Bioethanol supply chain system planning under supply and demand uncertainties

A mixed integer stochastic programming model is established to support strategic planning of bioenergy supply chain systems and optimal feedstock resource allocation in an uncertain decision environment. The two-stage stochastic programming model, together with a Lagrange relaxation based decomposition solution algorithm, was implemented in a real-world case study in California to explore the potential of waste-based bioethanol production. The model results show that biowaste-based ethanol can be a viable part of sustainable energy solution for the future.     

Multistage optimization of the supply chains of biofuels

In this study, a mathematical model that integrates spatial and temporal dimensions is developed for strategic planning of future bioethanol supply chain systems. The planning objective is to minimize the cost of the entire supply chain of biofuel from biowaste feedstock fields to end users over the entire planning horizon, simultaneously satisfying demand, resource, and technology constraints. This model is used to evaluate the economic potential and infrastructure requirements for bioethanol production from eight waste biomass resources in California as a case study. It is found that, through careful supply chain design, biowaste-based ethanol production can be sustained at a compatible cost around $1.1 per gallon.     

A hybrid decomposition algorithm for designing a multi-modal transportation network under biomass supply uncertainty

This study presents a two-stage stochastic programming model for the design and management of a biomass co-firing supply chain network under feedstock supply uncertainty. To represent a more realistic case, we generate scenarios from prediction errors of the historical and forecasted biomass supply availabilities. We solve the model using a hybrid decomposition algorithm that combines Sample average approximation with an enhanced Progressive hedging algorithm. The proposed algorithm is validated via a real-world case study using data from Mississippi and Alabama. Computational results indicate that the proposed algorithm is capable of producing high quality solutions in a reasonable amount of time.     

Locating manufacturing and distribution centers: An integrated supply chain-based spatial interaction approach

This study investigates the facility location problem of transnational personal computer (PC) manufacturing for the domestic market of China. An integrated supply chain-based spatial interaction model is formulated to determine facility locations of the transnational PC manufacturing centers and regional product distribution centers in China, with the goal of maximizing the potential rate of return on facility investment. The numerical results show that the Shanghai municipality is ranked highest for siting both the manufacturing and distribution centers for a transnational PC manufacturing enterprise.     

A two-stage residential location and transport mode choice model with exposure to traffic-induced air pollution

Air pollution is an increasing concern to urban residents. In response, residents are beginning to adapt their travel behaviour and to consider local air quality when choosing a home. We study implications of such behaviour for the morphology of cities and population exposure to traffic-induced air pollution. To do so, we propose a spatially explicit and integrated residential location and transport mode choice model for a city with traffic-induced air pollution. Intra-urban spatial patterns of population densities, transport mode choices, and resulting population exposure are analysed for urban settings of varying levels of health concern and air pollution information available to residents. Numerical analysis of the feedback between residential location choice and transport mode choice, and between residents' choices and the subsequent potential impact on their own health suggests that increased availability of information on spatially variable traffic-induced health concerns shifts population towards suburban areas with availability of public transport. Thus, health benefits result from reduced population densities close to urban centres in this context. To mitigate population exposure, our work highlights the need for spatially explicit information on peoples' air pollution concerns and, on this basis, spatially differentiated integrated land use and transport measures.     

A supply chain network design model for biomass co-firing in coal-fired power plants

We propose a framework for designing the supply chain network for biomass co-firing in coal-fired power plants. This framework is inspired by existing practices with products with similar physical characteristics to biomass. We present a hub-and-spoke supply chain network design model for long-haul delivery of biomass. This model is a mixed integer linear program solved using benders decomposition algorithm. Numerical analysis indicates that 100 million tons of biomass are located within 75 miles from a coal plant and could be delivered at $8.53/dry-ton; 60 million tons of biomass are located beyond 75 miles and could be delivered at $36/dry-ton.     

GIS-based method for assessing city parking patterns

Every car trip ends with a parking search and parking. However, current transportation research still lacks practical tools and methodologies to analyze parking needs and dynamics, which cannot be adequately performed at an aggregate level. This paper presents PARKFIT, a novel algorithm for estimating city parking patterns that is based on a spatially explicit high-resolution view of the inherently heterogeneous urban parking demand and supply. Using high-resolution data obtainable from most municipal GIS, we apply PARKFIT to evaluate the fit between overnight parking demand and parking capacity in the city of Bat Yam, both currently and within the framework of the Bat Yam 2030 transportation master plan. We then analyze PARKFIT’s capabilities and limitations, and supply PARKFIT as a free ArcGIS-based software.     

Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies

In this study, an approach to re-planning the multi-stage supply chain (SC) subject to disruptions is developed. We analyze seven proactive SC structures, compute recovery policies to re-direct material flows in the case of two disruption scenarios, and assess the performance impact for both service level and costs with the help of a SC (re)planning model containing elements of system dynamics and linear programming. In the result, an explicit connection of performance impact assessment and SC plan reconfiguration issues with consideration of the duration of disruptions and the costs of recovery has been achieved.     

Locating road-vehicle refueling stations

This study follows the concept of set cover for proposing a refueling-station-location model using a mixed integer programming method, based on vehicle-routing logics. Its solution uses only the easy-obtain data of the origin-destination distance matrix. A case study that focuses on the siting of refueling stations for achieving multiple origin-destination intercity travel via electric vehicles on Taiwan demonstrates the applicability of the model. Sensitivity analysis shows that greater vehicle range will result in a lower number of refueling stations that need to be sited. Range is crucial in reducing the facility-location costs, and therefore is an important issue in the development of alternative-fuel-vehicle technology.     

Accommodating spatial correlation across choice alternatives in discrete choice models: an application to modeling residential location choice behavior

This paper presents a modeling methodology capable of accounting for spatial correlation across choice alternatives in discrete choice modeling applications. Many location choice (e.g., residential location, workplace location, destination location) modeling contexts involve choice sets where alternatives are spatially correlated with one another due to unobserved factors. In the presence of such spatial correlation, traditional discrete choice modeling methods that are often based on the assumption of independence among choice alternatives are not appropriate. In this paper, a Generalized Spatially Correlated Logit (GSCL) model that allows one to represent the degree of spatial correlation as a function of a multi-dimensional vector of attributes characterizing each pair of location choice alternatives is formulated and presented. The formulation of the GSCL model allows one to accommodate alternative correlation mechanisms rather than pre-imposing restrictive correlation assumptions on the location choice alternatives. The model is applied to the analysis of residential location choice behavior using a sample of households drawn from the 2000 San Francisco Bay Area Travel Survey (BATS) data set. Model estimation results obtained from the GSCL are compared against those obtained using the standard multinomial logit (MNL) model and the spatially correlated logit (SCL) model where only correlations across neighboring (or adjacent) alternatives are accommodated. Model findings suggest that there is significant spatial correlation across alternatives that do not share a common boundary, and that the GSCL offers the ability to more accurately capture spatial location choice behavior.     

Charting the coast: spatial planning for tourism using public participation GIS

Coastal tourism continues to experience sustained growth on a global scale, leading to concerns regarding socio-cultural, economic and environmental impacts. To-date, the explicit integration of tourism development with coastal management has lagged behind more traditional planning concerns, presenting an opportunity and challenge for managers. Spatial planning using public participation geographical information systems (PPGIS) offers one solution for integration that is cognisant of the centrality of place in tourism. PPGIS was used to document spatially explicit data on place values, activities and development preferences along the remote, Aboriginal-managed Port Smith (Purnturrpurnturr) coastline in Western Australia. The research was developed and implemented as a collaborative partnership between Aboriginal custodians and University researchers. Ninety-seven questionnaires containing participatory mapping were conducted with residents and visitors. The participatory mapping approach successfully identified areas of potential conflict and allows tourism planners and managers to implement spatial planning that explicitly recognises and accounts for visitor values and preferences. Tourism, as well as marine spatial planning, can be enhanced by a holistic approach that considers both tangible and intangible socio-spatial data. Such an approach is likely to foster a more nuanced appreciation of what is valued in the landscape, providing greater insights to support sustainable long-term planning.     

Road accessibility and cohesion in lagging regions: Empirical evidence from Portugal based on spatial econometric models

A number of transportation infrastructure projects were conducted in Europe during the 1990s with the explicit objective of promoting regional cohesion goals through the improvement of the accessibility of peripheral regions. Given its peripheral situation in the Union, Portugal was a key target for some of these projects. A decade later, it is pertinent to ask whether the new infrastructure achieved its objective of promoting lagging regions. The objective of this paper is to analyze one of the lagging regions in Portugal, to try to establish the link between changes in accessibility levels brought by improvements in the transportation network, and two indicators commonly used in accessibility research (population and purchasing power), while controlling other variables of possible relevance, such as the levels of educational achievement of the population in the region. The analysis is based on the use of spatially autoregressive models which allow the representation of accessibility as spatially lagged variables of first and second order. The results place the contribution of accessibility changes in context, and point at the relevance of education as an alternative policy.     

Analyzing the impact of intermodal-related risk to the design and management of biofuel supply chain

This study presents a mathematical model that designs a reliable multi-modal transportation network for a biofuel supply chain system, where intermodal hubs are subject to site-dependent probabilistic disruptions. The disruption probabilities of intermodal hubs are estimated by using a probabilistic model which is developed using real world data. We developed an accelerated Benders decomposition algorithm to solve this challenging NP-hard problem. Numerical analysis show that the model selects to use intermodal hubs located in areas with low disruption probabilities. In case of a disaster, the reliable solution results in 6.21% savings over the minimum cost solution.     

Electric and hybrid car use in a free-floating carsharing system

A free-floating carsharing system is a flexible way to provide cars to members in a limited area. This paper discusses the user behavior and choice between electric and hybrid cars in a free-floating system. We use Auto-mobile's transactional and Global Positioning System (GPS) datasets to analyze car use. We develop a binomial logit model to find the probability of choosing an electric car when there is a choice. The analysis reveals that travel distance has a major influence on this probability: electric vehicles are less popular for distances of more than 24 km. Cold temperatures and being a female also decrease the probability, while higher energy level increases odds. The spatial analysis shows a difference in behavior between both car technologies. Trips made by electric cars are less dispersed spatially than those made by hybrid cars.     

Synchromodal hinterland freight transport: Model study for the port of Rotterdam

This paper develops a model that enables comparative analysis of intermodal and synchromodal operations from economic, societal, and environmental perspectives. The model captures relevant (day-to-day and within-day) dynamics in freight transport demand and supply, flexible multimodal routing with transfers and transhipments. The capacitated schedule-based assignment algorithm operating specifically at path level allows strategic modelling and evaluation accounting for the freight transport system at operational level. The Rotterdam hinterland container transport case study shows that synchromodal system is likely to improve transport service level, capacity utilization, and modal shift, but not to reduce delivery costs.     

Supplier hoarding,government intervention,and timing for post-disaster crop supply chain recovery

This work presents a multi-methodological approach to address the issue of post-disaster crop supply chain recovery under the influences of government intervention and supplier hoarding intention. A conceptual model which characterizes the antecedents of supplier hoarding intention is proposed, and validated using survey data. Grounded in the empirical study, an analytical model is then proposed for decision analysis of a two-tier crop supply chain recovering from post-disaster supply disruptions. Analytical results indicate that hoarding behavior reduces the time taken by supply chain members for supply recovery; and however, contributes to mixed effects on the expected profits of supply chain members.     

An empirical analysis of dockless bike-sharing utilization and its explanatory factors: Case study from Shanghai,China

Revealing dockless bike-sharing utilization pattern and its explanatory factors are essential for urban planners and operators to improve the utilization and turnover of public bikes. This study explores the dockless bike-sharing utilization pattern from the perspective of bike using GPS-based bike origin-destination data collected in Shanghai, China. In this paper, utilization patterns are captured by decoupling several spatially cohesive regions with intensive bike use via non-negative matrix factorization. We then measure the utilization efficiency of bikes within each sub-region by calculating Time to booking (ToB) for each bike and explore how the built environment and social-demographic characteristics influence the bike-sharing utilization with ordinary least squares (OLS) regression and geographically weighted regression (GWR) models. The matrix factorization results indicate that the shared bikes mainly serve a certain area instead of the whole city. In addition, the GWR model shows higher explanatory power (Adjusted R² = 0.774) than the OLS regression model (Adjusted R² = 0.520), which suggests a close relationship between bike-sharing utilization and the selected explanatory variables. The coefficients of the GWR model reveal the spatial variations of the linkage between bike-sharing utilization and its explanatory factors across the study area. This study can shed light on understanding the demand and supply of shared bikes for rebalancing and provide support for operators to improve the dockless bike-sharing utilization efficiency.     

How can GPS/GNSS tracking data be used to improve our understanding of informal transport? A discussion based on a feasibility study from Dar es Salaam

Informal transport is an important factor for people's daily mobility in most developing countries, in urban as well as in rural areas. It has grown rapidly in recent years and influences cities' appearances all around the world. But little is known about its operation, as informal transport is highly dynamic and its operation mostly unregulated by the state. This paper discusses how informal transport can be better understood by using GPS tracking data. The methodology is exemplified using results from a feasibility study from Dar es Salaam. The results show that GPS tracking has great potential to provide insights into the functionality of informal transport, such as its role as a feeder mode in the transport system, as well as into the comparative advantages and disadvantages of different modes of transport. Moreover, by delivering spatially locatable information on mobility developments, it can deliver important information for integrated planning with regard to better coordinating the interwoven developments of urban settlements, growing mobility demand, and transport supply.     

Spatial variations in active mode trip volume at intersections: a local analysis utilizing geographically weighted regression

Geographically weighted regression (GWR) models have been employed in previous studies regarding vehicular travel demands, but few studies have locally modeled walking travel demands at intersections to address the issue of spatially varying relationships. Harnessing a comprehensive collection of walking and bicycling traffic counts over 10 years in Chittenden County, Vermont, US, along with socioeconomic characteristics, transit accessibility indices, land use attributes and characteristics of intersections and roadway networks, this study utilizes GWR models to identify whether there are spatially varying relationships between active mode travel demands and ambient built-environment attributes. One Ordinary Least Square (OLS) model and two GWR models were parametrically calibrated: a full GWR model of all local variables and a mixed GWR model of both global and local variables. K-fold cross-validation method is used to select variables that significantly influence the volume of active travel modes in the OLS model. The uniform set of variables is investigated in two GWR models. Only residuals of the mixed GWR model exhibit spatial independence. The prediction accuracy of the three models is respectively compared by means of the k-fold cross-validation method. Results show that the mixed GWR model has higher prediction accuracy, while the other two models have roughly the same level of performance. We find that not all independent variables possess a spatially varying relationship with active mode volumes. The flexibility of the mixed GWR model that allows some independent variables to be global strengthens its prediction power. With these findings, transportation planners can dynamically estimate bicycle and pedestrian volumes at widespread intersections, and this geographical realism would facilitate local transportation planning, facility design, safety enhancement and operation analysis, as well as instilling new insights into interdisciplinary spatial research domain.     

A multiple dispatch and partial backup hypercube queuing model to analyze emergency medical systems on highways

The hypercube is a spatially distributed queuing model based on Markovian analysis approximations, used to analyze the configuration and operation of server-to-customer emergency systems. In the present study we adapted the model to analyze emergency medical systems (EMS) on highways, which operate within particular dispatching policies. The study takes into consideration that: the emergency calls are of different types; the servers are distinct (e.g., rescue ambulances, medical vehicles); only certain servers in the system can service calls in a given region (partial backup); and, depending on the type of call, one or more identical or distinct servers are immediately dispatched to service such calls (multiple dispatch). We also consider that the arriving calls take place either along the highway or at the home location of a server – in which case the server does not need to travel to the call location. Finally, we analyzed the computational results of applying such an approach to the case study of an EMS operating on Brazilian highways.