Hybrid Meta-heuristics with VNS and Exact Methods: Application to Large Unconditional and Conditional Vertex p-Centre Problems

Large-scale unconditional and conditional vertex p-centre problems are solved using two meta-heuristics. One is based on a three-stage approach whereas the other relies on a guided multi-start principle. Both methods incorporate Variable Neighbourhood Search, exact method, and aggregation techniques. The methods are assessed on the TSP dataset which consist of up to 71,009 demand points with p varying from 5 to 100. To the best of our knowledge, these are the largest instances solved for unconditional and conditional vertex p-centre problems. The two proposed meta-heuristics yield competitive results for both classes of problems

Location models in the public sector

The past four decades have witnessed an explosive growth in the field of networkbased facility location modeling. This is not at all surprising since location policy is one of the most profitable areas of applied systems analysis in regional science and ample theoretical and applied challenges are offered. Location-allocation models seek the location of facilities and/or services (e.g., schools, hospitals, and warehouses) so as to optimize one or several objectives generally related to the efficiency of the system or to the allocation of resources. This paper concerns the location of facilities or services in discrete space or networks, that are related to the public sector, such as emergency services (ambulances, fire stations, and police units), school systems and postal facilities. The paper is structured as follows: first, we will focus on public facility location models that use some type of coverage criterion, with special emphasis in emergency services. The second section will examine models based on the P-Median problem and some of the issues faced by planners when implementing this formulation in real world locational decisions. Finally, the last section will examine new trends in public sector facility location modeling.Location analysis, public facilities, covering models

Budget Constrained Location Problem with Opening and Closing of Facilities

In this paper, we study a budget constrained location problem in which we simultaneously consider opening some new facilities and closing some existing facilities. Motivations for this problem stem from applications where, due to a change in the distribution of customer demand, the existing facility system no longer provides adequate service. The objective is to minimize the total weighted travel distance for customers subject to a constraint on the budget for opening and/or closing facilities and a constraint on the total number of open facilities desired. For this problem, we develop a mathematical programming model and examine its theoretical properties. We then develop three heuristic algorithms (greedy interchange, tabu search and Lagrangian relax-ation approximation) for this NP-hard problem. Computational testing of these algorithms includes an analysis of the sensitivity of the solution to the budget and the desired number of facilities. The intended application in this testing is that of locating/relocating bank branches in a large-size town such as in our data set from Amherst, New York. We also discuss the situation where operating costs are part of the objective function

Development of transportation and supply chain problems with the combination of agent-based simulation and network optimization

Demand drives a different range of supply chain and logistics location decisions, and agent-based modeling (ABM) introduces innovative solutions to address supply chain and logistics problems. This dissertation focuses on an agent-based and network optimization approach to resolve those problems and features three research projects that cover prevalent supply chain management and logistics problems. The first case study evaluates demographic densities in Norway, Finland, and Sweden, and covers how distribution center (DC) locations can be established using a minimizing trip distance approach. Furthermore, traveling time maps are developed for each scenario. In addition, the Nordic area consisting of those three countries is analyzed and five DC location optimization results are presented. The second case study introduces transportation cost modelling in the process of collecting tree logs from several districts and transporting them to the nearest collection point. This research project presents agent-based modelling (ABM) that incorporates comprehensively the key elements of the pick-up and delivery supply chain model and designs the components as autonomous agents communicating with each other. The modelling merges various components such as GIS routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. The entire pick-up and delivery operation are modeled by ABM and modeling outcomes are provided by time series charts such as the number of trucks in use, facilities inventory and travel distance. In addition, various scenarios of simulation based on potential facility locations and truck numbers are evaluated and the optimal facility location and fleet size are identified. In the third case study, an agent-based modeling strategy is used to address the problem of vehicle scheduling and fleet optimization. The solution method is employed to data from a real-world organization, and a set of key performance indicators are created to assess the resolution's effectiveness. The ABM method, contrary to other modeling approaches, is a fully customized method that can incorporate extensively various processes and elements. ABM applying the autonomous agent concept can integrate various components that exist in the complex supply chain and create a similar system to assess the supply chain efficiency.Tuotteiden kysyntä ohjaa erilaisia toimitusketju- ja logistiikkasijaintipäätöksiä, ja agenttipohjainen mallinnusmenetelmä (ABM) tuo innovatiivisia ratkaisuja toimitusketjun ja logistiikan ongelmien ratkaisemiseen. Tämä väitöskirja keskittyy agenttipohjaiseen mallinnusmenetelmään ja verkon optimointiin tällaisten ongelmien ratkaisemiseksi, ja sisältää kolme tapaustutkimusta, jotka voidaan luokitella kuuluvan yleisiin toimitusketjun hallinta- ja logistiikkaongelmiin. Ensimmäinen tapaustutkimus esittelee kuinka käyttämällä väestötiheyksiä Norjassa, Suomessa ja Ruotsissa voidaan määrittää strategioita jakelukeskusten (DC) sijaintiin käyttämällä matkan etäisyyden minimoimista. Kullekin skenaariolle kehitetään matka-aikakartat. Lisäksi analysoidaan näistä kolmesta maasta koostuvaa pohjoismaista aluetta ja esitetään viisi mahdollista sijaintia optimointituloksena. Toinen tapaustutkimus esittelee kuljetuskustannusmallintamisen prosessissa, jossa puutavaraa kerätään useilta alueilta ja kuljetetaan lähimpään keräyspisteeseen. Tämä tutkimusprojekti esittelee agenttipohjaista mallinnusta (ABM), joka yhdistää kattavasti noudon ja toimituksen toimitusketjumallin keskeiset elementit ja suunnittelee komponentit keskenään kommunikoiviksi autonomisiksi agenteiksi. Mallinnuksessa yhdistetään erilaisia komponentteja, kuten GIS-reititys, mahdolliset tilojen sijainnit, satunnaiset puunhakupaikat, kaluston mitoitus, matkan pituus sekä monimuotokuljetukset. ABM:n avulla mallinnetaan noutojen ja toimituksien koko ketju ja tuloksena saadaan aikasarjoja kuvaamaan käytössä olevat kuorma-autot, sekä varastomäärät ja ajetut matkat. Lisäksi arvioidaan erilaisia simuloinnin skenaarioita mahdollisten laitosten sijainnista ja kuorma-autojen lukumäärästä sekä tunnistetaan optimaalinen toimipisteen sijainti ja tarvittava autojen määrä. Kolmannessa tapaustutkimuksessa agenttipohjaista mallinnusstrategiaa käytetään ratkaisemaan ajoneuvojen aikataulujen ja kaluston optimoinnin ongelma. Ratkaisumenetelmää käytetään dataan, joka on peräisin todellisesta organisaatiosta, ja ratkaisun tehokkuuden arvioimiseksi luodaan lukuisia keskeisiä suorituskykyindikaattoreita. ABM-menetelmä, toisin kuin monet muut mallintamismenetelmät, on täysin räätälöitävissä oleva menetelmä, joka voi sisältää laajasti erilaisia prosesseja ja elementtejä. Autonomisia agentteja soveltava ABM voi integroida erilaisia komponentteja, jotka ovat olemassa monimutkaisessa toimitusketjussa ja luoda vastaavan järjestelmän toimitusketjun tehokkuuden arvioimiseksi yksityiskohtaisesti.fi=vertaisarvioitu|en=peerReviewed

Topology Network Optimization of Facility Planning and Design Problems

The research attempts to provide a graphical theory-based approach to solve the facility layout problem. Which has generally been approached using Quadratic Assignment Problem (QAP) in the past, an algebraic method. It is a very complex problem and is part of the NP-Hard optimization class, because of the nonlinear quadratic objective function and (0,1) binary variables. The research is divided into three phases which together provide an optimal facility layout, block plan solution consisting of MHS (material handling solution) projected onto the block plan. In phase one, we solve for the position of departments in a facility based on flow and utility factor (weight for location). The position of all the departments is identified on the vertices of MPG (maximal planar graph), which maximizes the possibility of flow. We use named MPG produced in literature, throughout the research. The grouping of the department is achieved through GMAFLAD, a QSP (quadratic set packing) based optimizer. In Phase 2, the dual for the MPG’s is solved consisting of department location as per phase 1, to generate Voronoi graphs. These graphs are then, expanded by an ingenious parameter optimization formulation to achieve area fitting for individual cases. Optimization modeling software, Lingo17.0 is used for solving the parameter optimization for generating coordinates of the block plan. The plotting of coordinates for the block plan graphics is done via Autodesk inventor 2019. In phase 3, the solution for MHS is achieved using an RSMT (Rectilinear Steiner minimal tree) graph approach. The Voronoi seed coordinates produced through phase 2 results are computed by GeoSteiner package to generated the RSMT graph for projection onto the block plan (Also, done by Inventor 2019). The graphical method employed in this research, itself has complex and NP-hard problem segments in it, which have been relaxed to polynomial time complexity by fragmenting into groups and solving them in sections. Solving for MPG & RSMT are a class of NP-Hard problem, which have been restricted to N=32 here. Finally, to validate the research and its methodology a real-life case study of a shipyard building for the data set of PDVSA, Venezuela is performed and verified

Evaluating the Effectiveness of Tree Locations and Arrangements for Improving Urban Thermal Environment

abstract: Trees serve as a natural umbrella to mitigate insolation absorbed by features of the urban environment, especially building structures and pavements. For a desert community, trees are a particularly valuable asset because they contribute to energy conservation efforts, improve home values, allow for cost savings, and promote enhanced health and well-being. The main obstacle in creating a sustainable urban community in a desert city with trees is the scarceness and cost of irrigation water. Thus, strategically located and arranged desert trees with the fewest tree numbers possible potentially translate into significant energy, water and long-term cost savings as well as conservation, economic, and health benefits. The objective of this dissertation is to achieve this research goal with integrated methods from both theoretical and empirical perspectives. This dissertation includes three main parts. The first part proposes a spatial optimization method to optimize the tree locations with the objective to maximize shade coverage on building facades and open structures and minimize shade coverage on building rooftops in a 3-dimensional environment. Second, an outdoor urban physical scale model with field measurement is presented to understand the cooling and locational benefits of tree shade. The third part implements a microclimate numerical simulation model to analyze how the specific tree locations and arrangements influence outdoor microclimates and improve human thermal comfort. These three parts of the dissertation attempt to fill the research gap of how to strategically locate trees at the building to neighborhood scale, and quantifying the impact of such arrangements. Results highlight the significance of arranging residential shade trees across different geographical scales. In both the building and neighborhood scales, research results recommend that trees should be arranged in the central part of the building south front yard. More cooling benefits are provided to the building structures and outdoor microclimates with a cluster tree arrangement without canopy overlap; however, if residents are interested in creating a better outdoor thermal environment, open space between trees is needed to enhance the wind environment for better human thermal comfort. Considering the rapid urbanization process, limited water resources supply, and the severe heat stress in the urban areas, judicious design and planning of trees is of increasing importance for improving the life quality and sustaining the urban environment.Dissertation/ThesisDoctoral Dissertation Geography 201

A review of network location theory and models

Cataloged from PDF version of article.In this study, we review the existing literature on network location problems. The study has a broad scope that includes problems featuring desirable and undesirable facilities, point facilities and extensive facilities, monopolistic and competitive markets, and single or multiple objectives. Deterministic and stochastic models as well as robust models are covered. Demand data aggregation is also discussed. More than 500 papers in this area are reviewed and critical issues, research directions, and problem extensions are emphasized.Erdoğan, Damla SelinM.S

GIS and Location Theory Based Bioenergy Systems Planning

This research is concerned with bioenergy systems planning and optimization modelling in the context of locating biomass power plants and allocating available biomass feedstock to the active plants. Bioenergy, a promising renewable energy resource, has potentially significant benefits to climate change, global warming, and alternative energy supplies. As modern bioenergy applications in power production have the ability to generate cleaner electricity and reduce Green House Gas (GHG) emissions compared with traditional fossil fuels, many researchers have proposed various approaches to obtain competitive power generation prices from biomass in different ways. However, the highly dispersed geographical distribution of biomass is a big challenge for regional bioenergy systems planning. This thesis introduces an integrated methodology combining Geographic Information Systems (GIS) and discrete location theories for biomass availability assessment, biomass power plant candidate selection, and location-allocation of power plants and biomass supplies. Firstly, a well known discrete location model – the p-Median Problem (PMP) model is employed to minimize the weighted transportation costs of delivering all collectable biomass to active power plants. Then, a p-Uncapacitated Facility Location Problem (p-UFLP) model for minimizing the Levelized Unit Costs of Energy (LUCE) is proposed and genetic algorithms (GAs) for solving these optimization problems are investigated. To find the most suitable sites for constructing biomass power plants, the Analytic Hierarchy Process (AHP) and GIS based suitability analysis are employed subject to economical, societal, public health, and environmental constraints and factors. These methods and models are aimed at evaluating available biomass, optimally locating biomass power plants and distributing all agricultural biomass to the active power plants. The significance of this dissertation is that a fully comprehensive approach mixed with the applications of GIS, spatial analysis techniques, an AHP method and discrete location theories has been developed to address regional bioenergy systems planning, involving agricultural biomass potential estimation, power plants siting, and facility locations and supplies allocation scenarios. With the availability of the spatial and statistical data, these models are capable of evaluating and identifying electric power generation from renewable bioenergy on the regional scale optimally. It thus provides the essential information to decision makers in bioenergy planning and renewable bioenergy management. An application sited in the Region of Waterloo, Ontario Canada is presented to demonstrate the analysis and modelling process