A tabu search algorithm to solve the integrated planning of container on an inter-terminal network connected with a hinterland rail network

Transport demand for containers has been increasing for decades, which places pressure on road transport. As a result, rail transport is stimulated to provide better intermodal freight transport services. This paper investigates mathematical models for the planning of container movements in a port area, integrating the inter-terminal transport of containers (ITT, within the port area) with the rail freight formation and transport process (towards the hinterland). An integer linear programming model is used to formulate the container transport across operations at container terminals, the network interconnecting them, railway yards and the railway networks towards the hinterland. A tabu search algorithm is proposed to solve the problem. The practical applicability of the algorithm is tested in a realistic infrastructure case and different demand scenarios. Our results show the degree by which internal (ITT) and external (hinterland) transport processes interact, and the potential for improvement of overall operations when the integrated optimization proposed is used. Instead, if the planning of containers in the ITT system is optimized as a stand-alone problem, the railway terminals may suffer from longer delay times or additional train cancellations. When planning the transport of 4060 TEU containers within one day, the benefits of the ITT planning without considering railway operations account for 17% ITT cost reduction but 93% railway operational cost growth, while the benefits of integrating ITT and railway account for a reduction of 20% in ITT cost and 44% in railway operational costs.     

Using a stages of change approach to explore opportunities for increasing bicycle commuting

Given the potential benefits of bicycling to the environment, the economy, and public health, many U.S. cities have set ambitious goals for increasing the bicycle share of commute trips. The Transtheoretical Model of Behavior Change, which seeks to describe how positive and permanent change can be fostered in individuals, may shed light on how cities can most effectively increase bicycle commuting. We use the model’s “stages of change” framework to explore the potential for increased bicycle commuting to the UC Davis campus in Davis, California. Our analysis uses data from the 2012 to 2013 UC Davis Campus Travel Survey, an annual online survey that is randomly administered to students and employees at UC Davis. Based on their responses to questions about current commute mode and contemplation of bicycle commuting, respondents are divided into five stages of change: Pre-contemplation, Contemplation, Preparation, Action, and Maintenance. We construct a Bayesian multilevel ordinal logistic regression model to understand how differences in socio-demographic characteristics, travel attributes, and travel attitudes between individuals explain their membership in different stages of change. In addition, we use this model to explore the potential of various intervention strategies to move individuals through the stages of change toward becoming regular bicycle commuters. Our results indicate that travel attitudes matter more to progression toward regular commute bicycling than travel attributes, tentatively supporting the efficacy of “soft” policies focused on changing travel attitudes.     

Analysis and Potential Alternatives for the Disputed South China Sea from Ocean Governance in the Polar Regions

Sovereignty claims over insular features and maritime jurisdiction in the South China Sea have been disputed for decades, and a governance regime to address ocean-related issues is urgently needed. This article first introduces the notion of a regime, and examines details of cooperation mechanisms in the Polar Regions. Lessons that can be applied to the South China Sea include that both soft and hard law regimes work to bring States concerned together to cooperate on the “commons” issues even when military conflicts or sovereignty disputes still exist. Consensus among bordering States would be necessary to make the South China Sea a “zone of peace.” Mechanisms that accommodate the various sovereignty claims and freeze existing and new claims to, as well as to prohibit military activities in, the South China Sea are recommended. Lastly, if a cooperative mechanism were to be established in the future, the Arctic regime would be more applicable to the South China Sea than the Antarctic regime due to their geographic nature. Thus, only States bordering the South China Sea should have voting and decision-making rights in the cooperative mechanism. As always, the political will of all parties is paramount to the success of such an endeavor.     

Robust supply vessel routing and scheduling

We solve the problem of tactical supply vessel planning arising in the upstream offshore petroleum logistics. Supply vessels deliver all the necessary materials and equipment to offshore installations from an onshore supply base according to a delivery schedule. The planning of supply vessels should be done so that their number is minimized and at the same time provide a reliable flow of supplies from the base. The execution of a weekly sailing plan is affected by weather conditions, especially in winter time. Harsh weather conditions increase the number of vessels required to perform the operations as well as the service times at the installations, and thus disrupt the schedule, leading to additional costs and reduced service level. We present a methodology for robust supply vessel planning enabling a trade-off analysis to be made between the schedules’ service level and vessels’ cost. The methodology involves the generation of multiple vessel schedules with different level of robustness using an adaptive large neighbourhood search metaheuristic and a subsequent discrete event simulation procedure for the assessment of the service level. To control the level of robustness we developed a concept of slacks and incorporated it into the metaheuristic algorithm.     

Integrated scheduling of m-truck,m-drone,and m-depot constrained by time-window,drop-pickup,and m-visit using constraint programming

The idea of deploying unmanned aerial vehicles, also known as drones, for final-mile delivery in logistics operations has vitalized this new research stream. One conceivable scenario of using a drone in conjunction with a traditional delivery truck to distribute parcels is discussed in earlier literature and termed the parallel drone scheduling traveling salesman problem (PDSTSP). This study extends the problem by considering two different types of drone tasks: drop and pickup. After a drone completes a drop, the drone can either fly back to depot to deliver the next parcels or fly directly to another customer for pickup. Integrated scheduling of multiple depots hosting a fleet of trucks and a fleet of drones is further studied to achieve an operational excellence. A vehicle that travels near the boundary of the coverage area might be more effective to serve customers that belong to the neighboring depot. This problem is uniquely modeled as an unrelated parallel machine scheduling with sequence dependent setup, precedence-relationship, and reentrant, which gives us a framework to effectively consider those operational challenges. A constraint programming approach is proposed and tested with problem instances of m-truck, m-drone, m-depot, and hundred-customer distributed across an 8-mile square region.     

Design and development of a hybrid ant colony-variable neighbourhood search algorithm for a multi-depot green vehicle routing problem

The traditional distribution planning problem in a supply chain has often been studied mainly with a focus on economic benefits. The growing concern about the effects of anthropogenic pollutions has forced researchers and supply chain practitioners to address the socio-environmental concerns. This research study focuses on incorporating the environmental impact on route design problem. In this work, the aim is to integrate both the objectives, namely economic cost and emission cost reduction for a capacitated multi-depot green vehicle routing problem. The proposed models are a significant contribution to the field of research in green vehicle routing problem at the operational level. The formulated integer linear programming model is solved for a set of small scale instances using LINGO solver. A computationally efficient Ant Colony Optimization (ACO) based meta-heuristic is developed for solving both small scale and large scale problem instances in reasonable amount of time. For solving large scale instances, the performance of the proposed ACO based meta-heuristic is improved by integrating it with a variable neighbourhood search.     

Impact evaluation of a mass transit fare change on demand and revenue utilizing smart card data

Transit fares are an effective tool for demand management. Transit agencies can raise revenue or relieve overcrowding via fare increases, but they are always confronted with the possibility of heavy ridership losses. Therefore, the outcome of fare changes should be evaluated before implementation. In this work, a methodology was formulated based on elasticity and exhaustive transit card data, and a network approach was proposed to assess the influence of distance-based fare increases on ridership and revenue. The approach was applied to a fare change plan for Beijing Metro. The price elasticities of demand for Beijing Metro at various fare levels and trip distances were tabulated from a stated preference survey. Trip data recorded by an automatic fare collection system was used alongside the topology of the Beijing Metro system to calculate the shortest path lengths between all station pairs, the origin–destination matrix, and trip lengths. Finally, three fare increase alternatives (high, medium, and low) were evaluated in terms of their impact on ridership and revenue. The results demonstrated that smart card data have great potential with regard to fare change evaluation. According to smart card data for a large transit network, the statistical frequency of trip lengths is more highly concentrated than that of the shortest path length. Moreover, the majority of the total trips have a length of around 15 km, and these are the most sensitive to fare increases. Specific attention should be paid to this characteristic when developing fare change plans to manage demand or raise revenue.     

GHG emissions in daily travel and long-distance travel in Germany – Social and spatial correlates

There is considerable research on the climate effects of daily travel, including research on the spatio-temporal and socioeconomic impact factors of daily travel and associated climate change effects. However, this is less true with respect to long-distance trips. This paper uses national transport survey data from Germany to point out differences in GHG emissions related to demographic, socioeconomic and spatial characteristics for daily and long-distance travel. Daily travel and long-distance travel are investigated simultaneously and separately using Logit and OLS regressions. The results show that transport-related GHG emissions from long-distance trips and daily trips are affected by sociodemographics in largely the same direction. In contrast, spatial attributes, like municipality size or density grade of the region, show a different picture. Per capita emissions in rural and suburban areas are higher for daily trips, but lower for long-distance trips than emissions caused by urban residents. While we cannot rule out the possibility of residential self-selection, our findings challenge the idea that compact urban development may help reduce CO₂ emissions once long-distance trips are taken into account.     

Analysis of a proposed mechanism for carbon-neutral growth in international aviation

In October 2013, the International Civil Aviation Organization (ICAO) announced that it would put in place a market-based mechanism to cap net greenhouse gas emissions from international civil aviation at 2020 levels. This paper analyses the obligations that would be placed on real airlines under an initial draft “Strawman” proposal that was originally formulated as a starting point for discussions within ICAO, and the extent to which such a proposal would succeed in keeping emissions at or below the desired level. The provisions of the ICAO proposal were then applied to more than 100 existing airlines. In order to protect commercial sensitivities, we used hierarchical cluster analysis to identify groups of different types of airlines. We report the results for these groups rather than for individual airlines. While ambiguities in the Strawman proposal complicated the analysis, we found that, depending on their size and rate of growth, airlines will be required to offset very different proportions of their emissions from international flights. A system of de minimis exemptions, as currently proposed, would benefit some rich countries as well as poor ones. Targeting such exemptions more narrowly would raise practical difficulties, which we describe. We conclude by recommending that ICAO design and implement a much simpler system; and propose one alternative.     

Designing an integrated distribution system for catering services for high-speed railways: A three-echelon location routing model with tight time windows and time deadlines

An emerging task in catering services for high-speed railways (CSHR) is to design a distribution system for the delivery of high-quality perishable food products to trains in need. This paper proposes a novel model for integrating location decision making with daily rail catering operations, which are affected by various aspects of rail planning, to meet time-sensitive passenger demands. A three-echelon location routing problem with time windows and time budget constraints (3E-LRPTWTBC) is thus proposed toward formulating this integrated distribution system design problem. This model attempts to determine the capacities/locations of distribution centers and to optimize the number of meals delivered to stations. The model also attempts to generate a schedule for refrigerated cars traveling from distribution centers to rail stations for train loading whereby meals can be catered to trains within tight time windows and sold before a specified time deadline. By relaxing the time-window constraints, a relaxation model that can be solved using an off-the-shelf mixed integer programming (MIP) solver is obtained to provide a lower bound on the 3E-LRPTWTBC. A hybrid cross entropy algorithm (HCEA) is proposed to solve the 3E-LRPTWTBC. A small-scale case study is implemented, which reveals a 9.3% gap between the solution obtained using the HCEA and that obtained using the relaxation model (RM). A comparative analysis of the HCEA and an exhaustive enumeration algorithm indicates that the HCEA shows good performance in terms of computation time. Finally, a case study considering 156 trains on the Beijing-Shanghai high-speed corridor and a large-scale case study considering 1130 trains on the Chinese railway network are addressed in a comprehensive study to demonstrate the applicability of the proposed models and algorithm.