OPTIMASI ALOKASI SISTEM PENGOLAHAN SAMPAH ANORGANIK DENGAN METODE CAPACITATED MAXIMUM COVERING LOCATION PROBLEM

Province X in Indonesia has a significant problem with trash that has been challenging to address. The Environment Agency therefore desires to construct an inorganic Trash Management System. The Environment Agency has four potential locations with various capacities. The purpose of this research is to locate a trash treatment system's best possible site and allocate each TPS that is less than 30 km and more over 30 km from the chosen system, respectively, to ensure that the overall distance traveled is as little as possible. There are four possible locations, and it required to select three of them. This study use the LINGO 18.0 software to solve the Capacitated Maximum Covering Location Problem (CMCLP) approach. Distance parameters used by CMCLP are split into two stages. The first stage is to determine the location of the trash treatment system to be built and the allocation of trash and the amount of trash from each TPS using Mix Integer Programming. B is not chosen out of the options, which are A, B, C, and D. System A will receive 1,407,520 tons of trash totaling 24 TPS. System C receives trash from 147 TPS weighing 1,294,495 pounds. System D will receive trash weighing 819,142 tons from 88 TPS. The allocation of trash from TPS that are more than 30 km away takes place in the second stage. Three TPS are assigned to System A, and seven TPS are assigned to System D

New Model of Maximal Covering Location Problem with Fuzzy Conditions

The objective of Maximal Covering Location Problem is locating facilities such that they cover the maximal number of locations in a given radius or travel time. MCLP is applied in many different real-world problems with several modifications. In this paper a new model of MCLP with fuzzy conditions is presented. It uses two types of fuzzy numbers for describing two main parameters of MCLP - coverage radius and distances between locations. First, the model is defined, then Particle Swarm Optimization method for solving the problem is described and tested

Anticipatory routing of police helicopters

We have developed a decision support application for the Dutch Aviation Police and Air Support unit for routing their helicopters in anticipation of unknown future incidents. These incidents are not known in advance, yet do require a swift response. A response might include the dispatch of a police helicopter to support the police on the ground. If a helicopter takes too long to arrive at the crime scene, it might be too late to assist. Hence, helicopters have to be proximate when an incident happens to increase the likelihood of being able to support the police on the ground in apprehending suspects. We propose the use of a forecasting technique, followed by a routing heuristic to maximize the number of incidents where a helicopter provides a successful assist. We have implemented these techniques in a decision support application in collaboration with the Dutch Aviation Police and Air Support. Using numerical experiments, we show that our application has the potential to improve the success rate with a factor nine. The Dutch Air Support and Aviation Police are now using the application

On the fuzzy maximal covering location problem

This paper studies the maximal covering location problem, assuming imprecise knowledge of all data involved. The considered problem is modeled from a fuzzy perspective producing suitable fuzzy Pareto solutions. Some properties of the fuzzy model are studied, which validate the equivalent mixed-binary linear multiobjective formulation proposed. A solution algorithm is developed, based on the augmented weighted Tchebycheff method, which produces solutions of guaranteed Pareto optimality. The effectiveness of the algorithm has been tested with a series of computational experiments, whose numerical results are presented and analyzed.MINECO/FEDER grants MTM2017-89577-P, MTM2015-63779-R, MTM2016-74983-C2-1-R

Optimization of Turkish Air Force SAR Units’ Forward Deployment Points for a Central Based SAR Force Structure

Many developed countries that have a combatant Air Force and Search & Rescue (SAR) assets designed for their Air Force\u27s SAR service have been struggling with locating SAR units due to limited SAR assets, constrained budgets, logistic-maintenance problems, and high-risk level of military flights. In recent years, the Turkish Air Force (TUAF) has also been researching methods to gather all SAR units into a central base and deploying the needed number of SAR units to defined Deployment Points (DPs). This research applies three location optimization models to determine the optimum locations for TUAF SAR units. The first model, Set Covering Location Problem (SCLP), defines the minimum number of SAR DPs to cover all fighter aircraft training areas (TAs). The second model, Maximal Covering Location Problem (MCLP), aims to obtain maximum coverage with a given SAR DP number and response time. A weighted MCLP models is also applied with TAs risk values obtained by this research to maximize demanded coverage of TAs. Finally the last model, P-Median Location Problem, defines the locations of SAR DPs while obtaining minimum aggregate or average response time. These three models are applied via a Visual Basic for Applications (VBA) & LINGO Optimization Software interface that allows changing each exogenous variable of the models in a flexible way. The primary objective of this research is to provide the information for the required number of SAR units and their locations. The results indicate that the response time definition is as important as the required number of DPs. Additionally; some DP locations are indispensable because they have no alternative in their sectors

Maximal covering location problem (MCLP) with fuzzy travel times

This paper presents a fuzzy maximal covering location problem (FMCLP) in which travel time between any pair of nodes is considered to be a fuzzy variable. A fuzzy expected value maximization model is designed for such a problem. Moreover, a hybrid algorithm of fuzzy simulation and simulated annealing (SA) is used to solve FMCLP. Some numerical examples are presented, solved and analyzed to show the performance of the proposed algorithm. The results show that the proposed SA finds solutions with objective values no worse than 1.35% below the optimal solution. Furthermore, the simulation-embedded simulated annealing is robust in finding solutions

Metodologia de definição de rede de suprimentos para armazenagem de commodities agrícolas

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2020.A agricultura brasileira está entre as mais importantes do planeta, ocupando posição de destaque na exportação das commodities agrícolas de grãos como soja e milho. O fato das áreas de produção serem distantes dos portos e a sua velocidade de expansão territorial ser maior que a velocidade de instalação de novas estruturas de movimentação e armazenagem, emergem impactos negativos no sistema logístico. A ausência de um sistema logístico eficiente e com capacidade de armazenagem suficiente para todo o volume de colheita força os agricultores a venderem suas colheitas imediatamente, provocando uma redução de preços das commodities por excesso de oferta e a venda em um momento de preços em queda. Diante deste cenário, o presente estudo tem por objetivo principal desenvolver uma metodologia para definição de rede de suprimentos para armazenagem e escoamento de commodities agrícolas. Para alcançar tais objetivos foi desenvolvida uma metodologia composta de sete passos metodológicos que abrangem as etapas de execução da metodologia segundo um conjunto de restrições de definições teóricas para a aplicação do mesmo. De modo a validar a metodologia foi realizado um estudo de caso em que foram utilizados os mapas de estimativas das safras de soja e de milho do município de Nova Ubiratã no Estado do Mato Grosso – MT para determinar a configuração da rede suprimentos para o município que supra o déficit de capacidade estática por armazenagem para a safra 2017/2018 e que minimize o custo total da rede de suprimento. Para esse estudo de caso foram usados mapas da infraestrutura logística envolvida (rodovias e de armazenagem) para o escoamento destas safras, rede de suprimento e de localização p-mediana, linguagens de programação R e Python, e Sistemas de Informações Geográficas (SIG). Os resultados mostraram que a rede de suprimento com menor custo de transporte e armazenagem fora aquela que utilizou unidades de armazenamento de 162 mil toneladas de capacidade e com cobertura da produção de 120%. A metodologia possibilitou analisar a rede de suprimento atual sob a ótica de oferta-demanda e calcular as localizações e o número de instalações de facilidades logísticas necessárias para suprir o déficit da região do estudo de caso. Ademais, a metodologia demonstrou capacidade de tratar problemas de localização e otimização da rede de suprimentos em escala regional com agilidade e a eficiência preconizada.The Brazilian agriculture is one of the most important on the planet, with a prominent position in the agricultural commodities export of soybeans and corn. As the crop production areas are far from the ports and the speed of territorial expansion is greater than the speed of installation of new storage structures, important negative impacts on the logistics system emerge. The absence of an efficient logistical system with enough storage capacity for the entire harvest season forces farmers to sell their crops immediately after the harvest, causing a reduction in commodity prices due to oversupply and with sales at a time of falling prices. In this scenario, the main objective of this thesis was to develop a methodology for defining the supply network for the storage and flow of agricultural commodities. To achieve those objectives, the methodology developed consist of seven methodological steps that encompass the stages of the modeling execution according to a set of restrictions and definitions for its application. In order to validate the methodology, a study case was carried out using crop estimatives maps for soybean and corn in the municipality of Nova Ubiratã in the State of Mato Grosso – MT, Brazil. This maps to determine the configuration of the supply network for the municipality that overcomes the shortage of static storage capacity for the harvest of 2017/2018 and minimizes the total cost of the supply chain. For this case study, maps of the logistics infrastructure involved (highways and storage) were used for the flow of these crops, network model and p-median location, programming languages R and Python, and Geographic Information Systems (GIS). The results showed that the supply network with the lowest transport and storage cost was that one with storage units of 162 thousand tons of capacity and with production coverage of 120%. The model made it possible to analyze the current supply network from the perspective of supply-demand and calculate the locations and the number of logistical facilities necessary to supply the deficit of the region of the study case. In addition, the methodology demonstrated the ability to address problems of localization and optimization of the supply network on a regional scale with agility and the expected efficiency