Production Systems with Deteriorating Product Quality : System-Theoretic Approach

Manufacturing systems with perishable products are widely seen in practice (e.g., food, metal processing, etc.). In such systems, the quality of a part is highly dependent on its residence time within the system. However, the behavior and properties of these systems have not been studied systematically, and, therefore, is carried out in this dissertation. Specifically, it was assumed that the probability that each unfinished part is of good quality is a decreasing function of its residence time in the preceding buffer. Then, in the framework of serial production lines with machines having Bernoulli and geometric reliability models, closed-form formulas for performance evaluation in the two-machine line case were derived, and develop an aggregation-based procedure to approximate the performance measures in M\u3e2-machine lines. In addition, the monotonicity properties of these production lines using numerical experiments were studied. A case study in an automotive stamping plant is described to illustrate the theoretical results obtained. Also, Bernoulli serial lines with controlled parts released was analyzed for both deterministic and stochastic releases. Finally, bottleneck analysis in Bernoulli serial lines with deteriorating product quality were studied

ANALYTICAL SOLUTION OF STEADY-STATE BEHAVIOR OF BERNOULLI PRODUCTION LINE WITH TWO FINITE BUFFERS

Production system engineering is strongly relied on mathematical models of systems under consideration and analytical solution of the problem has an important role in development and validation of more complex numerical tools. Analytical solution of steady-state behavior of serial Bernoulli production line with three machines and two buffers is considered in this paper based on Markov chain approach. Transition matrices in general case, including three machines with different probabilities of failure and arbitrary occupancy of two buffers, are formulated along with equations for performance measures. The developed theory is illustrated using four different serial Bernoulli lines and the obtained results are compared to those determined using semi-analytical approach via aggregation procedure. Finally, the existing discrepancies are analyzed and pointed out for five different levels of buffers occupancy. From general perspective, good agreement has been found for most of the results within the examined space of system states

Serial production line performance under random variation:Dealing with the ‘Law of Variability’

Many Queueing Theory and Production Management studies have investigated specific effects of variability on the performance of serial lines since variability has a significant impact on performance. To date, there has been no single summary source of the most relevant research results concerned with variability, particularly as they relate to the need to better understand the ‘Law of Variability’. This paper fills this gap and provides readers the foundational knowledge needed to develop intuition and insights on the complexities of stochastic simple serial lines, and serves as a guide to better understand and manage the effects of variability and design factors related to improving serial production line performance, i.e. throughput, inter-departure time and flow time, under random variation

Identification of Moving Bottlenecks in Production Systems

Manufacturing sector have been plagued by bottlenecks from time immemorial, leading to loss of productivity and profitability, various research effort has been expended towards identifying and mitigating the effects of bottlenecks on production lines. However, traditional approaches often fail in identifying moving bottlenecks. The current data boom and giant strides made in the machine learning field proffers an alternative means of using the large volume of data generated by machines in identifying bottlenecks. In this study, a hierarchical agglomerative clustering algorithm is used in identifying potential groups of bottlenecks within a serial production line. A serial production line with five workstations and zero buffer was simulated in ARENA® with data regarding blocked, producing and starvation time extracted. The extracted data was preprocessed using Python 3.7 to obtain a matrix of ones and zeros. The resultant matrix was fed into a complete linkage hierarchical agglomerative clustering algorithm to obtain clusters containing potential bottleneck workstations. Results obtained was validated using results obtained from simulation and an Elbow plot

Analytical evaluation of the output variability in production systems with general Markovian structure

Performance evaluation models are used by companies to design, adapt, manage and control their production systems. In the literature, most of the effort has been dedicated to the development of efficient methodologies to estimate the first moment performance measures of production systems, such as the expected production rate, the buffer levels and the mean completion time. However, there is industrial evidence that the variability of the production output may drastically impact on the capability of managing the system operations, causing the observed system performance to be highly different from what expected. This paper presents a general methodology to analyze the variability of the output of unreliable single machines and small-scale multi-stage production systems modeled as General Markovian structure. The generality of the approach allows modeling and studying performance measures such as the variance of the cumulated output and the variance of the inter-departure time under many system configurations within a unique framework. The proposed method is based on the characterization of the autocorrelation structure of the system output. The impact of different system parameters on the output variability is investigated and characterized. Moreover, managerial actions that allow reducing the output variability are identified. The computational complexity of the method is studied on an extensive set of computer experiments. Finally, the limits of this approach while studying long multi-stage production lines are highlighted. © 2013 Springer-Verlag Berlin Heidelberg

MODELING AND ANALYSIS OF SPLIT AND MERGE PRODUCTION SYSTEMS

Many production systems have split and merge operations to increase production capac- ity and variety, improve product quality, and implement product control and scheduling policies. This thesis presents analytical methods to model and analyze split and merge production systems with Bernoulli and exponential reliability machines under circulate, priority and percentage policies. The recursive procedures for performance analysis are de- rived, and the convergence of the procedures and uniqueness of the solutions, along with the structural properties, are proved analytically, and the accuracy of the estimation is justi¯ed numerically with high precision. In addition, comparisons among the e®ects of di®erent policies in system performance are carried out

Energy Efficient Policies, Scheduling, and Design for Sustainable Manufacturing Systems

Climate mitigation, more stringent regulations, rising energy costs, and sustainable manufacturing are pushing researchers to focus on energy efficiency, energy flexibility, and implementation of renewable energy sources in manufacturing systems. This thesis aims to analyze the main works proposed regarding these hot topics, and to fill the gaps in the literature. First, a detailed literature review is proposed. Works regarding energy efficiency in different manufacturing levels, in the assembly line, energy saving policies, and the implementation of renewable energy sources are analyzed. Then, trying to fill the gaps in the literature, different topics are analyzed more in depth. In the single machine context, a mathematical model aiming to align the manufacturing power required to a renewable energy supply in order to obtain the maximum profit is developed. The model is applied to a single work center powered by the electric grid and by a photovoltaic system; afterwards, energy storage is also added to the power system. Analyzing the job shop context, switch off policies implementing workload approach and scheduling considering variable speed of the machines and power constraints are proposed. The direct and indirect workloads of the machines are considered to support the switch on/off decisions. A simulation model is developed to test the proposed policies compared to others presented in the literature. Regarding the job shop scheduling, a fixed and variable power constraints are considered, assuming the minimization of the makespan as the objective function. Studying the factory level, a mathematical model to design a flow line considering the possibility of using switch-off policies is developed. The design model for production lines includes a targeted imbalance among the workstations to allow for defined idle time. Finally, the main findings, results, and the future directions and challenges are presented

Real-Time Analysis and Control of Serial Production Lines for Energy Efficient Manufacturing

Productivity analysis, operation control and energy consumption reduction have been the central topics in manufacturing research and practice. They are closely related to each other. Control of production operations is considered as one of the most economical methods to improve energy efficiency in manufacturing systems, while system performance analysis serves as the base of production control. On the other hand, effective operation control can result to energy efficiency in manufacturing. Steady state analysis has been investigated extensively; however, transient analysis remained largely unexplored. Our research focuses on system modeling, performance analysis, and real-time operation control of serial production lines with unreliable machines and finite buffers, especially in transient period, with Bernoulli or geometric reliability. Analytical results, practical case studies and applications for energy efficient manufacturing are provided. A simulation using ARENA software to reproduce and analyze brewery production line is performed