A regret model applied to the maximum coverage location problem with queue discipline

This article discusses issues related to the location and allocation problems where is intended to demonstrate, through the random number generation, the influence of congestion of such systems in the final solutions. It is presented an algorithm that, in addition to the GRASP, incorporates the Regret with the pminmax method to evaluate the heuristic solution obtained in regard to its robustness for different scenarios. To the well know Maximum Coverage Location Problem from Church and Revelle [1] an alternative perspective is added in which the choice behavior of the server does not only depend on the elapsed time from the demand point looking to the center, but also includes the waiting time for service conditioned by a waiting queue.N/

A regret model applied to the maximum capture location problem

This article addresses issues related to location and allocation problems. Herein, we intend to demonstrate the influence of congestion, through the random number generation, of such systems in final solutions. An algorithm is presented which, in addition to the GRASP, incorporates the Regret with the pminmax method to evaluate the heuristic solution obtained with regard to its robustness for different scenarios. Taking as our point of departure the Maximum Capture Location Problem proposed by Church and Revelle [1, 26], an alternative perspective is added in which the choice behavior of the server does not depend only on the elapsed time from the demand point looking to the center, but includes also the service waiting time.N/

A DATA ANALYTICAL FRAMEWORK FOR IMPROVING REAL-TIME, DECISION SUPPORT SYSTEMS IN HEALTHCARE

In this dissertation we develop a framework that combines data mining, statistics and operations research methods for improving real-time decision support systems in healthcare. Our approach consists of three main concepts: data gathering and preprocessing, modeling, and deployment. We introduce the notion of offline and semi-offline modeling to differentiate between models that are based on known baseline behavior and those based on a baseline with missing information. We apply and illustrate the framework in the context of two important healthcare contexts: biosurveillance and kidney allocation. In the biosurveillance context, we address the problem of early detection of disease outbreaks. We discuss integer programming-based univariate monitoring and statistical and operations research-based multivariate monitoring approaches. We assess method performance on authentic biosurveillance data. In the kidney allocation context, we present a two-phase model that combines an integer programming-based learning phase and a data-analytical based real-time phase. We examine and evaluate our method on the current Organ Procurement and Transplantation Network (OPTN) waiting list. In both contexts, we show that our framework produces significant improvements over existing methods

An exact Method for Stochastic Maximal Covering Problem of Preventive Healthcare Facilities

Abstract Effective preventive healthcare services have a significant role in reducing fatality and medical expenses in all human societies and the level of accessibility of customers to these services can be considered as a measure of their efficiency and effectiveness. The main purpose of this paper is to develop a service network design model of preventive healthcare facilities with the principal objective of maximizing participation in the offered services. While considering utility constraints and incorporating demand elasticity of customers due to travel distance and congestion delays, optimal number, locations and capacities of facilities as well as customer assignment o facilities are determined. First, the primary nonlinear integer program is transformed, and then the linearized model is solved by developing an exact algorithm. Computational results show that large-sized instances can be solved in a reasonable amount of time. An illustrative case study of network of hospitals in Shiraz, Iran, is used to demonstrate the model and the managerial insights are discussed

Location analysis of electric vehicle charging stations for maximum capacity and coverage

Electric vehicle charging facility location is a critical component of long-term strategic planning. Integration of electric vehicles into mainstream adoption has unique characteristics as it requires a careful investigation of both electric and transportation networks. In this paper, we provide an overview of recent approaches in location analyses of electric vehicle charging infrastructures. We review approaches from classical operations research for fast and slow charging stations. Sample formulations along with case studies are presented to provide insights. We discuss that classical methods are appropriate to address the coverage of charging networks which is defined as average time or distance to reach a charging station when needed. On the other hand, calculating required capacity, defined as the individual charging resources at each node, is still an open research topic. In the final part, we present stochastic facility location theory that uses queuing and other probabilistic approaches

VI Workshop on Computational Data Analysis and Numerical Methods: Book of Abstracts

The VI Workshop on Computational Data Analysis and Numerical Methods (WCDANM) is going to be held on June 27-29, 2019, in the Department of Mathematics of the University of Beira Interior (UBI), Covilhã, Portugal and it is a unique opportunity to disseminate scientific research related to the areas of Mathematics in general, with particular relevance to the areas of Computational Data Analysis and Numerical Methods in theoretical and/or practical field, using new techniques, giving especial emphasis to applications in Medicine, Biology, Biotechnology, Engineering, Industry, Environmental Sciences, Finance, Insurance, Management and Administration. The meeting will provide a forum for discussion and debate of ideas with interest to the scientific community in general. With this meeting new scientific collaborations among colleagues, namely new collaborations in Masters and PhD projects are expected. The event is open to the entire scientific community (with or without communication/poster)

The Role of Consumer Behaviour in Service Operations Management

In this thesis, I study the impact of consumer behaviour on service providers’ operations. In the first study, I consider service systems where customers do not know the distribution of uncertain service quality and cannot estimate it fully rationally. Instead, they form their beliefs by taking the average of several anecdotes, the size of which measures their level of bounded rationality. I characterise the customers’ joining behaviour and the service provider’s pricing, quality control, and information disclosure decisions. Bounded rationality induces customers to form different estimates of the service quality and leads the service provider to use pricing as a market segmentation tool, which is radically different from the full rationality setting. When the service provider also has control over quality, I find that it may reduce both quality and price as customers gather more anecdotes. In addition, a high-quality service provider may not disclose quality information if the sample size is small. In the second study, I analyse the performance of opaque selling in countering the negative revenue impact from consumers’ strategic waiting behaviour in vertically differentiated markets. The advantage of opaque selling is to increase the firm’s regular price, whereas the disadvantage lies in the inflexibility of segmenting different types of consumers. Both the advantage and the disadvantage are radically different from their counterparts in horizontally differentiated markets, and this contrast generates opposite policy recommendations across the two settings. In the third study, I investigate an online store’s product return policy when competing with a physical store, in which consumers can try the product before purchase. I find that the online store should offer product return only if it is socially efficient. Moreover, it should allocate product return cost between the online store and the consumers to minimise the total return cost

Algorithmic contributions to bilevel location problems with queueing and user equilibrium : exact and semi-exact approaches

Bien que la littérature sur le problème d'emplacement soit vaste, la plupart des publications considèrent des modèles simples, dans lesquels une autorité centrale assigne les utilisateurs aux installations les plus proches. Des caractéristiques plus réalistes, telles que le comportement des usagers, la compétition et la congestion, sont souvent négligées, peut-être en raison de leur nature hautement non-linéaire «compliquée». Quelques articles ont incorporé ces traits, mais uniquement de facon séparée, et seulement des approches heuristiques ont été proposées comme méthodes de résolution. Le problème d'emplacement d'installations consiste à localiser un ensemble d'installations de manière optimale afin de répondre à une demande donnée. Dans un environnement congestioné où les usagers ont le choix, les installations sont généralement modélisées sous la forme de files d'attente. Les utilisateurs sélectionnent les installations à fréquenter en fonction de leur utilité perçue, qui est généralement écrite comme une combinaison linéaire de la distance de déplacement, du temps d'attente dans les installations, etc. En résulte un modèle dit "à deux niveaux" appartenant à la classe des programmes mathématiques à contraintes d'équilibre (MPEC en anglais), où l'équilibre peut être exprimé sous la forme d'une inéquation variationnelle. Notre travail est axé sur le problème d'emplacement d'installations où les usagers ont le choix (CC-FLP en anglais) et nous fournissons un certain nombre de contributions importantes. Du point de vue de la modélisation, nous proposons différents modèles qui capturent les principales caractéristiques du CC-FLP. Pour ces programmes non-linéaires, discrets, et NP-difficiles, nous avons conçu des algorithmes exactes et d'approximation, ainsi que des heuristiques sur-mesure. Notre travail couvre trois articles. Dans le premier article, nous considérons différents modèles qui intègrent l'abandon aux centres de services, en raison des places limitées dans la file d'attente, tandis que le comportement des utilisateurs peut être déterministe ou stochastique. Dans ce dernier cas, le comportement des usagers correspond au principe d'équilibre de Wardrop, tandis que dans le premier cas, les clients se distribuent entre les établissements selon un modèle de choix d'utilité aléatoire Logit. Au-delà de l'analyse des propriétés théoriques du modèle, nous concevons une heuristique menée par les usagers et un algorithme d'approximation linéaire pour lequel nous prouvons une borne d'erreur de l'approximation, dans le cas d'une file d'attente M/M/1. Le second article est consacré à la conception d'un nouvel algorithme de `Branch and Bound' (B&B) pour résoudre une sous-classe plus générale des MPEC. L'algorithme est implémenté et évalué sur un CC-FLP. L'idée est de traiter virtuellement chaque nœud de l'arbre B& B comme un problème d'optimisation distinct, afin de tirer parti de la puissance des solveurs MILP et de leur prétraitement fort au niveau de la racine. Notre approche algorithmique est basée sur une combinaison de programmation linéaire à nombres entiers et mixtes (MILP en anglais), de techniques de linéarisation et de la résolution itérative de sous-problèmes convexes, et nécessite une gestion d’arbre sophistiquée. Dans le troisième article, nous incorporons les prix dans le CC-FLP. Le prix est une variable de décision continue, tout comme la localisation et le niveaux et de service, et les utilisateurs l'intègrent dans leur utilité. Les concepts de tarification du réseaux et de CC-FLP étant fusionnés en un seul modèle, le problème devient extrêmement difficile, également en raison de la présence de variables de localisation et de niveau de service, ainsi que de délais d'attente bidimensionnels. Pour ce programme à deux niveaux non-convexe, nous avons conçu un algorithme basé sur des approximations linéaires emprunté à la fois à la littérature sur la localisation et à la tarification du réseau.While the location literature is vast, most papers consider simpler models, in which a central authority assigns users to the closest facilities. More realistic traits, such as user behaviour, competition, and congestion are often overlooked, perhaps due to their `complicating' highly non-linear nature. A few papers did incorporate them, but separately, and only heuristic approaches have been proposed as solution methods. The facility location problem consists in optimally locating a set of facilities in order to satisfy a given demand. In a congested user-choice environment, facilities are typically modeled as queues, and users select the facilities to patronize based on their perceived utility, which is, in general, written as linear combination of travel distance, waiting time at facilities, etc. The resulting bilevel model belongs to the class of mathematical programs with equilibrium constraints (MPECs), where the equilibrium can be expressed as a variational inequality. Our work is focused on the \emph{competitive congested user-choice facility location problem} (CC-FLP), and we provide a number of strong contributions. From the modeling point of view, we propose various models that capture the key features of CC-FLP. For these NP-hard discrete nonlinear programs we designed exact and approximated algorithms, as well as tailored heuristics. Our work spans three papers. In the first article we consider different models that incorporate balking at facilities, due to limited places in the queue, while user behaviour can be either deterministic or stochastic. In the latter case, user behaviour fits Wardrop's equilibrium principle, while in the former case, customers distribute among facilities according to a Logit random utility choice model. Beyond the analysis of the model's theoretical properties, we design a user-driven heuristic and a linear approximation algorithm, for which we prove a bound on the approximation error, for the M/M/1 queue. The second paper is dedicated to the design of a novel exact branch-and-bound (B&B) algorithm for solving a more general subclass of MPECs, which is implemented and evaluated on a CC-FLP. The idea is to virtually treat each node of the B&B tree as a separate optimization problem, in oder to leverage the strength of the MILP solvers and their strong preprocessing at the root node. Our algorithmic approach is based on a combination of Mixed-Integer Linear Programming (MILP), linearization techniques and the iterative solution of convex subproblems, and requires a sophisticated tree management. In the third paper we incorporate mill pricing into the CC-FLP. Price is a continuous decision variable, along with the location and service levels, and user incorporate it into their utility. Since concepts from network pricing and CC-FLP are merged into a single model, the problem becomes extremely challenging, also due to the presence of facility location and service level decision variables, as well as bivariate queueing delays. For this non-convex bilevel program we devise an algorithm based on linear approximations, that borrows from both location and network pricing literature

A comparative performance analysis of intelligence-based algorithms for optimizing competitive facility location problems

Most companies operate to maximize profits and increase their market shares in competitive environments. Since the proper location of the facilities conditions their market shares and profits, the competitive facility location problem (CFLP) has been extensively applied in the literature. This problem generally falls within the class of NP-hard problems, which are difficult to solve. Therefore, choosing a proper solution method to optimize the problem is a key factor. Even though CFLPs have been consistently solved and investigated, an important question that keeps being neglected is how to choose an appropriate solution technique. Since there are no specific criteria for choosing a solution method, the reasons behind the selection approach are mostly unclear. These models are generally solved using several optimization techniques. As harder-to-solve problems are usually solved using meta-heuristics, we apply different meta-heuristic techniques to optimize a new version of the CFLP that incorporates reliability and congestion. We divide the algorithms into four categories based on the nature of the meta-heuristics: evolution-based, swarm intelligence-based, physics-based, and human-based. GAMS software is also applied to solve smaller-size CFLPs. The genetic algorithm and differential evolution of the first category, particle swarm optimization and artificial bee colony optimization of the second, Tabu search and harmony search of the third, and simulated annealing and vibration damping optimization of the fourth are applied to solve our CFLP model. Statistical analyses are implemented to evaluate and compare their relative performances. The results show the algorithms of the first and third categories perform better than the others