No description available.

We consider a scheduling problem where each job requires multiple classes of resources, which we refer to as the multiple resource constrained scheduling problem(MRCSP). Potential applications include team scheduling problems that arise in service industries such as consulting and operating room scheduling. We focus on two general cases of the problem. The first case considers uncertainty of processing times, due dates, and resource availabilities consumption, which we denote as the stochastic MRCSP with uncertain parameters (SMRCSP-U). The second case considers uncertainty in the number of jobs to schedule, which arises in consulting and defense contracting when companies bid on future contracts but may or may not win the bid. We call this problem the stochastic MRCSP with job bidding (SMRCSP-JB).We first provide formulations of each problem under the framework of two-stage stochastic programming with recourse. We then develop solution methodologies for both problems. For the SMRCSP-U, we develop an exact solution method based on the L-shaped method for problems with a moderate number of scenarios. Several algorithmic enhancements are added to improve efficiency. Then, we embed the L-shaped method within a sampling-based solution method for problems with a large number of scenarios. We modify a sequential sampling procedure to allowfor approximate solution of integer programs and prove desired properties. The sampling-based method is applicable to two-stage stochastic integer programs with integer first-stage variables. Finally, we compare the solution methodologies on a set of test problems.For SMRCSP-JB, we utilize the disjunctive decomposition (D2 ) algorithm for stochastic integer programs with mixed-binary subproblems. We develop several enhancements to the D2 algorithm. First, we explore the use of a cut generation problem restricted to a subspace of the variables, which yields significant computational savings. Then, we examine generating alternative disjunctive cuts based on the generalized upper bound (GUB) constraints that appear in the second-stage of the SMRCSP-JB. We establish convergence of all D2 variants and present computational results on a set of instances of SMRCSP-JB.

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.

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.

No description available.

In this Thesis, we first deploy Gittins index theory to establish the indexability of inter-alia general families of restless bandits that arise in problems of stochastic scheduling with switching penalties and machine maintenance. We also give formulae for the resulting indices. Numerical investigations testify the strong performance of the index heuristics.The second class of problems concerns two families of Markov decision problems. The spinning plates problem concerns the optimal management of a portfolio of assets whose yields grow with investment but otherwise decline. In the model of asset exploitation called the squad system, the yield from an asset declines when it is utilised but will recover when the asset is at rest. Simply stated conditions are given which guarantee general indexability of the problem together with necessary and sufficient conditions for strict indexability. The index heuristics, which emerge from the analysis, are assessed numerically and found to perform strongly.

In this dissertation, we develop novel approaches to independently address two issues that are commonly encountered in machine scheduling problems: uncertainty of problem parameters (in particular, due to job processing times), and batching of jobs for processing on capacitated machines. Our approach to address the uncertainty issue regards the indeterminate parameters as random variables, and explicitly considers the resulting variability of a performance measure. To incorporate variability into the schedule selection process, we develop a method to evaluate both the expectation and variance of various performance measures for a given schedule. Our method is based on the use of mixture models to approximate a variety of distribution types. The Expectation-Maximization algorithm of Dempster et al. (1977) is applied to derive mixture models of processing time distributions. Our method, then, utilizes these mixture models to calculate the distributions of other random variables in order to derive the expectation and variance of various scheduling performance measures, assuming that the job sequencing decisions are known a priori. To make our method more computationally efficient, we adapt a mixture reduction method to control the number of mixture components used in the intermediate steps. We apply our method to two different scheduling problems: the job shop makespan scheduling problem and the single machine total weighted tardiness scheduling problem, and compare its performance with that of Monte-Carlo method. The results show the efficacy of our mixture approximation method. It generates fairly accurate results while requiring significantly less CPU times. The proposed method offers a good compromise between the Monte Carlo method, which requires extensive effort, and use of simple normal approximation, which produces lower-quality results. Next, we introduce and demonstrate for the first time in the literature the use of conditional-value-at-risk (CVaR) as a criterion for stochastic scheduling problems in order to obtain risk-averse solutions. This criterion has the tendency of minimizing both the expectation and variance of a performance measure simultaneously, which is an attractive feature in the scheduling area as most of the literature in this area considers the expectation and variance of a performance measure separately. Also, the CVaR has an added advantage of maintaining a linear objective function. We develop a scenario-based mixed integer programming formulation to minimize CVaR for the general scheduling problem involving various performance measures, and employ a decomposition-based approach for its solution. Furthermore, a set of valid inequalities are incorporated to strengthen the relaxed master problem of this decomposition scheme. The proposed approach is demonstrated on the single machine total weighted tardiness scheduling problem. Our computational investigation reveals the efficacy of the proposed decomposition approach and the effectiveness of using the CVaR as an optimization criterion for scheduling problems. Besides providing an exact approach to solve our stochastic scheduling problem, we also develop an efficient heuristic method to enable the use of CVaR for large-sized problems. To that end, we modify the Dynasearch method of Grosso et al. (2004) to minimize CVaR for a stochastic scheduling problem. Furthermore, we extend the application of CVaR to a parallel-machine total weighted tardiness problem. The use of CVaR appears to be quite promising for simultaneously controlling both the expected value and variability of a performance measure in a stochastic scheduling environment. Scenario-based formulations have frequently been used for stochastic scheduling problems. However, the determination of a lower bound can be a time-consuming task for this approach. Next, we develop a new method for scenario generation that is computationally competitive and that assures attainment of an exact lower bound. Our approach is based on discretization of random parameter distributions of job processing times. We use the idea of Recursive Stratified Sampling to partition the probability space, so that the conditional expectations in each region yield scenario-wise parameter values. These scenarios are, then, used to formulate a two-stage stochastic program, which yields a lower bound for the original stochastic problem. We provide theoretical basis of our bounding approach for both the expectation and CVaR objectives. Our discrete bounding method generates exact lower bounds, as against the probabilistic bounds generated by Sample Average Approximation. We also present results of our numerical experimentation to compare the performances of these two approaches in terms of the bound value obtained and the CPU time required. The problem pertaining to integrated batching and scheduling of jobs on capacitated parallel machines that we consider arises in the primary manufacturing sector of a pharmaceutical supply chain. We, first, develop a comprehensive mathematical programming model that can accommodate various realistic features of this problem. These features include batch production, sequence-dependent setup time/cost, and inter-period carryover of setup status. We further derive several valid inequalities that are based on the embedded subproblem structure. We also consider an alternative formulation (termed the Plant Location model) based on the lot-sizing perspective of the problem. Noting the resemblance of the campaign sequencing subproblem to the high multiplicity asymmetric traveling salesman problem (HMATSP), we adapt various ideas from the HMATSP to enforce the connectivity of the sequencing graph. Due to the complexity of this problem, we also explore the possibility of applying column generation technique for its solution. Various schemes of problem decomposition are considered, along with the use of dual stabilization technique to improve the convergence of the column generation procedure. We also develop heuristic methods to generate initial feasible solutions that further enhance the performance of the column generation method. A computational experimentation has been conducted on a data set that mimics real-life problem instances. It illustrates the effectiveness of using the proposed column generation method. / Ph. D.