Branch-and-price algorithm for the location-routing problem with time windows

This study proposes a branch-and-price algorithm to solve the Location-Routing Problem with Time Windows (LRPTW) which has never been attempted with the exact solutions before. The problem is solved by the simplex algorithm in the master problem and elementary shortest path problems with resource constraint corresponding to column generation in the subproblem until only the non-negative reduced cost columns remain. The proposed algorithm can solve many testing instances effectively. The computational results and the effect of time windows are also compared and discussed.     

A comprehensive evacuation planning model and genetic solution algorithm

We consider the problem of evacuating an urban area. Several planning aspects need to be considered in such a scenario, which are usually considered separately. We propose a macroscopic multi-criteria optimization model that includes several such questions simultaneously, and develop a genetic algorithm to solve the problem heuristically. Its applicability is extended by also considering how to aggregate instance data, and how to generate solutions for the original instance starting from a reduced solution. In computational experiments using real-world data, we demonstrate the effectiveness of our approach and compare different levels of data aggregation.     

A reactive tabu search algorithm for the multi-depot container truck transportation problem

A container truck transportation problem that involves multiple depots with time windows at both origins and destinations, including the reposition of empty containers, is formulated as a multi-traveling salesman problem with time windows (m-TSPTW) with multiple depots. Since the problem is NP-hard, a cluster method and a reactive tabu search (RTS) algorithm are developed to solve the problem. The two methods are compared with the mixed integer program which can be used to find optimum solutions for small size problems. The computational results show that the developed methods, particularly the RTS algorithm, can be efficiently used to solve the problem.     

An exact solution approach for vehicle routing and scheduling problems with soft time windows

A new column generation based exact optimization approach for the vehicle routing and scheduling problem with semi soft time windows (VRPSSTW) is presented. Elementary shortest path problem with resource constraints and late arrival penalties is solved as a subproblem, which rises from the Dantzig–Wolfe decomposition method. Exact solutions of VRPSSTW and hard time windows variant are compared on Solomon’s benchmark instances as well as on an instance based on Tokyo road network. It was found that the VRPSSTW solution results in fewer routes thus overall costs are reduced and late arrival penalties contribute only a small fraction to total cost.     

Traveling in the three-dimensional city: applications in route planning, accessibility assessment, location analysis and beyond

Indoor environments are increasingly important spaces in contemporary three-dimensional cities. Handling these micro-scale spaces within the broader context of outdoor - urban environments is a significant challenge in transportation analysis and geographic information science. This complexity stems from the greater computational requirements, the specificity of indoor network data structures, as well as the inherent perceptual intricacies of 3D visualization. As built environments grow in size and in complexity, two-dimensional urban geography research reveals its limits. This paper advocates for 3D network-based urban research and showcases the feasibility of this approach for three specific types of urban analytical functionalities, namely route planning, spatial accessibility assessment, and facility location planning. The latter modeling functionality can be viewed as foundational elements for comprehensive network-based three-dimensional analytics of urban environments. The effectiveness of the data modeling scheme and of the integrated software application 3DCityNet is discussed through the three coupled geospatial modeling functionalities.     

A polynomial-time heuristic for the quay crane double-cycling problem with internal-reshuffling operations

One of great challenges in seaport management is how to handle containers under reshuffling, called reshuffles. Repositioning reshuffles in a bay (internal reshuffling) can improve the efficiency of quay cranes and help ports to reduce ship turn-around time. This paper studies the quay crane double-cycling problem with internal-reshuffling operations, and presents a fast solution algorithm. To reduce the number of operations necessary to turn around a bay of a vessel, the problem is first formulated as a new integer program. A polynomial-time heuristic is then developed. The analysis is made on the worst-case error bound of the proposed algorithm. Results are presented for a suite of combinations of problem instances with different bay sizes and workload scenarios. Comparisons are made between our algorithm and the start-of-the-art heuristic. The computational results demonstrate that our model can be solved more efficiently with CPLEX than the model proposed by Meisel and Wichmann (2010), and the proposed algorithm can well solve real-world problem instances within several seconds.     

The trilogy of distance, speed and time

Over the recent past there has been a dramatic increase in travel, mainly driven by the desire to move faster and over ever greater distances. This growth is unsustainable, and the continued growth in levels of mobility needs to be reassessed through substantially reducing the levels of consumption (energy and carbon) in transport. This means that travel activities should be based on shorter distances and slower speeds, with a more flexible interpretation of time constraints. Transport geographers should have a strong and instrumental role to play in this debate. This paper outlines the changing patterns of movement, before concentrating on urban areas where most daily travel takes place, and it examines the trilogy of distance, speed and time. The focus of the paper is on distance, and the role that land use planning and development, and technology can play in encouraging new forms of travel in cities, but there are strong implications on the ways in which speed and time are conceptualised. The conventional transport paradigm is heavily embedded in the belief that travel time needs to be minimised and consequently speeds need to be increased. The resulting impacts on travel distances have not been part of that debate, but reducing travel distances is central to sustainable transport.     

On the formulation and solution of the convoy routing problem

In this work, we will identify important variables that contribute to vehicular movement in an emergency environment. In particular, we formulate and pose the convoy routing problem. We suggest a method for modeling the problem and formulate a precise problem statement that significantly reduces the number of variables under consideration relative to similar previous work; even so, we prove that the decision version of this problem is NP-complete. After devising an algorithm using artificial intelligence techniques, we then empirically analyze this model (via software simulation) to get computational results on a single instance of the problem.     

A strategic planning model for the railway system accident rescue problem

The present study examines the location of emergency rescue problems for urban ambulance and railway emergency systems. The proposed model considers probabilistic rescue demand, independent busy fractions of ambulances, and the corresponding risk levels in railway segments. We formulate the proposed model using fuzzy multi-objective programming and solve it using a generic algorithm and a non-dominated sorting genetic algorithm-II. Computation results are analyzed by applying the model to a real-world Taiwan railway system. Analytical results demonstrate that a proper adjustment of the rescue resource location improves rescue effectiveness for railway rescue and urban medical service demand.     

Inequality and access to social services in Latin America: space–time constraints of child health checkups and prenatal care in Montevideo

This paper explores the role of space–time constraints in determining the conditions under which people access to basic social services. To do so, the paper addresses two empirical questions: (i) how travel time impacts families’ strategies for choosing and using social services, and (ii) how service characteristics become part of the constraints families have to face when using services. Both questions refer to problems that are of “time nature” (e.g., long waiting hours or the imposition of additional tasks and travel to finally obtain access to the service). The use of a space–time approach introduces a theoretical innovation for studying access to urban public social services in the region, shedding light on the micro-processes underneath macro-aggregated indicators. This is performed by examining in a qualitative manner the way in which space–time constraints are distributed among different types of families and by looking at families’ strategies for accessing services in a city like Montevideo, the capital of Uruguay. The empirical evidence is a qualitative study with 13 households with at least one child between 0 and 3 years of age. These households were selected from a purposive sample that allowed the study of a group of cases from different socioeconomic strata living in different geographical locations. The paper results suggest that households use travel time as an input to manage total time investment (travel plus participation) when defining a strategy to reach better services. In addition, empirical evidence reflects the distorting effects that uncertainty about the time to be spent in using the services may have on the activities of households.     

Socioeconomic differences in public acceptability and car use adaptation towards urban road pricing

Urban road pricing is regarded as an effective instrument to reduce traffic congestion and environmental-related problems in metropolitan areas. Whereas the overall impact of urban road pricing on car use adaptation and public acceptability is known, there are only inconsistent results concerning the socioeconomic differences in the response towards road pricing. However, this knowledge is necessary for the development of urban road pricing packages. This paper uses a segmentation approach to identify groups of car users with a similar background in relevant socioeconomic variables and compares their responses towards road pricing. Three groups are identified: young families, suburban families, and singles and couples. These groups indeed differ in their car use adaptation towards urban road pricing as well as in their preferred revenues use. While all three groups significantly reduced their private car use, the young families reduced their car use most, followed by the group of singles and couples. Complementary measures are discussed that are believed to facilitate car use adaptation of each group in response towards urban road pricing.     

Factors associated with disability paratransit's travel time reliability

This paper identifies some of the characteristics of trips and pick-up and drop-off locations that are associated with paratransit's travel time reliability. Following convention, reliability has been defined as the inverse of variability. Four measures of travel time variability have been used to examine reliability: Standard Deviation, Percent Variation, Misery Index, and Buffer Index. Regression models have been used to estimate these four variables with trip data from Access Link, the paratransit service provided by NJ TRANSIT pursuant to the Americans with Disabilities Act (ADA). A number of characteristics of the pick-up and drop-off locations as well as selected characteristics of the trips were used as independent variables of the models. The statistical significance of the independent variables varied depending on which measure of reliability was estimated, but a few variables were consistently associated with reliability in all four models. These variables were trip distance, booking type, winter season, density of motor vehicle crashes in pick-up and drop-off locations, and whether pick-ups occurred in suburban bus corridors or urban core areas. Because of the significance of the variables on motor vehicle crash density in pick-up and drop-off locations, an additional regression model was used to examine the effect of crash incidents on trip duration by considering drop-offs that occurred in locations immediately after a crash. The model showed that trips take 4 to 5% longer when crashes occur in locations prior to a drop off. Planning implications of the findings are discussed.     

Supply chain network design under profit maximization and oligopolistic competition

In this paper, we model the supply chain network design problem with oligopolistic firms who are involved in the competitive production, storage, and distribution of a homogeneous product to multiple demand markets. The profit-maximizing firms select both the capacities associated with the various supply chain network activities as well as the product quantities. We formulate the governing Nash–Cournot equilibrium conditions as a variational inequality problem and identify several special cases of the model, notably, a generalization of a spatial oligopoly and a classical oligopoly problem to include design capacity variables. The proposed computational approach, which is based on projected dynamical systems, fully exploits the network structure of the problems and yields closed form solutions at each iteration. In order to illustrate the modeling framework and the algorithm, we also provide solutions to a spectrum of numerical supply chain network oligopoly design examples.This paper makes a contribution to game theoretic modeling of competitive supply chain network design problems in an oligopolistic setting.     

A robust mathematical model and heuristic algorithms for integrated aircraft routing and scheduling,with consideration of fleet assignment problem

One of the most important airline's products is to determine the aircraft routing and scheduling and fleet assignment. The key input data of this problem is the traffic forecasting and allocation that forecasts traffic on each flight leg. The complexity of this problem is to define the connecting flights when passengers should change the aircraft to reach the final destination. Moreover, as there exists various types of uncertainties during the flights, finding a solution which is able to absorb these uncertainties is invaluable. In this paper, a new robust mixed integer mathematical model for the integrated aircraft routing and scheduling, with consideration of fleet assignment problem is proposed. Then to find good solutions for large-scale problems in a rational amount of time, a heuristic algorithm based on the Simulated Annealing (SA) is introduced. In addition, some examples are randomly generated and the proposed heuristic algorithm is validated by comparing the results with the optimum solutions. The effects of robust vs non-robust solutions are examined, and finally, a hybrid algorithm is generated which results in more effective solution in comparison with SA, and Particle Swarm Optimization (PSO).     

Economic geography and transportation conditions with endogenous time distribution amongst work, travel, and leisure

This study examines possible effects of transportation conditions upon economic growth and geography. The paper proposes a model of economic growth with travel time and cost, housing, residential distribution, amenity, and endogenous time distribution amongst work, travel, and leisure. It deals with a dynamic interaction among capital accumulation, land use, housing market, environmental change, and transportation conditions in a linear economy. Although transportation systems are simple in this paper in the light of contemporary literature in transportation research, the paper demonstrates a way to integrating some important models in the literature in transportation research, economic growth theory and urban economics so that the significance of transportation systems upon economies can be properly analyzed.     

Space-time dynamics: A modeling approach for commuting departure time on linked datasets

Commuters' departure time related decisions are important in time geography. Analytic tools have been proposed to capture the inherent choice determinants both in time and space. Although the dynamic aspects of the problem have been identified, most of the existing studies are based on static models. In this paper, a dynamic modeling framework is proposed to explore the relationship between commuters' departure time choices and the evolution of en route traffic. A data linkage method is developed to create an integrated dataset that enables the observation of commuters' reaction to changes in travel time and traffic conditions over time. A regional household travel survey is linked to travel information obtained from the Google Maps application program interface (API), creating a synthetic longitudinal dataset. Two decision rules are applied to model commuters' response to the evolution of traffic. The results indicate that travel time, distance to work location, flexibility in working schedule, expected arrival time, and commuters' sociodemographic influence departure time choices. It is also found that accounting for dynamics improves model fit and out-of-sample predictions. Both the dynamic model and the proposed data linkage method contribute to the understanding of human activities in space and time and can be used to enhance transportation demand analysis and urban policy studies.     

An adaptive approach to implementing innovative urban transport solutions

Urban transport is facing an increasing number of problems. Innovative technological solutions have been proposed for many of these problems. The implementation of these solutions, however, is surrounded by many uncertainties—for example, future relevant developments for urban transport demand and supply, the possible consequences of these developments for urban transportation system performance, and the way crucial stakeholders will value these consequences. In order to deal with these uncertainties, a flexible or adaptive policy is proposed that takes some actions right away and creates a framework for future actions that allow for adaptations over time as knowledge about urban transport technologies accumulates and critical events with respect to the implementation of these technologies take place. The adaptive approach is illustrated for three promising technological solutions for urban transport problems: (1) intelligent speed adaptation, (2) personal intelligent travel assistant, and (3) underground freight transport.     

Safety and travel time in cost-benefit analysis: A sensitivity analysis for North Rhine-Westphalia

Decisions on large-scale infrastructure concepts are frequently based on cost benefit analysis (CBA). Using 431 road projects evaluated in the integrated transport planning process in North Rhine-Westphalia, Germany (IGVP NRW) this paper examines the evaluation dimensions traffic safety (fatalities) and travel time in private passenger transport. The unit values of traffic fatalities and travel time are varied, and the effects of the variations on the rank order of the projects are examined. Target conflicts between safety and travel time are studied as well as the contribution of these two dimensions to the total benefit values. The sensitivity analysis shows that the evaluation results are fairly stable against variations in unit values of travel time and fatalities. The relevance of traffic safety in terms of its contribution to total benefit as well as in terms of the unit value appears to be relatively minor. The unit value of travel time is higher than that of lifetime. Some projects turn out as feasible in the evaluation even though they are likely to increase the number of fatalities. The paper therefore suggests the higher weighting of traffic safety in CBA.     

Arc routing problems to restore connectivity of a road network

After a disaster, restoring accessibility in the affected area is critical for response operations. We study two arc routing problems for clearing blocked roads. The first problem minimizes the time to reconnect the road network, while the second maximizes the total benefit gained by reconnecting network components within a time limit. For each problem, we develop a mixed integer programming formulation and two versions of a heuristic algorithm. We conduct computational experiments on Istanbul data and instances adapted from the literature. The heuristics achieve near-optimal or optimal solutions quickly in most of the tested instances.