Solutions to the optimal network design problem with shipments related to transportation cost

Two versions of an optimal network design problem with shipments proportional to transportation costs are formulated. Extensions of an algorithm developed in prior research for solving these problems are proposed and tested. The performance of the algorithms is found to improve substantially as the dependence of shipments on costs is increased. Moreover, the optimal solutions obtained are unexpectedly robust with respect to a wide range of transportation cost assumptions. These findings could have important computational and policy implications if applicable to larger networks.     

Sequential optimization approach for the multi‐class user equilibrium problem in a continuous transportation system

We consider a city region with several facilities that are competing for customers of different classes. Within the city region, the road network is dense, and can be represented as a continuum. Customers are continuously distributed over space, and they choose a facility by considering both the transportation cost and market externalities. More importantly, the model takes into account the different transportation cost functions and market externalities to which different customer classes are subjected. A logit‐type distribution of demand is specified to model the decision‐making process of users' facility choice. We develop a sequential optimization approach to decompose the complex multi‐class and multi‐facility problem into a series of smaller single‐class and single‐facility sub‐problems. An efficient solution algorithm is then proposed to solve the resultant problem. A numerical example is given to demonstrate the effectiveness and potential applicability of the proposed methodology.     

Global optimization method for mixed transportation network design problem: A mixed-integer linear programming approach

This paper proposes a global optimization algorithm for solving a mixed (continuous/discrete) transportation network design problem (MNDP), which is generally expressed as a mathematical programming with equilibrium constraint (MPEC). The upper level of the MNDP aims to optimize the network performance via both expansion of existing links and addition of new candidate links, whereas the lower level is a traditional Wardrop user equilibrium (UE) problem. In this paper, we first formulate the UE condition as a variational inequality (VI) problem, which is defined from a finite number of extreme points of a link-flow feasible region. The MNDP is approximated as a piecewise-linear programming (P-LP) problem, which is then transformed into a mixed-integer linear programming (MILP) problem. A global optimization algorithm based on a cutting constraint method is developed for solving the MILP problem. Numerical examples are given to demonstrate the efficiency of the proposed method and to compare the results with alternative algorithms reported in the literature.     

A statistical approach to the traveling salesman problem

A statistical approach is shown to be adaptable to the N-city traveling salesman problem by considering route distances to be random variables which are continuous and normally distributed. A solution to the shortest route distance and path can be approximated by utilizing a Monte Carlo simulation to obtain a representative sample of possible journeys. The approach involves recursive statistical inference which is used to select next-city visits leading to the most probable minimum route path. A statistical selection of the minimum route path is computationally efficient and computer run time increases in proportion to the square of the number of cities as opposed to an (N - 1)! increase for a deterministic approach. The accuracy of the statistical approach is directly proportional to the number of Monte Carlo simulations.     

A justice-theoretic approach to the distribution of transportation benefits: Implications for transportation planning practice in the United States

Transportation improvements inevitably lead to an uneven distribution of user benefits, in space and by network type (private and public transport). This paper makes a moral argument for what would be a fair distribution of these benefits. The argument follows Walzer’s “Spheres of Justice” approach to define the benefits of transportation, access, as a sphere deserving a separate, non-market driven, distribution. That distribution, we propose, is one where the maximum gap between the lowest and highest accessibility, both by mode and in space, should be limited, while attempting to maximize average access. We then review transportation planning practice for a priori distributional goals and find little explicit guidance in conventional and even justice-oriented transportation planning and analyses. We end with a discussion of the implications for practice.     

On the dual approach to the traffic assignment problem

This paper attempts to explore the possibility of solving the traffic assignment problem with elastic demands by way of its dual problem. It is shown that the dual problem can be formulated as a nonsmooth convex optimization problem of which the objective function values and subgradients are conveniently calculated by solving shortest path problems associated with the transportation network. A subgradient algorithm to solve the dual problem is presented and limited computational experience is reported. The computational results are encouraging enough to demonstrate the effectiveness of the proposed approach.     

The management of the rural transportation problem in Nigeria

In spite of the fact that about 70% of Nigeria's population live in rural areas, these areas have remained largely inaccessible. Not only do they lack motorable roads and organized public transport but also field surveys indicate that nearly all rural inhabitants do not have a private car. A Directorate of Food, Roads and Rural Infrastructure has been set up to manage the inaccessibility problem, among others. Whereas there are several conventional approaches to the management of the rural transportation problem, the Nigerian approach relies on the provision of roads. This restrictive approach with an emphasis on locational to the neglect of personal accessibility has various limitations which are analysed.     

A discrete-convex programming approach to the simultaneous optimization of land use and transportation

Three design problems are discussed in this article. First, it is shown that the network design problem with congestion reduces to an all-or nothing traffic assignment problem under some assumptions on the congestion function and the investment cost function. Second, the land use design problem is formulated as an extension of the Koopmans-Beckmann problem and a heuristic is proposed to solve this problem. Third, it is shown that the seemingly more complex problem of designing jointly a land-use plan and a transportation network reduces to a pure land-use design problem. All that is needed to solve the joint optimization problem is a shortest path algorithm and a heuristic to solve the land use design problem. Computational experience is reported for each algorithm.     

Solving a discrete multimodal transportation network design problem

This paper investigates the multimodal network design problem (MMNDP) that optimizes the auto network expansion scheme and bus network design scheme in an integrated manner. The problem is formulated as a single-level mathematical program with complementarity constraints (MPCC). The decision variables, including the expanded capacity of auto links, the layout of bus routes, the fare levels and the route frequencies, are transformed into multiple sets of binary variables. The layout of transit routes is explicitly modeled using an alternative approach by introducing a set of complementarity constraints. The congestion interaction among different travel modes is captured by an asymmetric multimodal user equilibrium problem (MUE). An active-set algorithm is employed to deal with the MPCC, by sequentially solving a relaxed MMNDP and a scheme updating problem. Numerical tests on nine-node and Sioux Falls networks are performed to demonstrate the proposed model and algorithm.     

Using activity-based models and the capability approach to evaluate equity considerations in transportation projects

Transportation - The capability approach (CA) is a theoretical framework that emphasizes the moral significance of individuals’ capability to achieve outcomes that they value and have reason...     

Combinatorial programming,statistical optimization and the optimal transportation network problem

This paper presents and evaluates a branch and bound algorithm and two heuristic hill-climbing techniques to solve a discrete formulation of the optimal transportation network design problem. For practical applications it is proposed to combine a hill-climbing algorithm with a uniform random generation of the initial solutions, thereby inducing a statistical distribution of local optima. In order to determine when to stop sampling local optima and in order to provide an estimate of the exact optimum based on the whole distribution of local optima, we follow previous work and fit a Weibull distribution to the empirical distribution of local optima. Several extensions are made over previous work: in particular, a new confidence interval and a new stopping rule are proposed. The numerical application of the statistical optimization methodology to the network design algorithms consolidates the empirical validity of fitting a Weibull distribution to the empirical distribution of local optima. Numerical experiments with hill-climbing techniques of varying power suggest that the method is best applied with heuristics of intermediate quality: such heuristics provide many distinct sample points for statistical estimation while keeping the confidence intervals sufficiently narrow.     

运用QC方法解决管片生产质量问题

武汉长江隧道钢筋混凝土管片在开始生产之前,成立了QC质量攻关小组。小组运用QC方法,严格按照PDCA循环步骤,针对在管片生产过程中出现的质量问题,进行了大量的试验、研究及摸索,并采取有效措施对混凝土配合比及生产工艺等方面进行了改进,最终成功解决了质量问题。     

Multiple objective optimization of the fleet sizing problem for road freight transportation

A fleet sizing problem (FSP) in a road freight transportation company with heterogeneous fleet and its own technical back‐up facilities is considered in the paper. The mathematical model of the decision problem is formulated in terms of multiple objective mathematical programming based on queuing theory. Technical and economical criteria as well as interests of different stakeholders are taken into account in the problem formulation. The solution procedure is composed of two steps. In the first one a sample of Pareto‐optimal solutions is generated by an original program called MEGROS. In the second step this set is reviewed and evaluated, according to the Decision Maker's (DM's) model of preferences. The evaluation of solutions is carried out with an application of an interactive multiple criteria analysis method, called Light Beam Search (LBS). Finally, the DM selects the most desirable, compromise solution.     

Genetic algorithms approach to the problem of the automated vehicle identification equipment locations

The automated vehicle identification (AVI) equipment location problem entails determination of the best locations for the automated vehicle identification equipment. The paper attempts to solve the AVI equipment location problem as a multi‐objective optimization problem, thus determining the best locations on the basis of several criteria. The developed model is based on genetic algorithms. Testing of the model developed on the greater transportation networks is certainly one of the most important directions for the future research, as much as the development of models based on other metaheuristic approaches (Simulated Annealing, Taboo Search). The results obtained in this stage of the research are promising.     

A cost and expected consequence approach to planning and managing railroad transportation of hazardous materials

Although hazardous materials (hazmat) account for around 140 million tons of all railroad freight traffic in the US, it has not received much attention from academic researchers. This is surprising especially when one considers the volume of hazmat moved by railroads in both North America and Europe. In this paper we develop a bi-objective optimization model, where cost is determined based on the characteristics of railroad industry and the determination of transport risk incorporates the dynamics of railroad accident. The optimization model and the solution framework is used to solve a realistic-size problem instance based in south-east US, which is then analyzed to gain managerial insights. In addition, a risk-cost frontier depicting non-dominated solutions is developed, followed by conclusion.     

An artificial neural network approach for evaluating transportation network improvements

As demand increases over time, new links or improvements in existing links may be considered for increasing a network's capacity. The selection and timing of improvement projects is an especially challenging problem when the benefits or costs of those projects are interdependent. Most existing models neglect the interdependence of projects and their impacts during intermediate periods of a planning horizon, thus failing to identify the optimal improvement program. A multiperiod network design model is proposed to select the best combination of improvement projects and schedules. This model requires the evaluation of numerous network improvement alternatives in several time periods. To facilitate efficient solution methods for the network design model, an artificial neural network approach is proposed for estimating total travel times corresponding to various project selection and scheduling decisions. Efficient procedures for preparing an appropriate training data set and an artificial neural network for this application are discussed. The Calvert County highway system in southern Maryland is used to illustrate these procedures and the resulting performance.     

Granger-causality between transportation and GDP: A panel data approach

This paper investigates the Granger-causality relationship between income and transportation of EU-15 countries using a panel data set covering the period 1970–2008. In the study, inland freight transportation per capita in ton-km (TRP), inland passenger transportation per capita in passenger-km (PAS), and road sector gasoline fuel consumption per capita in kg of oil equivalent (GAS) are used as transportation proxies and GDP per capita is used as measure of income. Our findings indicate that the dominant type of Granger-causality is bidirectional. Instances of one-way or no Granger-causality were found to correspond with countries with the lowest income per capita ranks in 1970 and/or in 2008. Although we conclude that there is an endogenous relationship between income and transportation, this is not observed until after an economy has completed its transition in terms of economic development.     

British location rents: an interpretation of the dual transportation problem for commodity flows

The transportation problem is solved for a number of commodity flow matrices for Britain for 1972. The commodities were selected because of the likelihood of the transportation problem producing a relatively good fit. Commodities were also chosen when it was thought that accessibility was a key location factor. The rationalisation of the interpretation of the dual values as shadow prices and location rents is outlined and, after discussing the merits of alternative means of analysing and presenting the dual values, the distribution of these values are compared with the actual location of industry. Some difficulties and further implications of the approach are suggested.