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A novel integrated condition-based maintenance and stochastic flexible job shop scheduling problem: simulation-based optimization approach

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ANNALS OF OPERATIONS RESEARCH 2018年第1-2期269卷 583-621页

作者： Rahmati, Seyed Habib A. Ahmadi, Abbas Govindan, Kannan Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran Univ Southern Denmark Dept Technol & Innovat Ctr Sustainable Supply Chain Engn DK-5230 Odense M Denmark

Integrated consideration of production planning and maintenance processes is a real world assumption. Specifically, by improving the monitoring equipment such as various sensors or product-embedded information devices in recent years, joint assessment of these processes is inevitable for enhancing the level of the system optimization. By means of this equipment, managers can benefit from a condition-based maintenance (CBM) for monitoring and managing their system. The chief aim of the paper is to develop a stochastic maintenance problem based on CBM activities engaged with a complex applied production problem called flexible job shop scheduling problem (FJSP). This integrated problem considers two maintenance scenarios in terms of corrective maintenance (CM) and preventive maintenance (PM). The activation of scenario is done by monitoring the degradation condition of the system and comparing the associated value by predetermined PM and CM levels. Moreover, to make it more realistic, the developed problem allows breakdown of the system between inspection intervals. The event time and duration of performing the maintenance activities are also considered stochastic. The developed methodology copes with high stochastic complexity of proposed problem through simulation-based optimization (SBO) approach, which works based on harmony search optimization algorithm. The developed items of SBO are discussed on different generated test problems and assessed through statistical methods and new visualization approach. Numerical example shows that the proposed method is practical for proposed integrated CBM and FJSP problem. It also offers a basis for any implementation of CBM on production problems.

关键词： Condition-based maintenance (CBM) Flexible job shop scheduling problem (FJSP) simulation-based optimization Degradation visualization Harmony search optimization (HSO)

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Designing a resilient skip-stop schedule in rapid rail transit using a simulation-based optimization methodology

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OPERATIONAL RESEARCH 2021年第3期21卷 1691-1721页

作者： Shahabi, Ali Raissi, Sadigh Khalili-Damghani, Kaveh Rafei, Meysam Islamic Azad Univ Sch Ind Engn South Tehran Branch Tehran Iran

In recent years, rapid rail transit systems have played a unique role in transportation systems due to the demand increase in accommodating passengers. This study proposes a simulation-optimization method to improve the resiliency of the train timetable in rapid transit rail lines under uncertainty associated with the passenger flow and train running times. The aim is to evaluate the resiliency of the train timetable through a discrete-event simulation (DES) model and to provide an optimized schedule with the maximum degree of resiliency against random disruptions caused by passenger flow fluctuations. The problem is first formulated as a mixed-integer nonlinear programming model. The validity of the DES model is justified using convergence test analysis of the response variable, i.e., average passenger wait time, during the simulation run. Due to the complexity of the problem, a variable neighborhood search (VNS) and a genetic algorithm (GA) are proposed to solve large instances of the problem. A self-adaptive tuning approach is proposed to adjust the GA parameters. The benefit of the simulation-optimization approach is verified through numerical experiments based on real cases adopted from Line No. 1 of the Tehran underground metro system. The results indicate that the simulation-based optimization method could improve the resiliency of train services by almost 16.7%, on average, as against the all-stop service operation. The average improvement of using VNS as against the GA is about 47%. Also, VNS method provides better-quality solutions by average optimality gap of about 14% in all test instances when compared to an exact solution method, i.e., branch-and-reduce algorithm.

关键词： Rapid transit Resiliency simulation-based optimization Train delay Passenger demand

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simulation-based optimization of Mixed Road Pricing Policies in a large Real-world Network

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Transportation Research Procedia 2015年 8卷 215-226页

作者： Xiqun (Michael) Chen Zheng Zhu Lei Zhang College of Civil Engineering and Architecture Zhejiang University Hangzhou bChina 310058 Department of Civil and Environmental Engineering University of Maryland College Park MD 20742 United States of America

The joint optimization of various types of road pricing strategies for a large-scale real-world network is challenging. Dynamic network supply models can overcome this shortcoming, and provide more detailed information regarding the system performance. However, the computational burden of simulation is still a big challenge for the optimization. Utilizing a simulation-based dynamic traffic assignment model, this paper proposes a simulation-based optimization method to solve the mixed road pricing problem, which is characterized by expensive-to-evaluate and non-closed-form multi-objectives. The mixed road pricing problem is formulated to satisfy dynamic user equilibrium conditions. It enables to capture the time-varying network performance via dynamic traffic assignment. The simulation-based optimization method is successfully applied to the joint optimization of a variety of toll facilities in a real-world network of the Montgomery County in Maryland, i.e. an HOT lane, express toll lanes, and a toll road.

关键词： road pricing simulation-based optimization network modeling dynamic traffic assignment

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simulation-based optimization of Flexible Energy Systems in Manufacturing with Local Energy Production and Storage Components

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Procedia CIRP 2023年 120卷 434-439页

作者： Johannes Breitschopf Thomas Sobottka Gabriela Zabik Fazel Ansari Fraunhofer Austria Research GmbH Theresianumgasse 7 1040 Wien Austria TU Wien Institute of Management Science Department of Industrial and Systems Engineering 1040 Vienna Austria TU Wien Institute for Energy Systems and Thermodynamics Getreidemarkt 9/BA 1060 Wien Austria

This paper introduces a simulation-based planning method that metaheuristically optimizes production planning and the control of components of a flexible energy system for energy intensive production. Significantly increasing energy efficiency and energy flexibility are key requirements and success factors, especially for energy-intensive manufacturers, amid rising energy prices, volatility, and the overall need to reduce the energy and pollution footprint. The considerable potential for an optimized integrated planning and control of production and energy system components remains unexhausted, due to the underlying planning complexity, combined with a lack of available planning methods. This paper aims to introduce a novel planning approach that provides an automated integrated planning functionality to improve energy efficiency and flexibility alongside production goals. The method considers fluctuating short-term energy markets as well as local hydrogen-based energy storage components. Both method development and evaluation are based on a case study in energy-intensive steel production. The achieved results show a significant benefit from the developed dynamic planning and optimization for different variables particularly in two different heat-treatment process technologies.

关键词： flexible energy systems simulation-based optimization metaheuristics sustainable production production planning and control

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simulation-based optimization for modeling and mitigating tunnel-induced damages

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2021年 205卷

作者： Wang, Ying Zhang, Limao Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore 639798 Singapore

This research develops a simulation-based optimization approach that is capable of modeling and mitigating tunnel-induced damages. Two fuzzy cognitive maps (FCMs) (i.e., one with self-feedback and the other without self-feedback) are learned from historical datasets by using the real-coded genetic algorithm (RCGA) on a data-driven modeling manner. Then, the optimal variable value set is searched in the input space. Two new measures, namely "maximum response" and "average response", are proposed to search the optimal variable value set in the input space in the FCM dynamic simulation process. A realistic tunnel case in the Wuhan metro system in China is extensively investigated to demonstrate the applicability and effectiveness of the developed approach. Results indicate that (1) The FCM with self-feedback is more stable than the FCM without self-feedback considering its higher coefficient of determination in the testing samples, where less modification of input variables realizes comparable improvement in the objective in the FCM with self-feedback. (2) The measure "maximum response" shows a larger change in the objective than the measure "average response", where modifications in input space are similar. (3) It is revealed that the ground settlement is more sensitive to TBM operational parameters than tunnel geometry and geological conditions in the two learned FCMs. The developed approach provides insights into a better understanding of causal relationships among factors in tunnel-induced damages, enabling the planning of proactive control strategies for mitigating tunnel-induced damages.

关键词： simulation-based optimization Tunnel-induced damage FCM Data-driven modeling Mitigation control

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Efficient solution of many instances of a simulation-based optimization problem utilizing a partition of the decision space

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ANNALS OF OPERATIONS RESEARCH 2018年第1期265卷 93-118页

作者： Nedelkova, Zuzana Lindroth, Peter Patriksson, Michael Stromberg, Ann-Brith Chalmers Univ Technol Dept Math Sci S-41296 Gothenburg Sweden Univ Gothenburg S-41296 Gothenburg Sweden Volvo Grp Trucks Technol Chassis & Vehicle Dynam Engn Global Operat S-40508 Gothenburg Sweden

This paper concerns the solution of a class of mathematical optimization problems with simulation-based objective functions. The decision variables are partitioned into two groups, referred to as variables and parameters, respectively, such that the objective function value is influenced more by the variables than by the parameters. We aim to solve this optimization problem for a large number of parameter settings in a computationally efficient way. The algorithm developed uses surrogate models of the objective function for a selection of parameter settings, for each of which it computes an approximately optimal solution over the domain of the variables. Then, approximate optimal solutions for other parameter settings are computed through a weighting of the surrogate models without requiring additional expensive function evaluations. We have tested the algorithm's performance on a set of global optimization problems differing with respect to both mathematical properties and numbers of variables and parameters. Our results show that it outperforms a standard and often applied approach based on a surrogate model of the objective function over the complete space of variables and parameters.

关键词： simulation-based optimization Surrogate model Response surface Partition of variables Tyres

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simulation-based optimization for the Integrated Control of Production and Logistics: A Performance Comparison

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IFAC-PapersOnLine 2020年第2期53卷 10639-10644页

作者： Matheus C. Pires Enzo M. Frazzon Djonathan Quadras Eike Broda Michael Freitag Department of Production Engineering and Systems (EPS) Federal University of Santa Catarina Florianópolis Brazil Graduate Program in Production Engineering Federal University of Santa Catarina Florianópolis Brazil Faculty of Production Engineering University of Bremen Bremen Germany BIBA - Bremer Institut für Produktion und Logistik GmbH at the University of Bremen Bremen Germany

Manufacturing systems’ efficiency depends on the proper assignment of orders to resources. Due to existing interdependencies, the integrated consideration of production, inventory and delivery processes can improve the overall manufacturing performance. However, the integration can result in high complexity and stochasticity. Thus, the three areas are rarely addressed together. Thereof, this paper proposes an integrated simulation-based optimization method to cope with uncertainty and complexity. The proposed approach was compared to a benchmark approach and the obtained results show that the first is able to handle the complexity and stochasticity of real-world manufacturing systems, surpassing the performance of the latter.

关键词： Supply Chain Planning Control simulation-based optimization Integrated Planning simulation optimization Simulated Annealing

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Performance analysis of distributed solution approaches in simulation-based optimization

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COMPUTATIONAL MANAGEMENT SCIENCE 2005年第1期2卷 57-82页

作者： Gerdes, M. Barth, T. Grauer, M. Univ Siegen Inst Informat Syst Holderlinstr 3 D-57068 Siegen Germany

Applying computationally expensive simulations in design or process optimization results in long-running solution processes even when using a state-of-the-art distributed algorithm and hardware. Within these simulation-based optimization problems the optimizer has to treat the simulation systems as black-boxes. The distributed solution of this kind of optimization problem demands efficient utilization of resources (i.e. processors) and evaluation of the solution quality. Analyzing the parallel performance is therefore an important task in the development of adequate distributed approaches taking into account the numerical algorithm, its implementation, and the used hardware architecture. In this paper, simulation-based optimization problems are characterized and a distributed solution algorithm is presented. Different performance analysis techniques (e.g. scalability analysis, computational complexity) are discussed and a new approach integrating parallel performance and solution quality is developed. This approach combines a priori and a posteriori techniques and can be applied in early stages of the solution process. The feasibility of the approach is demonstrated by applying it to three different classes of simulation-based optimization problems from groundwater management.

关键词： Performance evaluation simulation-based optimization distributed computing scalability surrogate neural networks

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Parallel simulation-based optimization on Block Planning of Container Terminals

Parallel Simulation-based Optimization on Block Planning of ...

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20th Chinese Control and Decision Conference

作者： Li, Haoyuan Wang, Dingwei Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China

ISBN: (纸本)9781424417339

simulation is an effective means when solving the design and operation problems in modem container terminals. Applying object-oriented discrete event system simulation method, and analyzing the technological process of loading and unloading as well as the operational management in modem container terminals, a simulation model which can describe the whole operation system of container terminals is constructed. To solve the block planning problem of container terminals, the simulation-based optimization (SBO) method is proposed. The parallel computing technology which is realized by MPI is introduced to the solving process, and the computational time and computational cost of SBO are reduced effectively.

关键词： simulation-based optimization Container terminal Block planning Parallel computing

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Adaptive simulation-based optimization for Production Scheduling: A Comparative Study 10th

Adaptive Simulation-Based Optimization for Production Schedu...

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10th IFAC Triennial Conference on Manufacturing Modelling, Management and Control (MIM)

作者： Quadras, Djonathan Frazzon, Enzo M. Mendes, Lucio G. Pires, Matheus C. Rodriguez, Carlos M. T. Univ Fed Santa Catarina Grad Program Prod Engn Florianopolis SC Brazil

Production planning and control is an essential activity for all production systems, as its performance directly affects important factors that are fundamental for business competitiveness. The flow shop scheduling problem is one of the most common problems involved in the planning of production systems, being often applied in the real world. The solution to this problem cannot be easily found due to its complexity, which makes it difficult to use methods that return optimal results due to their high computational execution time. Thus, Adaptive simulation-based optimization is a tool with potential application as it considers the stochasticity of the problem, seeking solutions that are aligned with the real scenario through a simulation and optimizing the desired variables. The simulation-based optimization approach consists in a loop between an optimization heuristic/meta-heuristic and a simulation. The optimization inputs the simulation with a parameter set, while the simulation tests all the models generated by the optimization and returns the fitness function values. However, the literature presents several different heuristics, simulation approaches and optimization methodologies to solve the problem. The general objective of the present work is to comparatively evaluate different adaptive simulation-based optimization models for flow shop production systems. Two methods for comparison were considered, which apply a genetic algorithm integrated with a discrete events simulation. The first method seeks to find the best dispatching rule for each machine in the production system, while the second seeks to directly find the best sequencing of production orders without a predetermined rule. The results show that the rule-free sequencing of production orders returns a result 3.56% better than annual makespan and 5.62% better in execution time compared to the model based on dispatching rules. Copyright (C) 2022 The Authors.

关键词： Production Planning and Control Flow Shop Scheduling Adaptive simulation-based optimization simulation-based optimization Genetic Algorithm

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