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This paper proposes a computationally efficient simulation-based optimization (SO) algorithm suitable to address large-scale generally constrained urban transportation problems. The algorithm is based on a novel metamodel formulation. We embed the metamodel within a derivative-free trust region algorithm and evaluate the performance of this SO approach considering tight computational budgets. We address a network-wide traffic signal control problem using a calibrated microscopic simulation model of evening peak period traffic of the full city of Lausanne, Switzerland, which consists of more than 600 links and 200 intersections. We control 99 signal phases of 17 intersections distributed throughout the entire network. This SO problem is a high-dimensional nonlinear constrained problem. It is considered large-scale and complex in the fields of derivative-free optimization, traffic signal optimization, and simulation-based optimization. We compare the performance of the proposed metamodel method to that of a traditional metamodel method and that of a widely used commercial signal control software. The proposed method systematically and efficiently identifies signal plans with improved average city-wide travel times. [ABSTRACT FROM AUTHOR]

Advances in information technology and telecommunications, together with ever-growing amounts of data, offer opportunities for transportation companies to improve the quality of the service that they provide to their customers. This paper compares two methods motivated by the opportunity that the availability of data and technology gives to improve on current practice. In particular, the two solution approaches are explored in the context of a dynamic and stochastic routing problem in which a single, uncapacitated vehicle serves a set of known customers locations. One approach, sample-scenario planning, offers the potential for higher-quality solutions, but at the expense of greater computational effort. On the other hand, anticipatory insertion offers reduced computation and increased managerial ease, but with the potential for reduced solution quality due to restrictions on solution structure. Our results show that anticipatory insertion can often match the quality of sample-scenario planning, particularly when the degree of dynamism is low. [ABSTRACT FROM AUTHOR]

This paper considers a dynamic and stochastic routing problem in which information about customer locations and probabilistic information about future service requests are used to maximize the expected number of customers served by a single uncapacitated vehicle. The problem is modeled as a Markov decision process, and analytical results on the structure of the optimal policy are derived. For the case of a single dynamic customer, we completely characterize the optimal policy. Using the analytical results, we propose a real-time heuristic and demonstrate its effectiveness compared with a series of other intuitively appealing heuristics. We also use computational tests to determine the heuristic value of knowing both customer locations and probabilistic information about future service requests. [ABSTRACT FROM AUTHOR]

The single-sink, fixed-charge transportation problem has a variety of applications, including supplier selection, product distribution/fleet selection, and process selection. In this paper we present a dynamic programming algorithm for solving this important problem that is very easy to implement and that improves considerably in terms of computational attractiveness on the best methods in the literature. [ABSTRACT FROM AUTHOR]

Transfer optimization attempts to minimize the overall inconvenience to passengers who must transfer between lines in a transit network. Bus trips are scheduled to depart from their terminal so as to minimize some objective function measuring that inconvenience. In this paper, the transit network is assumed to be given, and the scheduled headway is treated as fixed on each line. We denote by t[sub 1] the departure time of the first bus on line I. {[sub l]} are termed "offset times," and constitute the decision variables of our model. To take into account stochastic travel times of buses, our treatment of transfer optimization employs a simulation procedure in combination with an optimization model. That model turns out to be a relaxation of the Quadratic Assignment Problem. It can incorporate a wide range of objective functions (measures of overall passenger disutility) and a variety of policies for holding buses at a transfer point. In the case where buses are not held at all, we show, for a number of different objective functions and transit networks, the negative consequences of optimizing transfers with a deterministic bus-travel-times assumption, if these travel times are in fact random variables. Suggestions are then made for future research. [ABSTRACT FROM AUTHOR]

In the dial-a-ride problem (DARP), a fleet of vehicles must serve transportation requests made by users that need to be transported from an origin to a destination. In this paper we develop the first exact algorithm which is able to either efficiently prove the infeasibility or to provide a feasible solution. Such an algorithm could be used in a dynamic setting for determining whether it is possible or not to accept an incoming request. The algorithm includes solution space reduction procedures, and filtering algorithms for some DARP relaxations. Computational results show that the filtering algorithms are effective and that the resulting algorithm is advantageous on the more constrained instances. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic (or multi-period) hub location problem. It proposes a branch-and-bound algorithm that uses a Lagrangian relaxation to obtain lower and upper bounds at the nodes of the tree. The Lagrangian function exploits the structure of the problem and can be decomposed into smaller subproblems that can be solved efficiently. In addition, some reduction procedures based on the Lagrangian bounds are implemented. These yield a considerable reduction of the size of the problem and thus help reduce the computational burden. Numerical results on a set of instances with up to 100 nodes and 10 time periods are reported. [ABSTRACT FROM AUTHOR]

This paper proposes an activity-based methodology for representing the allocation of time to discretionary activities during their programming and scheduling, based on the premise that both phases are to be considered contextually and two aspects of the same decision process. The aim of this work is to extend the treatment of utility maximization associated with carrying out two activities to J activities, so as to be able to segregate the time spent traveling from the total amount of time dedicated to out-of-home activities. The global structure of the model takes the form of a nested Tobit, particularly suited for reproducing a sequence of coupled choices. The first choice concerns dividing up overall discretionary time between activities inside and outside the home, then the second choice, subordinate to the first, involves rebudgeting the time between in- and out-of-home activities and trips. Thus the proposed model enables us to analyze the effects that each explicative variable exerts on trips segregated from activities outside the home and, last, during demand forecasting, the direct consequences of allocating discretionary time to trips following changes to an individual's time budget. A database created from a large-scale time-use survey (ISTAT 1988-1991) has been used for calibrating the model coefficients. [ABSTRACT FROM AUTHOR]

This paper presents simple formulae for the utility covariances of the nested logit (NL) model, and based on these defines a "scaled tree" that can be used as an aid for the interpretation of estimation results. The paper also shows that the full information log-likelihood function of linear-in-the-parameters NL models is concave in the utility parameters. Thus, conditional on the scaling parameters, full information maximum likelihood (FIML) searches cannot get trapped in local maxima. [ABSTRACT FROM AUTHOR]

We formulate the yield management problem as a continuous time, stochastic, dynamic programming model. We derive an expression for the expected revenue in terms of the stochastic booking processes and the control policies. The solution to the problem is found by maximizing the expected revenue over the possible control decisions. The solution is for an arbitrary number of fare classes and arbitrary booking curves. In particular, it requires no assumptions on the order of arrivals from different fare classes. The solution can be expressed in terms of a double recursion complex. At each node of the complex, the upper limit of a one-dimensional integral is solved to find a critical time for each fare class and for each value of remaining capacity. The critical times are the only values that need to be stored in the reservation control system to achieve optimal real-time control. This simple result is somewhat surprising given the complexity of even the static programming versions of the problem. We derive simple expressions of expected revenues and bid prices, which provide useful information to the user of a yield management system. [ABSTRACT FROM AUTHOR]

When studying multiclass-user transportation networks Dafermos made a strong assumption on symmetry in order to prove existence of a user-optimizing flow parterre In this paper existence is proved without the symmetry-hypothesis and it is shown that the methods of convex programming are not applicable. [ABSTRACT FROM AUTHOR]

This paper shows that the LOGIT type stochastic assignment/stochastic user equilibrium assignment can be represented as an optimization problem with only link variables. The conventional entropy function defined by path flows in the objective can be decomposed into a function consisting only of link flows. The idea of the decomposed formulation is derived from a consideration of the most likely link flow patterns over a network. Then the equivalence of the decomposed formulation to LOGIT assignment is proved by using the Markov properties that underlie Dial's algorithm. Through the analyses, some useful properties of the entropy function and its conjugate dual function (expected minimum cost function) have been derived. Finally, it is discussed that the derived results have a potential impact on the development of efficient algorithms for the stochastic user equilibrium assignment. [ABSTRACT FROM AUTHOR]

This paper discusses the mathematical and computational underpinnings of an optimization model that reduces United Parcel Service, Inc.'s trailer-on-flat-car (TOFC) usage costs by $4 million per year. It includes an informal statement of the TOFC problem, an integer linear program (ILP) formulation, and a transformation of this ILP into a pure minimum cost network flow program, whose software implementation provides the TOFC planner with a rich set of practical features. [ABSTRACT FROM AUTHOR]

This paper proposes different "dynamic" estimators using time-varying traffic counts to obtain (discrete) time-varying OD flows or average OD flows. All the estimators can combine counts with other available information, e.g., out-dated matrices and surveys, on a general network and can be formulated as optimization problems. In the case of time-varying OD flows, two types of estimators are proposed. Simultaneous estimators produce joint estimates of the whole set of OD matrices, one for each time slice, whereas sequential estimators produce a sequence of OD estimates for successive time slices. A particular type of simultaneous and sequential estimator (namely Generalized Least Squares) was tested on the Italian Brescia-Padua motorway for which "true" OD flows over a whole day were available. Results were generally satisfactory, showing that also in the "no a priori information" case significant estimates could be obtained. [ABSTRACT FROM AUTHOR]

This paper is concerned with some variational calculus/optimization problems involving the determination of constrained optimal path functions or routes of a single moving facility, which traverses a region encompassing a set of existing facilities. Situations of this type may involve a reconnaisance plane maintaining a surveillance of certain locations, a patrol car traveling in radio contact with several stations with the possibility of being dispatched to answer service calls originating over several designated regions, or an aircraft determining a flight plan over a region containing enemy missile sites. We formulate the general problem of maximizing total benefits, or maximizing the benefits per unit travel time, subject to constraints on the travel path function. The specific problems analyzed involve a direct distance related benefit (or cost) function, employing rectilinear or squared-Euclidean distance measures, and they constrain the paths to be straight lines between parallel boundaries or across rectangular regions, with both ends free or with one end fixed, or to be general paths between fixed end-points. In each case, the problem is reduced to a finite dimensional optimization problem. Illustrative examples and computational results on a personal computer are provided, and several extensions are proposed for future research. [ABSTRACT FROM AUTHOR]

In this paper we consider location-allocation problems in which the region to be served is a convex polygon having a uniform demand distribution. Both single and multifacility formulations are considered. For the single facility problem, an a algorithm which an efficient implementation of the Weiszfeld technique is developed and is shown to converge to a global optimal solution. This anlysis is extended to the nonconvex multifacility case. However, although global optimality of the fixed point that the algorithm converges to is not guaranteed, a method suggested for finding a good starting solution increases the likelihood of finding an optimal solution. Extensions of the above problems to include discrete de points and computational experience are also provided. [ABSTRACT FROM AUTHOR]

An operator of a network of battery swap stations for electric vehicles must make a long-term investment decision on the number of batteries and charging bays in the system and periodic short-term decisions on when and how many batteries to recharge. Both decisions must be made concurrently, because there exists a trade-off between the long-term investment in batteries and charging bays, and short-term expenses for operating the system. Costs for electric energy as well as demand rates for batteries are stochastic: We consider an infinite time horizon for operation of the system. We derive an optimization problem, which cannot be solved optimally in a reasonable time for real world instances. By optimally solving various small problem instances, we show the mechanics of the model and the influence of its parameters on the optimal cost. We then develop a near-optimal solution heuristic based on Monte Carlo sampling following the ideas of approximate dynamic programming for the infinite horizon dynamic program. We show that operating battery swap stations in a network where lateral transshipments are allowed can substantially decrease expected operating costs. The online appendix is available at https://doi.org/10.1287/trsc.2017.0781 [ABSTRACT FROM AUTHOR]

We study the mixed capacitated general routing problem (MCGRP) in which a fleet of capacitated vehicles has to serve a set of requests by traversing a mixed weighted graph. The requests may be located on nodes, edges, and arcs. The problem has theoretical interest because it is a generalization of the capacitated vehicle routing problem (CVRP), the capacitated arc routing problem (CARP), and the general routing problem. It is also of great practical interest since it is often a more accurate model for real-world cases than its widely studied specializations, particularly for so-called street routing applications. Examples are urban waste collection, snow removal, and newspaper delivery. We propose a new iterated local search metaheuristic for the problem that also includes vital mechanisms from adaptive large neighborhood search combined with further intensification through local search. The method utilizes selected, tailored, and novel local search and large neighborhood search operators, as well as a new local search strategy. Computational experiments show that the proposed metaheuristic is highly effective on five published benchmarks for the MCGRP. The metaheuristic yields excellent results also on seven standard CARP data sets, and good results on four well-known CVRP benchmarks, including improvement of the best known upper bound for one instance. [ABSTRACT FROM AUTHOR]

Time considerations have been largely ignored in the study of vehicle routing problems with stochastic demands, even though they are crucial in practice. We show that tour duration limits can effectively and efficiently be incorporated in solution approaches that build fixed, or a priori, tours for such problems. We do so by assuming that each tour must be duration feasible for all demand realizations, and determine the maximum duration of a given delivery tour by solving the optimization problem of an adversary. A computational study demonstrates the approach, and shows that enforcing tour duration limits impacts the structure of nearly-best solutions and may create the need for additional tours. However, for the instances considered, the price paid for robustness is small as the increase in total expected tour duration is modest. [ABSTRACT FROM AUTHOR]

The problem considered is that of locating N new facilities among M existing facilities with the objective of minimizing the maximum weighed Euclidean distance among all facilities. The application of nonlinear duality theory shows this problem can always be solved by maximizing a continuously differentiable concave objective subject to a small number of linear constraints. This leads to a solution procedure which produces very good numerical results. Computational experience is reported. [ABSTRACT FROM AUTHOR]

Patriksson (2004) analyzes the sensitivity of solutions of traffic equilibrium problems. One of his central results deals with directional differentiability in the elastic-demand case. Unfortunately, his proof contains crucial errors, and the purpose of this comment is to explain what they are and why they render the proof incorrect. The consequence is that Patriksson's proof of his Theorem 5 is invalid, so the theorem must be regarded as unproven. [ABSTRACT FROM AUTHOR]