Showing papers in "Transportation Science in 2013"
TL;DR: The flow refueling location model was reformulated and a flexible mixed-integer linear programming model was presented, which was able to obtain an optimal solution much faster than the previous set cover version and could be solved in the maximum cover form in a reasonable time on the large-sized networks.
Abstract: Serious environmental and economic problems of using fossil fuels in transportation sections force managers to think of alternative fuels such as hydrogen, ethanol, biodiesel, natural gas, or electricity. Meanwhile, lack of fuel network infrastructures is a major problem, which needs to be investigated considering the number and optimal location of alternative fuel stations. In the literature, two different flow-based demand modeling concepts (the maximum cover and set cover) have been proposed for solving this problem. Because of the huge number of combinations of fuel stations for covering the flow of each path, the models are impractical for the real size problems. In this paper, the flow refueling location model was reformulated and a flexible mixed-integer linear programming model was presented, which was able to obtain an optimal solution much faster than the previous set cover version. The model also could be solved in the maximum cover form in a reasonable time on the large-sized networks.
213 citations
TL;DR: This paper addresses a variant of the PDP where requests can change vehicle during their trip and proposes new heuristics capable of efficiently inserting requests through transfer points embedded into an Adaptive Large Neighborhood Search.
Abstract: The pickup and delivery problem PDP consists in defining a set of routes that satisfy transportation requests between a set of pickup points and a set of delivery points. This paper addresses a variant of the PDP where requests can change vehicle during their trip. The transfer is made at specific locations called “transfer points.” The corresponding problem is called the pickup and delivery problem with transfers PDPT. Solving the PDPT leads to new modeling and algorithmic difficulties. We propose new heuristics capable of efficiently inserting requests through transfer points. These heuristics are embedded into an adaptive large neighborhood search. We evaluate the method on generated instances and apply it to the transportation of people with disabilities. On these real-life instances we show that the introduction of transfer points can bring significant improvements up to 9% to the value of the objective function.
174 citations
TL;DR: An effective Variable Neighborhood Search algorithm based on the use of cyclic-exchange neighborhoods that incorporates an adaptive mechanism to bias the random shaking step is developed and successfully used to solve MDVRPPC.
Abstract: In this paper, we investigate a routing problem arising in the last-mile delivery of small packages. The problem, called Multi-Depot Vehicle Routing Problem with Private fleet and Common carriers MDVRPPC, is an extension of the Multi-Depot Vehicle Routing Problem MDVRP where customers can either be served by the private fleet based at self-owned depots or by common carriers, i.e., subcontractors. We develop an effective Variable Neighborhood Search algorithm based on the use of cyclic-exchange neighborhoods that incorporates an adaptive mechanism to bias the random shaking step. The approach is successfully used to solve MDVRPPC as well as closely related problems, such as the MDVRP and the single-depot VRP with Private fleet and Common carriers VRPPC, obtaining high quality solutions within short computing time. Our extensive testing on these problems shows the positive impact of the adaptive mechanism with respect to a standard VNS algorithm.
138 citations
TL;DR: A new synthetic population generator in the class of the Synthetic Reconstruction methods, which makes explicit use of both continuous and discrete optimization and uses the χ2 metric to estimate distances between estimated and generated distributions.
Abstract: The advent of microsimulation in the transportation sector has created the need for extensive disaggregated data concerning the population whose behavior is modeled. Because of the cost of collecting this data and the existing privacy regulations, this need is often met by the creation of a synthetic population on the basis of aggregate data. Although several techniques for generating such a population are known, they suffer from a number of limitations. The first is the need for a sample of the population for which fully disaggregated data must be collected, although such samples may not exist or may not be financially feasible. The second limiting assumption is that the aggregate data used must be consistent, a situation that is most unusual because these data often come from different sources and are collected, possibly at different moments, using different protocols. The paper presents a new synthetic population generator in the class of the Synthetic Reconstruction methods, whose objective is to obviate these limitations. It proceeds in three main successive steps: generation of individuals, generation of household type's joint distributions, and generation of households by gathering individuals. The main idea in these generation steps is to use data at the most disaggregated level possible to define joint distributions, from which individuals and households are randomly drawn. The method also makes explicit use of both continuous and discrete optimization and uses the χ2 metric to estimate distances between estimated and generated distributions. The new generator is applied for constructing a synthetic population of approximately 10,000,000 individuals and 4,350,000 households localized in the 589 municipalities of Belgium. The statistical quality of the generated population is discussed using criteria extracted from the literature, and it is shown that the new population generator produces excellent results.
133 citations
TL;DR: This paper introduces the multiconstraint team orienteering problem with multiple time windows MC-TOP-MTW, and presents a fast and effective algorithm for tackling this problem, by hybridizing iterated local search with a greedy randomized adaptive search procedure.
Abstract: This paper introduces the multiconstraint team orienteering problem with multiple time windows MC-TOP-MTW. In the MC-TOP-MTW, a set of vertices is given, each with a service time, one or more time windows, and a score. The goal is to maximize the sum of the collected scores, by a fixed number of tours. The tours are limited in length and restricted by the time windows and additional constraints. Next to a mathematical formulation of the MC-TOP-MTW, the main contribution of this paper is a fast and effective algorithm for tackling this problem, by hybridizing iterated local search with a greedy randomized adaptive search procedure. On a large test set, an average run has a score gap of only 5.19% with known high quality solutions, using 1.5 seconds of computational time. For 32% of the test instances, the known high quality solution was found or improved. This solution method also performs well on test instances of the TOPTW, the selective vehicle routing problem with time windows, and the MC-TOP-TW. A sensitivity analysis shows that the performance of the algorithm is insensitive to small changes in the parameter settings.
122 citations
TL;DR: A branch-and-price algorithm for the time-dependent vehicle routing problem with time windows TDVRPTW is presented, and a tailored labeling algorithm is used to solve the pricing problem.
Abstract: This paper presents a branch-and-price algorithm for the time-dependent vehicle routing problem with time windows TDVRPTW. We capture road congestion by considering time-dependent travel times, i.e., depending on the departure time to a customer, a different travel time is incurred. We consider the variant of the TDVRPTW where the objective is to minimize total route duration and denote this variant the duration minimizing TDVRPTW DM-TDVRPTW. Because of time dependency, vehicles' dispatch times at the depot are crucial as road congestion might be avoided. Because of its complexity, all known solution methods to the DM-TDVRPTW are based on meta-heuristics. The decomposition of an arc-based formulation leads to a set-partitioning problem as the master problem, and a time-dependent shortest path problem with resource constraints as the pricing problem. The master problem is solved by means of column generation, and a tailored labeling algorithm is used to solve the pricing problem. We introduce new dominance criteria that allow more label dominance. For our numerical results, we modified Solomon's data sets by adding time dependency. Our algorithm is able to solve about 63% of the instances with 25 customers, 38% of the instances with 50 customers, and 15% of the instances with 100 customers.
121 citations
TL;DR: An exact method for solving the symmetric two-echelon capacitated vehicle routing problem, a transportation problem concerned with the distribution of goods from a depot to a set of customers through aSet of satellite locations, based on an edge flow model that is a relaxation and provides a valid lower bound.
Abstract: This paper presents an exact method for solving the symmetric two-echelon capacitated vehicle routing problem, a transportation problem concerned with the distribution of goods from a depot to a set of customers through a set of satellite locations. The presented method is based on an edge flow model that is a relaxation and provides a valid lower bound. A specialized branching scheme is employed to obtain feasible solutions. Out of a test set of 93 instances the algorithm is able to solve 47 to optimality, surpassing previous exact algorithms.
116 citations
TL;DR: An integrated line planning and timetabling model is formulated with the objective of minimizing both user inconvenience and operational costs and is solved using a cross-entropy metaheuristic.
Abstract: An integrated line planning and timetabling model is formulated with the objective of minimizing both user inconvenience and operational costs. User inconvenience is modeled as the total time passengers spend in a railway system, including waiting at origin and transfer stations. The model is solved using a cross-entropy metaheuristic. The line plan and timetable of Israel Railways is used as a benchmark. Using the same amount of resources, the average journey time of passengers is reduced by 20%.
112 citations
TL;DR: This paper reviews container processing in railway yards from an operational research perspective and analyzes basic decision problems for the two most important yard types, namely conventional rail-road and modern rail-rail transshipment yards.
Abstract: In spite of extraordinary support programs initiated by the European Union and other national authorities, the percentage of overall freight traffic moved by train is in steady decline. This development has occurred because the macroeconomic benefits of rail traffic, such as the relief of overloaded road networks and reduced environmental impacts, are counterbalanced by severe disadvantages from the perspective of the shipper, e.g., low average delivery speed and general lack of reliability. Attracting a higher share of freight traffic on rail requires freight handling in railway yards that is more efficient, which includes technical innovations as well as the development of suitable decision support systems. This paper reviews container processing in railway yards from an operations research perspective and analyzes basic decision problems for the two most important yard types: conventional rail--road and modern rail--rail transshipment yards. Furthermore, we review the relevant literature and identify open research challenges.
110 citations
TL;DR: It is demonstrated that the new approach balances the cost drivers of a terminal much better than the conventional way of operations planning does and that it enables significant cost reductions.
Abstract: In seaport container terminals, berth allocation, quay crane assignment, and quay crane scheduling problems are solved sequentially. However, this sequential way of planning the seaside operations often hinders obtaining a sufficient resource utilization and service quality at low cost. This paper provides a framework for aligning all decisions that have to be made in an integrative manner. The framework is laid down in three phases. Phase I estimates productivity rates for the cranes from the vessels' stowage plans. The productivity rates are used in Phase II to make berthing decisions and to assign crane capacity to vessels. Phase III determines detailed crane schedules and aligns the decisions made. The framework supports using well-known heuristics for solving the contained subproblems. Computational tests reveal that the integrated planning is computationally tractable for problem scenarios of realistic size. It is also demonstrated that the new approach balances the cost drivers of a terminal much better than the conventional way of operations planning does and that it enables significant cost reductions.
108 citations
TL;DR: This paper proposes a model based on an exponential number of variables that is solved via column generation that allows for a comparative analysis between the hierarchical and the integrated solution approach that confirms the added value of integration in terms of cost reduction and efficient use of resources.
Abstract: In this paper we study the simultaneous optimization of berth allocation and quay crane assignment in seaport container terminals. We propose a model based on an exponential number of variables that is solved via column generation. An exact branch and price algorithm is implemented to produce optimal integer solutions to the problem. In particular, we present several accelerating techniques for the master and the pricing problem that can be generalized to other branch and price schemes. Computational results show that the proposed approach outperforms commercial solvers. Furthermore, the developed algorithm allows for a comparative analysis between the hierarchical and the integrated solution approach that confirms the added value of integration in terms of cost reduction and efficient use of resources. To the best of our knowledge, this is the first exact branch and price algorithm for both the berth allocation problem and the berth allocation problem with quay crane assignment.
TL;DR: Different scheduling algorithms embedded within a tabu search heuristic are developed and the computational results confirm the benefits of using a sophisticated scheduling procedure when planning long-haul transportation.
Abstract: Long-haul carriers must comply with various safety rules which are rarely taken into account in models and algorithms for vehicle routing problems. In this paper, we consider the rules on truck driver safety during long-haul trips in the United States. The problem under study has two dominant features: a routing component that consists of determining the sequence of customers visited by each vehicle and a scheduling component that consists of planning the rest periods and the service time of each customer. We have developed different scheduling algorithms embedded within a tabu search heuristic. The overall solution methods were tested on modified Solomon instances, and the computational results confirm the benefits of using a sophisticated scheduling procedure when planning long-haul transportation.
TL;DR: This study indicates that the Frank-Wolfe methods are competitive for accuracy requirements ensuring link flow stability, and shows that CFW is globally convergent.
Abstract: We present versions of the Frank-Wolfe method for linearly constrained convex programs, in which consecutive search directions are made conjugate. Preliminary computational studies in a MATLAB environment applying pure Frank-Wolfe, conjugate direction Frank-Wolfe CFW, bi-conjugate Frank-Wolfe BFW, and “partanized” Frank-Wolfe methods to some classical Traffic Assignment Problems show that CFW and BFW compare favorably to the other methods. This spurred a more detailed study, comparing our methods to an origin-based algorithm. This study indicates that our methods are competitive for accuracy requirements ensuring link flow stability. We also show that CFW is globally convergent. We further point at independent studies by other researchers that show that our methods compare favorably with recent bush-based and gradient projection algorithms on computers with several cores.
TL;DR: It is shown that multiobjective PVRP models can achieve a balance between workforce management and travel distance goals, and with the proper parameters in place, workforce management principles may be successfully applied without sacrificing other operational objectives.
Abstract: Service quality and driver efficiency in the delivery industry may be enhanced by increasing the regularity with which a driver visits the same set of customers. However, effectively managing a workforce of drivers may increase travel distance, a traditional metric of the vehicle routing problem VRP. This paper evaluates the effect that workforce management has on routing costs, providing insight for managerial decision making. The analysis is presented in the context of the period vehicle routing problem PVRP, an extension of the VRP with vehicle routes constructed to service customers according to preset visit frequencies over an established period of time. We develop models to apply workforce management principles. Through a computational study with standard PVRP test cases and real-world delivery data, we show that multiobjective PVRP models can achieve a balance between workforce management and travel distance goals. With the proper parameters in place, workforce management principles may be successfully applied without sacrificing other operational objectives.
TL;DR: The computational study shows that the short sea fuel oil distribution problem occurring in the archipelago at Cape Verde can be solved to optimality within reasonable time by the use of improved formulations based on a combination of such strategies.
Abstract: We consider a short sea fuel oil distribution problem occurring in the archipelago at Cape Verde. Here, an oil company is responsible for the routing and scheduling of ships between the islands such that the demand for various fuel oil products is satisfied during the planning horizon. Inventory management considerations are taken into account at the demand side but not at the supply side. The ports have restricted opening hours each day, so multiple time windows are considered. In contrast to the ships in many other studies within ship routing and scheduling, ships here spend considerable time in the ports compared to at sea. Hence, the time in port is modeled in detail by incorporating both a variable unloading time and a setup time for loading different products in the same ports. A mathematical model of the problem is presented and it includes a combined continuous and discrete time horizon because of the multiple time windows and a daily varying consumption rate of the various products in the different ports. We discuss several strategies to improve the proposed model, such as tightening bounds, using extended formulations, and including valid inequalities. The computational study shows that the real problem can be solved to optimality within reasonable time by the use of improved formulations based on a combination of such strategies.
TL;DR: A solution procedure for the min-max vehicle routing problem VRP is developed by applying tabu search within the framework of Multiobjective Adaptive Memory Programming and compared to an implementation of the Non-dominated Sorting Genetic Algorithm---a well-known approach to multiobjective optimization.
Abstract: The min-max vehicle routing problem VRP is a variant of the classical VRP in which the objective is to minimize the duration of the longest route. Examination of the VRP literature indicates that the min-max VRP has received less attention than other variants have over the years. However, the problem has important practical applications, such as those related to routing school buses. In this setting, in addition to the min-max criterion imposed on the time it takes to complete the longest route, school districts are concerned with the minimization of the total distance traveled, which is the objective of the classical VRP. Hence, the problem is formulated as a bi-objective optimization model that trades off service i.e., the minimization of the longest route and operational cost i.e., the minimization of the total distance traveled. We develop a solution procedure for this problem by applying tabu search within the framework of Multiobjective Adaptive Memory Programming and compare it to an implementation of the Non-dominated Sorting Genetic Algorithm---a well-known approach to multiobjective optimization. We also assess the merit of the solution method by comparing our approximations with solution frontiers obtained with an e-constraint implementation.
TL;DR: In this paper, the authors present integer programming models of the service network design problem faced by less-than-truckload LTL freight transportation carriers and a solution approach for the large-scale instances that result in practical applications.
Abstract: We present integer programming models of the service network design problem faced by less-than-truckload LTL freight transportation carriers and a solution approach for the large-scale instances that result in practical applications. To accurately represent freight consolidation opportunities, the models use a fine discretization of time. Furthermore, the models simultaneously route freight and empty trailers and thus explicitly recognize the efficiencies presented by backhaul lanes. The solution approach can generate the traditional service network designs commonly used by LTL carriers but also enables the construction of designs that allow more flexibility, e.g., that allow freight routes to vary by day of week. An iterative improvement scheme is employed that searches a large neighborhood, each iteration using an integer program. Computational experiments using data from a large U.S. carrier demonstrate that the proposed modeling and solution approach has the potential to generate significant cost savings.
TL;DR: The results suggest that the Portuguese legacy carrier can improve their expected profits significantly, while diminishing the total number of flights and slightly increasing the passengers' average connecting time, suggesting that the mixed-integer linear optimization model is a significant addition to the airline planning toolbox.
Abstract: Airport congestion is a major cause for the large delays that currently affect the air transport industry. These delays have huge cost implications—for the U.S. economy these costs were estimated at $32.9 billion in 2007. In this paper, we present a mixed-integer linear optimization model aimed at assisting airlines in the making of integrated flight scheduling and fleet assignment decisions that take aircraft and passenger delay costs explicitly into account. The objective of the model is to maximize the expected profits of an airline that faces a given origin/destination-based travel demand and operates in congested, slot-constrained airports. Both airline competition and airline cooperation are dealt with in the model, though in a simplified manner. The model was applied to a case study involving the main network of TAP Portugal, which comprises 31 airports and 100 daily flight legs. The results obtained through the model suggest that the Portuguese legacy carrier can improve their expected profits sig...
TL;DR: A compact polynomial-sized representation for the aircraft routing problem is presented, which is then linearized and lifted using the reformulation-linearization technique, and two root-node strategies for further augmenting the model formulation are proposed.
Abstract: Given a set of flights for a specific fleet type, the aircraft routing problem (ARP) determines the flying sequence for each individual aircraft while incorporating specific considerations of minimum turn time, maintenance checks, as well as restrictions on the total accumulated flying time, the total number of takeoffs, and the total number of days between two consecutive maintenances. This stage is significant to airline companies as it directly assigns operational routes and maintenance breaks for each aircraft in service. Most approaches related to the problem adopt set partitioning formulations that include exponentially many variables, which requires the design of specialized column generation or branch-and-price algorithms and codes. In this paper, we present a compact polynomial-sized representation for the ARP, which is then linearized and lifted using the reformulation-linearization technique. In addition, we propose two root-node strategies for further augmenting the model formulation. The resu...
TL;DR: This paper proposes a new network-based mixed-integer linear programming (LP) formulation for the WAMRP; namely, weekly rotation-tour network model (WRTNM), and develops a diving heuristic to solve WRTNM efficiently and effectively.
Abstract: Most studies in airline operations planning research are focused on the optimization problems that deal with a daily flight schedule, which is considered to be the same for every day in the week. While the weekly schedule is more realistic and practical, it increases the complexity of the optimization problems drastically. In this paper, we present a novel weekly rotation-tour network representation for the weekly aircraft maintenance routing problem (WAMRP). Based on this representation, we propose a new network-based mixed-integer linear programming (LP) formulation for the WAMRP; namely, weekly rotation-tour network model (WRTNM). The main advantage of this formulation is that the size of WRTNM only increases linearly with the size of the weekly schedule, and it provides a very tight LP relaxation. In addition, because of the tight LP relaxation, we develop a diving heuristic to solve WRTNM efficiently and effectively. To assess the performance of WRTNM, we tested the WRTNM using eight real-life test c...
TL;DR: Numerical results indicate that operating margins may decrease 10% for reasonable-length port-of-entry closures, that margins may be eliminated completely without contingency plans, and that expected holding and penalty costs may increase 20% for anticipated increases in port- of-entry utilization.
Abstract: Ports-of-entry are critical components of the modern international supply chain infrastructure, particularly container seaports and airfreight hubs. The potential operational and economic impact resulting from their temporary closure is unknown but is widely believed to be very significant. This paper investigates one aspect of this potential impact, focusing specifically on the use of supply chain inventory as a risk mitigation strategy for a one supplier, one customer system in which goods are transported through a port-of-entry subject to temporary closures. Closure likelihood and duration are modeled using a completely observed, exogenous Markov chain. Order lead times are dependent on the status of the port-of-entry, including potential congestion backlogs of unprocessed work. An infinite-horizon, periodic-review inventory control model is developed to determine the optimal average cost ordering policies under linear ordering costs with backlogged demand. When congestion is negligible, the optimal policy is state invariant. In the more complex case of nonnegligible congestion, this result no longer holds. For studied scenarios, numerical results indicate that operating margins may decrease 10% for reasonable-length port-of-entry closures, that margins may be eliminated completely without contingency plans, and that expected holding and penalty costs may increase 20% for anticipated increases in port-of-entry utilization.
TL;DR: This study proposes new static and dynamic single-leg overbooking models and proposes a dynamic programming model, which is based on two streams of events that corresponds to the arrival of booking requests and the cancellations.
Abstract: Airline revenue management is concerned with identifying the maximum revenue seat allocation policies. Because a major loss in revenue results from cancellations and no-shows, overbooking has received significant attention in the literature over the years. In this study, we propose new static and dynamic single-leg overbooking models. In the static case we introduce two models: the first one aims to determine the overbooking limit and the second one is about finding the overbooking limit and the booking limits to allocate the virtual capacity among multiple fare classes. Because the second static model is hard to solve, we also introduce computationally tractable models that give upper and lower bounds on its optimal expected net revenue. In the dynamic case, we propose a dynamic programming model, which is based on two streams of events. The first stream corresponds to the arrival of booking requests and the second one corresponds to the cancellations. We conduct simulation experiments to illustrate the effectiveness of the proposed models.
TL;DR: A model that integrates certain aspects of the schedule design, fleet assignment, and aircraft-routing processes, while considering flight retiming and demand recapture issues, along with optional legs, itinerary-based demands, and multiple fare classes is proposed.
Abstract: Airline profits critically depend on the nature and efficiency of service they provide, and accrue from a complex planning process involving schedule design, fleet assignment, aircraft routing, and crew scheduling, which are interrelated to each other within the overall system. We propose in this paper a model that integrates certain aspects of the schedule design, fleet assignment, and aircraft-routing processes, while considering flight retiming and demand recapture issues, along with optional legs, itinerary-based demands, and multiple fare classes. Maintenance routing decisions, as well as through-flight opportunities, are additionally incorporated in our model, and we apply the reformulation-linearization technique to reduce its complexity while introducing hierarchical symmetry-breaking constraints, along with other classes of valid inequalities, to enhance its solvability. A Benders' decomposition-based method is designed to handle the resulting large-scale model formulation. Computational results ...
TL;DR: A planning approach is proposed that seeks to obtain a favourable trade-off between the two contrasting objectives, passenger service and operating cost, by modifying the timetable, referred to as the simultaneous vehicle scheduling and passenger service problem (SVSPSP).
Abstract: Passengers using public transport systems often experience waiting times when transferring between two scheduled services. In this paper we propose a planning approach that seeks to obtain a favourable trade-off between the two contrasting objectives, passenger service and operating cost, by modifying the timetable. The planning approach is referred to as the simultaneous vehicle scheduling and passenger service problem (SVSPSP). The SVSPSP is modelled as an integer programming problem and solved using a large neighborhood search metaheuristic. The proposed framework is tested on data inspired by the express-bus network in the Greater Copenhagen area. The results are encouraging and indicate a potential decrease of passenger transfer waiting times in the network of up to 20%, with the vehicle scheduling costs remaining mostly unaffected.
TL;DR: A mixed-integer optimization model that determines the optimal location and number of stations along a railway line that will be introduced over an existing transportation network, taking into account the sensitivity of rail ridership to time losses because of stops at intermediate stations, as well as static competition from other modes.
Abstract: Rail transportation has experienced a rebirth in the last few decades, and a very large investment will certainly be made in new railway lines in the years to come---especially in high-speed rail lines. The success of such investment is heavily dependent on rail ridership, which in turn is dependent on the location of railway stations. In this paper, we present a mixed-integer optimization model that determines the optimal location and number of stations along a railway line that will be introduced over an existing transportation network. The stations are chosen within a set of possible locations defined a priori according to the objective of maximizing travel cost savings. The model takes into account the sensitivity of rail ridership to time losses because of stops at intermediate stations, as well as static competition from other modes. The practical usefulness of the model is illustrated with a case study involving a high-speed rail line expected to be built in Portugal in the future: the Lisbon-Porto line.
TL;DR: In this paper, an adaptive path relinking framework is proposed to evolve a set of reference solutions on the basis of a novel Adaptive Path Relinking framework to generate provisional solutions based on the recurrence of particular solution attributes.
Abstract: This paper deals with one-to-many-to-one vehicle routing and scheduling problems with pickups and deliveries and studies the effect of various backhauling strategies. Initially, focus is given on problem instances with clustered backhauls where all delivery customers must be visited before pickup customers. Afterward, operational settings with mixed backhauls and varying visiting sequence restrictions with respect to the capacity of the vehicles are examined. The proposed solution method evolves a set of reference solutions on the basis of a novel Adaptive Path Relinking framework. The latter encompasses an adaptive multisolution recombination procedure to generate provisional solutions based on the recurrence of particular solution attributes. On return, these solutions are used as guiding points for performing search trajectories from initial reference solutions via tunneling. Computational results on benchmark data sets of the literature illustrate the competitiveness and robustness of the proposed approach compared to state-of-the-art solution methods for well-known vehicle routing and scheduling problems. Finally, various experiments are also reported to demonstrate the economic effect of different mixing levels and densities of linehaul and backhaul customers.
TL;DR: Results predict significant fuel-burn benefits from absorbing some of the delay as stand hold, as well as delay benefits from indirectly aiding the runway controllers by reducing runway queue sizes.
Abstract: This paper considers the problem of allocating pushback times to departing aircraft, specifying the time at which they will be given permission to push back from their allocated stand, start their engines, and commence their taxi to the runway. The aim of this research is to first predict the delay (defined as the waiting time at the stand or runway) for each departure, then to use this to calculate a pushback time such that an appropriate amount of the delay is absorbed at the stand, prior to starting the engines. A two-stage approach is used, where the feasibility of the second stage (pushback time allocation) has to be considered within the first stage (takeoff sequencing). The characteristics of this real-world problem and the differences between it and similar problems are thoroughly discussed, along with a consideration of the important effects of these differences. Differences include a nonlinear objective function with a nonconvex component; the integration of two sequence dependent separation pro...
TL;DR: A computational study of the approximation algorithm for capacitated location routing on benchmark instances and large-scale randomly generated instances reveals that the quality of the computed solutions is much closer to optimality than the provable approximation factor.
Abstract: An approximation algorithm for an optimization problem runs in polynomial time for all instances and is guaranteed to deliver solutions with bounded optimality gap. We derive such algorithms for different variants of capacitated location routing, an important generalization of vehicle routing where the cost of opening the depots from which vehicles operate is taken into account. Our results originate from combining algorithms and lower bounds for different relaxations of the original problem; along with location routing we also obtain approximation algorithms for multidepot capacitated vehicle routing by this framework. Moreover, we extend our results to further generalizations of both problems, including a prize-collecting variant, a group version, and a variant where cross-docking is allowed. We finally present a computational study of our approximation algorithm for capacitated location routing on benchmark instances and large-scale randomly generated instances. Our study reveals that the quality of the computed solutions is much closer to optimality than the provable approximation factor.
TL;DR: This work proposes an innovative interactive method to address the R C S P P, based on a novel search strategy of the criteria space, and shows that the developed solution strategy is competitive with the most efficient strategies known thus far.
Abstract: The Resource Constrained Shortest Path Problem R C S P P is a variant of the classical shortest path problem and is of great practical importance. The aim is to find the shortest path between a given pair of nodes under additional constraints representing upper bounds on the consumption of resources along the path. In the scientific literature, different approaches have been defined to solve the R C S P P. In this work we propose an innovative interactive method to address the R C S P P, based on a novel search strategy of the criteria space. The performance of the proposed approach is evaluated on the basis of an extensive computational study by considering benchmark instances. A comparison with the state-of-the-art approaches developed for the R C S P P is also carried out. The computational results have shown that the developed solution strategy is competitive with the most efficient strategies known thus far.
TL;DR: Stochastic optimization models are developed to determine the numbers of slots to make available over the course of a day, controlling for the long-term uncertainty induced in arrival or departure capacities because of weather conditions.
Abstract: At many congested airports, access rights are governed by a system of slot controls. A slot conveys to its owner the right to schedule an operation (flight arrival or departure). In this paper, stochastic optimization models are developed to determine the numbers of slots to make available over the course of a day, controlling for the long-term uncertainty induced in arrival or departure capacities because of weather conditions. Three related integer programming formulations for this problem are presented, which vary both in their computational properties and the economic trade-offs modeled. The models are compared both analytically and computationally. Experiments using data from New York's LaGaurdia Airport are reported to demonstrate the impact of these models on optimizing slot profiles while considering long-term capacity uncertainty and several policy objectives.