The research work presented in this dissertation relates to lot sizing and its applications in the areas of operational planning and shop floor scheduling and control. Lot sizing enables a proper loading of requisite number of jobs on the machines in order to optimize the performance of an underlying production system. We address lot sizing problems that are encountered at the order entry level as well as those that are faced at the time of distributing the jobs from one machine to another and those that arise before shipping the jobs (orders) to customers. There are different issues and performance measures involved during each of these scenarios, which make the lot sizing problems encountered in these scenarios different from one another. We present algorithms and relevant theoretical analyses for each of the lot sizing problems considered, and also, present results of numerical experimentation to depict their effectiveness We first study the lot sizing problem encountered while transferring jobs from one machine to another. A lot of the jobs is to be split into smaller lots (called sublots) such that the lot is processed on multiple machines in an overlapping manner, a process which is known in the literature as lot streaming. Two lot streaming problems, FL2/n/C and FLm/1/C, are investigated in Chapter 2. FL2/n/C involves a two-machine flow shop in which multiple lots are to be processed. The objective is to minimize the combined cost of makespan and material handling (the latter is proportional to the number of sublots). A dynamic programming-based methodology is developed to determine the optimal sublot sizes and the number of sublots for each lot while assuming a known sequence in which to process the lots. We designate this problem as LSP-DP. This methodology is, then, extended to determine an optimal sequence in which to process the lots in conjunction with the number of sublots and sublot sizes for each lot. We designate this problem as LSSP-DP. Three multidimensional heuristic search procedures (denoted as LSSP-Greedy, LSSP-Cyclic and LSSP-ZP) are proposed for this problem in order to obtain good-quality solutions in a reasonable amount of computational time. Our experimentation reveals that both lot streaming and lot sequencing generate significant benefits, if used alone. However, for the objective of minimizing total handling and makespan cost, lot streaming is more beneficial than lot sequencing. The combined use of lot streaming and sequencing, expectedly, results in the largest improvement over an initial random solution. LSP-DP is found to be very efficient, and so are the three LSSP heuristics, all of which are able to generate near-optimal solutions. On the average, LSSP-Greedy generates the best solutions among the three, and LSSP-Cyclic requires the least time. FLm/1/C deals with the streaming of a single lot over multiple machines in a flow shop. The objective is a unified cost function that comprises of contributions due to makespan, mean flow time, work-in-process, transfer time and setup time. The distinctive features of our problem pertain to the inclusion of sublot-attached setup time and the fact that idling among the sublots of a lot is permitted. A solution procedure that relies on an approximation equation to determine sublot size is developed for this problem for equal-size sublots. The approximation avoids the need for numerical computations, and enables the procedure to run in polynomial time. Our experimentation shows that this solution procedure performs quite well and frequently generates the optimal solution. Since the objective function involves multiple criteria, we further study the marginal cost ratios of various pairs of the criteria, and propose cost sensitivity indices to help in estimating the impact of marginal cost values on the number of sublots obtained. The lot sizing problem addressed in Chapter 3 is motivated by a real-life setting associated with semiconductor manufacturing. We first investigate the integration of lot sizing (at the operational planning level) and dispatching (at the scheduling and control level) in this environment. Such an integration is achieved by forming a closed-loop control system between lot sizing and dispatching. It works as follows: lot sizing module determines lot sizes (loading quota) for each processing buffer based on the current buffer status via a detailed linear programming model. The loading quotas are then used by the dispatching module as a general guideline for dispatching lots on the shop floor. A dispatching rule called "largest-remaining-quota-first" (LRQ) is designed to drive the buffer status to its desired level as prescribed by the lot sizing module. Once the buffer status is changed or a certain amount of time has passed, loading quotas are updated by the lot sizing module. Our experimentation, using the simulation of a real-life wafer fab, reveals that the proposed approach outperforms the existing practice (which is based on "first-in-first-out" (FIFO) model and an ad-hoc lot sizing method). Significant improvements are obtained in both mean values and standard deviations of the performance metrics, which include finished-goods inventory, backlog, throughput and work-in-process. The integration of lot sizing and dispatching focuses on the design of an overall production system architecture. Another lot sizing problem that we present in Chapter 3 deals with input control (or workload control) that complements this architecture. Input control policies are responsible for feeding the production system with the right amount of work and at the right time, and are usually divided into "push" or "pull" categories. We develop a two-phase input control methodology to improve system throughput and the average cycle time of the lots. In phase 1, appropriate operational lot sizes are determined with regard to weekly demand, so as to keep the lot start rate at the desired level. In phase 2, a "pull" policy, termed CONLOAD, is applied to keep the bottleneck's workload at a target level by releasing new lots into the system whenever the workload level is below the desired level. Since the operators are found to be the bottleneck of the system in our preliminary investigation, the "operator workload" is used as system workload in this study. Using throughput and cycle time as the performance metrics, it is shown that this two-phase CONLOAD methodology achieves significant improvement over the existing CONWIP-like policy. Furthermore, a reference table for the target operator workload is established with varying weekly demand and lot start rate. The last lot sizing problem that we address has to do with the integration of production and shipping operations of a make-to-order manufacturer. The objective is to minimize the total cost of shipping and inventory (from manufacturer's perspective) as well as the cost of earliness and tardiness of an order (from customer's perspective). An integer programming (IP) model is developed that captures the key features of this problem, including production and delivery lead times, multiple distinct capacitated machines and arbitrary processing route, among others. By utilizing the generalized upper bound (GUB) structure of this IP model, we are able to generate a simplified first-level RLT (Reformulation Linearization Technique) relaxation that guarantees the integrity of one set of GUB variables when it is solved as a linear programming (LP) problem. This allows us to obtain a tighter lower bound at a node of a branch-and-bound procedure. The GUB-based RLT relaxation is complemented by a GUB identification procedure to identify the set of GUB variables that, once restricted to integer values, would result in the largest increment in the objective value. The tightening procedure described above leads to the development of a RLT-based branch-and-bound algorithm. Our experimentation shows that this algorithm is able to search the branch-and-bound tree more efficiently, and hence, generates better solutions in a given amount of time. / Ph. D.

Over the last decade, the semiconductor industry has witnessed a steady increase in its complexity based on improvements in manufacturing processes and equipment. Progress in the technology used is no longer the key to success, however. In fact, the semiconductor technology has reached such a high level of complexity that improvements appear at a slow pace. Moreover, the diffusion of technology among competitors shows that traditional approaches based on technological advances and innovations are not sufficient to remain competitive. A recent crisis in the semiconductor field in the summer 2001 made it even clearer that optimizing the operational control of semiconductor wafer fabrication facilities is a vital key to success. Operating research-oriented studies have been carried out to this end for the last 5 years. None of them, however, suggest a comprehensive model and solution to the operational control problem of a semiconductor manufacturing facility. Two main approaches, namely mathematical programming and dispatching rules, have been explored in the literature so far, either partially or entirely dealing with this problem. Adapting the Shifting Bottleneck (SB) procedure is a third approach that has motivated many studies. Most research focuses on optimizing a certain objective function under idealized conditions and thus does not take into consideration system disruptions such as machine breakdown. While many papers address the adaptations of the SB procedure, the problem of re-scheduling jobs dynamically to take disruptions and local disturbances (machines breakdown, maintenance...) into consideration shows interesting perspectives for research. Dealing with local disturbances in a production environment and analyzing their impact on scheduling policies is a complex issue. It becomes even more complex in the semiconductor industry because of the numerous inherent constraints to take into account. The problem that is addressed in this thesis consists of studying dynamic scheduling in a job-shop environment where local disturbances occur. This research focuses on scheduling a large job shop and developing re-scheduling policies when local disturbances occur. The re-scheduling can be applied to the whole production horizon considered in the instance, or applied to a restricted period T that becomes a decision variable of the problem. The length of the restricted horizon T of re-scheduling can influence significantly the overall results. Its impact on the general performance is studied. Future extensions can be made to include constraints that arise in the semiconductors industry, such as the presence of parallel and batching machines, reentrant flows and the lot dedication problem. The theoretical results developed through this research will be applied to data sets to study their efficiency. We hope this methodology will bring useful insights to dealing effectively with local disturbances in production environments. / Master of Science

This is a study to determine the present and future status of offset printing as compared to letterpress printing in Texas, based on the opinions of qualified teachers of printing and commercial printers taken from sixteen groups of different sizes.

The sponsor of the thesis is the Composite Unit of AIRBUS Nantes plant, which manufactures aircraft composite. The basic process to manufacture composite parts is to lay-up raw composite material on a tool and involves very costly means and raw material. This process can be modeled as a two-stage hybrid flow shop problem with specific constraints, particularly prescription constraints on the jobs. This thesis restates the practical problem as a scheduling problem by doing hypotheses and restrictions. Then, it designs a mathematical model based on time-indexed variables. This model has been implemented in an IP solver to solve real based scenarios. A heuristic algorithm is developed for obtaining good solutions quickly. Finally, the heuristic is used to increase the execution speed of the IP solver. This thesis concludes by a discussion on the advantages and disadvantages of each option (IP solver vs. heuristic software) for the sponsor. / Master of Science

Today, labor union membership has dropped to its lowest level in over 40 years. Attempts to boost aggregate union membership through large scale organizing drives have largely failed. This has placed a great deal of pressure on unions to provide services to existing union members. This would seem to be especially true for labor unions operating in right-to-work states where union members can simply quit the union if they are not satisfied with the union's efforts. Accordingly, this project sought to explain the extent to which local unions have been successful in achieving desirable bargaining outcomes for their members through the exercise of bargaining power. The purpose of this project was to assess the relationship between sources of plant-level bargaining power and changes in collective bargaining outcomes in an open-shop environment. Sources of power were grouped into those over which the union had relatively greater control (strikes, union strength, and decertification attempts) and those over the employer had relatively greater control (bargaining unit employment, plant closure communications, and degree of labor intensity). A three-page survey questionnaire was employed to collect plant-level data from Virginia and Iowa representing sources of bargaining power relevant to specific time periods to help identify whether sources of bargaining power were more or less effective in securing bargaining outcomes favorable to the local union during episodes of union militancy. Results demonstrate that strike incidence and union strength, two consistent traditional predictors of various bargaining outcomes were ineffective as sources of union bargaining power at least for these samples. Strike duration did lead to greater bargaining outcomes for union members in Iowa. Decertification activity was so low in these samples that meaningful relationships were not possible. Changes in bargaining unit employment, over which the employer has relatively greater influence were directly related to bargaining outcomes in the Iowa sample of plants which did not experience strikes. In Virginia, the threat of a plant closure by an employer during an impasse lead to lower bargaining outcomes for union members as predicted. The degree of labor intensity was unrelated to changes in bargaining outcomes for either state. When considering all significant relationships (supportive and nonsupportive), strikes demonstrated a particularly disruptive influence. Research results suggested that future research should consider industrial, union affiliation, and regional differences in plant level studies. / Ph. D. / Pages xiv-xix missing.

No / This article provides empirical data from the Tyneside Maritime Construction Industry (TMCI) to contribute to the union renewal/resilience debate. The aim of the study is a focus on the effectiveness of shop steward organisation in the industry, levels of activity in membership participation and its significance for union democracy, all of which are important factors in the debate. The paper supports the resilience argument and adds a further development to the debate by presenting evidence which suggests resilient renewal.

The aim of the writer in giving the Stenquist Mechanical Aptitude Test as a basis for this study was to try to find a reliable method of selecting the boys to be admitted to the shop classes in vocational and technical high schools.

The application of reinforcement learning to dynamic industrial scheduling has gained increasing attention due to its capability to optimize complex manufacturing processes. With the advent of Industry 4.0 and the rise of smart manufacturing, new challenges arise that require innovative approaches, particularly in environments where there is a high degree of variability and uncertainty. Previous research has demonstrated that reinforcement learning, in particular Monte Carlo methods, is highly effective in optimizing resource allocation in job-shop scheduling scenarios. Even though Monte Carlo methods are effective where reward functions are clear and retrospective, real-world manufacturing systems often require more dynamic decision-making capabilities in real-time, for which temporaldifference methods are more appropriate. Despite the effectiveness of reinforcement learning in this area, there is a gap in understanding how different reward functions affect the learning process. In this study, we systematically examined multiple reward functions within a temporal difference system, applying a sensitivity analysis to assess their effects during the training and evaluation phases. Our results demonstrated that the overall performance of the production line improved despite the inherent complexity and challenges posed by temporal difference methods. Our findings demonstrate the effectiveness of multi-agent reinforcement learning for improving manufacturing efficiency, and provide implications for future research on scalable, real-time industrial scheduling.

In modern manufacturing environments, it is essential to be able to react autonomously and dynamically to unpredictable events in an automated manner in order to schedule production in a cost-effective manner. One of the prerequisites for the development of this technology is the progressive integration of cyberphysical systems into industrial sectors. Data generated by the industry constitutes the basis for operative and strategic decision-making in this context. Collecting these data in real time, transforming it if necessary, and analyzing it in order to ensure time-critical decision-making is a major challenge. This paper presents a novel approach that simplifies dynamic scheduling through efficient action selection. YoloRL, the method presented in this paper, which is based on reinforcement learning, which allows for a reduction in the complexity of the training process in a substantial way. For the purpose of identifying promising action sequences, YoloRL does not take into consideration all of the state information of an episode; it only takes into account the initial state. As a result, training complexity is significantly reduced while at the same time robust and adaptive control can be achieved. This study improves the manufacturing system’s performance by minimizing the overall completion time (for any given order). Experimental results indicate that the proposed method results in a faster generalization of the domain knowledge and provides for a powerful policy that is both efficient and reliable in dynamic environments. With YoloRL, overall completion time is reduced by a moderate but quantifiable amount compared with the traditional approach. In accordance with our experimental results, the proposed methodology has the ability to accelerate and stabilize the training process. Thus, a reliable and generalizable policy network is established, which can nevertheless respond dynamically to unforeseen events and changing environmental conditions due to its adaptability. The policy ...

“Premium services will be offered by a cup of coffee” which is a relatively new business model in Taiwan. Thanks to the internet booming in recent years, it becomes less and less difficult for people to create new businesses; which leads to co-working spaces largely increasing their numbers in the world, especially in the U.S.A. and in Europe. It has become a global trend where people go to work and go to socialize. However, there are few co-working spaces existing in Asia, especially in Taiwan; their spaces are not cheap or one has to pay membership fee. You may or may not ever hear about the “Garage Café” in Beijing which is currently running a very successful business. It offers a low cost but high efficiency work space to entrepreneurs, start-up companies and many young people who have dreams of doing business. Our ideal is to introduce this type of business model in Taiwan; our goal is not aimed to make big profits but to bring a lot of business opportunities to boost the Taiwanese economy and to lead Taiwanese products to global markets.