The farm record keeping system is frequently used by the farm business manager for historical accounting, mainly to satisfy institutional requirements for information, particularly that of taxation. Little emphasis has been placed on the record system as an aid in forward planning. Farm planning aids frequently ignore the record keeping aspect of farm business management and are generally unrelated to record keeping systems used on the farm. The Canfarm Farm Record System and the various Canfarm Farm Planning Packages are likewise unrelated. This lack of a comprehensive Managerial Information and Decision System for the Canadian farm business manager may, in part, account for the slow rate of adoption of computerized record keeping systems and farm planning aids. The objective of this study was to integrate the Canfarm Farm Record System with a farm planning model, in order to suggest a format for the future development of an extension oriented Managerial Information and Decision System. The specific objectives were: 1) to identify the Canfarm Farm Record System, the statements that are generated from the Record System and the accounting items which make up these statements; 2) to construct, document and validate a linear programming farm model which incorporates the information contained in the Canfarm Farm Record System and generates output in statements that are consistent (identical) with the Record System statements; and, 3) to develop recommendations for standardization of projected financial reports that existing models could adopt, and which could be incorporated into future models. A significant cost of using any system is the investment in time required to become acquainted with that system. Consequently, the fewer the concepts to be learned, the lower would be the cost of learning for both the extension agent and the farm business manager. In addition to reducing the number of new concepts, integrating a record system with the planning function provides the control link through the comparison of planned versus actual values that is not available with fragmented packages. These considerations, in conjunction with management theory and theories of the firm formed the theoretical background to this study. Linear programming was chosen as an appropriate solution method due to its lack of conceptual complexity, the availability of a suitable algorithm and its maximizing capabilities. The Canfarm Farm Record System was described in terms of its accounting concepts. The flow of data was shown, from the farmer's journal entries, through the various detailed reports, to the summary financial statements and to the Balance Sheet. Four reports, the Farm Operating Statement, the Income Statement, the Statement of Assets, Liabilities and Owners Equity (Balance Sheet), plus the Cash Flow Statement, were chosen as the basic reports to be generated by the model. The empirical work was shown to consist of several stages, from the capturing of the historical records, the formulation of the farm plan, the generation of the LP matrix, to the solution of the projected farm plan. A report writer converts the output from the LP solution and produces the four financial statements. The final stage is the control link, in which the projected reports are compared to the actual records as they become available. The model was designed to be applicable to a variety of farm planning situations, in addition to being able to link up with other models. It was therefore subjected to several tests, including a simulation run using case farm data from the Canfarm Farm Record System to test the model structure, integration with two different farm planning models to demonstrate its flexibility and comparison with the same data fed through the Canfarm Farm Record System. Once this stage was reached, other potential applications were outlined and then a recommendation was proposed for the adoption of a standardization base for future models as well as for wider applications of this model. / Land and Food Systems, Faculty of / Graduate

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This dissertation develops a mixed integer linear program to establish the upper and lower bounds of the Alphorn of Uncertainty. For a project manager, planning for uncertainty is a staple of their jobs and education. But the uncertainty associated with a catastrophic event presents difficulties not easily controlled with traditional methods of risk management. This dissertation brings and modifies the concept of a project schedule as a bounded “Alphorn of Uncertainty” to the problem of how to reduce the risk of a catastrophic event wreaking havoc on a project and, by extension, the company participating in that project. The dissertation presents new mathematical models underpinning the methods proposed to reduce risk as well as simulations to demonstrate the accuracy of those models. The dissertation further assesses the complexity of the models and thus their practical application. Finally, the dissertation presents strategies to reduce the risk to a project of a catastrophic event using the upper bound of the Alphorn as the measure of risk.

The management of large-scale transportation infrastructure is becoming a very complex task for the urban areas of this century which are covering bigger geographic spaces and facing the inclusion of connected and self-controlled vehicles. This new system paradigm can leverage many forms of sensing and interaction, including a high-scale mobile sensing approach. To obtain a high penetration sensing system on urban areas more practical and scalable platforms are needed, combined with estimation algorithms suitable to the computational capabilities of these platforms. The purpose of this work was to develop a transportation framework that is able to handle different kinds of sensing data (e.g., connected vehicles, loop detectors) and optimize the traffic state on a defined traffic network. The framework estimates the traffic on road networks modeled by a family of Lighthill-Whitham-Richards equations. Based on an equivalent formulation of the problem using a Hamilton-Jacobi equation and using a semi-analytic formula, I will show that the model constraints resulting from the Hamilton-Jacobi equation are linear, albeit with unknown integer variables. This general framework solve exactly a variety of problems arising in transportation networks: traffic estimation, traffic control (including robust control), cybersecurity and sensor fault detection, or privacy analysis of users in probe-based traffic monitoring systems. This framework is very flexible, fast, and yields exact results. The recent advances in sensors (GPS, inertial measurement units) and microprocessors enable the development low-cost dedicated devices for traffic sensing in cities, 5 which are highly scalable, providing a feasible solution to cover large urban areas. However, one of the main problems to address is the privacy of the users of the transportation system, the framework presented here is a viable option to guarantee the privacy of the users by design.

In the past decades, linear programming (LP) has been successfully used to develop approximation algorithms for various optimization problems. In particular, the so-called assignment LP has lead to substantial progress for various allocation problems, including scheduling unrelated parallel machines. However, we have reached its limits for many problems, since the best-known approximation algorithms match the integrality gap of the assignment LP for these problems. The natural question is then whether a different LP formulation can lead to better algorithms. We answer this question positively for variants of two allocation problems: max-min fair allocation and maximum budgeted allocation. This is achieved by using a more powerful LP formulation called the configuration LP that has an exponential number of variables, but can be approximated in polynomial time. The restricted max-min fair allocation problem, also known as the restricted Santa Claus problem, is one of few problems that have a better polynomial estimation algorithm than approximation algorithm. An estimation algorithm estimates the value of the optimal solution, but is not necessarily able to find the optimal solution. The configuration LP can be used to estimate the optimal value within a factor of 1/(4+ɛ) for any ɛ&gt;0, but it is only known how to efficiently find a solution achieving this value in exponential time. We give an approximation algorithm with the same approximation ratio but improve the running time to quasi-polynomial: n^O(log n). Our techniques also have the interesting property that although we use the rather complex configuration LP in the analysis, we never actually solve it and therefore the resulting algorithm is purely combinatorial. For the maximum budgeted allocation (MBA) the integrality gap of the assignment LP is exactly 3/4. We prove that the integrality gap of the configuration LP is strictly better than 3/4 and provide corresponding polynomial time rounding algorithms for two variants of the problem: the restricted MBA and the graph MBA. Finally, we improve the best-known upper bound on the integrality gap for the general case from 0.833 to 0.828 and also show hardness of approximation results for both variants studied. / Under de senaste decennierna har linjärprogrammering (LP) framgångsrikt använts för att utveckla approximeringsalgoritmer. I synnerhet har det så kallade tilldelnings-LP lett till betydande framsteg för olika allokeringsproblem, som scheduling unrelated parallel machines. Vi verkar dock ha nått dess gräns, eftersom de bästa approximeringsalgoritmerna har samma kvalitet som heltalsgapet för dessa problem. Den naturliga frågan är då om någon annan LP-formulering kan leda till bättre algoritmer. Vi besvarar denna fråga positivt för varianter av två fördelningsproblem: max-min fair allocation och maximal budgeted allocation. Vi använder en mer kraftfull LP-formulering som kallas konfigurations-LP och har ett exponentiellt antal variabler men kan approximeras i polynomisk tid. Problemet restricted max-min fair allocation, som är även känt som restricted Santa Claus problem, är ett av få problem som har en bättre polynomisk värderingsalgoritm än approximeringsalgoritm. En värderingsalgoritm approximerar det optimala värdet, men hittar inte nödvändigtvis den optimala lösningen. Konfigurations-LP kan användas för att approximera det optimala värdet inom en faktor 1 / (4 + ɛ) för något ɛ &gt; 0, men man vet bara hur man hittar en lösning med sådan kvalitet i exponentiellt tid. Vi ger en approximeringsalgoritm med samma approximeringskvalitet men förbättrar tidskomplexitet till kvasipolynomisk: n^O(log n). Våra tekniker har också den intressanta egenskapen att även om vi använder det ganska komplext konfigurations-LP:t i analysen, löser vi aldrig det och vår algoritm är rent kombinatorisk. För maximal budgeted allocation (MBA) är heltalsgapet av tilldelnings-LP:et är precis 3/4. Vi bevisar att heltalsgapet av konfiguration-LP är strikt bättre än 3/4 och vi ger en motsvarande polynomisk avrundningsalgoritm för två varianter av problemet: restricted MBA och graph MBA. Slutligen förbättrar vi den bäst kända övre gränsen på heltalsgapet för det allmänna fallet från 0.833 till 0.828 samt ger approximeringssvårighetsresultat för båda två studerade varianter. / <p>QC 20150305</p> / ERC APPROXNP 226203

The major purpose of this study was to determine the least- cost method of producing red meat in Kenya. Linear programming was used in the study. A simulated reduction of grazing land available in one of the settlement areas was carried out to indicate what effect this had on the overall regional production pattern of meat in the country. Kenya was divided into eleven livestock producing and consuming regions. 1979 was used as the base year, and the demand projection was based on the 1979 population. Input and output coefficients, livestock unit requirements, and market prices were developed. A linear programming model was then used to generate the optimal production and marketing of both cattle and small stock .

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21130号 / 工博第4494号 / 新制||工||1698(附属図書館) / 京都大学大学院工学研究科化学工学専攻 / (主査)教授 長谷部 伸治, 教授 大嶋 正裕, 教授 佐野 紀彰 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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Transportation underlines the vehicle industry's critical role in a country's economic future.The amount of goods moved, specically by trucks, is only expected to increase inthe near future. This work attempts to tackle the problem of optimizing fuel consumptionin Volvo trucks, when there are hard constraints on the delivery time and speed limits.Knowledge of the truck such as position, state, conguration etc., along with the completeroute information of the transport mission is used for fuel optimization.Advancements in computation, storage, and communication on cloud based systems, hasmade it possible to easily incorporate such systems in assisting modern eet. In this work,an algorithm is developed in a cloud based system to compute a speed plan for the completemission for achieving fuel minimization. This computation is decoupled from thelocal control operations on the truck such as prediction control, safety, cruise control, etc.;and serves as a guide to the truck driver to reach the destination on time by consumingminimum fuel.To achieve fuel minimization under hard constraints on delivery (or arrival) time andspeed limits, a non-linear optimization problem is formulated for the high delity modelestimated from real-time drive cycles. This optimization problem is solved using a Nonlinearprogramming solver in Matlab.The optimal policy was tested on two drive cycles provided by Volvo. The policy wascompared with two dierent scenarios, where the mission demands hard constraints ontravel time and the speed limits in addition to no trac uncertainties (deterministic). with a cruise controller running at a constant set speed throughout the mission. Itis observed that there is no signicant fuel savings. with maximum possible fuel consumption; achieved without the help of optimalspeed plan (worst case). It is seen that there is a notable improvement in fuelsaving.In a real world scenario, a transport mission is interrupted by uncertainties such as trac ow, road blocks, re-routing, etc. To this end, a stochastic optimization algorithm is proposedto deal with the uncertainties modeled using historical trac ow data. Possiblesolution methodologies are suggested to tackle this stochastic optimization problem.

An Optimization Approach to Employee Scheduling Using Fuzzy Logic William G. Spence Selection of sales employees is critical because the sales employees represent the company’s image, competitive advantage, technology, and values. In many service systems the majority of consumer contact is with the sales department. Since there are different types of customers, scheduling quality salespersons who can adequately help consumers may affect revenue. This thesis proposes a new methodology for the scheduling of employees in a service system. The methodology uses Fuzzy Logic to calculate possible sales and Linear Programming to create an optimal schedule. This approach enables the rating of sales employees with respect to three customer’s types (Lookie Lou, Price Shopper and Buyer). The salesperson rating, along with customer arrival distribution is then used to optimize sale person scheduling, with the objective of revenue maximization. The uniqueness of this thesis lies in the combination of Fuzzy Logic and Linear Programming. The combination of these two disciplines provides an adaptive tool that can be used to optimize employee scheduling based on personality traits.