Simulation Modelling Practice and Theory 46 (2014) 1–3
Contents lists available at ScienceDirect
Simulation Modelling Practice and Theory journal homepage: www.elsevier.com/locate/simpat
Editorial
Simulation–optimization of complex systems: Methods and applications Simulation models are widely used to provide insight on the behavior of various types of complex systems. Decision makers can benefit from simulation models mainly in two ways: (i) the results of simulation-based analyses are used to improve the performance of the system under study by some specific changes to its design parameter values; (ii) the simulation model is repeatedly analyzed to find a set of design parameters providing the best (simulated) performance. The latter case leads to so-called simulation–optimization approaches, which combine simulation and optimization techniques. Several types of approaches can be used, which are chosen on the basis of several types of considerations, such as: the mathematical characteristics of the objective function and constraints, the nature of the corresponding region of interest (e.g., discrete or continuous), and how uncertainty is accounted for. Furthermore, the computational cost is often a very important aspect. Many studies have been made, for example, using ranking and selection, random search and meta-heuristics (especially if the simulation experiments do not require too much computing time). When the function to be optimized is differentiable, then derivative-based methods or metamodel-based optimization are often exploited. Several useful reviews about existing simulation–optimization techniques have been published by highly reputed researchers. An internet search using a popular web browser with the keyword ‘‘Simulation Optimization’’ returns about two hundred thousand pages, while the more focalized Google Scholar gives about twenty-four thousand pages mainly containing scientific and technical articles, conference publications, research reports and academic manuscripts. Clearly, simulation– optimization is a field that captures as much interest from researchers and simulation practitioners dealing with real problems. As one of the results of this great deal of work on simulation–optimization in the literature, dedicated optimization routines have been recently incorporated into several commercial simulation software packages. One important reason of this popularity is that many real world optimization problems are too complex to be addressed directly through analytical mathematical formulations so that simulation models avoid major simplification (e.g., stochastic issues can be taken into account). Consequently, a common goal in both the optimization and simulation communities is to develop methods to guide and help the analyst to produce high quality solutions, in the absence of tractable mathematical structures. It is worth noting that several issues and topics emerge in the area of simulation–optimization to enlarge the number of problems that can be handled using this approach and to improve the existing methodologies. They include:
Multi-response simulation–optimization. Random constraints management. Best solution assessment. Definition of effective neighborhoods for local-search based optimizations. Effective schemes for sampling and exploration of the experimental area of interest. Management of computational costs for functions estimations (obtained by simulation) with respect to optimization efforts. Inclusion of simulation–optimization in decision support and/or control systems. Specific methods for combinatorial problems (e.g., scheduling and planning). Distributed or parallel simulation–optimization techniques. Web and cloud based simulation–optimization.
This Special Issue aims to report recent advances in simulation–optimization techniques for complex systems and how they are applied in practice. It also aims at contributing to reduce the gap between methods that have been extensively studied in the research literature and those commonly used in practice. The Special Issue includes ten papers, selected from the numerous submissions we received. They were evaluated by qualified referees, through several rounds of reviews, in accordance with the usual selection process of Simulation Modelling http://dx.doi.org/10.1016/j.simpat.2014.05.001 1569-190X/Ó 2014 Published by Elsevier B.V.
2
Editorial / Simulation Modelling Practice and Theory 46 (2014) 1–3
Practice and Theory. The selected articles cover a variety of important and challenging topics. They bring several types of contributions, which highlight new methodologies, improve the knowledge about how simulation–optimization is used and contribute to application-oriented problems by addressing theoretical issues arising in production, transportation, logistics, energy management, supply chain management, and engineering. The first article by E. Quaglietta presents an optimal design approach devoted to identify the signaling layout which minimizes the investment and management costs, while respecting the required level of capacity in railway networks. The usefulness of the proposed framework is supported by results on a real mass rapid transit line in Naples, Italy. The following article by C. Hamarat et al. proposes a multi-objective robust simulation–optimization scheme to address policy-making issues with an adaptive approach. A case study is discussed to provide evidence for effective support to policy makers in the transition towards renewable energy systems in the European Union. A combination of simulation and heuristic optimization algorithms is proposed by A.A. Juan et al. to solve stochastic variants of the well-known Inventory-Routing Problem. A set of benchmark test problems has been developed to illustrate the methodology through a number of scenarios, which analyze the impact of uncertainty on inventory costs. The article by G. Zambrano Rey et al. reports on a study of a semi-heterarchical architecture based on simulation– optimization mechanisms devoted to operations management and production control in Flexible Manufacturing Systems (FMSs). The proposed approach has been applied to a real assembly cell; experimental results show a reduction in the myopic behavior, measured by the completion time variance, while improving the system capability to promptly react to disturbances. The article by J.T. Lin and C.-J. Huang studies material movement in the photolithography area of a semiconductor manufacturing facility. It aims to analyze the impact of dispatching rules, inter-arrival time and number of vehicles on the design and performance indicators, combining Particle Swarm Optimization (PSO) and discrete-event simulation in an Automated Material Handling Systems framework. While the article by A.A. Juan, B.B. Barrios et al. illustrates a simulation–optimization procedure for a stochastic version of the Permutation Flow Shop Problem combining Monte Carlo simulation with an Iterated Local Search meta-heuristic. The benefits provided by the proposed approach are illustrated through a computational campaign based on a wide set of tests under several scenarios characterized by different levels of uncertainty. The article by G. Figueira and B. Almada-Lobo offers a broad view of research methods that combines simulation and optimization in various ways – accounting for both problems and solution methods. The proposed taxonomy provides guidance on practical problem solving, encouraging cross-fertilization between different areas. While the article by A. Huerta-Barrientos et al. analyzes the structure, collaboration patterns and the time-evolution of the co-authorship network of simulation–optimization of supply chains. This study shows that, although simulation–optimization methods are numerous, they have not yet been extensively applied to support decision-making in many areas of manufacturing and supply chain. The article by I. Nasri et al. presents an analytical approach using (max, +) algebra to model High-Variety, Low-Volume scheduling problems subject to preventive maintenance under machines availability constraints. Results show that the proposed non-linear constrained optimization problem effectively minimizes the makespan, while having a limited impact on the manufacturing planning program. Finally, the study of A. Elsheikh deals with the adoption of derivative-based optimization methods within hybrid optimization strategies in order to improve the solution quality of continuous-time simulation–optimization problems. In particular, hybrid strategies are implemented by mixing multistart derivative-based optimization methods with population-based meta-heuristics experimentally showing significant improvements in the solutions quality. The Guest Editors would like to thank all those who have contributed to this special issue by their support, advice and contributions. First among these are all the contributing authors (including those whose papers were not selected) for submitting their recent research results. We are deeply grateful to the authors for bearing with our repeated requests for improvements and revisions to meet the high scientific standard required by the journal. The numerous reviewers for the time and effort they spent to help in selecting and improving the submitted manuscripts are also gratefully acknowledged. Thanks are also due to Professor Helen Karatza, Editor in Chief of this journal, for supporting our editorial project, and to Journal Managers for their permanent help. We hope that the articles of this special issue will stimulate further research in the growing area of simulation–optimization and contribute to address new challenging problems. Gabriella Dellino STAR⁄AgroEnergy Group, University of Foggia, Via A. Gramsci 89/91 – 71122 Foggia, Italy Istituto per le Applicazioni del Calcolo ‘‘Mauro Picone’’ – CNR, via Amendola, 122/I – 70126 Bari, Italy E-mail address: [email protected]
Editorial / Simulation Modelling Practice and Theory 46 (2014) 1–3
3
Carlo Meloni Dipartimento di Ingegneria Elettrica e dell’Informazione, Politecnico di Bari, via E. Orabona, 4 – 70125 Bari, Italy Istituto per le Applicazioni del Calcolo ‘‘Mauro Picone’’ – CNR, via Amendola, 122/I – 70126 Bari, Italy E-mail address: [email protected] Henri Pierreval Clermont University, IFMA-LIMOS, UMR CNRS 6158 - Campus de Clermont-Ferrand, Les Cézeaux CS 20265, F-63175 Aubière Cedex, France E-mail address: [email protected] Available online 29 May 2014
Contents lists available at ScienceDirect
Simulation Modelling Practice and Theory journal homepage: www.elsevier.com/locate/simpat
Editorial
Simulation–optimization of complex systems: Methods and applications Simulation models are widely used to provide insight on the behavior of various types of complex systems. Decision makers can benefit from simulation models mainly in two ways: (i) the results of simulation-based analyses are used to improve the performance of the system under study by some specific changes to its design parameter values; (ii) the simulation model is repeatedly analyzed to find a set of design parameters providing the best (simulated) performance. The latter case leads to so-called simulation–optimization approaches, which combine simulation and optimization techniques. Several types of approaches can be used, which are chosen on the basis of several types of considerations, such as: the mathematical characteristics of the objective function and constraints, the nature of the corresponding region of interest (e.g., discrete or continuous), and how uncertainty is accounted for. Furthermore, the computational cost is often a very important aspect. Many studies have been made, for example, using ranking and selection, random search and meta-heuristics (especially if the simulation experiments do not require too much computing time). When the function to be optimized is differentiable, then derivative-based methods or metamodel-based optimization are often exploited. Several useful reviews about existing simulation–optimization techniques have been published by highly reputed researchers. An internet search using a popular web browser with the keyword ‘‘Simulation Optimization’’ returns about two hundred thousand pages, while the more focalized Google Scholar gives about twenty-four thousand pages mainly containing scientific and technical articles, conference publications, research reports and academic manuscripts. Clearly, simulation– optimization is a field that captures as much interest from researchers and simulation practitioners dealing with real problems. As one of the results of this great deal of work on simulation–optimization in the literature, dedicated optimization routines have been recently incorporated into several commercial simulation software packages. One important reason of this popularity is that many real world optimization problems are too complex to be addressed directly through analytical mathematical formulations so that simulation models avoid major simplification (e.g., stochastic issues can be taken into account). Consequently, a common goal in both the optimization and simulation communities is to develop methods to guide and help the analyst to produce high quality solutions, in the absence of tractable mathematical structures. It is worth noting that several issues and topics emerge in the area of simulation–optimization to enlarge the number of problems that can be handled using this approach and to improve the existing methodologies. They include:
Multi-response simulation–optimization. Random constraints management. Best solution assessment. Definition of effective neighborhoods for local-search based optimizations. Effective schemes for sampling and exploration of the experimental area of interest. Management of computational costs for functions estimations (obtained by simulation) with respect to optimization efforts. Inclusion of simulation–optimization in decision support and/or control systems. Specific methods for combinatorial problems (e.g., scheduling and planning). Distributed or parallel simulation–optimization techniques. Web and cloud based simulation–optimization.
This Special Issue aims to report recent advances in simulation–optimization techniques for complex systems and how they are applied in practice. It also aims at contributing to reduce the gap between methods that have been extensively studied in the research literature and those commonly used in practice. The Special Issue includes ten papers, selected from the numerous submissions we received. They were evaluated by qualified referees, through several rounds of reviews, in accordance with the usual selection process of Simulation Modelling http://dx.doi.org/10.1016/j.simpat.2014.05.001 1569-190X/Ó 2014 Published by Elsevier B.V.
2
Editorial / Simulation Modelling Practice and Theory 46 (2014) 1–3
Practice and Theory. The selected articles cover a variety of important and challenging topics. They bring several types of contributions, which highlight new methodologies, improve the knowledge about how simulation–optimization is used and contribute to application-oriented problems by addressing theoretical issues arising in production, transportation, logistics, energy management, supply chain management, and engineering. The first article by E. Quaglietta presents an optimal design approach devoted to identify the signaling layout which minimizes the investment and management costs, while respecting the required level of capacity in railway networks. The usefulness of the proposed framework is supported by results on a real mass rapid transit line in Naples, Italy. The following article by C. Hamarat et al. proposes a multi-objective robust simulation–optimization scheme to address policy-making issues with an adaptive approach. A case study is discussed to provide evidence for effective support to policy makers in the transition towards renewable energy systems in the European Union. A combination of simulation and heuristic optimization algorithms is proposed by A.A. Juan et al. to solve stochastic variants of the well-known Inventory-Routing Problem. A set of benchmark test problems has been developed to illustrate the methodology through a number of scenarios, which analyze the impact of uncertainty on inventory costs. The article by G. Zambrano Rey et al. reports on a study of a semi-heterarchical architecture based on simulation– optimization mechanisms devoted to operations management and production control in Flexible Manufacturing Systems (FMSs). The proposed approach has been applied to a real assembly cell; experimental results show a reduction in the myopic behavior, measured by the completion time variance, while improving the system capability to promptly react to disturbances. The article by J.T. Lin and C.-J. Huang studies material movement in the photolithography area of a semiconductor manufacturing facility. It aims to analyze the impact of dispatching rules, inter-arrival time and number of vehicles on the design and performance indicators, combining Particle Swarm Optimization (PSO) and discrete-event simulation in an Automated Material Handling Systems framework. While the article by A.A. Juan, B.B. Barrios et al. illustrates a simulation–optimization procedure for a stochastic version of the Permutation Flow Shop Problem combining Monte Carlo simulation with an Iterated Local Search meta-heuristic. The benefits provided by the proposed approach are illustrated through a computational campaign based on a wide set of tests under several scenarios characterized by different levels of uncertainty. The article by G. Figueira and B. Almada-Lobo offers a broad view of research methods that combines simulation and optimization in various ways – accounting for both problems and solution methods. The proposed taxonomy provides guidance on practical problem solving, encouraging cross-fertilization between different areas. While the article by A. Huerta-Barrientos et al. analyzes the structure, collaboration patterns and the time-evolution of the co-authorship network of simulation–optimization of supply chains. This study shows that, although simulation–optimization methods are numerous, they have not yet been extensively applied to support decision-making in many areas of manufacturing and supply chain. The article by I. Nasri et al. presents an analytical approach using (max, +) algebra to model High-Variety, Low-Volume scheduling problems subject to preventive maintenance under machines availability constraints. Results show that the proposed non-linear constrained optimization problem effectively minimizes the makespan, while having a limited impact on the manufacturing planning program. Finally, the study of A. Elsheikh deals with the adoption of derivative-based optimization methods within hybrid optimization strategies in order to improve the solution quality of continuous-time simulation–optimization problems. In particular, hybrid strategies are implemented by mixing multistart derivative-based optimization methods with population-based meta-heuristics experimentally showing significant improvements in the solutions quality. The Guest Editors would like to thank all those who have contributed to this special issue by their support, advice and contributions. First among these are all the contributing authors (including those whose papers were not selected) for submitting their recent research results. We are deeply grateful to the authors for bearing with our repeated requests for improvements and revisions to meet the high scientific standard required by the journal. The numerous reviewers for the time and effort they spent to help in selecting and improving the submitted manuscripts are also gratefully acknowledged. Thanks are also due to Professor Helen Karatza, Editor in Chief of this journal, for supporting our editorial project, and to Journal Managers for their permanent help. We hope that the articles of this special issue will stimulate further research in the growing area of simulation–optimization and contribute to address new challenging problems. Gabriella Dellino STAR⁄AgroEnergy Group, University of Foggia, Via A. Gramsci 89/91 – 71122 Foggia, Italy Istituto per le Applicazioni del Calcolo ‘‘Mauro Picone’’ – CNR, via Amendola, 122/I – 70126 Bari, Italy E-mail address: [email protected]
Editorial / Simulation Modelling Practice and Theory 46 (2014) 1–3
3
Carlo Meloni Dipartimento di Ingegneria Elettrica e dell’Informazione, Politecnico di Bari, via E. Orabona, 4 – 70125 Bari, Italy Istituto per le Applicazioni del Calcolo ‘‘Mauro Picone’’ – CNR, via Amendola, 122/I – 70126 Bari, Italy E-mail address: [email protected] Henri Pierreval Clermont University, IFMA-LIMOS, UMR CNRS 6158 - Campus de Clermont-Ferrand, Les Cézeaux CS 20265, F-63175 Aubière Cedex, France E-mail address: [email protected] Available online 29 May 2014
