to disruptions of the prescribed boarding sequences and identify robustness against such disruptions as a bottleneck for further improvements.
{"title":"Minimizing Airplane Boarding Time","authors":"Felix J. L. Willamowski, Andreas M. Tillmann","doi":"10.1287/trsc.2021.1098","DOIUrl":"https://doi.org/10.1287/trsc.2021.1098","url":null,"abstract":"The time it takes passengers to board an airplane is known to influence the turn-around time of the aircraft and thus bears a significant cost-saving potential for airlines. Although minimizing boarding time therefore is the most important goal from an economic perspective, previous efforts to design efficient boarding strategies apparently never tackled this task directly. In this paper, we first rigorously define the problem and prove its NP-hardness. While this generally justifies the development of inexact solution methods, we show that all commonly discussed boarding strategies may in fact give solutions that are far from optimal. We complement these theoretical findings by a simple time-aware boarding strategy with guaranteed approximation quality (under reasonable assumptions) as well as a local improvement heuristic and an exact mixed-integer programming (MIP) formulation. Our numerical experiments with simulation data show that for several airplane cabin layouts, provably high-quality or even optimal solutions can be obtained within reasonable time in practice by means of our MIP approach. We also empirically assess the sensitivity of boarding strategies with respect to disruptions of the prescribed boarding sequences and identify robustness against such disruptions as a bottleneck for further improvements.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"16 1","pages":"1196-1218"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85387533","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}
Several works published over the last two decades have shown for a stylized set-up with homogeneous users that metering-based priority (MBP) schemes may generate Pareto improving departure time adjustments similar to those induced by congestion pricing, but without any financial transaction. We investigate whether MBP (i) still generates significant savings and (ii) remains Pareto-improving, with various sources of heterogeneity (in schedule flexibility, desired arrival time, and capacity usage). We consider two types of schemes: one where the priority status is allocated randomly (R-MBP) and another (HOV-MBP), which only prioritizes users with small capacity usage (e.g., carpoolers). We find that the relative total cost savings of R-MBP decrease with heterogeneity in flexibility, but may increase with heterogeneity in desired arrival time. It fails however to be Pareto-improving, as nonprioritized users are almost systematically worse-off. HOV-MBP circumvents this issue by generating an ordering effect and a modal shift, which both contribute to a better distribution of benefits among users. Under favorable circumstances, they may even restore a Pareto improvement. Overall, MBP appears as a realistic way to alleviate congestion, scoring well both in terms of efficiency and social acceptability.
{"title":"Impacts of Metering-Based Dynamic Priority Schemes","authors":"Raphaël Lamotte, A. Palma, N. Geroliminis","doi":"10.1287/trsc.2021.1091","DOIUrl":"https://doi.org/10.1287/trsc.2021.1091","url":null,"abstract":"Several works published over the last two decades have shown for a stylized set-up with homogeneous users that metering-based priority (MBP) schemes may generate Pareto improving departure time adjustments similar to those induced by congestion pricing, but without any financial transaction. We investigate whether MBP (i) still generates significant savings and (ii) remains Pareto-improving, with various sources of heterogeneity (in schedule flexibility, desired arrival time, and capacity usage). We consider two types of schemes: one where the priority status is allocated randomly (R-MBP) and another (HOV-MBP), which only prioritizes users with small capacity usage (e.g., carpoolers). We find that the relative total cost savings of R-MBP decrease with heterogeneity in flexibility, but may increase with heterogeneity in desired arrival time. It fails however to be Pareto-improving, as nonprioritized users are almost systematically worse-off. HOV-MBP circumvents this issue by generating an ordering effect and a modal shift, which both contribute to a better distribution of benefits among users. Under favorable circumstances, they may even restore a Pareto improvement. Overall, MBP appears as a realistic way to alleviate congestion, scoring well both in terms of efficiency and social acceptability.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"33 1","pages":"358-380"},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85857664","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 growth of e-commerce has increased demand for last-mile deliveries, increasing the level of congestion in the existing transportation infrastructure in urban areas. Crowdsourcing deliveries can provide the additional capacity needed to meet the growing demand in a cost-effective way. We introduce a setting where a crowd-shipping platform sells heterogeneous products of different sizes from a central depot. Items sold vary from groceries to electronics. Some items must be delivered within a time window, whereas others need a customer signature. Furthermore, customer presence is not guaranteed, and some deliveries may need to be returned to the depot. Delivery requests are fulfilled by a fleet of professional drivers and a pool of crowd drivers. We present a crowd-shipping platform that standardizes crowd drivers’ capacities and compensates them to return undelivered packages back to the depot. We formulate a two-stage stochastic model, and we propose a branch and price algorithm to solve the problem exactly and a column generation heuristic to solve larger problems quickly. We further develop an analytical method to calculate upper bounds on the supply of vehicles and an innovative cohesive pricing problem to generate columns for the pool of crowd drivers. Computational experiments are carried out on modified Solomon instances with a pool of 100 crowd vehicles. The branch and price algorithm is able to solve instances of up to 100 customers. We show that the value of the stochastic solution can be as high as 18% when compared with the solution obtained from a deterministic simplification of the model. Significant cost reductions of up to 28% are achieved by implementing crowd drivers with low compensations or higher capacities. Finally, we evaluate what happens when crowd drivers are given the autonomy to select routes based on rational and irrational behavior. There is no cost increase when crowd drivers are rational and select routes that have a higher compensation first. However, when crowd drivers are irrational and select routes randomly, the cost can increase up to 4.2% for some instances.
{"title":"Vehicle Routing with Stochastic Supply of Crowd Vehicles and Time Windows","authors":"Fabian Torres, M. Gendreau, W. Rei","doi":"10.1287/trsc.2021.1101","DOIUrl":"https://doi.org/10.1287/trsc.2021.1101","url":null,"abstract":"The growth of e-commerce has increased demand for last-mile deliveries, increasing the level of congestion in the existing transportation infrastructure in urban areas. Crowdsourcing deliveries can provide the additional capacity needed to meet the growing demand in a cost-effective way. We introduce a setting where a crowd-shipping platform sells heterogeneous products of different sizes from a central depot. Items sold vary from groceries to electronics. Some items must be delivered within a time window, whereas others need a customer signature. Furthermore, customer presence is not guaranteed, and some deliveries may need to be returned to the depot. Delivery requests are fulfilled by a fleet of professional drivers and a pool of crowd drivers. We present a crowd-shipping platform that standardizes crowd drivers’ capacities and compensates them to return undelivered packages back to the depot. We formulate a two-stage stochastic model, and we propose a branch and price algorithm to solve the problem exactly and a column generation heuristic to solve larger problems quickly. We further develop an analytical method to calculate upper bounds on the supply of vehicles and an innovative cohesive pricing problem to generate columns for the pool of crowd drivers. Computational experiments are carried out on modified Solomon instances with a pool of 100 crowd vehicles. The branch and price algorithm is able to solve instances of up to 100 customers. We show that the value of the stochastic solution can be as high as 18% when compared with the solution obtained from a deterministic simplification of the model. Significant cost reductions of up to 28% are achieved by implementing crowd drivers with low compensations or higher capacities. Finally, we evaluate what happens when crowd drivers are given the autonomy to select routes based on rational and irrational behavior. There is no cost increase when crowd drivers are rational and select routes that have a higher compensation first. However, when crowd drivers are irrational and select routes randomly, the cost can increase up to 4.2% for some instances.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"461 1","pages":"631-653"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86696003","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}
Sadeque Hamdan, Ali Cheaitou, O. Jouini, T. Granberg, Zied Jemaï, I. Alsyouf, M. Bettayeb, B. Josefsson
Despite various planning efforts, airspace capacity can sometimes be exceeded, typically because of disruptive events. Air traffic flow management (ATFM) is the process of managing flights in this situation. In this paper, we present an ATFM model that accounts for different rerouting options (path rerouting and diversion) and preexisting en route flights. The model proposes having a central authority to control all decisions, which is then compared with current practice. We also consider interflight and interairline fairness measures in the network. We use an exact approach to solve small- to medium-sized instances, and we propose a modified fix-and-relax heuristic to solve large-sized instances. Allowing a central authority to control all decisions increases network efficiency compared with the case where the ATFM authority and airlines control decisions independently. Our experiments show that including different rerouting options in ATFM can help reduce delays by up to 8% and cancellations by up to 23%. Moreover, ground delay cost has much more impact on network decisions than air delay cost, and network decisions are insensitive to changes in diversion cost. Furthermore, the analysis of the tradeoff between total network cost and overtaking cost shows that adding costs for overtaking can significantly improve fairness at only a small increase in total system cost. A balanced total cost per flight among airlines can be achieved at a small increase in the network cost (0.2%–3.0%) when imposing airline fairness. In conclusion, the comprehensiveness of the model makes it useful for analyzing a wide range of alternatives for efficient ATFM.
{"title":"Central Authority-Controlled Air Traffic Flow Management: An Optimization Approach","authors":"Sadeque Hamdan, Ali Cheaitou, O. Jouini, T. Granberg, Zied Jemaï, I. Alsyouf, M. Bettayeb, B. Josefsson","doi":"10.1287/trsc.2021.1087","DOIUrl":"https://doi.org/10.1287/trsc.2021.1087","url":null,"abstract":"Despite various planning efforts, airspace capacity can sometimes be exceeded, typically because of disruptive events. Air traffic flow management (ATFM) is the process of managing flights in this situation. In this paper, we present an ATFM model that accounts for different rerouting options (path rerouting and diversion) and preexisting en route flights. The model proposes having a central authority to control all decisions, which is then compared with current practice. We also consider interflight and interairline fairness measures in the network. We use an exact approach to solve small- to medium-sized instances, and we propose a modified fix-and-relax heuristic to solve large-sized instances. Allowing a central authority to control all decisions increases network efficiency compared with the case where the ATFM authority and airlines control decisions independently. Our experiments show that including different rerouting options in ATFM can help reduce delays by up to 8% and cancellations by up to 23%. Moreover, ground delay cost has much more impact on network decisions than air delay cost, and network decisions are insensitive to changes in diversion cost. Furthermore, the analysis of the tradeoff between total network cost and overtaking cost shows that adding costs for overtaking can significantly improve fairness at only a small increase in total system cost. A balanced total cost per flight among airlines can be achieved at a small increase in the network cost (0.2%–3.0%) when imposing airline fairness. In conclusion, the comprehensiveness of the model makes it useful for analyzing a wide range of alternatives for efficient ATFM.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"26 1","pages":"299-321"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84779401","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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