Shelter location and transportation planning under hurricane conditions

This paper develops a scenario-based bilevel programming model to optimize the selection of shelter locations with explicit consideration of a range of possible hurricane events and the evacuation needs under each of those events. A realistic case study for the state of North Carolina is presented. Through the case study, we demonstrate (i) the criticality of considering multiple hurricane scenarios in the location of shelters, and; (ii) the importance of considering the transportation demands of all evacuees when selecting locations for public shelters.     

A Linear Programming approach for robust network revenue management in the airline industry

The classical revenue management problem consists of allocating a fixed network capacity to different customer classes, so as to maximize revenue. This area has been widely applied in service industries that are characterized by a fixed perishable capacity, such as airlines, cruises, hotels, etc.It is traditionally assumed that demand is uncertain, but can be characterized as a stochastic process (See Talluri and van Ryzin (2005) for a review of the revenue management models). In practice, however, airlines have limited demand information and are unable to fully characterize demand stochastic processes. Robust optimization methods have been proposed to overcome this modeling challenge. Under robust optimization framework, demand is only assumed to lie within a polyhedral uncertainty set (Lan et al. (2008); Perakis and Roels (2010)).In this paper, we consider the multi-fare, network revenue management problem for the case demand information is limited (i.e. the only information available is lower/upper bounds on demand). Under this interval uncertainty, we characterize the robust optimal booking limit policy by use of minimax regret criterion. We present an LP (Linear Programming) solvable mathematical program for the maximum regret so our model is able to solve large-scale problems for practical use. A genetic algorithm is proposed to find the booking limit control to minimize the maximum regret. We provide computational experiments and compare our methods to existing ones. The results demonstrate the effectiveness of our robust approach.     

Uncertainty propagation in a supply chain or supply network

A supply chain is characterized by uncertain demands (demand-side uncertainty) and uncertainties associated with the performances of the production facilities (supply-side uncertainty). In this paper, a method is proposed to plan production in a supply chain with a multi-echelon supply process with unreliable production facilities working in markets with uncertain demand. In such a system it is necessary to consider the global and cumulative effects on the performance of the entire supply chain. We introduce the salient features of uncertainty propagation in supply chains and demonstrate their impact quantitatively using a test problem from the automotive industry.     

基于行程时间可靠性的弹性需求SUE配流模型

从路段通行能力和路段行程时间关系的角度,提出行程时间可靠性和广义出行费用的概念,基于出行者根据行程时间和行程时间可靠性选择路径,以及路径上的交通流量满足Logit模型,建立弹性需求交通网络随机用户平衡(SUE)配流模型,并将其转化成等价的变分不等式形式,给出模型解的等价性和唯一性证明,最后对模型进行算法分析。     

Assessing the effects of access policies on travel mode choices in an Alpine tourist destination

Alternative Transportation Systems (ATS) can contribute to an overall reduction of visitation-related impacts in natural areas if their design and management are informed by a clear understanding of the factors influencing visitors’ mode choice. This is particularly relevant in areas served by multiple alternative transportation options at different locations because mode choices in this case can largely modify visitation patterns. This study investigated mode choices in a popular hiking area of the Dolomites (Italian Alps) that is reachable by car, bus, and lifts (i.e. cable car and chairlift). A stated preference survey was used to elicit visitor sensitivities to a series of management and experiential conditions, while simulation was applied to predict mode choice as a consequence of various access policies. While indicating lift as the most preferred transportation option, results suggest the existence of two main kinds of visitor: one preferring road-accessible trailheads and another preferring lift-accessible trailheads. These two kinds seem to reflect a traditional view (i.e., very sensitive to fares, road closures, and overcrowding) and a more modern one (i.e., moderately sensitive to lift fares, relatively insensitive to crowding), respectively. Simulations performed for both groups led to four management principles: road traffic is not reduced significantly without disincentives for car use; overly cheap lift fares are counterproductive; fare-frequency trade-off is key to ensure adequate bus ridership within both groups; and road closures may be comprehensively more effective than road tolls. The findings of this study may support managers and administrators in setting up access policies that better preserve natural resources and visitors’ recreational experience.     

The impacts of Radio Frequency Identification (RFID) technology on supply chain costs

Radio Frequency Identification (RFID) is regarded as a promising technology for the optimization of supply chain processes since it improves manufacturing and retail operations from forecasting demand to planning, managing inventory, and distribution. This study uses a simulation model to calculate the expected benefits of an integrated RFID system on a three-echelon supply chain obtained through performance increases in efficiency, accuracy, visibility, and security level. The study investigates how the product value, lead time, and demand uncertainty affect the performance of the integrated RFID supply chain in terms of cost factors at the echelon level.     

Optimizing the benefits of urban road user charging

Traffic congestion is a feature of most modern cities but attempts to control it or limit its effects have met with only modest success. There is significant and continuing interest in the concept of charging city vehicle users, although apart from the use of parking charges actual operational schemes are few and far between. In this paper, we compare three alternative charging policies using a simplified model of travel demand and supply, which we combine with cost benefit techniques. The charging policies are area-based charging in which users pay to locate in or enter an area, terminal-charging based on supplementary parking fees in residential and non-residential locations and distance-based charging which is a charge related to how far users travel. The model allows for behavioural effects resulting from trip diversion and demand suppression, as well as capacity restraint (speed-flow feedback effects based on limited route capacity). In the case study, we parameterize the model using data and geographical dimensions based on London. We show that area based charging delivers the least benefits whilst a hybrid policy based on terminal and distance-based charges delivers the most. Because it is of topical interest, we compare our results and predictions with the Mayor's strategy for London, which is an area-based scheme. We conclude that the revenue generated using a hybrid policy would be as great as for an area based scheme whilst at the same time delivering substantially greater benefits to road users in terms of travel time and other savings.     

Benders’ decomposition for concurrent redesign of forward and closed-loop supply chain network with demand and return uncertainties

This paper attempts to design a reverse supply chain network (SCN), add it to an existing multi-product forward SCN and simultaneously redesign the existing forward supply chain (SC). The problem considers uncertainty on products demand and and also returned products in multi-period context. Benders’ decomposition is applied to solve the stochastic mixed-integer model to optimality. The scenarios are generated based on the demand distribution function using Cholesky’s factorization method to consider correlation among different products’ demands. To decrease the computational effort, the number of scenarios is reduced using k-means clustering algorithm. The method is tested on a cell phone SC.     

How does demand uncertainty affect food and beverage capacity in the hotel industry?

This study investigates the effect of demand uncertainty on hotels’ food and beverage (F&B) capacity using the operation data of international tourist hotels in Taiwan. The empirical results of this study show that demand uncertainty leads to an increase in F&B capacity. Moreover, the magnitude of this effect increasingly strengthens for hotels with larger F&B scales. Our results together with other studies on room capacity collectively indicate that hotels’ overcapacity problem resulting from demand uncertainty considerably varies with hotel scales and between different hotel sectors.     

Expanding airport capacity of cities under uncertainty: Strategies to mitigate congestion

Airport capacity constraints are increasingly challenging the growth of air traffic. At the same time, decision-making about airport capacity investments is extremely complex, involving trade-offs. This paper’s objective is to optimise a privately owned airport system’s capacity investment decision in a city under demand uncertainty. Next to the investment size, our real options model incorporates the timing of the investment, as well as the cost of congestion. The results reveal that the larger a city’s initial airport capacity, the smaller its investment will relatively be and the lower the occupancy rate threshold at which investment will take place. We also show that, in case of a higher demand growth combined with more demand uncertainty, the city will benefit from a significantly larger investment, but made later at a higher occupancy rate. In this case, cities with a small initial capacity will sometimes even more than double current capacity. Higher airport charges and an increase in non-aeronautical revenues both lead to a later investment in more capacity, due to the increased project attractiveness. An increase in congestion costs results in a larger investment made earlier, in order to eliminate delays. Airport operational cost and capacity holding cost increases both lead to smaller investments.     

Evaluating spatial accessibility to healthcare services under travel time uncertainty: A reliability-based floating catchment area approach

Most existing healthcare accessibility studies ignore the travel time uncertainty that are commonly encountered in road networks. This study aims to examine the impacts of travel time uncertainty on healthcare accessibility. A reliability-based two-step floating catchment area (2SFCA) method is proposed to evaluate healthcare accessibility under travel time uncertainty. The proposed measure generalizes the conventional 2SFCA measure by explicitly considering individuals' reliability constraints when scheduling visits to healthcare facilities in the face of travel time uncertainty. The proposed measure is further used to investigate travel time uncertainty impacts in a comprehensive case study. A big dataset of taxi trajectories is collected in the case study to extract dynamic information on travel time distributions. The results of the case study highlight the significant but heterogeneous impacts of travel time uncertainty on healthcare accessibility for various parts of the city at different times of the day. They also have several methodological implications for the evaluation of healthcare accessibility under travel time uncertainty.     

Coordination with a backup supplier through buy-back contract under supply disruption

This paper studies a buy-back contract between a buyer and a backup supplier when the buyer’s main supplier experiences disruptions. The expected profit functions and the optimal decisions of the contract players are derived through a sequential optimization process. The common properties of the contract as well as the differences under the demand uncertainty and the main supplier’s recurrent supply uncertainty are explored through comparative studies and numerical examples. The study contributes to the literature by providing a better understanding of the impacts of demand and supply uncertainties and by shedding insights on the value of a backup supply.     

Joint analysis of the impacts of built environment on bikeshare station capacity and trip attractions

Shared micromobility is proliferating throughout the world. Many researchers have extensively studied the links among factors representing the built and natural environment and bikeshare demand. One common feature of the existing demand models is that they view bikeshare infrastructure as a group of exogenous variables along with other influential factors. Indeed, this assumption is seldom true in planning practices. Bikeshare system operators usually allocate resources in dense urban areas based upon the environmental correlates. This study contributes to the literature by jointly exploring the determinants of bikeshare station capacity (i.e., the number of docking points) and trip arrivals at the station-level. The research dataset is constructed from the Citi Bike system in New York City in September 2016. The analytical results reveal that the effects of built environment characteristics on bikeshare usage could be carefully considered during the system installation process. We find existing bicycle facilities do not significantly influence the supply of docking points at the station-level. However, they exert direct and positive effects on hourly trip arrivals. The findings improve our understanding of the bikeshare system installation process.     

Determining flight frequencies on an airline network with demand–supply interactions

This paper determines flight frequencies on an airline network with demand–supply interactions between passenger demand and flight frequencies. The model consists of two submodels, a passenger airline flight choice model and an airline flight frequency programming model. The demand–supply interactions relevant to determining flight frequency on an airline’s network are analyzed by integrating these two submodels. The necessary condition for the convergence of the demand–supply interaction is discussed. An example demonstrates the feasibility of applying the proposed models. The results are more accurate than those obtained without considering demand–supply interactions, and the models provide ways to consider demand–supply interactions well in advance to determine flight frequencies on an airline network.     

Insights into carsharing demand dynamics: Outputs of an agent-based model application to Lisbon,Portugal

Two important claims for carsharing systems are their increased flexibility and potential contribution to reducing transport externalities such as pollution. Carsharing typically involves a fleet of vehicles in stations around a city that clients may use on an hourly-payment basis. Classical round-trip systems address a niche market of shopping and errand trips. However, a growing market is now arising providing one-way trips to clients. Great uncertainty remains on the economic viability of this type of carsharing given the complex relation between supply and demand, and how this may influence the level of service provided. Realistic modeling tools that include both supply and demand characterization and allow testing several carsharing operational parameters are scarce. In this sense, a detailed agent-based model was developed to simulate one-way carsharing systems. The simulation incorporates a stochastic demand model discretized in time and space and a detailed environment characterization with realistic travel times. The operation includes maintenance operations, relocations and reservations. The model was applied to the case-study city of Lisbon. Our results show that comparing to other modes, carsharing performs worse than private cars both in terms of time and cost. Nevertheless, it clearly outperforms taxis in terms of cost, and outperforms buses, metro and walking in terms of travel time. The competitiveness of carsharing is highly determined by trip length, becoming more competitive than other modes (travel-time wise) as trips become longer. The operational policies as car-fleet relocation and car reservation showed significant effects in enhancing profit while preserving good customers' satisfaction.     

Pre-distribution and post-distribution cross-docking operations

In this paper, we construct analytical models for distribution strategies with two major types of cross-docking, pre-distribution cross-docking (Pre-C) and post-distribution cross-docking (Post-C). A traditional distribution center system is also discussed for comparison purposes. Three models are compared pair-wisely. Analytical results show that, Pre-C is preferred for environments with shorter supply lead time and lower uncertainty of demand, without the benefits of risk-pooling. The Post-C mitigates the weakness of the Pre-C at the expense of higher operations cost spent at the cross-dock. Numerical experiments are conducted to support our results and explore other findings.     

Price,capacity and concession period decisions of Pareto-efficient BOT contracts with demand uncertainty

In this paper, we study the impact of demand uncertainty on the build-operate-transfer (BOT) contract design by optimizing a bi-objective problem via three critical decisions: toll, capacity and concession period. We derive the optimums and identify the public and private sector’s economic incentives. We find that the optimal length of concession period and the service quality of the infrastructure depend on the two parties’ operational costs and negotiation powers. Under mild conditions, we prove that the government will build a larger capacity but charge less than the private sector. Furthermore, the efficiency of BOT contract is improved with demand uncertainty.     

Robust optimization for United States Department of Agriculture food aid bid allocations

The U.S. Department of Agriculture (USDA) currently uses a bidding system to determine carriers and suppliers that would partner in providing food aid annually in response to global emergencies and famine. We mimic the USDA approach via a robust optimization model featuring box and ellipsoid uncertainty frameworks to account for uncertainties in demand, supplier and carrier bid prices. Through a case study utilizing historical invoice data, we demonstrate our model applicability in improving ocean carrier and food supplier bid pricing strategy and similar supply chain network optimization problems. Through a validation algorithm we demonstrate the value of our robust models.     

Modelling the effects of capacity constraints on air travellers’ airport choice

This paper analyses the effects of limited capacity on air travellers’ airport choice. The analysis is based on a market-segment specific airport choice model that accounts for limited capacities. The region of Stuttgart in Germany serves as a case study to examine the impact of limited airport capacity on air travellers’ airport choice. Air travellers’ choice depends on the supply of flights and accessibility of the airports in their choice set as well as on their preferences and willingness-to-pay. To elaborate the effects of limited airport capacity, scenarios relating to the capacity situation at airports serving the air travel demand of the Stuttgart region are analysed. This paper reveals the mutual dependence among airports. Capacity constraints at one airport cause spill-over effects and thus influence air travel demand served at other airports. In some cases this may even lead to new capacity constraints elsewhere.     

Integrated production and intermodal transportation planning in large scale production–distribution-networks

We present a model and solution approach for combining production and intermodal transportation planning in a supply network. A close and detailed integration of both decision fields is missing in the literature so far. The model includes relevant decisions regarding production setups and output volumes of plants, cargo consolidation at intermodal terminals, and capacity bookings for road and rail transports. A Branch-and-Cut method and heuristics are designed for solving the problem. A comprehensive case study for a chemical company identified a 6%-cost saving from the integrated planning. At the same time, companies are successfully supported in establishing eco-friendly distribution processes.