Search

This paper addresses the issue of convergence to equilibrium in dynamic traffic assignment. Within-day time is considered to be a continuous variable, so that traffic flows and costs are functions of within-day time. Flow propagates along routes connecting origin-destination (OD) pairs with the demand for travel between each OD pair considered to be rigid (fixed from day to day although it can vary within day). Day-to-day time is also modelled as continuous with the day-to-day dynamical system derived naturally from the usual dynamical user equilibrium (DUE) condition. This paper focuses on the bottleneck model, which has deterministic vertical queueing at bottleneck link exits when flow exceeds capacity. A new property called decay monotonicity is introduced. The link delay (and hence link cost) function is shown to be a decay monotone function of link flow provided that the link capacity is continuously differentiable and positive. In a restricted version of the single bottleneck per route case, it is shown that link cost decay monotonicity implies route cost decay monotonicity. Decay monotonicity of the route cost function is shown to be sufficient for convergence to equilibrium of the dynamical system. [ABSTRACT FROM AUTHOR]

The vehicle routing problem with time windows is a hard combinatorial optimization problem that has received considerable attention in the last decades. This paper proposes a two-stage hybrid algorithm for this transportation problem. The algorithm first minimizes the number of vehicles, using simulated annealing. It then minimizes travel cost by using a large neighborhood search that may relocate a large number of customers. Experimental results demonstrate the effectiveness of the algorithm, which has improved 10 (17%) of the 56 best published solutions to the Solomon benchmarks, while matching or improving the best solutions in 46 problems (82%). More important perhaps,the algorithm is shown to be very robust. With a fixed configuration of its parameters, it returns either the best published solutions (or improvements thereof) or solutions very close in quality on all Solomon benchmarks. Very preliminary results on the extended Solomon benchmarks are also given. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of finding the minimum number of vehicles required to visit a set of nodes subject to time window and capacity constraints. The fleet is homogeneous and is located at a common depot. Each node requires the same type of service. An exact method is introduced based on branch and cut. In the computations, ever increasing lower bounds on the optimal solution are obtained by solving a series of relaxed problems that incorporate newly found valid inequalities. Feasible solutions or upper bounds are obtained with the help of greedy randomized adaptive search procedure (GRASP). A wide variety of cuts is introduced to tighten the linear programming (LP) relaxation of the original mixed-integer program. To find violated cuts, it is necessary to solve a separation problem. A substantial portion of the paper is aimed at describing the heuristics developed for this purpose. A new approach for obtaining feasible solutions from the LP relaxation is also discussed. Numerical results for standard 50- and 100-node benchmark problems are reported. [ABSTRACT FROM AUTHOR]

There is a vivid debate in cities all over the world on how to distribute the restricted space in urban areas among stakeholders. Urban design movements such as new pedestrianism or Copenhagenization advocate that too much space is attributed to cars. In this context, our research investigates the optimization of parking lots with the help of mathematical programming. For the given ground plot of a parking lot, we maximize the number of parking spaces each reachable via a driving lane, so that the urban space attributed to the parking of cars is efficiently used. Based on a grid of squares in which we rasterize the ground plot, this paper presents mixed-integer programs based on three different resolutions for orthogonal parking. Our computational study explores the tradeoff between the additional parking spaces promised by a higher resolution and the increased computational effort because of the larger solution space (and vice versa). We compare our optimization approaches with a sample of 177 real-world parking lots and show that optimization can be a serviceable car park design tool with the help of a case study. [ABSTRACT FROM AUTHOR]

Continuous location models generally use the Euclidean or rectangular norm to approximate travel in a transportation network. This paper considers a new and more accurate distance measure, termed the weighted one-two norm, which is a positive linear combination of the preceding norms. A directional bias function is introduced to show the equivalence of this distance measure to the well known l[sub p] norm. We then formulate a simple linear regression model to fit the parameters of our distance function to a given data set. Some novel applications based on standard statistical tests are derived which provide practical insights into the nature of networks with rectangular bias. The results are readily extended to other types of networks. [ABSTRACT FROM AUTHOR]

In this paper we study a vehicle routing problem where customers request multiple commodities. We study the impact on transportation cost from using vehicles dedicated to a single commodity compared with using flexible vehicles capable of carrying any set of commodities. With vehicles that carry multiple commodities, we consider when the delivery to a customer can be made by more than one vehicle. If multiple vehicles can be used, we examine when deliveries of individual commodities may be split and when they may not be split. The latter problem has not previously been studied, and we present a mathematical programming model for it. We use worst case and computational analysis to compare these different models. [ABSTRACT FROM AUTHOR]

In this paper,we present a solution approach for the inventory-routing problem. The inventory-routing problem is a variation of the vehicle-routing problem that arises in situations where a vendor has the ability to make decisions about the timing and sizing of deliveries, as well as the routing, with the restriction that customers are not allowed to run out of product. We develop a two-phase approach based on decomposing the set of decisions: A delivery schedule is created first, followed by the construction of a set of delivery routes. The first phase utilizes integer programming, whereas the second phase employs routing and scheduling heuristics. Our focus is on creating a solution methodology appropriate for large-scale real-life instances. Computational experiments demonstrating the effectiveness of our approach are presented. [ABSTRACT FROM AUTHOR]

There has been considerable recent interest in the dynamic vehicle routing problem, but the complexities of this problem class have generally restricted research to myopic models. In this paper, we address the simpler dynamic assignment problem, where a resource (container, vehicle, or driver) can serve only one task at a time. We propose a very general class of dynamic assignment models,and propose an adaptive,nonmyopic algorithm that involves iteratively solving sequences of assignment problems no larger than what would be required of a myopic model. We consider problems where the attribute space of future resources and tasks is small enough to be enumerated,and propose a hierarchical aggregation strategy for problems where the attribute spaces are too large to be enumerated. Finally,we use the formulation to also test the value of advance information,which offers a more realistic estimate over studies that use purely myopic models. [ABSTRACT FROM AUTHOR]

Modeling of transportation systems must invariably balance behavioral richness and computational tractability. In this paper, we develop a transportation equilibrium model and an algorithm for the simultaneous prediction of trip generation, trip distribution, modal split, and trip assignment on large-scale networks. The model achieves a practical compromise between behavioral and computational aspects of modeling the problem. It is formulated as an equivalent convex optimization problem, yet it is behaviorally richer than other models that can be cast as equivalent convex programs. Although the model is not as behaviorally rich as the most general equilibrium models, it has computational advantages. The applications reported in this paper of the model to real systems, i.e., the intercity transport network of Egypt and the urban transportation network of Austin, Texas, illustrate the computational attractiveness of the approach. These results indicate that equivalent optimization formulations are not as restrictive as previously thought, and hence, the equivalent convex programming approach for modeling and predicting equilibrium on transportation systems remains a viable and fruitful avenue for future consideration. [ABSTRACT FROM AUTHOR]

Numerous transportation applications as diverse as capital investment decision- making, vehicle fleet planning, and traffic light signal setting all involve some form of (discrete choice) network design. In this paper, we review some of the uses and limitations of integer programming-based approaches to network design, and describe several discrete and continuous choice models and algorithms. Our objectives are threefold--to provide a unifying view for synthesizing many network design models, to propose a unifying framework for deriving many network design algorithms, and to summarize computational experience in solving design problems. We also show that many of the most celebrated combinatorial problems that arise in transportation planning are specializations and variations of a generic design model. Consequently, the network design concepts described in this paper have great potential application in a wide range of problem settings. [ABSTRACT FROM AUTHOR]

In the locomotive-scheduling problem (or the locomotive-assignment problem), we must assign a consist (a set of locomotives) to each train in a preplanned train schedule so as to provide each train with sufficient locomotive power to pull the train from its origin to its destination. Locomotive-scheduling problems are among the most important problems in railroad scheduling. In this paper, we report the results of a study on the locomotive-scheduling problem as it is faced by CSX Transportation, a major U.S. railroad company. We consider the planning version of the locomotive-scheduling model (LSM) in which multiple types of locomotives exist, and we need to decide which set of locomotives should be assigned to each train. We present an integrated model that determines: the set of active and deadheaded locomotives for each train; the light-traveling locomotives from power sources to power sinks; and train-to-train connections (for which we specify which inbound trains and outbound trains can directly connect). An important feature of our model is that we explicitly consider consist bustings and consistency. A consist is said to be busted when a set of locomotives coming on an inbound train is broken into subsets to be reassigned to two or more outbound trains. A solution is consistent over a week if a train receives the same locomotive assignment each day that it runs. We will provide a mixed-integer programming (MIP) formulation of the locomotive-assignment problem. However, an MIP of this size cannot be solved to optimality or near optimality in acceptable running times using commercially available software. Using problem decomposition, integer programming, and very large-scale neighborhood search, we have developed a solution technique to solve this problem within 30 minutes of computation time on a Pentium III computer. Our solution obtained a potential savings of over 400 locomotives over the solution obtained by the in-house software developed by CSX. [ABSTRACT FROM AUTHOR]

We present a best-first heuristic search approach for determining an optimal policy for stochastic shortest path problem. A vehicle is to travel from an origin, starting at time t[sub 0], to a destination, where once the destination is reached a terminal cost is accrued. The terminal cost depends on the time of arrival. Travel time along each arc in the network is modeled as a random variable with a distribution dependent on the time that travel along the arc is begun. The objective is to determine a routing policy that minimizes expected total cost. A routing policy is a rule that assigns the next arc to traverse, given the current node and time. The heuristic search algorithm that we specialize to this stochastic problem is AO[sub *]. We demonstrate the significant computational advantages of AO[sub *], relative to dynamic programming, on the basis of run time and storage, using a 131-intersection network of the major roads in Cleveland, Ohio. Further computational experience is based on grid networks that were randomly generated to have characteristics similar to transportation networks, and on randomly generated unstructured networks. [ABSTRACT FROM AUTHOR]

The Vehicle Routing Problem with Back hauls is an extension of the capacitated Vehicle Routing Problem where the customers' set is partitioned into two subsets. The first is the set of Linehaul, or Delivery, customers, while the second is the set of Backhaul, or Pickup, customers. The problem is known to be NP-hard in the strong sense and finds many practical applications in distribution planning. In this paper we consider, in a unified framework, both the symmetric and the asymmetric versions of the vehicle routing problem with backhauls, for which we present a new integer linear programming model and a Lagrangian lower bound which is strengthened in a cutting plane fashion. The Lagrangian lower bound is then combined, according to·the additive approach, with a lower bound obtained by dropping the capacity constraints, thus obtaining an effective overall bounding procedure. A branch-and-bound algorithm, reduction procedures and dominance criteria are also described. Computational tests on symmetric and asymmetric instances from the literature, involving up to 100 customers, are given, showing the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Many transportation problems can be formulated as a linearly-constrained convex programming problem whose objective function consists of entropy functions and other cost-related terms. In this paper, we propose an unconstrained convex programming dual approach to solving these problems. In particular, we focus on a class of linearly-constrained entropy maximization problem with quadratic cost, study its Lagrangian dual, and provide a globally convergent algorithm with a quadratic rate of convergence. The theory and algorithm can be readily applied to the trip distribution problem with quadratic cost and many other entropy-based formulations, including the conventional trip distribution problem with linear cost, the entropy-based modal split model, and the decomposed problems of the combined problem of trip distribution and assignment. The efficiency and the robustness of this approach are confirmed by our computational experience. [ABSTRACT FROM AUTHOR]

In this paper we examine the following problem arising in vehicle routing and in production scheduling. Consider a single uncapacitated vehicle and n customers j with deadlines dj. The objective is to minimize the number of deliveries completed after their deadline. This problem is NP-hard, but a class of solvable cases can be characterized. An integer linear programming formulation is proposed, and the problem is solved optimally by means of a specialized enumerative algorithm. Computational results are presented for problems involving up to 100 customers. [ABSTRACT FROM AUTHOR]

In this paper, we present a primal-dual, heuristic solution approach for large-scale multicommodity network flow problems. The original problem is solved indirectly by repeatedly solving restated feasibility problems. Restrictions on problem size imposed by exact methods are overcome by solving the restated problems with a pure network-based heuristic procedure. To control the heuristic solution process, the network-based procedure is embedded within an iterative primal-dual framework. At each iteration, feasible dual solutions are generated, the dual objective function value strictly ascends, and primal solutions that are measurably closer to feasibility are determined. The algorithm terminates when the heuristic network-based procedure cannot determine an improved primal solution or when optimality is achieved. To demonstrate the effectiveness of the network-based solution strategy, a large-scale freight assignment problem encountered in the trucking industry is formulated as a multicommodity network flow problem. Two linear programming based, exact solution strategies (a primal-dual algorithm and a price-directive algorithm) are unable to achieve even an initial solution for this problem due to excessive memory requirements. The network-based heuristic, however, determines an optimal solution. We compare the performance of the new heuristic with that of the exact procedures using a set of smaller test problems. The effects of problem formulation and congestion are evaluated for each of the alternative solution strategies. [ABSTRACT FROM AUTHOR]

Location problems on a sphere can be solved easily by mathematical programming or geometrical methods i/it is known that all the demand points are located on a hemisphere. This paper presents an algorithm for determining whether m given demand points are on a hemisphere or not. In this algorithm, the great circle is rotated successively until the hemisphere which contains the m given demand points is found or it becomes known using our criteria that the m given demand points are not on a hemisphere. The convergence of this algorithm is proved, and two illustrative examples are presented. [ABSTRACT FROM AUTHOR]

This paper presents an overview of a decision-support model for the tactical scheduling of freight railroad traffic which is designed to support the weekly or monthly scheduling of rail operations. This initial version of the Schedule Analysis (SCAN) system is based upon notions from both simulation and combinatorial optimization, and is designed to provide decision makers with a tool for scheduling which provides a truly interactive system. After describing the conceptual underpinnings of the SCAN system and its associated algorithms, examples taken from a major railroad are used to illustrate the capabilities and limitations of the current system. Future research directions aimed at removing SCAN I's limitations are then discussed in light of recent advances in railroad management philosophies and technological advancements in this industry. [ABSTRACT FROM AUTHOR]

In this paper we consider two network optimization problems which have the following characteristics: control parameters vary continuously and network users behave according to War-drop's first principle of traffic equilibrium ("user-optimization"). For each problem, we study an exact algorithm based on constraint accumulation and a heuristic algorithm previously proposed in the literature. [ABSTRACT FROM AUTHOR]

This paper presents transportation equilibrium results that apply to both discrete choice models and network problems. Specifically, it shows that many network equilibrium problems admit an unconstrained extremal formulation and that unconstrained optimization algorithms may be used for their solution. Similar results are derived for equilibrium problems involving discrete choice models. It also shows that a certain class of stochastic networks exhibit unique equilibria and that simulation algorithms with fixed step sizes converge almost surely to the equilibrium point. [ABSTRACT FROM AUTHOR]

The combined distribution and assignment problem has been studied by many authors. The problems examined have been of different types. Some authors focus their attention on the user optimized combined distribution and assignment problem whereas others study a system optimized version. Apart from this difference there is also a difference in the way the distribution part and the assignment part are incorporated into the models. In this paper we study a combined distribution and assignment problem with entropy objective and cost constraints and give an algorithm based on Benders decomposition for its solution. The resulting solution method will be a "dual" method. [ABSTRACT FROM AUTHOR]

A probabilistic version of the maximal covering location problem is introduced here. The maximum available location problem (MALP) positions p servers in such a way as to maximize the population which will find a server available within a time standard with α reliability. The maximum availability problem builds on the probabilistic location set covering problem in concept and on backup covering and expected covering models in technical detail. MALP bears the same relation to the probabilistic location set covering problem as the deterministic maximal covering problem bears to the deterministic location set covering problem. The maximum availability problem is structured here as a zero--one linear programming problem and solved on a medium-sized transportation network representing Baltimore City. [ABSTRACT FROM AUTHOR]

In this paper, we describe an algorithm to solve conditional location problems (such as the conditional p-median problem or the conditional p-center problem) on networks, where demand points are served by the closest facility whether existing or new. This algorithm requires the one-time solution of a (p + 1)-unconditional corresponding location problem using an appropriate shortest distance matrix. [ABSTRACT FROM AUTHOR]

Time-constrained deliveries are one of the fastest growing segments of the delivery business, and vet there is surprisingly little literature that addresses time constraints in the context of stochastic customer presence. We begin to fill that void by introducing the probabilistic traveling salesman problem with deadlines (PTSPD). The PTSPD is an extension of the well-known probabilistic traveling salesman problem (PTSP) in which, in addition to stochastic presence, customers must also be visited before a known deadline. We present two recourse models and a chance constrained model for the PTSPD. Special cases are discussed for each model, and computational experiments are used to illustrate under what conditions stochastic and deterministic models lead to different solutions. [ABSTRACT FROM AUTHOR]

We consider a system in which multiple items are transferred from a warehouse or a plant to a retailer through identical capacitated vehicles, or by identical freight wagons. Any mixture of the items may be loaded onto a vehicle. The retailer is facing dynamic deterministic demand for several items, over a finite planning horizon. A vehicle incurs a fixed cost for each trip made from the warehouse to the retailer. In addition, there exist item-dependent variable shipping costs and inventory holding costs at the retailer, which are both constant over time. The objective is to find a shipment schedule that minimizes the total cost,while satisfying demand on time. We address and partially resolve the question regarding the problem's complexity by introducing a dynamic programming algorithm whose complexity is polynomial for a fixed number of items, but exponential otherwise. Our dynamic programming formulation is based on properties satisfied by the optimal solution, and uses an innovative way for partitioning the problem into subproblems. [ABSTRACT FROM AUTHOR]

We Considers the problem of determining a set of optimal tolls on the arcs of a multicommodity transportation network. The problem is formulated as a bilevel mathematical program where the upper level consists in a firm that raises revenues from tolls set on arcs of the network, while the lower level is represented by a group of users travelling on shortest paths with respect to a generalized travel cost. [ABSTRACT FROM AUTHOR]

We consider a bilevel programming formulation of a freight tariff-setting problem where the leader consists in one among a group of competing carriers and the follower is a shipper. At the upper level, the leader's revenue corresponds to the total tariffs levied, whereas the shipper minimizes its transportation cost, given the tariff schedule set by the leader. We propose for this problem a class of heuristic procedures whose relative efficiencies, on small problem instances, could be validated with respect to optimal solutions obtained from a mixed integer reformulation of the mathematical model. We also present numerical results on large instances that could not be solved to optimality by an exact method. [ABSTRACT FROM AUTHOR]

We analyze a randomized version of the deterministic linear programming (DLP) method for computing network bid prices. The method consists of simulating a sequence of realizations of itinerary demand and solving deterministic linear programs to allocate capacity to itineraries for each realization. The dual prices from this sequence are then averaged to form a bid price approximation. This randomized linear programming (RLP) method is only slightly more complicated to implement than the DLP method. We show that the RLP method can be viewed as a procedure for estimating the gradient of the expected perfect information (PI) network revenue. That is, the expected revenue obtained with full information on future demand realizations. The expected PI revenue can, in turn, be viewed as an approximation to the optimal value function. We establish conditions under which the RLP procedure provides an unbiased estimator of the gradient of the expected PI revenue. Computational tests are performed to evaluate the revenue performance of the RLP method compared to the DLP. [ABSTRACT FROM AUTHOR]

Railway passenger transportation plays a fundamental role in Europe, particularly in view of the growing number of trains offering valuable services such as high speed travel, high comfort, etc. Hence, it is advantageous to submit seat inventories to a Yield Management system to get the maximum revenue. We consider a deterministic linear programming model and a probabilistic nonlinear programming model for the network problem with non-nested seat allocation. A first comparative analysis of the computational results obtained by the two models, both in terms of the overall expected revenue and in terms of CPU time, is carried out. Furthermore, we describe a new nonlinear algorithm for the solution of the probabilistic nonlinear programming model that exploits the structure of the optimization problem. The numerical results obtained on a set of real data show that, for this class of problems, this algorithm is more efficient than other standard algorithms for nonlinear programming problems. [ABSTRACT FROM AUTHOR]

An investigation of the single-vehicle, many-to-many, immediate-request dial-a-ride problem is developed in two parts (I and II). Part I focuses on the "static" case of the problem. In this case, intermediate requests that may appear during the execution of the route are not considered. A generalized objective function is examined, the minimization of a weighted combination of the time to service all customers and of the total degree of "dissatisfaction" experienced by them while waiting for service. This dissatisfaction is assumed to be a linear function of the waiting and riding times of each customer. Vehicle capacity constraints and special priority rules are part of the problem. A Dynamic Programming approach is developed. The algorithm exhibits a computational effort which, although an exponential function of the size of the problem, is asymptotically lower than the corresponding effort of the classical Dynamic Programming algorithm applied to a Traveling Salesman Problem of the same size. Part II extends this approach to solving the equivalent "dynamic" case. In this case, new customer requests are automatically eligible for consideration at the time they occur. The procedure is an open-ended sequence of updates, each following every new customer request. The algorithm optimizes only over known inputs and does not anticipate future customer requests. Indefinite deferment of a customer's request is prevented by the priority rules introduced in Part L Examples in both "static" and "dynamic" cases are presented. [ABSTRACT FROM AUTHOR]