In this study, we investigate several facets of a due-date quotation problem and develop a model considering jointly due-date quotation, order acceptance and base-stock decisions in a hybrid make-to-stock (MTS) / make-to-order (MTO) and multi-class system with lead time sensitive Poisson demand and exponentially distributed service times. We seek to maximize profit considering lateness penalties and holding costs in the model. We consider three alternative due-date quotation policies each having different properties in terms of due-date flexibility as well as the utilization of state information. In order to evaluate the value of due-date flexibility as well as state information, the performances of the optimal policy and alternative policies are evaluated for various performance measures under different operating conditions. We also discuss the benefit of joint pooling of inventory and capacity under optimal policy and an accept-all policy.

In this thesis the scheduling algorithms Earliest Due Date (EDD) and Tube-tap were compared to each other. They were compared to see which algorithm generated more net profit. They were compared to each other in four different scenarios. In each scenario there were two different machines and five different jobs. Each machine had a cost and a processing rate, and each job had a length, a deadline, and a profit. In every scenario all jobs could not be completed before their deadline. The net profit was calculated by subtracting the gross profit by the machine cost. The results for the Tube-tap and EDD algorithms were almost the same for both algorithms in the different scenarios. There was one scenario where Tube-tap gained 0.5 more in profit. This was because Tube-tap had 0.5 in machine cost for one of the scenarios. The conclusions was that there should be more experimentation to see how more profitable Tube-tap can be. Specifically, there should be testing on scenarios where all jobs cannot be completed before their deadlines, but the number of operations does not exceed the available space before the last deadline.

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O presente trabalho aborda o problema de determinação de datas de entrega e o sequenciamento de tarefas com tempos de processamento estocásticos. O ambiente considerado constitui em uma máquina simples e tarefas com custos individuais e distintos de adiantamento e atraso de entrega (earliness e tardiness ou simplesmente E/T). O objetivo é determinar a sequência e as datas de entrega ótimas que simultaneamente minimizam o custo total esperado de E/T. Para a determinação de sequências candidatas, são apresentadas diversas heurísticas construtivas com tempo de execução polinomial baseadas em um método de inserção de tarefas. Considerando tarefas com distribuição normal, experimentos computacionais comprovam a eficácia dos algoritmos para problemas de menor porte, os quais fornecem soluções ótimas em 99,85% dos casos avaliados. Quando aplicadas a um conjunto com uma maior quantidade de tarefas, as heurísticas apresentaram resultados melhores do que o algoritmo disponível na literatura em mais de 80% dos casos. Consideradas tarefas com distribuição lognormal, obteve-se um percentual de otimalidade entre 93,87% e 96,45%, a depender da heurística aplicada. Demonstra-se ainda para o caso com distribuição normal que os métodos propostos são assintoticamente ótimos e, portanto, são indicados para a resolução de problemas de grande porte. / This work addresses the problem of concurrent due-date assignment and sequencing of a set of jobs on a stochastic single-machine environment with distinct job earliness and tardiness penalty costs. It is assumed that the jobs processing times are statistically independent and follow a normal distribution whose mean and variance are provided and they are not necessarily integer values. The objective is to determine the job sequence and the integer due dates which minimize the expected total earliness and tardiness costs. Several efficient insertion-based construction heuristics are proposed in order to find candidates for the optimal sequence with polynomial time complexity. For the normal distribution problem, numerical experiments show that the proposed heuristic methods are able to find the optimal solution in 99,85% when applied to problems with a smaller size. When applied to problems with a bigger size, the solutions found by the proposed heuristics had better costs than the solutions described in the literature in more than 80% of cases. For the lognormal distribution problem, the proposed heuristic methods provided solutions with a percentage of optimality between 93,87% and 96,45%. Furthermore, for the normal distribuition case, it was proven that the heuristics are asymptotically optimal, i.e., it can be used for problems of any size.

In the make-to-order (MTO) industry, offering competitive due dates and on-time delivery for customer orders is important to the survival of MTO companies. Workload control is a production planning and control approach designed to meet the need of the MTO companies. In this dissertation, a multi-agent workload control methodology that simultaneously deals with due date setting, job release and scheduling is proposed to discourage job early or tardy completions. The earliness and tardiness objectives are consistent with the just-in-time production philosophy which has attracted significant attention in both industry and academic community. This methodology consists of the order entry agent, job release agent, job routing and sequencing agent, and information feedback agent. Two new due date setting rules are developed to establish job due dates based on two existing rules. A feedback mechanism to dynamically adjust due date setting is introduced. Both new rules are nonparametric and easy to be implemented in practice. A job release mechanism is applied to reduce job flowtimes (up to 20.3%) and work-in-process inventory (up to 33.1%), without worsening earliness and tardiness, and lead time performances. Flexible job shop scheduling problems are an important extension of the classical job shop scheduling problems and present additional complexity. A multi-agent scheduling method with job earliness and tardiness objectives in a flexible job shop environment is proposed. A new job routing and sequencing mechanism is developed. In this mechanism, different criteria for two kinds of jobs are proposed to route these jobs. Two sequencing algorithms based on existing methods are developed to deal with these two kinds of jobs. The proposed methodology is implemented in a flexible job shop environment. The computational results indicate that the proposed methodology is extremely fast. In particular, it takes less than 1.5 minutes of simulation time on a 1.6GHz PC to find a complete schedule with over 2000 jobs on 10 machines. Such computational efficiency makes the proposed method applicable in real time. Therefore, the proposed workload control methodology is very effective for the production planning and control in MTO companies.

Majority of the studies in the scheduling literature is devoted to time based performance measures. In this thesis, we develop a model that considers monetary issues in single machine scheduling environments. We assume all the jobs should be completed by a common due date. An early revenue is earned if the completion time is before or on the due date, and a tardy revenue is gained if the job is completed after the due date. We consider restricted and unrestricted due date versions of the problem. Our objective is the maximization of the net present value of all revenues. We first investigate some special cases of the problem, and present polynomial time algorithms to solve them. Then, we develop branch and bound algorithms with lower and upper bounding mechanisms. Computational experiments have shown that the branch and bound algorithms can solve large-sized problems in reasonable times.

In this thesis we analyze the procurement process of buyers and supply decisions of manufacturers. Companies are looking for ways to decrease their procurement costs, which account for a large percentage of the supply chain costs. We study the effects of demand aggregation and collaborative procurement on buyers' profitability. First, we make a high-level analysis and consider a market with multiple buyers and suppliers where multi-unit transactions for multiple items take place. The procurement costs are effected by economies of scale in the suppliers' production costs and by economies of scope in transportation. We design buyer strategies that model different collaboration levels and assess the role of collaboration under varying market conditions. Next, we analyze the procurement process at a lower level and identify benefits of inter-firm collaboration among buyers who are potential competitors in the end market. We adopt a game-theoretic approach to explore the economics of the basic mechanism underlying collaborative procurement, and determine the conditions that makes it beneficial for the participants. Besides low procurement costs, important considerations in supplier selection are responsiveness and the reliability of the suppliers in meeting demand. Hence, manufacturers face the pressure for quoting short and reliable lead times. We cover several aspects of the manufacturer's problem, such as quoting reliable due-dates based on current workload in the system, maximizing profit considering the lateness cost incurred due to late deliveries, and deciding on the level of inventory to increase responsiveness. We employ a model where demand arrival and manufacturing processes are stochastic, and obtain insights on the optimal due-date quotation policy and on the optimal inventory level.

O presente trabalho aborda o problema de determinação de datas de entrega e o sequenciamento de tarefas com tempos de processamento estocásticos. O ambiente considerado constitui em uma máquina simples e tarefas com custos individuais e distintos de adiantamento e atraso de entrega (earliness e tardiness ou simplesmente E/T). O objetivo é determinar a sequência e as datas de entrega ótimas que simultaneamente minimizam o custo total esperado de E/T. Para a determinação de sequências candidatas, são apresentadas diversas heurísticas construtivas com tempo de execução polinomial baseadas em um método de inserção de tarefas. Considerando tarefas com distribuição normal, experimentos computacionais comprovam a eficácia dos algoritmos para problemas de menor porte, os quais fornecem soluções ótimas em 99,85% dos casos avaliados. Quando aplicadas a um conjunto com uma maior quantidade de tarefas, as heurísticas apresentaram resultados melhores do que o algoritmo disponível na literatura em mais de 80% dos casos. Consideradas tarefas com distribuição lognormal, obteve-se um percentual de otimalidade entre 93,87% e 96,45%, a depender da heurística aplicada. Demonstra-se ainda para o caso com distribuição normal que os métodos propostos são assintoticamente ótimos e, portanto, são indicados para a resolução de problemas de grande porte. / This work addresses the problem of concurrent due-date assignment and sequencing of a set of jobs on a stochastic single-machine environment with distinct job earliness and tardiness penalty costs. It is assumed that the jobs processing times are statistically independent and follow a normal distribution whose mean and variance are provided and they are not necessarily integer values. The objective is to determine the job sequence and the integer due dates which minimize the expected total earliness and tardiness costs. Several efficient insertion-based construction heuristics are proposed in order to find candidates for the optimal sequence with polynomial time complexity. For the normal distribution problem, numerical experiments show that the proposed heuristic methods are able to find the optimal solution in 99,85% when applied to problems with a smaller size. When applied to problems with a bigger size, the solutions found by the proposed heuristics had better costs than the solutions described in the literature in more than 80% of cases. For the lognormal distribution problem, the proposed heuristic methods provided solutions with a percentage of optimality between 93,87% and 96,45%. Furthermore, for the normal distribuition case, it was proven that the heuristics are asymptotically optimal, i.e., it can be used for problems of any size.

Dissertation with title: Housing Financing in the Czech Republic using mortgage loan deals with analysis of mortgage loans. Work aims to acquaint with issue and options of financing of housing. First part is focused on legislation and theory of mortgage loans, where is also characterised process of mortgage loans. Further is followed up comparison mortgage loans with loans from building savings, their advantages and disadvantages when are used to financing of housing. In the second part are presented macroeconomic indicators, that affect state of mortgage market. In the third part are analysed mortgage products of four selected banks , that are Hypotecni banky a.s , Komercni banky a.s, Raiffeisenbank a.s. and České spořitelny a.s. Last part is devoted to model example of two hypothetical clients, where it is carried out comparison of the mortgage loan in the selected banks in terms of fundamental parameters and also the APR. Goal of dissertation is to provide recommendation within this form financing of housing and choose the best variant of mortgage loan for hypothetical clients.

Neste trabalho abordamos o problema de programação de tarefas em um ambiente flow shop permutacional com mais de duas máquinas. Restringimos o estudo para o caso em que todas as tarefas têm uma data de entrega comum e restritiva, e onde o objetivo é minimizar a soma total dos adiantamentos e atrasos das tarefas em relação a tal data de entrega. É assumido também um ambiente estático e determinístico. Havendo soluções com o mesmo custo, preferimos aquelas que envolvem menos tempo de espera no buffer entre cada máquina. Devido à dificuldade de resolver o problema, mesmo para instâncias pequenas (o problema pertence à classe NP-difícil), apresentamos uma abordagem heurística para lidar com ele, a qual está baseada em busca local e faz uso de um algoritmo linear para atribuir datas de conclusão às tarefas na última máquina. Este algoritmo baseia-se em algumas propriedades analíticas inerentes às soluções ótimas. Além disso, foi desenvolvida uma formulação matemática do problema em programação linear inteira mista (PLIM) que vai permitir validar a eficácia da abordagem. Examinamos também o desempenho das heurísticas com testes padrões (benchmarks) e comparamos nossos resultados com outros obtidos na literatura. / In this work we approach the permutational flow shop scheduling problem with more than two machines. We restrict the study to the case where all the jobs have a common and restrictive due date, and where the objective is to minimize the total sum of the earliness and tardiness of jobs relative to the due date. A static and deterministic environment is also assumed. If there are solutions with the same cost, we prefer those that involve less buffer time between each machine. Due to the difficulty of solving the problem, even for small instances (the problem belongs to the NP-hard class), we present a heuristic approach to dealing with it, which is based on local search and makes use of a linear algorithm to assign conclusion times to the jobs on the last machine. This algorithm is based on some analytical properties inherent to optimal solutions. In addition, a mathematical formulation of the problem in mixed integer linear programming (MILP) was developed that will validate the effectiveness of the approach. We also examined the performance of our heuristics with benchmarks and compared our results with those obtained in the literature.