[pt] Este estudo apresenta um modelo de programação inteira mista para a roteirização de navios com controle de estoque nos portos para a movimentação de múltiplos produtos com uma frota heterogênea. O modelo contempla a possibilidade de transformação de produtos dentro de navios, o que representa uma flexibilidade para o modelo optar por qual produto utilizar para atender um cliente com demanda com qualidade flexível. Esta habilidade não foi encontrada em nenhum outro estudo. Ele também combina o atendimento de demandas obrigatórias com opcionais. O modelo então é aplicado em um caso real de movimentação de derivados escuros de petróleo em uma empresa de petróleo brasileira, cujo modelo atual utilizado apresenta problemas que dificultam seu uso. Devido ao longo tempo que leva para obter a solução ótima para estes tipos de modelos, são utilizadas as matheurísticas de relax-and-fix e fix-and-optimize para obter soluções boas em um tempo reduzido. São apresentados experimentos computacionais em uma série de cenários para validar a qualidade das soluções encontradas pelos métodos propostos, testando diferentes configurações e discretizações de tempo. Os resultados apresentados comprovam a superioridade dos métodos em comparação com o modelo matemático puro. O modelo proposto apresentou grande potencial de substituir o modelo atual da empresa e para alcançar a melhoria pretendida na programação dos navios. / [en] This dissertation presents a mixed integer program model to solve a ship routing and scheduling with stock control in ports, also known as maritime inventory routing. This model considers a heterogeneous fleet, carrying multiples products. It also has the ability to transform one product into another inside ships. This aspect allows the model to choose which product it wishes to deliver to a client with a less restrict quality specification in his demand. No model presented in other studies has this capability. Another possibility covered by this model is to combine mandatory demands with optional ones. The model is applied to a real case of maritime transportation of dirty oil products in a Brazilian oil company, whose current model has a series of small problems that hinders its use. Due to the long time it takes to get the optimal solution, the relax-and-fix and fix-andoptimize heuristics are used to get good solutions in a reduced time. With the use of computational experiments in a series of scenarios, it has proved the quality of the solutions found by the proposed methods, testing different configurations and discretizations of time. The results presented prove the superiority of the methods in comparison to the pure mathematical model. The proposed model has shown great potential to replace the current one and to achieve the improvement for the ship routing intended by the company.

Purpose: This study aims to find how the integration between marketing and operations can improve demand management in order to have efficient inventory level and avoid excess inventory. Method used: In order to have optimize inventory level and managing demand, integrating business process between marketing, supply planning and inventory management team was considered in this study. Qualitative data from nine interviews among three direct sales cosmetics companies was gathered. Findings: Empirical findings address excess inventory is caused by several issue in the company as poor management of demand forecasting, wide product portfolio, long lead time, the lack of sharing information between the company and its suppliers and ineffective strategy to avoid excess inventory within the company. Regarding to improve forecast accuracy and manage demand, findings indicate the role of marketing in obtaining knowledge about customer insight is not good enough. Practical Implication: It is critical that works and plans from each function be integrated to optimize inventory. In order to support optimize inventory strategy all pertinent departments must continue reviewing meeting with the aim to reach a consensus about the products planning for the both side of demand and operation align with overall strategic goals of the company. Contribution: Empirical data demonstrate the best way for effective implication of demand management occurs when marketing can provide demand information in time, as well as supply chain management can react flexibly to demand changes in time. Moreover, the lack of integration between marketing and supply chain management is a major barrier in optimizing inventory.

The aim with this report is to formulate a strategic method to optimize selection processes of DNA-samples from a faeces inventory to identify as many individuals in as few analyzes as possible, and by that keep down the costs of brown bear management. Brown bear management in Sweden founds today on results from faeces inventory and is substantially led by the county administration boards. Data from the years of 2004 and 2009´s inventories in Västerbotten was used to test and evaluate different methods in selection processes of which faeces that should be sampled. Comparison were made between making selection by chance, by spatial distribution and by calculating variations in logistic regressions coefficient b, in other words bear density and probability in finding same individual in several faeces. We can show making selection by chance is the most uncertain method. Making selection by spatial distribution, without take in account variations in b, provides the highest number of identified individuals at a low labour and thus a low cost. Therefor we strongly recommend future brown bear management to, if not possible to sample all faces in a dataset, make selection by spatial distribution to minimize the risk of sampling the same bear several times.

This thesis analyzes the general performance and design requirements of photovoltaic(PV) systems, and specifically how they relate to the design of a system intended to supply power to a rotating satellite. The PV array geometry was discussed, different DC-DC converter topologies were analyzed, and optimum array geometry and converter topologies were determined. The potential reference quantities for use in control of the system are examined. Due to its comparably greater linearity with respect to changes in apparent load and its relative insensitivity to insolation changes, voltage was determined to be the best reference quantity for use in stable tracking of the maximum power operating point of photovoltaic modules. The preceding work is used to design and model a photovoltaic system for a rotating satellite ensuring the supply of the maximum available power as well as stable operation. Simulations of the system are performed at rotational velocities up to 300 rev/min and its behavior is analyzed to demonstrate the validity of the preceding work. It was concluded that: ● parallel connected photovoltaic panels provide greater efficiency than series connected panels. ● Buck, Boost, and Cuk Converter architectures are best suited to PV applications ● PV Voltage is the best reference quantity for use in stable control of PV systems.

The increasing penetration of distributed generation (DG) in power distribution systems presents technical and economic benefits as well as integration challenges to utility engineers. Governments are beginning to acknowledge DG as an economically viable alternative to deferring investment at generation, transmission and distribution levels, meeting demand growth and improving distribution network performance and security. DG technology is rapidly maturing in Ontario due to government economic incentives promoting connection, specifically, the Ontario’s Feed-In-Tariff (FIT) Program. Optimal sizing and siting of DG is well researched, traditionally studying the technical impact on distribution system such as real power loss reduction and voltage profile improvement. Equally common objectives studied are the economics of DG installation which are useful for the developer when deciding when and where to install. Although DG represents a “non-wires” solution to network asset reinforcement, the direct economic benefit to the host utility from promoting DG uptake is not fully understood by utility planners and asset managers. Some DG based asset reinforcement deferral work has been performed in the UK and Italy but is mainly at the transmission level and is not part of an overall strategy that could be applied by a utility. This research presents a comprehensive three stage technique: optimal siting, optimal sizing and financial evaluation of cost savings over a defined planning period to quantify the economic benefit to a Local Distribution Company (LDC) of solar photovoltaic (PV) DG connections on an actual distribution feeder. Optimal sites for PV DG are determined by applying the power loss sensitivity factor method to the test feeder. The objective functions used to determine cost savings consist of loss minimization, asset investment deferral, and peak demand reduction to identify an optimal DG penetration limit. Furthermore, a utility planner can identify an optimal DG penetration limit, encourage uptake at preferred locations that would benefit the LDC, and use the positive impact of DG at existing locations as part of an asset management strategy to prioritize and schedule future asset reinforcement upgrades.

Renewable Energy will be the key to preserving the Earth's remaining resources and continuing this surge of technological progress that we have experienced this past century. New philosophies of how/when/where energy should be consumed and produced are attempting to improve upon the current grid infrastructure. The massive advancement in communications, renewable and control systems will allow this new-age electric grid to maximize its efficiency while reducing cost. Renewable, "green" energy is now at the forefront of innovation. As the world population increases, there will be a need to free ourselves from natural resources as much as possible. Advanced Energy Storage Systems (AESS) will play a vital and large role in this new-age infrastructure. Because renewable energy is not constant (aside from hydroelectricity), this energy needs to be conserved and used at appropriate times. The Sustainable Electric Energy Delivery System (SEEDS) project features an AESS made from Lithium-ion phosphate (LiFeP04) and a Photovoltaic (PV) source connected to the grid. Every current technology has different parameters, efficiency, charge/discharge rates, lifespan, etc. The current Li-FeP04 system will be used as an example and a model. This project acts as a pilot project for future large scale smart grid endeavors. This thesis is written in conjunction with the SEEDS project and will outline and discuss in detail the findings. For the PV system, the performance is analyzed. For the storage system, the round-trip efficiency (measured) and life cycle are broken down. The thesis concludes with a capacity sizing estimation of the storage system which is based on the renewable energy source (solar).

A simple model that includes energy and carbon dioxide (CO2) emissions in the economics of machining is proposed, which has been published in the highly respected and cited journal, Annals of CIRP (International Academy for Production Engineering). This is a timely analysis in current government discussions on a proposed carbon tax or a carbon cap and trade regime and greater energy efficiency. The new cost model is based on life cycle analysis methodology for the initial part production. An illustrative example is given showing that the cheapest electrical grid should not be chosen, if it also has the highest CO2 emissions. Accurate pricing is important, because the more expensive product was highly dependent on the carbon price. A comprehensive review of machining economic models is covered. However, there is a dearth of actual machining data in the literature. This work includes studies in milling and single point incremental forming (SPIF) which can be used by other manufacturing engineers in their machining economic model development. The first milling study involved simple straight cuts. In general, as feed rate (FD) increased (increasing the material removal rate, MRR), the energy consumed decreased as process time decreased. In contrast, as spindle speed (N) increased, energy consumed increased, since more power is drawn by the motor, without a process time reduction. Given the inverse power relationship observed for the time, energy, process CO2 and cost against MRR, the recommended parameters were the same at the highest FD and lowest N permissible. In the second milling study with constant N for a more complex part (sprocket), similar relationships were observed. However, for sprockets made at constant chip load (allowing FD and N to change together), there were varying prescribed MRRs for time, energy, process CO2 and cost minimization. The SPIF studies showed a similar relationship to the constant N milling, and, that results for a simple part can be extrapolated to improve efficiency in more complex parts. Finally, although the energy and carbon costs represented a small contribution to the final cost, their significance increased for higher efficiency parameters or user conditions, e.g. low labour rate. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2011-12-03 19:58:07.76

The increasing penetration of distributed generation (DG) in power distribution systems presents technical and economic benefits as well as integration challenges to utility engineers. Governments are beginning to acknowledge DG as an economically viable alternative to deferring investment at generation, transmission and distribution levels, meeting demand growth and improving distribution network performance and security. DG technology is rapidly maturing in Ontario due to government economic incentives promoting connection, specifically, the Ontario’s Feed-In-Tariff (FIT) Program. Optimal sizing and siting of DG is well researched, traditionally studying the technical impact on distribution system such as real power loss reduction and voltage profile improvement. Equally common objectives studied are the economics of DG installation which are useful for the developer when deciding when and where to install. Although DG represents a “non-wires” solution to network asset reinforcement, the direct economic benefit to the host utility from promoting DG uptake is not fully understood by utility planners and asset managers. Some DG based asset reinforcement deferral work has been performed in the UK and Italy but is mainly at the transmission level and is not part of an overall strategy that could be applied by a utility. This research presents a comprehensive three stage technique: optimal siting, optimal sizing and financial evaluation of cost savings over a defined planning period to quantify the economic benefit to a Local Distribution Company (LDC) of solar photovoltaic (PV) DG connections on an actual distribution feeder. Optimal sites for PV DG are determined by applying the power loss sensitivity factor method to the test feeder. The objective functions used to determine cost savings consist of loss minimization, asset investment deferral, and peak demand reduction to identify an optimal DG penetration limit. Furthermore, a utility planner can identify an optimal DG penetration limit, encourage uptake at preferred locations that would benefit the LDC, and use the positive impact of DG at existing locations as part of an asset management strategy to prioritize and schedule future asset reinforcement upgrades.

abstract: In this thesis, a single-level, multi-item capacitated lot sizing problem with setup carryover, setup splitting and backlogging is investigated. This problem is typically used in the tactical and operational planning stage, determining the optimal production quantities and sequencing for all the products in the planning horizon. Although the capacitated lot sizing problems have been investigated with many different features from researchers, the simultaneous consideration of setup carryover and setup splitting is relatively new. This consideration is beneficial to reduce costs and produce feasible production schedule. Setup carryover allows the production setup to be continued between two adjacent periods without incurring extra setup costs and setup times. Setup splitting permits the setup to be partially finished in one period and continued in the next period, utilizing the capacity more efficiently and remove infeasibility of production schedule. The main approaches are that first the simple plant location formulation is adopted to reformulate the original model. Furthermore, an extended formulation by redefining the idle period constraints is developed to make the formulation tighter. Then for the purpose of evaluating the solution quality from heuristic, three types of valid inequalities are added to the model. A fix-and-optimize heuristic with two-stage product decomposition and period decomposition strategies is proposed to solve the formulation. This generic heuristic solves a small portion of binary variables and all the continuous variables rapidly in each subproblem. In addition, the case with demand backlogging is also incorporated to demonstrate that making additional assumptions to the basic formulation does not require to completely altering the heuristic. The contribution of this thesis includes several aspects: the computational results show the capability, flexibility and effectiveness of the approaches. The average optimality gap is 6% for data without backlogging and 8% for data with backlogging, respectively. In addition, when backlogging is not allowed, the performance of fix-and-optimize heuristic is stable regardless of period length. This gives advantage of using such approach to plan longer production schedule. Furthermore, the performance of the proposed solution approaches is analyzed so that later research on similar topics could compare the result with different solution strategies. / Dissertation/Thesis / Masters Thesis Industrial Engineering 2015

Residential and commercial buildings consume approximately 20% of the global energy generation. This value is continuously growing and the governments across the globe have realized the importance of regulating the building construction to optimize the energy utilization. Energy efficient building codes have been developed to optimize the energy efficiency in buildings. OTTV (Overall Thermal Transfer Value) is a key parameter for evaluating energy efficiency of building envelops in the present building code of Sri Lanka. In this research, the prescriptive requirements mentioned in the building code for the building envelops to optimize the energy efficiency of five (05) commercial buildings has been analyzed. The indoor climate was modeled and the annual cooling energy variation with Overall Thermal Transfer Value was studied using “DesignBuilder” software. A cost benefit analysis was carried out for enhanced energy efficiency building envelops applications. It was attempted to develop a general relationship between the OTTV and annual cooling energy requirement for each building. It has been observed that a second order polynomial relationship with R2 of 0.861 exists for RDA building, linear relationship with R2 of 0.838 exists for AirMech building. However a specific relationship could not be observed for BMICH, SLSI and WTC buildings. The impact on cooling energy requirement from envelop parameter modification is unique for each building. In some instances the reduction of OTTV has not resulted in any reduction of the cooling energy requirement. There is a combined effect from each building component which affects the final cooling energy requirement. A simulation based technique to be used to find the optimum building envelops design.