Explore the vast range of titles available.

Praise for the First Edition This book is refreshing to read since it takes an important topic... and presents it in a clear and concise manner by using examples that include visual presentations of the problem, solution methods, and results along with an explanation of the mathematical and procedural steps required to model the problem and work through to a solution.\" -Journal of Classification Thoroughly updated and revised, Network and Discrete Location: Models, Algorithms, and Applications, Second Edition remains the go-to guide on facility location modeling. The book offers a unique introduction to methodological tools for solving location models and provides insight into when each approach is useful and what information can be obtained. The Second Edition focuses on real-world extensions of the basic models used in locating facilities, including production and distribution systems, location-inventory models, and defender-interdictor problems. A unique taxonomy of location problems and models is also presented. Featuring examples using the author's own software-SITATION, MOD-DIST, and MENU-OKF-as well as Microsoft Office® Excel®, the book provides: A theoretical and applied perspective on location models and algorithms An intuitive presentation of the uses and limits of modeling techniques An introduction to integrated location-inventory modeling and defender-interdictor models for the design of reliable facility location systems A full range of exercises to equip readers with an understanding of the basic facility location model types Network and Discrete Location: Models, Algorithms, and Applications, Second Edition is an essential resource for practitioners in applied and discrete mathematics, operations research, industrial engineering, and quantitative geography. The book is also a useful textbook for upper-level undergraduate, graduate, and MBA courses.

Praise for the First Edition This book is refreshing to read since it takes an important topic... and presents it in a clear and concise manner by using examples that include visual presentations of the problem, solution methods, and results along with an explanation of the mathematical and procedural steps required to model the problem and work through to a solution.\" —Journal of Classification Thoroughly updated and revised, Network and Discrete Location: Models, Algorithms, and Applications, Second Edition remains the go-to guide on facility location modeling. The book offers a unique introduction to methodological tools for solving location models and provides insight into when each approach is useful and what information can be obtained. The Second Edition focuses on real-world extensions of the basic models used in locating facilities, including production and distribution systems, location-inventory models, and defender-interdictor problems. A unique taxonomy of location problems and models is also presented. Featuring examples using the author's own software—SITATION, MOD-DIST, and MENU-OKF—as well as Microsoft Office® Excel®, the book provides: • A theoretical and applied perspective on location models and algorithms • An intuitive presentation of the uses and limits of modeling techniques • An introduction to integrated location-inventory modeling and defender-interdictor models for the design of reliable facility location systems • A full range of exercises to equip readers with an understanding of the basic facility location model types Network and Discrete Location: Models, Algorithms, and Applications, Second Edition is an essential resource for practitioners in applied and discrete mathematics, operations research, industrial engineering, and quantitative geography. The book is also a useful textbook for upper-level undergraduate, graduate, and MBA courses.

Purpose: Disposal of infectious waste remains one of the most serious problems in the social and environmental domains of almost every nation. Selection of new suitable locations and finding the optimal set of transport routes to transport infectious waste, namely location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management. Design/methodology/approach: Due to the complexity of this problem, location routing problem for a case study, forty hospitals and three candidate municipalities in sub-Northeastern Thailand, was divided into two phases. The first phase is to choose suitable municipalities using hybrid fuzzy goal programming model which hybridizes the fuzzy analytic hierarchy process and fuzzy goal programming. The second phase is to find the optimal routes for each selected municipality using hybrid genetic algorithm which hybridizes the genetic algorithm and local searches including 2-Opt-move, Insertion-move and [lambda]-interchange-move. Findings: The results indicate that the hybrid fuzzy goal programming model can guide the selection of new suitable municipalities, and the hybrid genetic algorithm can provide the optimal routes for a fleet of vehicles effectively. Originality/value: The novelty of the proposed methodologies, hybrid fuzzy goal programming model, is the simultaneous combination of both intangible and tangible factors in order to choose new suitable locations, and the hybrid genetic algorithm can be used to determine the optimal routes which provide a minimum number of vehicles and minimum transportation cost under the actual situation, efficiently.

This paper proposes a cluster partitioning technique to calculate improved upper bounds to the optimal solution of maximal covering location problems. Given a covering distance, a graph is built considering as vertices the potential facility locations, and with an edge connecting each pair of facilities that attend a same client. Coupling constraints, corresponding to some edges of this graph, are identified and relaxed in the Lagrangean way, resulting in disconnected subgraphs representing smaller subproblems that are computationally easier to solve by exact methods. The proposed technique is compared to the classical approach, using real data and instances from the available literature.

This paper considers a location problem in a supply chain network. The problem addressed in this paper is motivated by an initiative to develop an efficient supply chain network for supporting the agricultural activities. The supply chain network consists of regions, warehouses, distribution centers, plants, and markets. The products include a set of inbound products and a set of outbound products. In this paper, definitions of the inbound and outbound products are seen from the region’s point of view.  The inbound product is the product demanded by regions and produced by plants which flows on a sequence of the following entities: plants, distribution centers, warehouses, and regions. The outbound product is the product demanded by markets and produced by regions and it flows on a sequence of the following entities: regions, warehouses, and markets. The problem deals with determining locations of the warehouses and the distribution centers to be opened and shipment quantities associated with all links on the network that minimizes the total cost. The problem can be considered as a strategic supply chain network problem. A solution approach based on genetic algorithm (GA) is proposed. The proposed GA is examined using hypothetical instances and its results are compared to the solution obtained by solving the mixed integer linear programming (MILP) model. The comparison shows that there is a small gap (0.23%, on average) between the proposed GA and MILP model in terms of the total cost. The proposed GA consistently provides solutions with least total cost. In terms of total cost, based on the experiment, it is demonstrated that coefficients of variation are closed to 0.

Determining the best location to serve companies’ profitability and sustainability is becoming more crucial every day, since the rivalry between companies is getting more intense. The transformation of economies from manufacturing orientation towards service based activities has resulted in a growing contribution of the service based economy in gross domestic product and workforce of developing countries. These recent changes in the economy are indicators that service facility related location science has received greater interest. Service location problems has been studied since the 1900s and interest on these types of problems has started to grow especially after the aforementioned economic transformation in the 2000s. A large number of problems have been investigated for different service facilities. However, there is a need for a survey that systematically classifies these papers in order to comprehend them thoroughly due to their prominence and complexity. This paper examines 90 papers that have been published on service facility location problems since 2000. The paper presents a classification based on 19 main characteristics including key features and descriptive dimensions of location problems in order to develop a taxonomy from an operations research perspective to assist the location scientists and practitioners who work on service facility location problems. Furthermore, service facility location problems are categorized according to their application fields and investigated in detail relating to each characteristic. We also draw interesting comparisons of characteristics between facility location problems in different application fields and highlight directions for future research.

In this paper, a bi-objective mixed-integer linear programming model is formulated for designing a perishable pharmaceutical supply chain network under demand uncertainty. The objectives of the proposed model are to simultaneously minimize the total cost of the network and lost demand amount. The proposed model is multi-product and multi-period and includes simultaneous facilities location, vehicle routing, and inventory management; hence, it is considered an operational-strategic model. Procurement discounts, the lifetime of products, storing products for future periods, lost demand, and soft and hard time windows are the main assumptions of the proposed model. A novel hybrid approach, based on fuzzy theory, chance constrained programming, and goal programming approach, is developed for solving the proposed bi-objective model. The validity of the proposed model and developed solution approach is evaluated using data from Avonex, a prefilled syringe distribution chain serving 11 health centers in Tehran. The proposed model indicates that some lost sales exist, and to overcome the lost sales, the case company needs to invest a little more in addition to the initial investment of around 2 billion tomans. The results obtained from implementing the model and the sensitivity analysis, using real-world data, confirm the efficiency and validity of the proposed mathematical model and solution approach.

Serious environmental and economic problems of using fossil fuels in transportation sections force managers to think of alternative fuels such as hydrogen, ethanol, biodiesel, natural gas, or electricity. Meanwhile, lack of fuel network infrastructures is a major problem, which needs to be investigated considering the number and optimal location of alternative fuel stations. In the literature, two different flow-based demand modeling concepts (the maximum cover and set cover) have been proposed for solving this problem. Because of the huge number of combinations of fuel stations for covering the flow of each path, the models are impractical for the real size problems. In this paper, the flow refueling location model was reformulated and a flexible mixed-integer linear programming model was presented, which was able to obtain an optimal solution much faster than the previous set cover version. The model also could be solved in the maximum cover form in a reasonable time on the large-sized networks.

This paper presents a new multi-objective mathematical model for the hub location and routing problem under uncertainty in flows, costs, times, and number of job opportunities. This model aims at minimizing the total transportation cost consisting of routing and fixed cost and maximizing the employment and regional development as social responsibility. An M/M/c/K queue system is applied to estimate the waiting time at hub nodes and maximize the responsiveness. Also, a fuzzy queuing method is applied to model the uncertainties in this network. A powerful evolutionary meta-heuristic algorithm based on fuzzy invasive weed optimization, variable neighborhood search, and game theory is developed to solve the introduced model and obtain near-optimal Pareto solutions. Many experiments as well as a real transportation case-study show the superiority of the proposed approaches compared to the state-of-the-art algorithm.

•The addition of flu could cripple the health care system during the COVID-19 pandemic.•Fears of coronavirus have intensified the shortage of flu vaccine in developing countries.•We present an optimization model for equitable flu vaccine distribution.•The model utilizes an equitable objective function to distribute vaccines to high-risk people.•We present a case study to exhibit efficacy and demonstrate the model’s applicability. The addition of other respiratory illnesses such as flu could cripple the healthcare system during the coronavirus disease 2019 (COVID-19) pandemic. An annual seasonal influenza vaccine is the best way to help protect against flu. Fears of coronavirus have intensified the shortage of influenza shots in developing countries that hope to vaccinate many populations to reduce stress on their health services. We present an inventory-location mixed-integer linear programming model for equitable influenza vaccine distribution in developing countries during the pandemic. The proposed model utilizes an equitable objective function to distribute vaccines to critical healthcare providers and first responders, elderly, pregnant women, and those with underlying health conditions. We present a case study in a developing country to exhibit efficacy and demonstrate the optimization model’s applicability.