Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH. / Includes bibliographies. / by Julia Marie Wondolleck. / M.C.P.

Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH. / Includes bibliographical references. / by Deborah Fae Cohen Shmueli. / M.C.P.

No description available.

A localização de fábricas, fornecedores e/ou CDs é um problema de grande complexidade, principalmente em empresas com operações internacionais. Na literatura mundial são poucos os modelos desenvolvidos que tratam da localização de instalações em escala global. Este trabalho propõe um modelo de Programação Linear Inteira Mista que soluciona o problema minimizando os custos logísticos (custos fixos, custos de transferência, custos de distribuição, impostos não recuperáveis, estoques, take-or-pay, matéria-prima e custos de agregação de valor e transbordo) levando em consideração vários elos da cadeia de suprimentos. Como principais contribuições do modelo citam-se a inclusão de benefícios fiscais em transações internacionais, tais como o drawback, o cálculo pioneiro para custo no carregamento dos estoques e o tratamento dado aos custos tipo take-or-pay. São sugeridas adaptações do modelo para utilização em quaisquer segmentos de indústria. O modelo foi aplicado com sucesso em um caso real de uma indústria do segmento químico. / The location of factories, suppliers and distribution centers has always been a problem of great complexity, mainly for companies with global operations. In the literature, there are a few models developed for facilities location in a global scale. This work proposes a Mixed Integer Linear Programming model that solves the problem minimizing the total logistic cost (fixed, transfer and distribution costs, non recoverable duties, carrying, take-or-pay, raw material, added value and handling costs) taking into consideration several echelons in the supply chain network. The main contributions of the proposed model are the considerations regarding fiscal benefits in international trade such as drawback, the pioneer carrying cost calculation and the treatment given to the take-or-pay costs. Some adaptations of the model have been suggested for use in any type of industry. The model was successfully applied in a real case of a chemical industry.

A localização de fábricas, fornecedores e/ou CDs é um problema de grande complexidade, principalmente em empresas com operações internacionais. Na literatura mundial são poucos os modelos desenvolvidos que tratam da localização de instalações em escala global. Este trabalho propõe um modelo de Programação Linear Inteira Mista que soluciona o problema minimizando os custos logísticos (custos fixos, custos de transferência, custos de distribuição, impostos não recuperáveis, estoques, take-or-pay, matéria-prima e custos de agregação de valor e transbordo) levando em consideração vários elos da cadeia de suprimentos. Como principais contribuições do modelo citam-se a inclusão de benefícios fiscais em transações internacionais, tais como o drawback, o cálculo pioneiro para custo no carregamento dos estoques e o tratamento dado aos custos tipo take-or-pay. São sugeridas adaptações do modelo para utilização em quaisquer segmentos de indústria. O modelo foi aplicado com sucesso em um caso real de uma indústria do segmento químico. / The location of factories, suppliers and distribution centers has always been a problem of great complexity, mainly for companies with global operations. In the literature, there are a few models developed for facilities location in a global scale. This work proposes a Mixed Integer Linear Programming model that solves the problem minimizing the total logistic cost (fixed, transfer and distribution costs, non recoverable duties, carrying, take-or-pay, raw material, added value and handling costs) taking into consideration several echelons in the supply chain network. The main contributions of the proposed model are the considerations regarding fiscal benefits in international trade such as drawback, the pioneer carrying cost calculation and the treatment given to the take-or-pay costs. Some adaptations of the model have been suggested for use in any type of industry. The model was successfully applied in a real case of a chemical industry.

Neste trabalho foi desenvolvido um modelo de programação linear inteira mista para localização das instalações da rede logística do biodiesel no Brasil, de forma a que se possa, com sua aplicação, avaliar o potencial de produção de oleaginosas no país, assim como identificar as zonas mais promissoras para a localização dos diversos elos da cadeia do biodiesel, a partir da demanda gerada pela mistura de um percentual de biodiesel ao diesel fóssil. O modelo incorpora quatro elos da cadeia produtiva (fase agrícola, extração de óleo, produção de biodiesel e pontos de demanda). Os parâmetros do modelo foram estimados com base em informações reais de mercado disponíveis (base de dezembro/2007). Obteve-se com a aplicação do modelo a diversos cenários, os municípios mais indicados para produção das oleaginosas, as oleaginosas utilizadas, o volume de produção em cada local e, por fim, a localização e porte das fábricas de óleo e das usinas de biodiesel. Análises de sensibilidade de alguns parâmetros foram executadas para verificação do comportamento do modelo face a incertezas. O trabalho incorpora sugestões e recomendações para aprimoramento do modelo. / In this research a mixed integer linear programming model was developed to locate facilities related to the biodiesel supply chain in Brazil, making possible to evaluate the oleaginous production potential, as well as the most promising regions to became the location of the several levels of the biodiesel chain, in accordance to the biodiesel future demand. The model incorporates four levels of the productive chain (agricultural phase, extraction of oil, biodiesel production and demand points). The model parameters were estimated based on market information available (base of december/2007). The application of the model to several sceneries led to the indication of the most promising regions for production of the oleaginous, the used oleaginous ones, the volume of production in each place and, finally, the location and scale of oil and biodiesel factories. Sensibility analyses were conducted to verify the results related to parameters uncertainty. The research contains suggestion and recommendations for improvement of the model.

This study deals with four location problems. The first problem is a brand new location problem on a line and considers the location decisions for depots and quarries in a highway construction project. We develop optimal solution properties of the problem. Using these properties, a dynamic programming algorithm is proposed. The second problem is also a brand new dynamic location problem on a line and locates concrete batching mobile and immobile facilities for a railroad construction project. We develop two mixed integer models to solve the problem. For solving large size problems, we propose a heuristic. Performances of models and the heuristic are tested on randomly generated instances plus a case study data and results are presented. The third problem is a generalization of the second problem to network locations. It is a dynamic version of the well known p-median problem and incorporates mobile facilities. The problem is to locate predetermined number of mobile and immobile facilities over a planning horizon such that sum of facility movement and allocation costs is minimized. Three constructive heuristics and a branch-and-price algorithm are proposed. Performances of these solution procedures are tested on randomly generated instances and results are presented. In the fourth problem we consider a special case of the third problem, allowing only conventional facilities. The algorithm for the third problem is improved so that generating columns and solving a mixed integer model are used repetitively. Performance of the algorithm is tested on randomly generated instances and results are presented.

No description available.

Η χωροθέτηση εγκαταστάσεων σε ένα ανταγωνιστικό περιβάλλον αποτελεί σημαντικό πρόβλημα που μπορεί να επηρεάσει την αποτελεσματικότητα και αποδοτικότητα μιας επιχείρησης. Διάφορες ποσοτικές μέθοδοι έχουν προταθεί ως βοήθεια για τη λήψη τέτοιων αποφάσεων. Ενώ τα συνήθη μοντέλα χωροθέτησης μελετούν την εγκατάσταση μονάδων μιας επιχείρησης, τα μοντέλα ανταγωνιστικής χωροθέτησης μελετούν και τις αντιδράσεις των ανταγωνιστικών επιχειρήσεων. Η παρούσα εργασία παρουσιάζει τα πιο σημαντικά και ευρέως γνωστά μοντέλα, που χρησιμοποιούνται στη χωροθέτηση ανταγωνιστικών εγκαταστάσεων. Εστιάζει κυρίως σε μοντέλο ακέραιου προγραμματισμού 0-1 για εγκατάσταση μονάδων ιεραρχικών επιπέδων, όταν στην περιοχή ενδιαφέροντος δραστηριοποιούνται και ανταγωνιστικές μονάδες. Το μοντέλο επιτρέπει τη χωροθέτηση νέων μονάδων καθώς και την επανεγκατάσταση υπαρχόντων. Τέλος, παρουσιάζεται μια εφαρμογή αυτής της μεθόδου βασισμένη σε πραγματικά δεδομένα μεγάλης Ελληνικής τράπεζας. / Facilities Location in a competitive environment is an important problem that can significantly affect the future performance of an organization. Various quantitative methods have been proposed as an aid to such decisions. Whereas the usual location models allow only one firm or facility to locate, competitive models permit other facilities to react to their competitor’s actions. This work presents the most important and common models, that are used in such situations. It mostly focuses on a 0-1 integer programming model for locating services with a hierarchical structure when there is competition in the region of interest. The model allows the location of new servers as well as the relocation of existing servers. Finally, an application of this technique is presented, based on real data from a big Greek bank.

Neste trabalho foi desenvolvido um modelo de programação linear inteira mista para localização das instalações da rede logística do biodiesel no Brasil, de forma a que se possa, com sua aplicação, avaliar o potencial de produção de oleaginosas no país, assim como identificar as zonas mais promissoras para a localização dos diversos elos da cadeia do biodiesel, a partir da demanda gerada pela mistura de um percentual de biodiesel ao diesel fóssil. O modelo incorpora quatro elos da cadeia produtiva (fase agrícola, extração de óleo, produção de biodiesel e pontos de demanda). Os parâmetros do modelo foram estimados com base em informações reais de mercado disponíveis (base de dezembro/2007). Obteve-se com a aplicação do modelo a diversos cenários, os municípios mais indicados para produção das oleaginosas, as oleaginosas utilizadas, o volume de produção em cada local e, por fim, a localização e porte das fábricas de óleo e das usinas de biodiesel. Análises de sensibilidade de alguns parâmetros foram executadas para verificação do comportamento do modelo face a incertezas. O trabalho incorpora sugestões e recomendações para aprimoramento do modelo. / In this research a mixed integer linear programming model was developed to locate facilities related to the biodiesel supply chain in Brazil, making possible to evaluate the oleaginous production potential, as well as the most promising regions to became the location of the several levels of the biodiesel chain, in accordance to the biodiesel future demand. The model incorporates four levels of the productive chain (agricultural phase, extraction of oil, biodiesel production and demand points). The model parameters were estimated based on market information available (base of december/2007). The application of the model to several sceneries led to the indication of the most promising regions for production of the oleaginous, the used oleaginous ones, the volume of production in each place and, finally, the location and scale of oil and biodiesel factories. Sensibility analyses were conducted to verify the results related to parameters uncertainty. The research contains suggestion and recommendations for improvement of the model.