Production and inventory control in complex production systems using approximate dynamic programming.

Production systems focus not only on providing enough product to supply the market, but also on delivering the right product at the right price, while lowering the cost during the production process. The dynamics and uncertainties of modern production systems and the requirements of fast response often make its design and operation very complex. Thus, analytical models, such as those involving the use of dynamic programming, may fail to generate an optimal control policy for modern production systems. Modern production systems are often in possession of the features that allow them to produce various types of product through multiple working stations interacting with each other. The production process is usually divided into several stages, thus a number of intermediate components (WIP) are made to stock and wait to be handled by the next production stage. In particular, development of an efficient production and inventory control policy for such production systems is difficult, since the uncertain demand, system dynamics and large changeover times at the work stations cause significant problems. Also, due to the large state and action space, the controlling problems of modern production systems often suffer from the curse of dimensionality

Numerical Analyses and Integration of Split Lot Sizing Using Lean Benchmark Model for Small Lot Manufacturing in High Mix Low Volume Production

As the global demand for automobiles has increased rapidly over the last fifty years, customers have become more particular about the characteristics of the autos they want. This change in demand, in part has pushed manufacturing to become more flexible and created a demand for alternative, more efficient processes like the High Mix Low Volume (HMLV) production of vehicles. During HMLV, manufacturers create production lot sizes and schedule to synchronize the production processes to meet customer demand on time. The demand for the automobile parts may not be uniform or parts may not be consumed by the customer immediately, Due to this variation in demand, companies avoid shortages by large production lots and storing excess inventory. However, excess inventory has to be managed differently during the production large lots. It increases the inventory holding cost; hence it is essential to know what, when and how much to produce. An excellent example of introducing controls for efficiencies is the Toyota Production System, which allows Toyota Motors to progress implement Just in Time (JIT) production, However, to achieve the JIT, needs for producing small lots have to be met. Hence, this thesis aims to assess a lot-sizing model that focuses on how to combine the production methods of high to low demand parts one machine to achieve JIT. The method was divided in two parts; first, it assesses the variable production of high to medium demand parts within a fixed amount of time described as Fixed Period Variable Amount (FPVA). The split lot technique used to minimize the inventory. Second, parts that have assess low demand were assessed within a Fixed Amount Variable Period (FAVP). It is proposed that a time-oriented method with the external changeover parameter can appropriately minimize the inventory of FAVP parts and avoid idling of the workforce. Also discussed the kaizen or continuous improvement approach for changeover with directed sequencing approaches to minimize longer changeover times, significant obstacle for the production of small lot production. The outcome of the propose model is then compared with two industry lot sizing and scheduling models, conventional lot sizing and lean benchmark lot sizing. The objective of conventional model is to minimize the cost without considering and HMLV environment and external changeover parameter. The objective of the lean benchmark model is to minimize inventory without creating idle time for the workforce. The thesis also investigates the integration and working of the Kanban scheduling system in the lean benchmark mode

Evaluating performance of production scheduling from an economic perspective

Production scheduling which is a part of the planning and control of production units lies at the heart of the performance of manufacturing organizations. Production scheduling determines organizational performance. The need for efficient scheduling has greatly increased in recent decades owing to market demands for product quality, flexibility and order flow times, and other measures. However, although scheduling research activities have in the same period moved from purely academic exercises to serious attempts to solve practical problems in companies, successful implementations of scheduling techniques in practice are still scarce [1-6] and less attempt on solving the same from an economic perspective. In many companies, scheduling is still a typically human domain. However, the task of scheduling production units can become very complex. Humans are not very well equipped to barely control or optimize large and complex systems without computational tools, and the relations between actions and effects are difficult to assess. This paper will focus on problems that are related to the complexity of scheduling in practice. Scheduling based on this technique is often changed by the scheduler due to random disruptions or are not carried out exactly as preplanned on the shop floor. Because of the complex production processes, schedules are often difficult to assess mainly in terms of production cost. This paper takes a leap approach by assessing production performance in terms of cost. A new criterion of optimality is also proposed and used. This criterion is termed “total opportunity cost” and takes into account the different single criterion in a weighed term

QUALITY AND PRODUCTIVITY IMPROVEMENTS IN ADDITIVE MANUFACTURING

Additive manufacturing (AM) is a relatively new manufacturing technology compared to the traditional manufacturing methods. Even though AM processes have many advantages, they also have a series of challenges that need to be addressed to adapt this technology for a wide range of applications and mass production. AM faces a number of challenges, including the absence of methods/models for determining whether AM is the best manufacturing process for a given part. The first study of this thesis proposes a framework for choosing specific AM processes by considering the complexity level of a part. It has been proven that the method works effectively through numerical experiments. Optimization of process parameters through expensive and time-consuming experiments is another issue with AM. To address this issue, an empirical model is presented in the second study to optimize parameters for minimizing building costs through maximizing the trade-off between productivity and quality. The proposed model proves to be effective in reducing building costs at any quality level. The results indicate that process parameters can be optimized quickly and accurately, as compared to the time-consuming and expensive experimental methods. Another limitation of AM is the lack of capability to use multiple materials, which is a concern when adapting this technology to mass production. To address this issue, a new scheduling model with considering multi-material types is introduced in the third study. Based on the numerical results, the proposed model can provide optimal sequence by maximizing the trade-off between tardiness and material switching cost

Optimization of product assignment to assembly lines

Dissertação de mestrado em Industrial engineering and ManagementThe work presented in this dissertation was developed in an industrial context integrated in the production control and management department of the Bosch Car Multimedia Portugal S.A – Braga automatic insertion. The problem addressed in this dissertation was finding the best distribution of product families to assign in different lines according to the physical and technical constraints of the assembly lines. In the approach of the problem, it was used tools and techniques of the Operational Research discipline through mathematical modeling, in order to analyze complex situation and obtain more efficient solutions to help in the decision-making process. Based on production data, production needs forecasts and assembly line physical availability, models with different sets of constraints and objective functions were created to present solutions that best fit the question and the specific problem of the present production context. Through specific software that suited the problem, the previously created models were solved, and the solutions were analyzed and evaluated to suit the company’s current needs and for possible and feasible implementation of the solutions.O trabalho apresentado nesta dissertação foi desenvolvido em contexto industrial integrado no departamento de planeamento e controlo de produção da área de inserção automática da Bosch Car Multimédia Portugal S.A - Braga. O problema abordado nesta dissertação foi encontrar a melhor distribuição de famílias de produtos a alocar nas diferentes linhas de produção de acordo com as suas restrições físicas e técnicas. Na abordagem do problema recorreu-se a técnicas de Investigação Operacional através de modelação matemática, para analisar situações complexas e obter soluções mais eficientes. Tendo como base dados da produção, previsões de necessidades e disponibilidade física da produção, foram criados modelos com diferentes conjuntos de restrições e funções objetivo por forma a apresentar soluções que melhor se adequassem à pergunta e ao problema específico do contexto produtivo presente. Através da utilização de software, foram resolvidos os modelos criados anteriormente, sendo que as soluções foram analisadas e avaliadas para a adequação às necessidades atuais da empresa e para a sua possível e viável implementação

Manufacturing System Lean Improvement Design Using Discrete Event Simulation

Lean manufacturing (LM) has been used widely in the past for the continuous improvement of existing production systems. A Lean Assessment Tool (LAT) is used for assessing the overall performance of lean practices within a system, while a Discrete Event Simulation (DES) can be used for the optimization of such systems operations. Lean improvements are typically suggested after a LAT has been deployed, but validation of such improvements is rarely carried out. In the present article a methodology is presented that uses DES to model lean practices within a manufacturing system. Lean improvement scenarios are then be simulated and investigated prior to implementation, thereby enabling a systematic design of lean improvements

Improving Changeover Efficiency in Opticap XL Encapsulation Process

This project studied MilliporeSigma’s changeover efficiency within the Opticap® XL encapsulation process to alleviate throughput issues associated with increasing demand. Our team conducted time and observational studies, together with stakeholder interviews, to identify and prioritize improvement areas. We developed a production schedule optimization tool, Single Minute Exchange of Dies analysis for changeover tasks, and conditions to streamline melt-check procedures. We recommend our deliverables be implemented to improve changeover efficiency, and estimate that 230 minutes can be saved in changeover time over two days