Essen, Universiẗat, Diss., 2003.

Universiẗat, Diss., 2005--Hohenheim.

This research aims at a study of the hybrid flow shop problem which has parallel batch-processing machines in one stage and discrete-processing machines in other stages to process jobs of arbitrary sizes. The objective is to minimize the makespan for a set of jobs. The problem is denoted as: FF|batch1, sj|Cmax. The problem is formulated as a mixed-integer linear program. The commercial solver, AMPL/CPLEX, is used to solve problem instances to their optimality. Experimental results show that AMPL/CPLEX requires considerable time to find the optimal solution for even a small size problem, i.e., a 6-job instance requires 2 hours in average. A bottleneck-first-decomposition heuristic (BFD) is proposed in this study to overcome the computational (time) problem encountered while using the commercial solver. The proposed BFD heuristic is inspired by the shifting bottleneck heuristic. It decomposes the entire problem into three sub-problems, and schedules the sub-problems one by one. The proposed BFD heuristic consists of four major steps: formulating sub-problems, prioritizing sub-problems, solving sub-problems and re-scheduling. For solving the sub-problems, two heuristic algorithms are proposed; one for scheduling a hybrid flow shop with discrete processing machines, and the other for scheduling parallel batching machines (single stage). Both consider job arrival and delivery times. An experiment design is conducted to evaluate the effectiveness of the proposed BFD, which is further evaluated against a set of common heuristics including a randomized greedy heuristic and five dispatching rules. The results show that the proposed BFD heuristic outperforms all these algorithms. To evaluate the quality of the heuristic solution, a procedure is developed to calculate a lower bound of makespan for the problem under study. The lower bound obtained is tighter than other bounds developed for related problems in literature. A meta-search approach based on the Genetic Algorithm concept is developed to evaluate the significance of further improving the solution obtained from the proposed BFD heuristic. The experiment indicates that it reduces the makespan by 1.93% in average within a negligible time when problem size is less than 50 jobs.

The research work presented in this dissertation relates to lot streaming in two-stage flow shops and assembly shops. Lot streaming refers to the process of splitting a production lot into sublots, and then, processing the sublots on different machines simultaneously in an overlapping manner. Such a strategy allows finished material at each stage to be transferred downstream sooner than if production and transfer batches were restricted to be the same size. In the case when each sublot consists of just one item, a single-piece-flow is obtained. Such a continuous flow is a key element of the Toyota Production System. However, single-piece-flow increases the number of transfers and the total transportation cost (time). As a result, it may not be economically justifiable in many cases, and therefore, material may have to be transferred in batches (called transfer batches, or sublots). Lot streaming addresses the problems of determining optimal sublot sizes for use in various machine environments and optimizes different performance measures.Given this relationship between lot streaming and the Toyota Production System, lot streaming can be considered a generalization of lean principles. In this dissertation, we first provide a comprehensive review of the existing literature related to lot streaming. We show that two-stage flow shop problems have been studied more frequently than other machine environments. In particular, single-lot two-machine flow shops have been very well researched and efficient solution techniques have been discovered for a large variety of problems. While two-stage flow shop lot streaming problems have been studied extensively, we find that the existing literature assumes that production rates at each stage remain constant. Such an assumption is not valid when processing rates change, for example, due to learning. Learning here, refers to the improvements in processing rates achieved due to experience gained from processing units. We consider the case when the phenomenon of learning affects processing and setup times in a two-stage flow shop processing a single lot, and when, sublot-attached setup times exist. The decrease in unit-processing time, or sublot-attached setup time, is given by Wright's learning curve. We find closed-form expressions or simple search techniques to obtain optimal sublot sizes that minimize the makespan when the effect of learning reduces processing times, sublot-attached setup times, or, both. Then, we provide a general method to transform a large family of scheduling problems related to lot streaming in the presence of learning, to their equivalent counterparts that are not influenced by learning. This transformation is valid for all integrable learning functions (including the Wright's learning curve). As a result, a large variety of new problems involving learning can be solved using existing solution techniques. We then consider lot streaming in stochastic environments in the context of sourcing material. Such problems have been well studied in the literature related to lot streaming for cost-based objective functions when demand is continuous, and when processing times are deterministic, or, for material sourcing problems when the time required to procure a lot is stochastic but is independent of the lot size. We extend this study to the case when the time required to produce a given quantity of products is stochastic and dependent on the number of units produced. We consider the case when two sublots are used, and also compare the performance of lot streaming to the case when each sublot is sourced from an independent supplier. Next, we address a new problem related to lot streaming in a two-stage assembly shop, where we minimize a weighted sum of material handling costs and makespan. We consider the case when several suppliers provide material to a single manufacturer, who then assembles units from different suppliers into a single item. We assume deterministic, but not necessarily constant, lead times for each supplier, who may use lot streaming to provide material to the manufacturer. Lead times are defined as the length of the time interval between a supplier beginning to process material and the time when the first sublot is delivered to the manufacturer; Subsequent sublots must be transported early enough so that the manufacturer is not starved of material. The supplier may reduce this lead time by using lot streaming, but at an increased material handling cost. The decrease in lead time is also affected by other factors such as lot attached/detached setup times, transportation times etc. We allow these factors to be different for each supplier, and each lot processed by the same supplier. We refer to this problem as the Assembly Lot Streaming Problem (ALSP). We show that the ALSP can be solved using two steps. The first step consists of solution to several two-stage, single-lot, flow shop, makespan minimization problems. The solution to these problems generate prospective sublot sizes. Solution methods outlined in the existing literature can be used to complete this step. The second step obtains optimal number of sublots and production sequence. For a given production sequence, this step can be executed in polynomial-time; otherwise, the second step problem is NP-hard and integer programming formulations and decomposition-based methodologies are investigated for their solution. We make very limited assumptions regarding the handling cost and the relationship between the supplier lead time and number of sublots used. As a result, our solution methodology has a wide scope. / Ph. D.

Este trabalho dedica-se ao problema de programação em flow shop permutacional com tempos de setup separados dos tempos de processamento e independentes da seqüência de execução das tarefas com o objetivo de minimizar a duração total da programação (Makespan). Por intermédio de investigações realizadas sobre as características estruturais do problema de programação e sua solução, uma propriedade deste problema é apresentada. Esta propriedade, denominada \"Propriedade LBY\", considerando quaisquer duas tarefas adjacentes Ju e Jv (Ju imediatamente precede Jv) independentemente de suas posições na seqüência de tarefas, fornece, um limitante inferior do tempo de espera para a tarefa Jv entre o fim do seu processamento na máquina Mk e o início do seu processamento na máquina seguinte. Dois novos métodos heurísticos são desenvolvidos, com base na propriedade apresentada e no procedimento de inserção de tarefas dos conhecidos métodos N&M e NEH: um construtivo, denominado BMc; e, um melhorativo, denominado BMm. Os métodos heurísticos propostos são comparados com os métodos heurísticos melhorativos de Cao; Bedworth (1992) e Rajendran; Ziegler (1997), através de um grande número de problemas gerados aleatoriamente. Os tempos de processamento são distribuídos no intervalo [1, 99] e os tempos de setup nos intervalos de [1, 49], [1, 99], [51, 149] e [101, 199]. Os métodos são avaliados quanto à porcentagem de sucesso em obter a melhor solução, ao desvio relativo médio e o tempo médio de computação. Os resultados da experimentação computacional mostram a qualidade do método construtivo BMc e a melhor performance do método melhorativo BMm. Estes resultados são apresentados e discutidos. / This work addresses the permutation flow shop scheduling problem with separated, non-batch, and sequence-independent setup times with the objective of minimizing the total time to complete the schedule (Makespan). Following an investigation of problem structural characteristics and your solution a property of this scheduling problem is presented. This property, denoted by \"Property LBY\", given any two adjacent jobs Ju e Jv (Ju immediately precedes Jv), regardless of their position in the sequence of jobs, provides an lower bound of the waiting time for job Jv between the end of its operations on the machine Mk and the beginning on machine M(k+1). Two news heuristics methods are development, on the basis of the presented property and in the job insertion procedure of the known methods named N&M and NEH: one constructive, denote by BMc; and, one improvement, denote by BMm. The proposed heuristics methods are compared with the improvement heuristics methods of Cao; Bedworth (1992) and Rajendran; Ziegler (1997), by a large number of randomly generated problems. The processing time are sampled from a distribution ranging from [1, 99] and, the setup times are sampled from distributions ranging from [1, 49], [1, 99], [51, 149] and [101, 199]. The methods are evaluated by the percentage of success in find the best solution, the average relative deviation and the average computation time. The results of the computational investigation show the quality of the constructive heuristic method BMc and that the improvement heuristic method BMc outperforms all others. These results are presented and discussed.

Este trabalho dedica-se ao problema de programação em flow shop permutacional com tempos de setup separados dos tempos de processamento e independentes da seqüência de execução das tarefas com o objetivo de minimizar a duração total da programação (Makespan). Por intermédio de investigações realizadas sobre as características estruturais do problema de programação e sua solução, uma propriedade deste problema é apresentada. Esta propriedade, denominada \"Propriedade LBY\", considerando quaisquer duas tarefas adjacentes Ju e Jv (Ju imediatamente precede Jv) independentemente de suas posições na seqüência de tarefas, fornece, um limitante inferior do tempo de espera para a tarefa Jv entre o fim do seu processamento na máquina Mk e o início do seu processamento na máquina seguinte. Dois novos métodos heurísticos são desenvolvidos, com base na propriedade apresentada e no procedimento de inserção de tarefas dos conhecidos métodos N&M e NEH: um construtivo, denominado BMc; e, um melhorativo, denominado BMm. Os métodos heurísticos propostos são comparados com os métodos heurísticos melhorativos de Cao; Bedworth (1992) e Rajendran; Ziegler (1997), através de um grande número de problemas gerados aleatoriamente. Os tempos de processamento são distribuídos no intervalo [1, 99] e os tempos de setup nos intervalos de [1, 49], [1, 99], [51, 149] e [101, 199]. Os métodos são avaliados quanto à porcentagem de sucesso em obter a melhor solução, ao desvio relativo médio e o tempo médio de computação. Os resultados da experimentação computacional mostram a qualidade do método construtivo BMc e a melhor performance do método melhorativo BMm. Estes resultados são apresentados e discutidos. / This work addresses the permutation flow shop scheduling problem with separated, non-batch, and sequence-independent setup times with the objective of minimizing the total time to complete the schedule (Makespan). Following an investigation of problem structural characteristics and your solution a property of this scheduling problem is presented. This property, denoted by \"Property LBY\", given any two adjacent jobs Ju e Jv (Ju immediately precedes Jv), regardless of their position in the sequence of jobs, provides an lower bound of the waiting time for job Jv between the end of its operations on the machine Mk and the beginning on machine M(k+1). Two news heuristics methods are development, on the basis of the presented property and in the job insertion procedure of the known methods named N&M and NEH: one constructive, denote by BMc; and, one improvement, denote by BMm. The proposed heuristics methods are compared with the improvement heuristics methods of Cao; Bedworth (1992) and Rajendran; Ziegler (1997), by a large number of randomly generated problems. The processing time are sampled from a distribution ranging from [1, 99] and, the setup times are sampled from distributions ranging from [1, 49], [1, 99], [51, 149] and [101, 199]. The methods are evaluated by the percentage of success in find the best solution, the average relative deviation and the average computation time. The results of the computational investigation show the quality of the constructive heuristic method BMc and that the improvement heuristic method BMc outperforms all others. These results are presented and discussed.

In this thesis we have presented a mathematical model of a scheduling problem which arises in the Brewing Industry. We have implemented two different types of global optimization algorithms to find the global minimum of the problem. Several instances of the scheduling problem are considered and the results thereof are presented. The results show that significant savings can be made if the global optimization techniques are used in brewery Industry.

The flow shop scheduling problems with fuzzy processing times and fuzzy due dates are investigated in this paper. The concepts of earliness and tardiness are interpreted by using the concepts of possibility and necessity measures that were developed in fuzzy sets theory. And the objective function will be taken into account through the different combinations of possibility and necessity measures. The genetic algorithm will be invoked to tackle these objective functions. A new idea based on longest common substring will be introduced at the best-keeping step. This new algorithm reduces the number of generations needed to reach the stopping criterion. Also, we implement the algorithm on CUDA. The numerical experiments show that the performances of the CUDA program on GPU compare favorably to the traditional programs on CPU.

O problema de escalonamento num flow shop (ou flow shop scheduling problem, FSSP) é um modelo de sistemas de produção muito comum que é bem estudado na literatura. No entanto, quase toda a literatura foca-se em escalonamentos permutacionais, desconsiderando soluções ótimas e quase ótimas que são escalonamentos não-permutacionais. Além disso, a prática comum padroniza os tempos de processamento de cada operação, mesmo que estes tempos variem dependendo das diferentes capacidades dos operadores das máquinas, cuja diversidade deve ser considerada no processo de escalonamento quando seja significativa, e.g., em centros de emprego para deficientes (CEDs). Nesta tese, propomos métodos para resolver o FSSP não-permutacional, usando o mesmo tempo e esforço que os métodos do estado da arte usam para o FSSP permutacional, e produzindo escalonamentos não-permutacionais com melhor qualidade do que escalonamentos permutacionais e não-permutacionais produzidos por métodos do estado da arte. Também propomos métodos para resolver o problema combinado de designação de trabalhadores heterogêneos e escalonamento de tarefas num flow shop (ou heterogeneous workforce assignment and flow shop scheduling problem, Het-FSSP), produzindo soluções que compensam as diferentes capacidades e deficiências dos trabalhadores com pequenas perdas nos objetivos da produção. Além do mais, a designação de trabalhadores heterogêneos pode ser integrada em outros problemas de escalonamento, como fizemos com o problema combinado de designação de trabalhadores heterogêneos e escalonamento de tarefas num job shop (ou heterogeneous workforce assignment and job shop scheduling problem, Het-JSSP). / The flow shop scheduling problem (or FSSP) is a very common model of production systems that is well studied in the literature. However, almost all the literature focuses on the permutation FSSP, disregarding optimal and near optimal solutions that are non-permutation schedules. Besides, common practice standardizes the processing times of each operation, even when those times may vary depending on different capabilities of the machine operators, whose diversity must be considered in the scheduling process when it is significant, e.g., in Sheltered Work centers for Disabled (SWDs). In this thesis, we propose methods to solve the non-permutation FSSP, using the same time and effort as state-of-the-art methods for the permutation FSSP, and producing non-permutation schedules with better quality than permutation and non-permutation schedules produced by state-of-the-art methods. We also propose methods to solve the combined heterogeneous workforce assignment and flow shop scheduling problem (or Het-FSSP), producing solutions that compensate the different capabilities and disabilities of the workers with minor or null losses in the productivity objectives. Moreover, the heterogeneous workforce assignment may be integrated into other shop scheduling models, as we did with the heterogeneous workforce assignment and job shop scheduling problem (or Het-JSSP) with similar results.

O problema de escalonamento num flow shop (ou flow shop scheduling problem, FSSP) é um modelo de sistemas de produção muito comum que é bem estudado na literatura. No entanto, quase toda a literatura foca-se em escalonamentos permutacionais, desconsiderando soluções ótimas e quase ótimas que são escalonamentos não-permutacionais. Além disso, a prática comum padroniza os tempos de processamento de cada operação, mesmo que estes tempos variem dependendo das diferentes capacidades dos operadores das máquinas, cuja diversidade deve ser considerada no processo de escalonamento quando seja significativa, e.g., em centros de emprego para deficientes (CEDs). Nesta tese, propomos métodos para resolver o FSSP não-permutacional, usando o mesmo tempo e esforço que os métodos do estado da arte usam para o FSSP permutacional, e produzindo escalonamentos não-permutacionais com melhor qualidade do que escalonamentos permutacionais e não-permutacionais produzidos por métodos do estado da arte. Também propomos métodos para resolver o problema combinado de designação de trabalhadores heterogêneos e escalonamento de tarefas num flow shop (ou heterogeneous workforce assignment and flow shop scheduling problem, Het-FSSP), produzindo soluções que compensam as diferentes capacidades e deficiências dos trabalhadores com pequenas perdas nos objetivos da produção. Além do mais, a designação de trabalhadores heterogêneos pode ser integrada em outros problemas de escalonamento, como fizemos com o problema combinado de designação de trabalhadores heterogêneos e escalonamento de tarefas num job shop (ou heterogeneous workforce assignment and job shop scheduling problem, Het-JSSP). / The flow shop scheduling problem (or FSSP) is a very common model of production systems that is well studied in the literature. However, almost all the literature focuses on the permutation FSSP, disregarding optimal and near optimal solutions that are non-permutation schedules. Besides, common practice standardizes the processing times of each operation, even when those times may vary depending on different capabilities of the machine operators, whose diversity must be considered in the scheduling process when it is significant, e.g., in Sheltered Work centers for Disabled (SWDs). In this thesis, we propose methods to solve the non-permutation FSSP, using the same time and effort as state-of-the-art methods for the permutation FSSP, and producing non-permutation schedules with better quality than permutation and non-permutation schedules produced by state-of-the-art methods. We also propose methods to solve the combined heterogeneous workforce assignment and flow shop scheduling problem (or Het-FSSP), producing solutions that compensate the different capabilities and disabilities of the workers with minor or null losses in the productivity objectives. Moreover, the heterogeneous workforce assignment may be integrated into other shop scheduling models, as we did with the heterogeneous workforce assignment and job shop scheduling problem (or Het-JSSP) with similar results.