Applying the flow-capturing location-allocation model to an authentic network: Edmonton, Canada

Traditional location-allocation models aim to locate network facilities to optimally serve demand expressed as weights at nodes. For some types of facilities demand is not expressed at nodes, but as passing network traffic. The flow-capturing location-allocation model responds to this type of demand and seeks to maximize one-time exposure of such traffic to facilities. This new model has previously been investigated only with small and contrived problems. In this paper, we apply the flow-capturing location-allocation model to morning-peak traffic in Edmonton, Canada. We explore the effectiveness of exact, vertex substitution, and greedy solution procedures; the first two are computationally demanding, the greedy is very efficient and extremely robust. We hypothesize that the greedy algorithm's robustness is enhanced by the structured flow present in an authentic urban road network. The flow-capturing model was derived to overcome flow cannibalization, wasteful redundant flow-capturing; we demonstrate that this is an important consideration in an authentic network. We conclude that real-world testing is an important aspect of location model development

Evasive Flow Capture

The flow-capturing location-allocation problem (FCLAP) consists of locating facilities in order to maximize the number of flow-based customers that encounter at least one of these facilities along their predetermined travel paths. In FCLAP, it is assumed that if a facility is located along (or "close enough"' to) a predetermined path of a flow, the flow of customers is considered captured. However, existing models for FCLAP do not consider the likelihood that targeted users may exhibit non-cooperative behavior by changing their travel paths to avoid fixed facilities. Examples of facilities that targeted subjects may have an incentive to avoid include weigh-in-motion stations used to detect and fine overweight trucks, tollbooths, and security and safety checkpoints. The location of these facilities cannot be adequately determined with the existing flow-capturing models. This dissertation contributes to the literature on facility location by introducing a new type of flow capturing framework, called the "Evasive Flow Capturing Problem" (EFCP), in which targeted flows exhibit non-cooperative behavior by trying to avoid the facilities. The EFCP proposed herein generalizes the FCLAP and has relevant applications in transportation, revenue management, and security and safety management. This work formulates several variants of EFCP. In particular, three optimization models, deterministic, two-stage stochastic, and multi-stage stochastic, are developed to allocate facilities given different availability of information and planning policies. Several properties are proved and exploited to make the models computationally tractable. These results are crucial for solving optimally the instances of EFCP that include real-world road networks, which is demonstrated on case studies of Nevada and Vermont

Multi-objective network optimization: models, methods, and applications

There can be an array of planning objectives to consider when identifying alternatives for using, modifying, or restoring natural or built environments. In this respect, multi-objective network optimization models can provide decision support to both managers and users of the system. While there can be an infinite number of feasible solutions to any multi-objective optimization problem in large networks (e.g., urban transportation systems), the efficient ones are usually more desirable in the decision-making process. However, identification of efficient solutions can be challenging in practical applications. To address this issue, this dissertation details mathematical formulations and solution algorithms for a range of real-world planning problems in the context of intelligent transportation systems, vehicle routing problem, natural conservation and landscape connectivity. While the combination of objectives being optimized is unique for each application, the underlying phenomena involves modeling movement between origins and destinations of a networked system. To demonstrate the type of insights that can be achieved using these modeling approaches, the location and number of times solutions appear in different realizations of system and given different solution approaches (e.g., exact and approximate methods) are visualized on network using a commercial geographic information system

Optimization and Integration of Electric Vehicle Charging System in Coupled Transportation and Distribution Networks

With the development of the EV market, the demand for charging facilities is growing rapidly. The rapid increase in Electric Vehicle and different market factors bring challenges to the prediction of the penetration rate of EV number. The estimates of the uptake rate of EVs for light passenger use vary widely with some scenarios gradual and others aggressive. And there have been many effects on EV penetration rate from incentives, tax breaks, and market price. Given this background, this research is devoted to addressing a stochastic joint planning framework for both EV charging system and distribution network where the EV behaviours in both transportation network and electrical system are considered. And the planning issue is formulated as a multi-objective model with both the capital investment cost and service convenience optimized. The optimal planning of EV charging system in the urban area is the target geographical planning area in this work where the service radius and driving distance is relatively limited. The mathematical modelling of EV driving and charging behaviour in the urban area is developed

Infrastructure planning for electrified transportation

Due to the climate crisis, the importance of reducing greenhouse gas (GHG) has been recognized by governments, private companies and the general public alike. Yet carbon capturing-based approaches are difficult to integrate with transportation, which is one of the largest GHG producing sectors, Therefore, electrification is the only viable approach to reduce emissions from transportation, by greatly increasing the market share of electric vehicles (EVs). However, the mass adoption of either (or both) of battery EVs (BEVs) and fuel cell EVs (FCEVs) require a large amount of supporting infrastructures, particularly the construction of EV charging stations (EVCSs) for BEVs and hydrogen refuelling stations (HRSs) for FCEVs. The goal of this study is to provide effective approaches for the sizing and sitting of EVCSs and HRSs to facilitate the deployment of BEVs and FCEVs. The background and an overview of the thesis are provided in Chapter 1, where the gaps in the current research are pointed out and the objectives of the thesis are formulated. Chapter 2 reviewed the current state of technologies regarding the hydrogen life cycle as well as the popular planning models for EVCSs and HRSs. In Chapter 3, to achieve a competitive strategy from the perspective of private companies, a market-based framework is proposed for the problem of EVCS planning by leveraging Graph Convolutional Network (GCN) and game theory. In Chapter 4, a multi-objective planning model is developed for EVCSs and the expansion of distribution network with significant renewable components while considering uncertainties in EV charging behaviour. Additionally, in Chapter 5, a planning model of HRS maximises the long-term profit while considering different practical constraints. The HRS planning model also addresses short-term demand uncertainty via redistribution. The models that are developed in this study are validated using either synthetic or real-world case studies, and the simulation results showed the effectiveness of the proposed models. Finally Chapter 6 summarises the major achievements of the thesis and provides directions for further research

The design of effective and robust supply chain networks

Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2009-2010Pour faire face aux risques associés aux aléas des opérations normales et aux périls qui menacent les ressources d'un réseau logistique, une méthodologie générique pour le design de réseaux logistiques efficaces et robustes en univers incertain est développée dans cette thèse. Cette méthodologie a pour objectif de proposer une structure de réseau qui assure, de façon durable, la création de valeur pour l'entreprise pour faire face aux aléas et se prémunir contre les risques de ruptures catastrophiques. La méthodologie s'appuie sur le cadre de prise de décision distribué de Schneeweiss et l'approche de modélisation mathématique qui y est associée intègre des éléments de programmation stochastique, d'analyse de risque et de programmation robuste. Trois types d'événements sont définis pour caractériser l'environnement des réseaux logistiques: des événements aléatoires (ex. la demande, les coûts et les taux de changes), des événements hasardeux (ex. les grèves, les discontinuités d'approvisionnement des fournisseurs et les catastrophes naturelles) et des événements profondément incertains (ex. les actes de sabotage, les attentats et les instabilités politiques). La méthodologie considère que l'environnement futur de l'entreprise est anticipé à l'aide de scénarios, générés partiellement par une méthode Monte-Carlo. Cette méthode fait partie de l'approche de solution et permet de générer des replications d'échantillons de petites tailles et de grands échantillons. Elle aide aussi à tenir compte de l'attitude au risque du décideur. L'approche générique de solution du modèle s'appuie sur ces échantillons de scénarios pour générer des designs alternatifs et sur une approche multicritère pour l'évaluation de ces designs. Afin de valider les concepts méthodologiques introduits dans cette thèse, le problème hiérarchique de localisation d'entrepôts et de transport est modélisé comme un programme stochastique avec recours. Premièrement, un modèle incluant une demande aléatoire est utilisé pour valider en partie la modélisation mathématique du problème et étudier, à travers plusieurs anticipations approximatives, la solvabilité du modèle de design. Une approche de solution heuristique est proposée pour ce modèle afin de résoudre des problèmes de taille réelle. Deuxièmement, un modèle incluant les aléas et les périls est utilisé pour valider l'analyse de risque, les stratégies de resilience et l'approche de solution générique. Plusieurs construits mathématiques sont ajoutés au modèle de base afin de refléter différentes stratégies de resilience et proposer un modèle de décision sous risque incluant l'attitude du décideur face aux événements extrêmes. Les nombreuses expérimentations effectuées, avec les données d'un cas réaliste, nous ont permis de tester les concepts proposés dans cette thèse et d'élaborer une méthode de réduction de complexité pour le modèle générique de design sans compromettre la qualité des solutions associées. Les résultats obtenus par ces expérimentations ont pu confirmer la supériorité des designs obtenus en appliquant la méthodologie proposée en termes d'efficacité et de robustesse par rapport à des solutions produites par des approches déterministes ou des modèles simplifiés proposés dans la littérature

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

\u3ci\u3eThe Conference Proceedings of the 2003 Air Transport Research Society (ATRS) World Conference, Volume 1\u3c/i\u3e

UNOAI Report 03-5https://digitalcommons.unomaha.edu/facultybooks/1131/thumbnail.jp

Infrastructure Design for Electric and Autonomous Vehicles

This thesis focuses on infrastructure design for the disruptive transportation technologies of electric vehicles (EVs) and autonomous vehicles (AVs) to enable their adoption at large scale. Particularly, two EV-related problem frameworks concerning the spatial distribution of charging stations and their respective capacity levels are studied, and a new problem is introduced to determine the optimal deployment of AV lanes and staging facilities to enable shared autonomous transportation in urban areas. The first problem is centered around determining optimal locations of fast-charging stations to enable long-distance transportation with EVs. A new mathematical model is developed to address this problem. This model not only determines optimal facility locations but also finds optimal routes for every origin-destination (OD) trip which follows the path that leads to the minimum total en route recharging. Through computational experiments, this model is shown to outperform the widely used maximum and set cover problem settings in the literature in terms of several routing-related performance measures. A Benders decomposition algorithm is developed to solve large-scale instances of the problem. Within this algorithm, a novel subproblem solution methodology is developed to accelerate the performance of the classical Benders implementation. Computational experiments on real-world transportation networks demonstrate the value of this methodology as it turns out to speed the classical Benders up to 900 times and allows solving instances with up to 1397 nodes. The second problem extends the previous one by seeking to determine EV charging station locations and capacities under stochastic vehicle flows and charging times. It also considers the route choice behavior of EV users by means of a bilevel optimization model. This model incorporates a probabilistic service requirement on the waiting time to charge, and it is studied under a framework where charging stations operate as M/M/c queuing systems. A decomposition-based solution methodology, that uses a logic-based Benders algorithm for the location-only problem, is developed to solve the proposed bilevel model. This methodology is designed to be versatile enough to be tailored for the cooperative or uncooperative EV user behavior. Computational experiments are conducted on real-life highway networks to evaluate how service level requirements, deviation tolerance levels, and route choice behavior affect the location and sizing decisions of charging stations. The third problem entails the staging facility location and AV lane deployment problem for shared autonomous transportation. The proposed problem aims to find the optimal locations of staging facilities utilizing a bi-objective model that minimizes total travel distance and the total AV travel not occurring on AV lanes with respect to a given AV lane deployment budget and a number of staging facilities to locate. A Benders decomposition algorithm with Pareto-optimal cuts is developed and the trade-offs with optimal solutions on benchmark instances are evaluated. Computational experiments are performed to analyze the effects of AV lane budget, staging facility count, and the objective preferences of decision makers on optimal solutions