An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: A case study of medical needle and syringe supply chain

This paper proposes a multi-objective possibilistic programming model to design a sustainable medical supply chain network under uncertainty considering conflicting economic, environmental and social objectives. Effective social and environmental life cycle assessment-based methods are incorporated in the model to estimate the relevant environmental and social impacts. An accelerated Benders decomposition algorithm utilizing three efficient acceleration mechanisms is devised to cope with computational complexity of solving the proposed model. Computational analysis is also provided by using a medical industrial case study to present the significance of the proposed model as well as the efficiency of the accelerated Benders decomposition algorithm.     

Twenty years of accessibility improvements. The case of the Spanish motorway building programme

This paper presents an exploratory spatial data analysis of accessibility impacts of a large-scale national motorway building programme. Accessibility is calculated for Spanish municipalities from 1980 to 2000 using two indicators: motorway network access and market potential accessibility. The average distance from municipalities to their nearest motorway has been reduced markedly over this period. Market potential accessibility maps show that there have been gains in all locations, but some of the highest gains occurred in some of the more peripheral regions. A review of the theoretical and empirical literature on the economic impacts of transport investments indicates important implications for regional development.     

Modeling downstream petroleum supply chain: The importance of multi-mode transportation to strategic planning

This paper proposes a deterministic mixed integer linear programming (MILP) model for downstream petroleum supply chain (PSC) network to determine the optimal distribution center (DC) locations, capacities, transportation modes, and transfer volumes. The model minimizes multi-echelon multi-product cost along the refineries, distribution centers, transportation modes and demand nodes. The relationship between strategic planning and multimodal transportation is further elucidated. A case study was considered with real data from the U.S. petroleum industry and transportation networks within Geographic Information System (GIS). A scenario analysis is also conducted to demonstrate the impact of key parameters on PSC decisions and total cost.     

Using cellular data to analyze the tourists' trajectories for tourism destination attributes: A case study in Hualien,Taiwan

This study adopts social network analysis (SNA) to identity the spatial role of nodes in a tourism region. The trajectory of multi-destination tourists will provide great information for tourism management, but tracking tourists' movements is a big challenge. Thanks to the popularity of mobile communication devices (mobile phones) nowadays, these can passively or actively provide information on owners' movement. This study uses call detail records (CDR), which are mobile phone positioning data, to construct a trajectory network of tourists in Hualien, Taiwan. Three types of centralities, as well as brokerage analysis and role analysis, are used to measure the relations in a complicated network. Degree centrality (DC) reveals popular nodes that have good relationships with others directly. Reach closeness centrality (RCC) shows indirect relationships with others. Betweenness centrality (BC) indicates the most important mediator nodes that help others to connect. BC and brokerage analysis are used to classify destinations into five brokers: the coordinator, consultant, gatekeeper, representative, and liaison. We also identify social positions of each destination by role analysis. This study identifies a total of 78 nodes (origins and destinations) and their functions in the tourism network. We find some nodes have more than one mission, while some only have one. The functions of nodes can be different on weekdays and weekends. We distinguish the tourism region into different size of districts. The results of this study can help managers consolidate strategies for tourism marketing.     

The spatial impacts model of trans-strait fixed links: A case study of the Pearl River Delta,China

The mage trans-strait fixed links are constructed in different countries to promote regional economic and social development. This paper proposes a spatial impacts model for the trans-strait fixed links in the Pearl River Delta, China. To verify the rationality of the model, four quantitative indicators, including weighted average travel time, the economic linkage intensity, the economic linkage membership grade, and the fractal index are used to investigate the effect by construction of Humen Bridge, Hong Kong–Zhuhai–Macao Bridge and Shenzhen–Zhongshan Bridge. The results show that the Hong Kong–Zhuhai–Macao Bridge and Shenzhen–Zhongshan Bridge greatly improve the regional accessibility with a maximum decrease weighted average travel time of 1.38 h and 0.4 h. The central part of the Delta has greatest impacts. The links increase the economic linkage of cities of each side. The cities directly connected to the fixed links (Hong Kong, Shenzhen, Macao and Zhongshan) experience the highest increasing economic linkage. The regional spatial pattern evolves from unipolarity to multi-polarity and integrates a more advanced and sophisticated spatial network. The theoretical spatial impacts model for the trans-strait fixed links accords with the actual development in the Pearl River Delta.     

Winners and losers in the Arab uprisings: a Mediterranean tourism perspective

This paper analyses the effects of the recent Arab uprisings on tourism destinations located along the Mediterranean coastline. It uses time-series analysis to model international tourist arrivals. First, autoregressive integrated moving average models are estimated for the period 1980–2009 and predicted for 2010–2014 to establish suitable country controls. Second, Bayesian structural time-series models – designed primarily to determine causal impacts in on-line marketing campaigns – are used to establish the effects of the events in Tunisia, Egypt, Morocco, Turkey, Spain and Greece. The main conclusion is that the models clearly capture the negative impact of the uprisings in Tunisia and Egypt and their positive impacts on Morocco and Turkey. However, the effects on Spain and Greece are less clear and depend crucially on the set of controls used in the analysis.     

Predicting airline passengers’ loyalty using artificial neural network theory

The study explores a model for predicting airline loyalty using the antecedents indicated in previous studies. Data was collected using a questionnaire distributed to 614 domestic air passengers using the snowball sampling method. The measurement tool had 16 scale items constructed on the recommendations of previous studies. Passenger satisfaction, airline service quality, passenger perceived value, and airline image are identified as determinants for airline loyalty. The predictive analytical approach of Artificial Neural Network theory and covariance-based Structural Equation Modelling for determining causality is employed in the study. The artificial neural network model predicts airline loyalty with 89% accuracy. Sensitivity analysis suggests passenger satisfaction as the most significant predictor of airline loyalty. The causal study supports that passenger satisfaction mediates the relationship between airline service quality and airline loyalty.     

The structures and flows of a large tourist itinerancy network

This paper undertakes a detailed network analysis (NA) of the structures and flows of a large tourist itinerancy network emerging in six Southeast Asian countries, developed by aggregating 1668 tour itineraries accessed from 149 tour marketing websites. The analysis includes the examination of the network and of seven extracted subnetworks (modules). It identifies various macro-patterns in the network influenced by scale freeness, preferential attachment, and growth. It proposes that a small number of nodes and edges form supercores. These dynamical elements influence the network's emergence, which is occurring without central coordination. A network backbone formed by these supercores emphasizes the flows in the network. The implications are visualized by several conceptual models of tourist and related resource and revenue flows. It identifies that a basin and sink topology, which drains flows from the periphery towards a network backbone sink, is consistent with preferential attachment processes in the network. This paper assists in turning NA in tourism from textile metaphors towards research centred upon empirical relational data. In this case, it exposes a network emerging from the inherent physical connectedness of tourism.     

Entry effect of low-cost carriers on airport-pairs demand model using market concentration approach

The trend of open sky policies and growth of low-cost airlines, the topic of airport-pairs demand is gradually being addressed in the golden aviation circle of Northeast Asia. The variety of flight services among the four major metropolises with dual-airport systems leads to a competition-cooperation relationship existing between various airports and airlines. Therefore, this study investigates the causal relationship between the route-level passenger demand and influential factors using aggregate data collected through website observations. The empirical study focuses on direct flights of airport-pair routes among Taipei, Shanghai, Seoul, and Tokyo. Results of the passenger regression model indicate that frequency, code-share, and morning flights have positive impacts on increasing passenger numbers for airlines. Further, the market concentration degree of Herfindahl-Hirschman Index and entry effect of low-cost carriers are important for the route-level passenger demand. In addition, routes with departures and arrivals in hub airports have a considerable attraction relative to other airport-pair routes. Finally, the proposed passenger model performs well in predicting market share, especially for routes with high demand.     

Adding a freight network to a national interstate input–output model: a TransNIEMO application for California

The state of the nation’s infrastructure is the subject of widespread discussion and comment because it is thought to include many deteriorating and unsafe bridges. Ever since the terrorist attacks of 9/11, there has been increasing concern over the extent to which an attack on infrastructure could result in serious economic disruption. This research develops a model to analyze the economic consequences of an attack on a major element of the highway network. We add a freight network to a national multiregional economic impact model and make freight traffic flows endogenous. The use of a sub-national interstate model recognizes that most infrastructure planning is at the state level and most political leaders’ interest is local. We base our approach on the National Interstate Economic Model (NIEMO) and refer to an elaboration that we name Transportation network and the National Interstate Economic Model (TransNIEMO). The new model enables us to study the state-specific and industry-specific economic impacts of some significant changes in the nature of highway freight movements. We tested the model for selected freight movements in and out of California. The results are entirely plausible and encourage us to elaborate and test the model for hypothetical disruptions of freight traffic throughout the US.     

The impacts of high-speed trains on British economic geography: a study of the UK’s InterCity 125/225 and its effects

The arrival of high-speed trains (HSTs) leads to unprecedented time-space effects, potentially assisting face-to-face contacts in knowledge-generating process, yet its wider spatial-economic impacts are still open to debate. This research attempts to fill in this gap with empirical evidence by studying the inter-regional impacts of upgraded British InterCity 125/225 on British economic geography over 30 years. The six study routes of London-outbound trains serving major railways stations in 26 local authorities were selected as units of analysis, with the observation of differential effects both between HSTs and non-HSTs towns and among each group of towns, to understand time-space effects of InterCity 125/225 whose potential impacts on local economic strength and knowledge intensive development. The key findings suggest that the differential HST effects on British economic geography could be classified into three influential zones, namely 1-h, 2-h, and over-2-h towns. HST has had substantial and demonstrable effects in aiding this transition within a 2-h travel limit of London, thus helping to generate renewed economic growth, but that the effects have not been automatic or universal. Regarding future British HST policy, the implications arising from this study is that cities connected to a new HST could seize opportunities which non-HST cities will not be able to do so, but this is not a zero-sum situation. Instead, it involves a national strategy to develop a hierarchical network with HST between London and key regional hubs, well integrated at these hubs with intra-regional transport systems. This indicates a strong case for a finer-grained and deeper probing analysis at an intra-regional level of the potential for rail improvement as an agent of change in city-region development. We hope to report further on this in a subsequent paper.     

A Comparative Analysis of Three Types of Tourism Demand Forecasting Models: Individual,Linear Combination and Non‐linear Combination

This paper investigates the combination of individual forecasting models and their roles in improving forecasting accuracy and proposes two non‐linear combination forecasting models using Radial Basis Function and Support Vector Regression neural networks. These two non‐linear combination models plus the standard Multi‐layer Perceptron neural network‐based non‐linear combination model are examined and compared with the linear combination models. The UK inbound tourism quarterly arrival data is used and the empirical results demonstrate that the proposed non‐linear combination models are robust and outperform the linear combination models that currently dominate in the tourism forecasting literature. Copyright © 2013 John Wiley & Sons, Ltd.     

City resident attitudes to proposed tourism development and its impacts on the community

Much of the literature on community attitudes to tourism development relates to rural areas in developing countries while urban environments in developed countries and pre‐development scenarios have been neglected. The paper is concerned with resident attitudes to tourism development proposals and their perceived impacts in the Worsley area of the city of Salford, England. The results show that the community is divided on the issue of support for tourism development based on the perceived benefits and costs of tourism and that the anticipated negative environmental consequences are significantly more influential than positive economic or social impacts. The theoretical contribution and practical implications of the findings are discussed. Copyright © 2010 John Wiley & Sons, Ltd.     

Dynamic network design for reverse logistics operations under uncertainty

The design of reverse logistics network has attracted growing attention with the stringent pressures from environmental and social requirements. In general, decisions about reverse logistics network configurations are made on a long-term basis and factors influencing such reverse logistics network design may also vary over time. This paper proposes dynamic location and allocation models to cope with such issues. A two-stage stochastic programming model is further developed by which a deterministic model for multiperiod reverse logistics network design can be extended to account for the uncertainties. A solution approach integrating a recently proposed sampling method with a heuristic algorithm is also proposed in this research. A numerical experiment is presented to demonstrate the significance of the developed stochastic model as well as the efficiency of the proposed solution method.     

Analysis of intermodal freight from China to Indian Ocean: A goal programming approach

This paper presents an intermodal network optimization model to examine the competitiveness of 36 alternative routings for freight moving from China to and beyond Indian Ocean. The proposed model, which is built upon the principles of goal programming, is able to handle multiple and conflicting objective functions such as minimizing transportation cost, transit time and transit time variability while ensuring flow continuity and transit nodes compatibility among the rail, road, ocean vessel, airplane and inland waterway transports. Transportation time and cost obtained from comprehensive industry sources are then fed into the intermodal transport network connecting two important Chinese origins and four Indian destinations, from which the most attractive routes are identified. In addition, the paper investigates several non-dominated transportation cases through sensitivity analysis tests, and analyzes the potential competitiveness and possible influences of future route developments to current transportation patterns in Asia.     

On the design of closed-loop networks for product life cycle management: Economic,environmental and geography considerations

The management of the product life cycle needs industrial synergies along large-scale networks to collect, recycle, reuse, and recover the end-of-life products. This paper provides a tool to assess the enabling economic, environmental, and transport geography conditions to design sustainable closed-loop networks for the management of a generic product along its life-cycle. The proposed tool is built through a mixed-integer linear programming (MILP) model for the strategic design of a multi-echelon closed-loop network. The product life cycle is handled via a cascade through seven stages, including raw material suppliers, manufacturing plants, distribution centers, retailers, collection nodes for waste and by-products, recycling centers, and landfills.The model minimizes a cost-based and a carbon-based function to determine the optimal geographic location of the nodes of the network and the allocation of transport flows. The model is applied to a case study inspired by the furniture industry over the Italian geography and a multi-scenario analysis is illustrated. The resulting considerations on the economic, environmental performances of the network couple with the transport geography to provide guidelines for designer, logistics planners and regional geographers toward a circular economy scenario.     

Using causal machine learning for predicting the risk of flight delays in air transportation

Delays in air transportation are a major concern that has negative impacts on the airline industry and the economy. Given the complexity of the National Air Space system, predicting the risk of flight delays and identifying significant predictors is vital to risk mitigation. The purpose of this paper is to perform data mining using causal machine learning algorithms in the USELEI process (Understanding, Sampling, Exploring, Learning, Evaluating, and Inferring) to predict the probability of flight delays in air transportation using data collected from different sources. The findings indicated significant effects of predictors, including reported arrivals and departures, arrival and departure demands, capacity, efficiency, and traffic volume at the origin and destination airports on the risk of flight delays. More importantly, causal interrelationships among variables in a fully structural network are presented to how these predictors interact with one another and how these interactions lead to delay incidents. Finally, sensitivity analysis and causal inference can be performed to evaluate various what-if scenarios and form effective strategies to mitigate the risk of delays.     

Network-based spatial interpolation of commuting trajectories: application of a university commuting management project in Kyoto,Japan

This study presents an application of network-based spatial interpolation of student commuting trajectories from a series of origin–destination trip datasets. In particular, we incorporated multimodal public transportation networks, including bus networks, to estimate the student commuting routes. The student samples for this study were collected from an online travel diary survey conducted by Ritsumeikan University in Kyoto, Japan. The ArcGIS Network Analyst was used to construct spatial network datasets and reconstruct trajectories from the origin–destination trip dataset. In addition, line densities of estimated trajectories were calculated and displayed on maps for geovisualization. These maps helped us understand the precise locations of congestion and spatial patterns of student commuting, unlike linear representations of people’s movements that connect origins and destinations. Our study also showed that estimated trajectories can simulate quantitative impacts on travel time by promoting walking or the use of public transportation.     

A threshold covering flow-based location model to build a critical mass of alternative-fuel stations

To facilitate the transition to alternative-fuel vehicles (AFVs), researchers have developed models for optimally locating an initial refueling infrastructure for AFVs with limited driving range. Recently, clustering of stations has emerged as a strategy to encourage consumers to purchase AFVs by building a critical mass of stations. Clustering approaches, however, have focused on serving demands represented as nodes or arcs rather than origin-destination (O-D) trips. This study proposes a Threshold Coverage extension to the original Flow Refueling Location Model that focuses on the percentage of a zone's O-D trips that can be successfully completed given a typical driving range and location of stations. It is motivated by the idea that drivers in an area will not purchase an AFV unless a critical mass of the trips they regularly make can be completed. Therefore, the new model optimally locates p refueling stations on a network to maximize the sum of weighted demand of covered origin zones, where “covered” means that the zone exceeds a specified threshold percentage of their total outbound round trips that are refuelable. The model is tested on networks for Orlando and the state of Florida. As the threshold percentage is raised, fewer zones can surpass the threshold. Covered nodes increasingly cluster together, as do stations for serving their O-D flows. The model's policy implementation will provide managerial insights for some key concerns of the industry, such as geographic equity vs. critical mass, from a new perspective.     

Research on China's Tourism: A 35‐Year Review and Authorship Analysis

This study reviewed 333 articles on China's tourism from 96 English‐language academic journals within and outside of tourism from 1978 to 2012. Tourism policy and impacts, and tourism industry development and promotion were the two predominant research themes. Tourism development, policy, ecological impacts, attractions and markets were the five most popular topics. On the basis of an authorship relationship analysis, three research networks were producing most of the China tourism research output in English. A majority of the authors were based in Mainland China, the USA and Hong Kong. Trends and gaps in the literature on China's tourism were identified. Copyright © 2013 John Wiley & Sons, Ltd.