A novel algorithm for optimal buffer allocation in automated asynchronous unreliable lines

The Buffer Allocation Problem is a well-known optimization problem aiming at determining the optimal buffer sizes in a manufacturing system composed by various machines decoupled by buffers. This problem still has scientific relevance because of problem complexity and trade-off between conflicting goals. Moreover, it assumes industrial relevance in reconfigurable manufacturing lines, where buffer sizes can be easily adapted to the production scenario. This work proposes a novel algorithm integrating performance evaluation and optimization by means of throughput cuts based on a linear approximation. Numerical results show the validity of the proposed approach with respect to the traditional gradient-based method. Moreover, an industrial case study integrating the proposed approach into a decision-support system for the buffer allocation and reallocation is analyzed

A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order); how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost) are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA) is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm

Optimization of buffer allocations in stochastic flow lines

This thesis develops exact solution methods which efficiently optimize the buffer allocation in flow lines under general assumptions. First, an overview on existing literature in the field of buffer optimization is given. A classification scheme is developed to facilitate the comparison of different algorithms. Then, exact mixed-integer programming approaches to calculate optimal buffer capacities are investigated. Finally, new exact algorithms are proposed in order to overcome the shortcomings of the mixed integer programs

Wind Power Performance Optimization Considering Redundancy and Opportunistic Maintenance

In This paper the redundancy and imperfect opportunistic maintenance optimization of a multi-state weighted k-out-of-n system is formulated. The objective is to determine the k-out-of-n system redundancy level and the maintenance strategy which will minimize the wind farm life cycle cost subject to an availability constraint. A new condition based opportunistic maintenance approach is developed. Different component health state thresholds are introduced for imperfect maintenance of failed subsystems and working subsystems and preventive dispatching of maintenance teams. In addition, a simulation method is developed to evaluate the performance measures of the system considering different types of subsystems, maintenance activation delays and durations, limited number of maintenance teams, and discrete inspection of the system. Also, a multi-seed tabu search heuristic algorithm is also proposed to solve the formulated problem. An application to the optimal design of a wind farm is provided to illustrate the proposed approach

The line balancing algorithm for optimal buffer allocation in production lines

Cataloged from PDF version of article.Buffer allocation is a challenging design problem in serial production lines that is often faced in the industry. Effective use of buffers (i.e. how much buffer storage to allow and where to place it) in production lines is important since buffers can have a great impact on the efficiency of the production line. Buffers reduce the blocking of the upstream station and the starvation of the downstream station. However, buffer storage is expensive both due to its direct cost and the increase of the work-in-process inventories it causes. Thus, there is a trade-off between performance and cost. This means that the optimal buffer capacity and the allocation of this capacity have to be determined by analysis. In this thesis, we focus on the optimal buffer allocation problem. We try to maximize the throughput of the serial production line by allocating the total fixed number of buffer slots among the buffer locations and in order to achieve this aim we introduced a new heuristic algorithm called “Line Balancing Algorithm (LIBA)”applicable to all types of production lines meaning that there is no restriction for the distributions of processing, failure and repair times of any machine, the disciplines such as blocking, failure etc. and the assumptions during the application of LIBA in the line.Selvi, ÖmerM.S

Hybrid metaheuristic approach GA-SA for the buffer allocation problem that minimizes the work in process in open serial production lines

[EN] The Buffer Allocation Problem (BAP) is a problem of combinatorial NP-Hard optimization in the design of production lines. This consists of defining the allocation of storage places (buffers) within a production line, in order to maximize the efficiency of the process. The methods of optimization have been reported with greater success in recent years are metaheuristic techniques. In this work, a hybrid approach is proposed that uses the metaheuristic techniques of Genetic Algorithms (GA) and Simulated Annealing (SA), with the objective of determining the required buffers that minimize the average work in process (WIP) in open serial production lines M/M/1/K. The evaluation is carried out with an analytical method of decomposition. The results obtained demonstrate the computational efficiency of the proposed hybrid algorithm with respect to a simple SA or GA.[ES] El problema de asignación del buffer (BAP, por sus siglas en inglés) es clasificado como un problema de optimización combinatorio NP-Duro en el diseño de las líneas de producción. Éste consiste en definir la asignación de lugares de almacenamiento (buffers) dentro de una línea de producción, con el fin de aumentar al máximo la eficiencia del proceso. Los métodos de optimización que han sido reportados con mayor éxito en los últimos años son las técnicas metaheurísticas. En este trabajo, se propone un enfoque híbrido que utiliza las técnicas metaheurísticas de: Algoritmos Genéticos (AG) y Recocido Simulado (RS), con el objetivo de determinar los buffers requeridos que minimicen el promedio de inventario en proceso (WIP, por sus siglas en inglés) en líneas de producción abiertas en serie M/M/1/K. La evaluación se realiza con un método analítico de descomposición. Los resultados obtenidos demuestran la eficiencia computacional del algoritmo híbrido propuesto con respecto a un RS o AG estándar.Se agradece al Consejo Nacional de Ciencia y Tecnología (CONACYT) por el financiamiento de esta investigación con número de registro CVU: 375571.Hernández-Vázquez, JO.; Hernández-González, S.; Jiménez-García, JA.; Hernández-Ripalda, MD.; Hernández-Vázquez, JI. (2019). Enfoque híbrido metaheurístico AG-RS para el problema de asignación del buffer que minimiza el inventario en proceso en líneas de producción abiertas en serie. Revista Iberoamericana de Automática e Informática. 16(4):447-458. https://doi.org/10.4995/riai.2019.10883SWORD447458164Amiri, M., & Mohtashami, A. (2011). Buffer allocation in unreliable production lines based on design of experiments, simulation, and genetic algorithm. International Journal of Advanced Manufacturing Technology, 62, 371-383. https://doi.org/10.1007/s00170-011-3802-8Ariyani, A. K., Mahmudy, W. F., & Anggodo, Y. P. (2018). Hybrid genetic algorithms and simulated annealing for multi-trip vehicle routing problem with time windows. International Journal of Electrical and Computer Engineering, 8(6), 4713-4723. https://doi.org/10.11591/ijece.v8i6.pp4713-4723Blum, C., Blesa Aguilera, M. J., Roli, A., & Sampels, M. (2008). Hybrid metaheuristics an emerging approach to optimization, Springer, Berlin. https://doi.org/10.1007/978-3-540-78295-7Costa, A., Alfieri, A., Matta, A., & Fichera, S. (2015). A parallel tabu search for solving the primal buffer allocation problem in serial production systems. Computers & Operations Research, 97-112. https://doi.org/10.1016/j.cor.2015.05.013Cruz, F. R., Kendall, G., While, L., Duarte, A. R., & Brito, N. L. (2012). Throughput maximization of queueing networks with simultaneous minimization of servicer rates and buffers. Mathematical Problems in Engineering, 1-19. https://doi.org/10.1155/2012/692593Curry, G., & Feldman, R. (2009). Manufacturing Systems Modeling and Analysis, Springer, Berlin.Demir, L., Tunali, S., & Tursel Eliiyi, D. (2014). The state of the art on buffer allocation problem: a comprehensive survey. Journal of Intelligent Manufacturing, 25(3), 371-392. https://doi.org/10.1007/s10845-012-0687-9Demir, L., & Tunali, S. (2008). A new approach for optimal buffer allocation in unreliable production lines. Pcoceedings of 38th International Conference on Computers, (págs. 1962-1970).Dolgui, A., Eremeev, A. V., & Sigaev, V. S. (2007). HBBA: hybrid algorithm for buffer allocation in tandem production lines. Journal of Intelligent Manufacturing, 18, 411-420. https://doi.org/10.1007/s10845-007-0030-zGoldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning (Primera ed.), Addison-Wesley Professional, United States of America.Gutiérrez Pulido, H., & De la Vara Salazar, R. (2012). Análisis y diseño de experimentos (Tercera ed.), McGraw-Hill, México.Huilcapi, V., Lima, B., Blasco, X., & Herrero, J. M. (2018). Multi-objective optimization in modeling and control for rotary inverted pendulum. Revista Iberoamericana de Automática e Informática Industrial, 15(4), 363-373. https://doi.org/10.4995/riai.2018.8739Kose, S. Y., & Kilincci, O. (2015). Hybrid approach for buffer allocation in open serial production lines. Computers & Operations Research, 60, 67-78. https://doi.org/10.1016/j.cor.2015.01.009Kose, S. Y., & Kilincci, O. (2018). A multi-objective hybrid evolutionary approach for buffer allocation in open serial production lines. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1435-6Liu, C., & Tu, F. S. (1994). Buffer allocation via the genetic algorithm. In: Proceedings of 33rd conference on decision and control, 609-610.Mohtashami, A. (2014). A new hybrid method for buffer sizing and machine allocation in unreliable production and assembly lines with general distribution time-dependent parameters. International Journal of Advanced Manufacturing Technology, 74, 1577-1593. https://doi.org/10.1007/s00170-014-6098-7Nahas, N., & Nourelfath, M. (2018). Joint optimization of maintenance, buffers and machines in manufacturing lines. Engineering Optimization, 50(1), 37-54. https://doi.org/10.1080/0305215X.2017.1299716Nahas, N., Nourelfath, M., & Ait-Kadi, D. (2009). Selecting machines and buffers in unreliable series-parallel production lines. International Journal of Production Research, 47(14), 3741-3774. https://doi.org/10.1080/00207540701806883Nahas, N., Nourelfath, M., & Gendreau, M. (2014). Selecting machines and buffers in unreliable assembly/disassembly manufacturing networks. International Journal of Production Economics, 154, 113-126. https://doi.org/10.1016/j.ijpe.2014.04.011Narasimhamu, K. L., Reddy, V. V., & Rao, C. (2014). Optimal buffer allocation in tandem closed queuing network with single server using PSO. Procedia Materials Science, 5, 2084-2089. https://doi.org/10.1016/j.mspro.2014.07.543Narasimhamu, K. L., Reddy, V. V., & Rao, C. (2015). Optimization of buffer allocation in manufacturing system using particle swarm optimization. International Review on Modelling and Simulations, 8(2). https://doi.org/10.15866/iremos.v8i2.5666Ortiz-Quisbert, M. E., Duarte-Mermoud, M. A., Milla, F., & Castro-Linares, R. (2016). Fractional adaptive control optimized by genetic algorithms, applied to automatic voltage regulators. Revista Iberoamericana de Automática e Informática industrial, 13(4), 403-409. https://doi.org/10.1016/j.riai.2016.07.004Papadopoulos, C. T., O'Kelly, M. E., Vidalis, M. J., & Spinellis, D. (2009). Analysis and design of discrete part production lines. New York: Springer. https://doi.org/10.1007/978-0-387-89494-2_2Papadopoulos, H. T., & Vidalis, M. I. (2001). Minimizing WIP inventory in reliable production lines. International Journal of Production Economics, 70, 185-197. https://doi.org/10.1016/S0925-5273(00)00056-6Rodríguez-Blanco, T., Sarabia, D., & De Prada, C. (2018). Real-time optimization using the modifier adaptation methodology. Revista Iberoamericana de Automática e Informática industrial, 15(2), 133-144. https://doi.org/10.4995/riai.2017.8846Shi, L., & Men, S. (2003). Optimal buffer allocation in production lines. IIE Transactions, 35, 1-10. https://doi.org/10.1080/07408170304431Shortle, J., Thompson, J., Gross, D., & Harris, C. (2018). Fundamentals of Queueing Theory (Fifth ed.), Wiley, United States of America. https://doi.org/10.1002/9781119453765Spinellis, D. D., & Papadopoulos, C. T. (2000a). A simulated annealing approach for buffer allocation in reliable production lines. Annals of Operations Research, 93, 373-384. https://doi.org/10.1023/A:1018984125703Spinellis, D. D., & Papadopoulos, C. T. (2000b). Stochastic algorithms for buffer allocation in reliable production lines. Mathematical Problems in Engineering, 5, 441-458. https://doi.org/10.1155/S1024123X99001180Spinellis, D., Papadopoulos, C., & Smith, J. M. (2000). Large production line optimisation using simulated annealing. International Journal of Production Research, 38(3), 509-541. https://doi.org/10.1080/002075400189284Su, C., Shi, Y., & Dou, J. (2017). Multi-objective optimization of buffer allocation for remanufacturing system based on TS-NSGAII hybrid algorithm. Journal of Cleaner Production, 166, 756-770. https://doi.org/10.1016/j.jclepro.2017.08.064Takahashi, Y., Miyahara, H., & Hasegawa, T. (1980). An approximation method for open restricted queueing networks. Operations Research, 28(3), 594-602. https://doi.org/10.1287/opre.28.3.594Vergara, H. A., & Kim, D. S. (2009). A new method for the placement of buffers in serial production lines. International Journal of Production Research, 47(16), 4437-445. https://doi.org/10.1080/00207540801939022Wei, H., Li, S., Jiang, H., Hu, J., & Hu, J. (2018). Hybrid genetic simulated annealing algorithm for improved flow shop scheduling with makespan criterion. Applied Sciences, 8(2621), 1-20. https://doi.org/10.3390/app8122621Weiss, S., Schwarz, J. A., & Stolletz, R. (2018). The buffer allocation problem in production lines: Formulations, solution methods, and instances. IISE Transactions. https://doi.org/10.1080/24725854.2018.144203

Production Scheduling

Generally speaking, scheduling is the procedure of mapping a set of tasks or jobs (studied objects) to a set of target resources efficiently. More specifically, as a part of a larger planning and scheduling process, production scheduling is essential for the proper functioning of a manufacturing enterprise. This book presents ten chapters divided into five sections. Section 1 discusses rescheduling strategies, policies, and methods for production scheduling. Section 2 presents two chapters about flow shop scheduling. Section 3 describes heuristic and metaheuristic methods for treating the scheduling problem in an efficient manner. In addition, two test cases are presented in Section 4. The first uses simulation, while the second shows a real implementation of a production scheduling system. Finally, Section 5 presents some modeling strategies for building production scheduling systems. This book will be of interest to those working in the decision-making branches of production, in various operational research areas, as well as computational methods design. People from a diverse background ranging from academia and research to those working in industry, can take advantage of this volume

Two-Stage Vehicle Routing Problems with Profits and Buffers: Analysis and Metaheuristic Optimization Algorithms

This thesis considers the Two-Stage Vehicle Routing Problem (VRP) with Profits and Buffers, which generalizes various optimization problems that are relevant for practical applications, such as the Two-Machine Flow Shop with Buffers and the Orienteering Problem. Two optimization problems are considered for the Two-Stage VRP with Profits and Buffers, namely the minimization of total time while respecting a profit constraint and the maximization of total profit under a budget constraint. The former generalizes the makespan minimization problem for the Two-Machine Flow Shop with Buffers, whereas the latter is comparable to the problem of maximizing score in the Orienteering Problem. For the three problems, a theoretical analysis is performed regarding computational complexity, existence of optimal permutation schedules (where all vehicles traverse the same nodes in the same order) and potential gaps in attainable solution quality between permutation schedules and non-permutation schedules. The obtained theoretical results are visualized in a table that gives an overview of various subproblems belonging to the Two-Stage VRP with Profits and Buffers, their theoretical properties and how they are connected. For the Two-Machine Flow Shop with Buffers and the Orienteering Problem, two metaheuristics 2BF-ILS and VNSOP are presented that obtain favorable results in computational experiments when compared to other state-of-the-art algorithms. For the Two-Stage VRP with Profits and Buffers, an algorithmic framework for Iterative Search Algorithms with Variable Neighborhoods (ISAVaN) is proposed that generalizes aspects from 2BF-ILS as well as VNSOP. Various algorithms derived from that framework are evaluated in an experimental study. The evaluation methodology used for all computational experiments in this thesis takes the performance during the run time into account and demonstrates that algorithms for structurally different problems, which are encompassed by the Two-Stage VRP with Profits and Buffers, can be evaluated with similar methods. The results show that the most suitable choice for the components in these algorithms is dependent on the properties of the problem and the considered evaluation criteria. However, a number of similarities to algorithms that perform well for the Two-Machine Flow Shop with Buffers and the Orienteering Problem can be identified. The framework unifies these characteristics, providing a spectrum of algorithms that can be adapted to the specifics of the considered Vehicle Routing Problem.:1 Introduction 2 Background 2.1 Problem Motivation 2.2 Formal Definition of the Two-Stage VRP with Profits and Buffers 2.3 Review of Literature on Related Vehicle Routing Problems 2.3.1 Two-Stage Vehicle Routing Problems 2.3.2 Vehicle Routing Problems with Profits 2.3.3 Vehicle Routing Problems with Capacity- or Resource-based Restrictions 2.4 Preliminary Remarks on Subsequent Chapters 3 The Two-Machine Flow Shop Problem with Buffers 3.1 Review of Literature on Flow Shop Problems with Buffers 3.1.1 Algorithms and Metaheuristics for Flow Shops with Buffers 3.1.2 Two-Machine Flow Shop Problems with Buffers 3.1.3 Blocking Flow Shops 3.1.4 Non-Permutation Schedules 3.1.5 Other Extensions and Variations of Flow Shop Problems 3.2 Theoretical Properties 3.2.1 Computational Complexity 3.2.2 The Existence of Optimal Permutation Schedules 3.2.3 The Gap Between Permutation Schedules an Non-Permutation 3.3 A Modification of the NEH Heuristic 3.4 An Iterated Local Search for the Two-Machine Flow Shop Problem with Buffers 3.5 Computational Evaluation 3.5.1 Algorithms for Comparison 3.5.2 Generation of Problem Instances 3.5.3 Parameter Values 3.5.4 Comparison of 2BF-ILS with other Metaheuristics 3.5.5 Comparison of 2BF-OPT with NEH 3.6 Summary 4 The Orienteering Problem 4.1 Review of Literature on Orienteering Problems 4.2 Theoretical Properties 4.3 A Variable Neighborhood Search for the Orienteering Problem 4.4 Computational Evaluation 4.4.1 Measurement of Algorithm Performance 4.4.2 Choice of Algorithms for Comparison 4.4.3 Problem Instances 4.4.4 Parameter Values 4.4.5 Experimental Setup 4.4.6 Comparison of VNSOP with other Metaheuristics 4.5 Summary 5 The Two-Stage Vehicle Routing Problem with Profits and Buffers 5.1 Theoretical Properties of the Two-Stage VRP with Profits and Buffers 5.1.1 Computational Complexity of the General Problem 5.1.2 Existence of Permutation Schedules in the Set of Optimal Solutions 5.1.3 The Gap Between Permutation Schedules an Non-Permutation Schedules 5.1.4 Remarks on Restricted Cases 5.1.5 Overview of Theoretical Results 5.2 A Metaheuristic Framework for the Two-Stage VRP with Profits and Buffers 5.3 Experimental Results 5.3.1 Problem Instances 5.3.2 Experimental Results for O_{max R, Cmax≤B} 5.3.3 Experimental Results for O_{min Cmax, R≥Q} 5.4 Summary Bibliography List of Figures List of Tables List of Algorithm

Analysis of buffer allocations in time-dependent and stochastic flow lines

This thesis reviews and classifies the literature on the Buffer Allocation Problem under steady-state conditions and on performance evaluation approaches for queueing systems with time-dependent parameters. Subsequently, new performance evaluation approaches are developed. Finally, a local search algorithm for the derivation of time-dependent buffer allocations is proposed. The algorithm is based on numerically observed monotonicity properties of the system performance in the time-dependent buffer allocations. Numerical examples illustrate that time-dependent buffer allocations represent an adequate way of minimizing the average WIP in the flow line while achieving a desired service level