E-commerce and retail companies are seeking ways to cut delivery time and cost by exploring opportunities to use drones for making last-mile deliveries. In recent years, drone routing and scheduling have become a highly active area of research. This research addresses the concept of a truck-drone combined delivery by allowing autonomous drones to fly from delivery trucks, make deliveries, and fly to delivery trucks nearby. The first part of the research considers the synchronized truck drone routing model by allowing multiple drones to fly from any truck, serve customers and immediately return to any available truck or depot in the system. The goal is to find the optimal routes of both trucks and drones which minimize the arrival time of both trucks and drones at the depot after completing the deliveries. The problem can be solved by the formulated Mixed Integer Programming (MIP) for the small-size problems and our proposed heuristic called Adaptive Insertion Heuristics (ADI) which is based on the insertion technique for the medium/large-size problems. The second part of the research extends the first studied problem by allowing drones to serve multiple customers before merging with trucks as well as considering the capacity requirement for both vehicles. The problem is mathematically formulated and two efficient heuristic algorithms are designed to solve the large-size problems: Drone Truck Route Construction (DTRC) and Large Neighborhood Search (LNS). In the third study, the goal is to study the potential benefits of combining different types of fleet vehicles to deliver packages to the customers. Three types of vehicles are considered in this study including large drones, traditional trucks and hybrid trucks. The problem can be optimally solved by a mathematical formulation on a small scale. Two efficient metaheuristics based on Variable Neighborhood Search (VNS) and Large Neighborhood Search (LNS) are proposed to solve for approximate solutions of the large-size problems. A case study and numerical analysis demonstrate the better delivery time of the proposed model when compared with the delivery time of other delivery models with a single fleet type.

The location of distribution centers is an important strategic decision in supply chain design, particularly as it relates to service quality, productivity, and profitability of the firm. There has been extensive research performed on distribution location models which require the use of complex algorithms and assumptions that make use of these models difficult in practice for small and medium enterprises (SMEs) that have limited capital and resources. Studies have also failed to capture and quantify potential business results of using more sophisticated methods. In this study, a deterministic and static location-allocation model is designed using a prototype software tool. The tool is a collection of Excel/VBA programs formulated as a mixed integer programming (MIP) model. Research was done in conjunction with a personal care products company that provided a unique opportunity to evaluate the manual methods typically used in SMEs with the results of the software tool and the potential business impact. Both quantitative data, including customer locations and order information, as well as qualitative data were collected from the company. A total of five models were simulated using the prototype software tool, including one model of the current supply chain for use as a base comparison, and four future-state models of potential distribution center (DC) location scenarios. The objective in each of these models was to minimize transportation costs while maintaining the desired service fulfillment levels. The use of the prototype software tool resulted in a more optimal supply chain solution. The optimized DC location resulted in a network design with a 6.5% reduction in transportation costs from the base model, and a 0.8% reduction in transportation costs from a location previously chosen by the company. The results also provided insight into considering weighted shipping volume in location analysis as it can serve as a magnifier of business impact and rapid diminishing returns when shipping product below an average of 10 pounds. The use of an optimization tool was shown to mitigate many issues SMEs encounter in attempting to synthesize multiple variables in the DC location problem.

Municipalities globally are recognizing their role in mitigating climate change and are actively working to reduce carbon emissions. This complex challenge is heightened in areas like Northern Sweden, where municipalities are adapting to accommodate new industries essential for meeting global climate targets, subsequently changing the energy landscape. The local administration must not only decarbonize existing energy use but also develop infrastructure for the new industries, all while fostering sustainable and appealing cities where residents aspire to live. However, the trajectory of these changes and the subsequent future energy requirements remain uncertain. This study aims to assist the local administration in navigating through these uncertainties and setting ambitious climate and energy targets aligned with the goals of the Paris Agreement and sustainable developments. The research explores how model based scenario analysis can be improved to identify a set of relevant pathways that the municipalities can adopt by employing system analysis, energy system optimization, and scenario analysis. The study focuses on Gällivare municipality in Northern Sweden and employs the TIMES-City model to develop the energy system model of the municipality (RQ1). To identify relevant scenarios for local energy transition a framework for developing "Glocal" scenarios has been established (RQ2). These glocal scenarios incorporate global, national, and local socioeconomic trends into a coherent narrative and provide a more holistic and realistic view of potential future pathways (Paper 2). Additionally, a set of SDG indicators for evaluating the sustainability of different scenarios has been developed and applied in the model (RQ3, Paper 3). While the study focuses on Gällivare, the "glocal" scenario framework and SDG indicators developed in this research can be utilized by municipalities across the globe for identifying their climate and energy targets.

This research adds to the literature and provides insight to practice via three essays that increase understanding about the applications and consequences of the two new approaches to the after-sales service governance: warranty contract and performance-based contracts. First, we attempted to enhance our knowledge of the modeling of the after-sales service process. In the first essay, the research papers with analytical models of after-sales services to present current trends, issues, and future research directions in the literature are classified. In the second essay, the effect of the warranty contract on the supplier's product quality improvement efforts in the context of capital goods is examined. Three sets of optimization models reveal that the existence of a warranty improves product quality. In the third essay, the performance-based contract is examined in the context of the warranty contract. The numerical experimentations conducted demonstrate that the performance-based contract is superior to the warranty contract in terms of the supplier's product quality efforts and the customer's total cost of after-sales services. The alignment of incentives based on the product performance tackles the issues presented in the traditional after-sales service contracting. Collectively, the three studies presented in this research expand our understanding of after-sales service contracts. Thus, the research presents managerial implications and adds to the existing body of knowledge in after-sales service research.

Master of Agribusiness / Agricultural Economics / Vincent R. Amanor-Boadu / The United States was the world’s third largest producer of raspberries (by pounds) in 2013, behind Russia and Poland. Raspberries are the third most popular berry in the United States behind strawberries and blueberries. Most U.S. production of red raspberries occurs in the states of Washington and Oregon during July and August depending on variety. Harvest and production for industrial pack typically runs for five weeks. Sieved red raspberries or single strength red raspberry puree is one of many industrial packs produced in the Pacific Northwest of the United States. Sieved red raspberries are produced by forcing fresh, cleaned and sorted red raspberries and red raspberry crumbles and pieces through a mesh screen, collected in drums or pails and stored for use in further processed products such as pies, confectioneries and other consumer food products. For this thesis, sieved berries are packed in 55-gallon steel drums lined with food grade plastic bags. They are shipped from the processing plant to a third party warehouse to be frozen and stored. The final processing plant draws on these stored frozen products for use in the production of the Company’s consumer food products. The purpose of this thesis is to review the Company’s current procurement practices of sieved red raspberries and determine how these practices may be improved to reduce its total delivered cost. We use an optimization modelling approach to assess the procurement process used by the Company. The results indicate that it is possible to reduce procurement costs and improve efficiencies by making changes to the current procurement strategy. By implementing the procurement strategy developed in this study, we show that the Company can save as much as $1.69 million per year, which is equivalent to about 20.3% of the current spend. This would suggest that adopting the optimization strategy could allow the Company to increase its total sieved raspberry utilization by as much as 0.9 million pounds per annum, all other things remaining unchanged.

The expansion of the electrical grid and infrastructure is an essential part of development since it contributes to improved standard of living among the population. Solar home systems (SHS) are one solution to generate electricity for households where the national grid does not reach or is too sparsely populated to build a local mini-grid. Solar home system programs have been used as a solution for rural electrification in developing countries all over the world with various success, one of these countries is Namibia. A large fraction of the population in Namibia lacks access to electricity where most of the people live in rural areas not reached by the national electrical grid. However, several SHS clients in Namibia have been dissatisfied with their systems due to several issues regarding the service providers. Several service providers have limited technical know-how and therefore frequently over- and undersize system components and make mistakes during installations. An opportunity to improve SHS in Namibia is to develop a software tool that service provider can use to quickly calculate an optimum SHS in a user friendly way based on the electricity demands of the clients. An optimization model was developed using MS Excel which calculates the optimal SHS component capacities regarding cost and reliability with the use of Visual Basic macros. Various field studies and sensitivity analyses were conducted with the MS Excel model. The results were validated and compared with other software programs such as PVsyst and a Matlab model used in a previous study regarding solar power. Results show that several components in existing systems are incorrectly sized and that the MS Excel model could improve future installations and improve the reputation of SHS. The sensitivity analyses focused on cost, system reliability, system size and PV-module tilt and were implemented in the MS Excel model to optimize the results in a techno-economic perspective. The MS-Excel model was approved by Namibia Energy Institute and will be available for all service providers in Namibia.

<p>Energy management has become a major concern in the past two decades with the increasing energy prices, overutilization of natural resources and increased carbon emissions. According to the department of Energy the industrial sector solely consumes 22.4% of the energy produced in the country [1]. This calls for an urgent need for the industries to design and implement energy efficient practices by analyzing the energy consumption, electricity data and making use of energy efficient equipment. Although, utility companies are providing incentives to consumer participating in Demand Response programs, there isn’t an active implementation of energy management principles from the consumer’s side. Technological advancements in controls, automation, optimization and big data can be harnessed to achieve this which in other words is referred to as “Smart Manufacturing”. In this research energy management techniques have been designed for two SEU (Significant Energy Use) equipment HVAC systems, Compressors and load shifting in manufacturing environments using control and optimization.</p> <p>The addressed energy management techniques associated with each of the SEUs are very generic in nature which make them applicable for most of the industries. Firstly, the loads or the energy consuming equipment has been categorized into flexible and non-flexible loads based on their priority level and flexibility in running schedule. For the flexible loads, an optimal load scheduler has been modelled using Mixed Integer Linear Programming (MILP) method that find carries out load shifting by using the predicted demand of the rest of the plant and scheduling the loads during the low demand periods. The cases of interruptible loads and non-interruptible have been solved to demonstrate load shifting. This essentially resulted in lowering the peak demand and hence cost savings for both “Time-of-Use” and Demand based price schemes. </p> <p>The compressor load sharing problem was next considered for optimal distribution of loads among VFD equipped compressors running in parallel to meet the demand. The model is based on MILP problem and case studies was carried out for heavy duty (>10HP) and light duty compressors (<=10HP). Using the compressor scheduler, there was about 16% energy and cost saving for the light duty compressors and 14.6% for the heavy duty compressors</p> <p>HVAC systems being one of the major energy consumer in manufacturing industries was modelled using the generic lumped parameter method. An Electroplating facility named Electro-Spec was modelled in Simulink and was validated using the real data that was collected from the facility. The Mean Absolute Error (MAE) was about 0.39 for the model which is suitable for implementing controllers for the purpose of energy management. MATLAB and Simulink were used to design and implement the state-of-the-art Model Predictive Control for the purpose of energy efficient control. The MPC was chosen due to its ability to easily handle Multi Input Multi Output Systems, system constraints and its optimal nature. The MPC resulted in a temperature response with a rise time of 10 minutes and a steady state error of less than 0.001. Also from the input response, it was observed that the MPC provided just enough input for the temperature to stay at the set point and as a result led to about 27.6% energy and cost savings. Thus this research has a potential of energy and cost savings and can be readily applied to most of the manufacturing industries that use HVAC, Compressors and machines as their primary energy consumer.</p><br>

Submitted by Fabielle Cheuczuk (fabielle.cheuczuk@unioeste.br) on 2017-11-30T13:22:04Z No. of bitstreams: 2 liro s. w. 2017.pdf: 2354107 bytes, checksum: 3a3d7bd6afbbedf2b5055cb60c32b347 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-11-30T13:22:05Z (GMT). No. of bitstreams: 2 liro s. w. 2017.pdf: 2354107 bytes, checksum: 3a3d7bd6afbbedf2b5055cb60c32b347 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-08-18 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / This work seeks to identify and to analyze the agricultural activities which optimize income in a rural family property using linear programming, called software Lindo, considering limits or restrictions of size, labor and financial resources. The methodology included the IBGE data analysis, among other official data sources to define the main agricultural activities of the region, including forest production or reforestation activities, fruits and vegetables, characterized as optimization model. Six scenarios were considered in the model, varying the amount of labor, as a restriction in each scenario. The results showed that, regardless of the amount of labor used, garlic is the main activity of the model, being cultivated in all scenarios, with demand of approximately 70% of all labor. Another clear point of the result in the optimization model is that in the scenarios with greater availability of labor the model opt for more intensive activities, this happens in the scenarios 04, 05 and 06, from the use of four people or above , during the year, with peach and strawberry cultivation. In contrast, with less labor availability, as in scenarios 01, 02 and 03, it opts for the use of most of the area with less labor-intensive activities, such as soybeans with maize (crop and mini crop) and soybean (crop and mini crop). Although the size of the property is a limiting factor, with the use of a larger number of people, that is, of labor, income generation is possible and feasible, respecting the choice of activities that compensate for its use. / Este trabalho procura identificar e analisar as atividades agrícolas que otimizam a renda em uma propriedade familiar rural utilizando-se da programação linear, por meio do software Lindo, considerando limites ou restrições de tamanho, mão de obra e recursos financeiros. A metodologia incluiu a análise de dados do IBGE, dentre outras fontes de dados oficiais para definição das principais atividades agrícolas da região, incluindo atividades de produção florestal ou reflorestamento, frutas e verduras, caracterizadas como modelo de otimização. Foram considerados seis cenários no modelo, variando a quantidade de mão de obra, como restrição em cada cenário. Os resultados apontaram que independentemente da quantidade de mão de obra utilizada, o alho é a principal atividade do modelo, sendo cultivada em todos os cenários, com demanda de aproximadamente 70% de toda a mão de obra. Outro apontamento claro do resultado do modelo de otimização, é que nos cenários com maior disponibilidade de mão de obra o modelo opta por atividades mais intensivas por esta, isto acontece nos cenários 04, 05 e 06, a partir do uso de quatro pessoas ou acima, durante o ano, com cultivo também de pêssego e morango. De modo contrário com menor disponibilidade de mão de obra, como nos cenários 01, 02 e 03, opta pelo uso da maior parte da área com atividades menos intensivas em mão de obra, como soja com milho (safra e safrinha) e soja (safra e safrinha). Apesar do tamanho da propriedade ser um fator limitante, com o uso de um maior número de pessoas, ou seja, de mão de obra, a geração de renda é possível e viável , respeitando-se a escolha de atividades que compensem a sua utilização.

Context: Data mining as a technique is used to find interesting and valuable knowledge from huge amount of stored data within databases or data warehouses. It encompasses classification, clustering, association rule learning, etc., whose goals are to improve commercial decisions and behaviors in organizations. Amongst these, hierarchical clustering method is commonly used in data selection preprocessing step for customer segmentation in business enterprises. However, this method could not treat with the overlapped or diverse clusters very well. Thus, we attempt to combine clustering and optimization into an integrated and sequential approach that can substantially be employed for segmenting customers and subsequent membership cards generation. Clustering methods is used to segment customers into groups while optimization aids in generating the required membership cards. Objectives: Our master thesis project aims to develop a methodological approach for customer segmentation based on their characteristics in order to define membership cards based on mathematical optimization model in a hypermarket. Methods: In this thesis, literature review of articles was conducted using five reputed databases: IEEE, Google Scholar, Science Direct, Springer and Engineering Village. This was done to have a background study and to gain knowledge about the current research in the field of clustering and optimization based method for membership card generating in a hypermarket. Further, we also employed video interviews as research methodologies and a proof-of-concept implementation for our solution. Interviews allowed us to collect raw data from the hypermarket while testing the data produces preliminary results. This was important because the data could be regarded as a guideline to evaluate the performance of customer segmentation and generating membership cards. Results: We built clustering and optimization models as a two-step sequential method. In the first step, the clustering model was used to segment customers into different clusters. In the second step, our optimization model was utilized to produce different types of membership cards. Besides, we tested a dataset consisting of 100 customer records consequently obtaining five clusters and five types of membership cards respectively. Conclusions: This research provides a basis for customer segmentation and generating membership cards in a hypermarket by way of data mining techniques and optimization. Thus, through our research, an integrated and sequential approach to clustering and optimization can suitably be used for customer segmentation and membership card generation respectively.

This thesis presents two Models of portfolio optimization, namely the Markowitz Mean Variance Optimization Model and the Rockefeller and Uryasev CVaR Optimization Model. It then presents an application of these models to a portfolio of clean energy assets for optimal allocation of financial resources in terms of maximum returns and low risk. This is done by writing GAMS programs for these optimization problems. An in-depth analysis of the results is conducted, and we see that the difference between both models is not very significant even though these results are data-specific.