A Development Of Optimal Buffer Allocation Determination Method For Μ-Unbalanced Unpaced Production Line

This research deals with a buffer allocation problem in an unpaced (asynchronous) μ-unbalanced production line. Kajian ini membincangkan masalah peruntukan pemampan di dalam talian pengeluaran tidak melangkah dengan ketidakseimbangan-μ

A hybrid meta-heuristic approach for buffer allocation in remanufacturing environment

Remanufacturing system is complicated due to its stochastic nature. Random customer demand, return product rate and system unreliability contribute to this complexity. Remanufacturing systems with unreliable machines usually contain intermediate buffers which are used to decouple the machines, thereby, reducing mutual interference due to machine breakdowns. Intermediate buffers should be optimized to eliminate waste of resources and avoid loss of throughput. The Buffer Allocation Problem (BAP) deals with allocating optimally fixed amount of available buffers to workstations located in manufacturing or remanufacturing systems to achieve specific objectives. Optimal buffer allocation in manufacturing and remanufacturing systems not only minimizes holding cost and stock space, but also makes facilities planning and remanufacturing decisions to be effectively coordinated. BAP in a non-deterministic environment is certainly one of the most difficult optimization problems. Therefore, a mathematical framework is provided to model the dependence of throughput on buffer capacities. Obviously, based on the survey undertaken, not only there exists no algebraic relation between the objective function and buffer size but the current literature does not offer analytical results for buffer capacity design in remanufacturing environment. Decomposition principle, expansion method for evaluating system performance and an efficient hybrid Meta-heuristic search algorithm are implemented to find an optimal buffer allocation for remanufacturing system. The proposed hybrid Simulated Annealing (SA) with Genetic Algorithm (GA) is compared to pure SA and GA. The computational experiments show better quality, more accurate, efficient and reliable solutions obtained by the proposed hybrid algorithm. The improvement obtained is more than 4.18 %. Finally, the proposed method is applied on toner cartridge remanufacturing company as a case study, and the numerical results from hybrid algorithm are presented and compared with results from SA and GA

Decision elements in the design of a consumer electronics assembly plant

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1999.Includes bibliographical references (p. 53).by Thomas M. Furey.S.M.M.B.A

Reinforcement learning for energy-efficient control of multi-stage production lines with parallel machine workstations

An effective approach to enhancing the sustainability of production systems is to use energy-efficient control (EEC) policies for optimal balancing of production rate and energy demand. Reinforcement learning (RL) algorithms can be employed to successfully control production systems, even when there is a lack of prior knowledge about system parameters. Furthermore, recent research demonstrated that RL can be also applied for the optimal EEC of a single manufacturing workstation with parallel machines. The purpose of this study is to apply an RL for EEC approach to more workstations belonging to the same industrial production system from the automotive sector, without relying on full knowledge of system dynamics. This work aims to show how the RL for EEC of more workstations affects the overall production system in terms of throughput and energy consumption. Numerical results demonstrate the benefits of the proposed model

Reinforcement learning for energy-efficient control of multi-stage production lines with parallel machine workstations

An effective approach to enhancing the sustainability of production systems is to use energy-efficient control (EEC) policies for optimal balancing of production rate and energy demand. Reinforcement learning (RL) algorithms can be employed to successfully control production systems, even when there is a lack of prior knowledge about system parameters. Furthermore, recent research demonstrated that RL can be also applied for the optimal EEC of a single manufacturing workstation with parallel machines. The purpose of this study is to apply an RL for EEC approach to more workstations belonging to the same industrial production system from the automotive sector, without relying on full knowledge of system dynamics. This work aims to show how the RL for EEC of more workstations affects the overall production system in terms of throughput and energy consumption. Numerical results demonstrate the benefits of the proposed model

Modelling and analysis of pull production systems

Ankara : Industrial Engineering and the Institute of Engineering and Science of Bilkent Univ., 1995.Thesis (Ph.D.) -- Bilkent University, 1995.Includes bibliographical references.A variety of production systems appearing in the literature are reviewed in order to develop a classification scheme for production systems. A number of pull production systems appearing in the classification are found to be equivalent to a tandem queue so that accurate tandem queue decomposition methods can be used to find the performance of such systems. The primary concern of this dissertation is to model and analyze non-tandem queue equivalent periodic pull production systems. In this research, an exact performance evaluation model is developed for a singleitem periodic pull production system. The processing and demand interarrival times are assumed to be Markovian. For large systems, which are difficult to evaluate exactly because of large state spaces involved, an approximate decomposition method is proposed. A typical approximate decomposition procedure takes individual stages or pairs of stages in isolation to analyze the system and then it aggregates the results to obtain an approximate performance for the whole system. An experiment is designed in order to investigate the general behavior of the decomposition. The results are worth attention. A second aspect of this study is to investigate an allocation methodology to achieve the maximum throughput rate with providing two sets of allocation parameters regarding the number of kanbans and the workload at each stage of the system. Together with some structural properties, the experimental results provide some insight into the behavior of pull production systems and also provide a basis for the proposed allocation methodology. Finally, we conclude our findings together with some directions for future research.Kırkavak, NureddinPh.D

The impact of unequal processing time variability on reliable and unreliable merging line performance

Research on merging lines is expanding as their use grows significantly in the contexts of remanufacturing, reverse logistics and developing economies. This article is the first to study the behavior of unpaced, reliable, and unreliable merging assembly lines that are deliberately unbalanced with respect to their coefficients of variation (CV). Conducting a series of simulation runs with varying line lengths, buffer storage capacities and unbalanced CV patterns delivers intriguing results. For both reliable and unreliable lines, the best pattern for generating higher throughput is found to be a balanced configuration (equal CVs along both parallel lines), except for unreliable lines with a station buffer capacity of six. In that case, the highest throughput results from the descending configuration, i.e. concentrating the variable stations close to the beginning of both parallel lines and the steady stations towards the end of the line. Ordering from the least to most steady station also provides the best average buffer level. By exploring the experimental Pareto Frontier, this study shows the combined performance of unbalanced CV patterns for throughput and average buffer level. Study results suggest that caution should be exercised when assuming equivalent behavior from reliable and unreliable lines, or single serial lines and merging lines, since the relative throughput performance of some CV patterns changed between the different configurations

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

Optimal Reconfiguration of Complex Production Lines for Profit Maximization via Simulation Modeling

With the recent trend of re-shoring, transferring manufacturing systems from a workforce-intensive to a capital-intensive production environment becomes more common. One challenge multinational manufacturing companies may face in such an endeavor is reconfiguration of the transferred manufacturing system according to the availability of better machinery in the capital-intensive environment. In this dissertation, based on a real-life problem, I develop several simulation optimization methods for the problem of production line reconfiguration. The case is a reverse transfer of manufacturing system/technology, i.e. transfer from a workforce-intensive environment to a capital-intensive one. I investigate the performances of nine different simulation optimization approaches based on the real-life case in automotive industry to illustrate their relative strengths under different parameter scenarios. I also create a test-bed problem to determine the specifications of these methods, and further analyze their performances. Numerical results may guide the practitioners facing similar challenges in choosing a suitable solution approach depending on the problem size and solution time availability