The design of cement distribution network in Myanmar : a case study of "X" cement industry

The network design problem is one of the most comprehensive strategic decision issues that need to be optimized for the long-term efficient operation of whole supply chain. The problem treated in this thesis is a capacitated location allocation planning of distribution centers for the distribution network design. The distribution network in this research is considered from plants to distribution centers and distribution centers to demand points. The research will explore the optimal number and locations of cement distribution center of “X” cement industry in Myanmar. The Mixed Integer Linear Programming (MILP) was developed as a tool to solve optimization problem which involves 3 manufacturing plants, 6 distribution centers and 6 market regions. The data collection was done by the company. The (MILP) model provides useful information for the Company about which distribution centers should be opened and what would be the best distribution network in order to maximize profit while still satisfies the customers’ demand. In this study, we proposed three scenarios which are scenario two, six and eight. In all scenarios, the solution was to have only two distribution centers from Mandalay and Meikhtila markets are recommended to open in the distribution network

Enhanced cell-based algorithm with dynamic radius in solving capacitated multi-source weber problem

Capacitated Multi-source Weber Problem (CMSWP) is a type of Location Allocation Problem (LAP) which have been extensively researched because they can be applied in a variety of contexts. Random selection of facility location in a Cell-based approach may cause infeasible or worse solutions. This is due to the unprofitable cells are not excluded and maybe selected for locating facilities. As a result, the total transportation cost increases, and solution quality is not much improved. This research finds the location of facilities in a continuous space to meet the demand of customers which minimize the total cost using Enhanced Cell-based Algorithm (ECBA). This method was derived from previous study that divides the distribution of customers into smaller cells of promising locations. The methodology consists of three phases. First, the profitable cells were constructed by applying ECBA. Second, initial facility configuration was determined using fixed and dynamic radius. Third, Alternating Transportation Problem (ATL) was applied to find a new location. The algorithm was tested on a dataset of three sizes which are 50, 654 and 1060 customers. The computational results of the algorithm prove that the results are superior in terms of total distance compared to the result of previous studies. This study provides useful knowledge to other researchers to find strategic facilities locations by considering their capacities

Developing dynamic maximal covering location problem considering capacitated facilities and solving it using hill climbing and genetic algorithm

The maximal covering location problem maximizes the total number of demands served within a maximal service distance given a fixed number of facilities or budget constraints. Most research papers have considered this maximal covering location problem in only one period of time. In a dynamic version of maximal covering location problems, finding an optimal location of P facilities in T periods is the main concern. In this paper, by considering the constraints on the minimum or maximum number of facilities in each period and imposing the capacity constraint, a dynamic maximal covering location problem is developed and two related models (A, B) are proposed. Thirty sample problems are generated randomly for testing each model. In addition, Lingo 8.0 is used to find exact solutions, and heuristic and meta-heuristic approaches, such as hill climbing and genetic algorithms, are employed to solve the proposed models. Lingo is able to determine the solution in a reasonable time only for small-size problems. In both models, hill climbing has a good ability to find the objective bound. In model A, the genetic algorithm is superior to hill climbing in terms of computational time. In model B, compared to the genetic algorithm, hill climbing achieves better results in a shorter time

Multi-period maximal covering location problem with capacitated facilities and modules for natural disaster relief services

The paper aims to study a multi-period maximal covering location problem with the configuration of different types of facilities, as an extension of the classical maximal covering location problem (MCLP). The proposed model can have applications such as locating disaster relief facilities, hospitals, and chain supermarkets. The facilities are supposed to be comprised of various units, called the modules. The modules have different sizes and can transfer between facilities during the planning horizon according to demand variation. Both the facilities and modules are capacitated as a real-life fact. To solve the problem, two upper bounds-(LR1) and (LR2)-and Lagrangian decomposition (LD) are developed. Two lower bounds are computed from feasible solutions obtained from (LR1), (LR2), and (LD) and a novel heuristic algorithm. The results demonstrate that the LD method combined with the lower bound obtained from the developed heuristic method (LD-HLB) shows better performance and is preferred to solve both small- and large-scale problems in terms of bound tightness and efficiency especially for solving large-scale problems. The upper bounds and lower bounds generated by the solution procedures can be used as the profit approximation by the managerial executives in their decision-making process

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

Adquisición de capital intelectual mediante sistemas de información geográfica y geomarketing: aplicaciones en la localización de instalaciones

El objetivo general de la tesis doctoral es analizar y proponer el uso de Sistemas de Información Geográfica y Técnicas de Geomarketing para mejorar los niveles de capital (incrementando su Capital Intelectual) de las organizaciones, en especial de pequeñas y medianas empresas (PYMES), a través de una mejora en los procesos de toma de decisiones relacionada con la elección de la ubicación física de sus instalaciones. En primer lugar y partiendo de que la Gestión del Conocimiento (Knowledge Management) y el Capital Intelectual constituye un pilar básico para muchas organizaciones, esta Tesis Doctoral analiza documentación especializada en este campo de investigación, incluyendo, por una parte, el estado del arte relacionado con la gestión del conocimiento y el capital intelectual, y por otra, como el uso de los Sistemas de información adecuados para cada organización permiten el desarrollo de ello, particularizando en los modelos teóricos de localización de instalaciones, que es una de las decisiones estratégicas más importantes que debe tomar una empresas, centrándose la tesis en PYMES y Microempresas. Se plantea y analiza estado actual de los sistemas de información en las organizaciones, con especial atención al uso de los Sistemas de Información Geográfica (SIG), y su integración con técnicas básicas de Geomarketing que mejoren los procesos de toma de decisiones, y con ello su capital intelectual. Por tanto, con este objetivo se presentan dos modelos para optimizar la localización de instalaciones en empresas. En ambos modelos se ha dado un enfoque basado en los problemas de cobertura, de forma que se trata, en todo momento, de maximizar el número de potenciales clientes que estuvieran dentro del área de influencia de las instalaciones propias. Los modelos presentados han sido validados haciendo uso de Sistema de Información Geográfico ArcGIS, y tomando datos reales de una extensa área urbana de la ciudad de Murcia de una extensión aproximada de 1,5 Km. El primer modelo presentado trata de seleccionar la localización de una nueva instalación en una organización. Para ello, se analiza la localización actual del resto de instalaciones (propias y de otras organizaciones del sector), así como de las alternativas de localización existentes, de forma que mediante un proceso iterativo de evaluación de alternativas, se llega a la mejor solución posible en términos de optimización. El segundo modelo presentado trata de seleccionar aquella instalación, dentro de un conjunto de instalaciones existentes, que deba ser eliminada (al objeto de reducir costes y/u optimizar recursos) de forma que el impacto negativo derivado de dicha eliminación sea mínimo en términos de cobertura del resto de instalaciones de la propia empresa. Los resultados obtenidos en los casos reales analizados, cuya información se presenta de forma detallada en términos numéricos y gráficos, muestran claramente cómo es posible que cualquier emprendedor que inicie una actividad económica (microempresa o PYME), así como aquellas que estén ya en funcionamiento, pueden controlar el tamaño de la organización desde el punto de vista del número de establecimientos, en base a estos procedimientos.Ingeniería, Industria y ConstrucciónAdministración y Dirección de EmpresasTurism