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Changeover minimization in the production of metal parts for car seats

COMPUTERS & INDUSTRIAL ENGINEERING 2024年 198卷

作者： Colmenar, J. Manuel Laguna, Manuel Martin-Santamaria, Raul Univ Rey Juan Carlos Calle Tulipan S-N Madrid 28933 Spain Univ Colorado Boulder Leeds Sch Business 995 Regent Dr Boulder CO 80309 USA

We tackle a capacitated lot-sizing and scheduling problem (CLSP) with the main objective of minimizing changeover time in the production of metal parts for car seats. Changeovers occur when a machine (or production line) is reconfigured to produce a different product or part, leading to production downtime and loss of efficiency. In this study, we first provide a mixed-integer programming (MIP) formulation of the problem. We test the limits of solving the problem with commercial mathematical programming software. We also propose two approaches to tackle instances found in practice for which the mathematical programming model is not a viable solution method. Both approaches are based on partitioning the entire production of a part into production runs (or work slots). In the first approach, the work slots are assigned to machines and sequenced by a metaheuristic that follows the search principles of the GRASP (greedy randomized adaptive procedure) and VNS (variable neighborhood search) methodologies. In the second approach, we develop a Hexaly Optimizer (formerly known as LocalSolver) model to assign and sequence work slots. The study provides insights into how to minimize changeovers and improve production efficiency in metal parts manufacturing for car seats. The findings of this study have practical implications for the auto-part manufacturing industry, where efficient and cost-effective production is critical to meet the demands of the market.

关键词： Lot sizing multi-period production scheduling Nonidentical parallel machines Metaheuristic optimization

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A Reconfigurable Cellular Remanufacturing Architecture: a multi-objective design approach

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Journal of Remanufacturing 2024年第2期14卷 185-217页

作者： Mejía-Moncayo, Camilo Kenné, Jean-Pierre Hof, Lucas A. Department of Mechanical Engineering École de technologie supérieure 1100 Notre-Dame St W Montreal H3C 1K3 QC Canada

Remanufacturing is a practice that postpones the product ‘end-of-life’ by returning the properties or features of a new product to a used product. Such a process represents an efficient circular economy strategy to extend product life, reducing its environmental footprint. However, remanufacturing systems must overcome distinct challenges related to information uncertainties about quantity and conditions of used products. Current strategies to address these issues include smart approaches towards smart remanufacturing systems. However, remanufacturing architectures must manage the negative effects on operations derived from the used product’s deterioration and return rate variability, as well as remanufactured products demand fluctuations. This study contributes to address this issue by a Reconfigurable Cellular Remanufacturing Architecture that is integrated in a business strategy towards smart sustainable remanufacturing. The design process is based on a multi-objective optimization model that minimizes the grouping cost, the workload balancing cost, the investment cost, the makespan cost and the reconfigurable cost. A customized version of the well-known multi-objective evolutionary non-dominated sorting genetic algorithm II (NSGA2), a mono-objective genetic algorithm (GA-mo) and a GAMS model were implemented to obtain the potential architecture’ configurations for an explanatory case study based on real-world industrial and random data, respectively. Two procedures to identify the best architecture were also considered. A sensitivity analysis is presented to illustrate the robustness of the proposed model. Managerial insights illustrate about solution methods, and best architecture selection. practical implications also are provided to offer useful options for practitioners. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： Cellular manufacturing Genetic algorithm multi-objective optimization NSGA2 Reconfigurable manufacturing Remanufacturing Smart manufacturing Uncertainty

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Unconventional Surrogate-Assisted approaches to EM-Driven Antenna Design. Modeling and optimization: Global, multi-objective, Statistical 18

Unconventional Surrogate-Assisted Approaches to EM-Driven An...

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18th European Conference on Antennas and Propagation (EuCAP)

作者： Koziel, Slawomir Pietrenko-Dabrowska, Anna Reykjavik Univ Engn Optimizat & Modeling Ctr IS-102 Reykjavik Iceland Gdansk Univ Technol Fac Elect Telecommun & Informat PL-80233 Gdansk Poland

ISBN: (纸本)9788831299091;9798350394436

Design of contemporary antenna systems relies heavily on full-wave electromagnetic (EM) simulation tools. EM analysis ensures reliability but tends to be computationally expensive, which becomes a serious issue in the context of design automation. In recent years, utilization of surrogate modeling methods has been fostered to alleviate the cost-related difficulties. Yet, the existing procedures typically follow a few major approaches, e.g., different architectural variations of deep neural networks, hyper-parameter/model adjustments in Bayesian optimization, or more or less standard machine learning frameworks. This paper reviews several unconventional surrogate-assisted techniques that employ less common algorithmic tools such as the response feature technology, domain confinement, dimensionality reduction, or supplementary inverse predictors. We demonstrate how these tools can be incorporated into practical procedures for global and multi-criterial optimization, statistical analysis, and design- oriented behavioral modeling. A discussion of the algorithm operating principles is supplemented by antenna design cases.

关键词： Antennas computer-aided engineering design automation EM analysis optimization surrogate modeling

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A multi-objective optimal PMU placement considering fault-location topological observability of lengthy lines: A case study in OMAN grid

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ENERGY REPORTS 2023年 9卷 1113-1123页

作者： Al-Hinai, Amer Karami-Horestani, Alireza Alhelou, Hassan Haes Sultan Qaboos Univ Dept Elect & Comp Engn POB 33 Muscat 123 Oman Sultan Qaboos Univ Sustainable Energy Res Ctr Muscat Oman Monash Univ Dept Elect & Comp Syst Engn Clayton Vic 3800 Australia

Using PMU data, the fault location algorithm has gradually been trended in the literature. Besides the optimality of the algorithm, the fault location application should directly receive the data of the faulty line from the PMU devices to have the best performance;using state estimation to create the input data of fault location algorithm not only wastes the golden time, but it also decreases the reliability. Given, it is not possible to install PMU devices in all buses of the system, it is better to install them for the lengthy lines having the most probability of the failure. Although various approaches have been presented in the literature to solve optimal PMU placement (OPP), the fault location observability has less been considered in OPP formulation Accordingly, this paper considers a new index, named fault location observability (FLO), as a second objective function in its OPP formulation. Using this index, in addition to minimizing PMU installation cost, the number of lengthy lines being directly observable are also maximized. Using the epsilon-constraint method, the multi-objective problem has been solved several times as single-objective optimizations. Given the constraints and objective functions are linear, the work presents a mixed integer linear programming (MILP) formulation for the placement problem. Using the GAMS under CPLEX solver, the global optimal solutions of MILP model corresponding to the various epsilon-constraints yields to a Pareto optimal front. The simulation results, next, implements the proposed method on the IEEE 300 bus test system as well as the practical OMAN grid to prove the effectiveness of the proposed method.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Phasor measurement unit Optimal PMU placement OMAN grid Fault location observability (FLO) index MILP-based multi-objective optimization

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Addressing uncertainty in closed-loop supply chain networks: a multi-objective approach to integrated production and transportation problems

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JOURNAL OF MODELLING IN MANAGEMENT 2024年第6期19卷 1849-1882页

作者： Varshney, Niharika Gupta, Srikant Ahmed, Aquil Aligarh Muslim Univ Dept Stat & Operat Res Aligarh Uttar Pradesh India Jaipuria Inst Management Dept Operat & Decis Sci Jaipur Rajasthan India

PurposeThis study aims to address the inherent uncertainties within closed-loop supply chain (CLSC) networks through the application of a multi-objective approach, specifically focusing on the optimization of integrated production and transportation processes. The primary purpose is to enhance decision-making in supply chain management by formulating a robust multi-objective ***/methodology/approachIn dealing with uncertainty, this study uses Pythagorean fuzzy numbers (PFNs) to effectively represent and quantify uncertainties associated with various parameters within the CLSC network. The proposed model is solved using Pythagorean hesitant fuzzy programming, presenting a comprehensive and innovative methodology designed explicitly for handling uncertainties inherent in CLSC *** research findings highlight the effectiveness and reliability of the proposed framework for addressing uncertainties within CLSC networks. Through a comparative analysis with other established approaches, the model demonstrates its robustness, showcasing its potential to make informed and resilient decisions in supply chain *** limitations/implicationsThis study successfully addressed uncertainty in CLSC networks, providing logistics managers with a robust decision-making framework. Emphasizing the importance of PFNs and Pythagorean hesitant fuzzy programming, the research offered practical insights for optimizing transportation routes and resource allocation. Future research could explore dynamic factors in CLSCs, integrate real-time data and leverage emerging technologies for more agile and sustainable supply chain ***/valueThis research contributes significantly to the field by introducing a novel and comprehensive methodology for managing uncertainty in CLSC networks. The adoption of PFNs and Pythagorean hesitant fuzzy programming offers an original and valuable approach to addressing uncertainties, providing practitioners and d

关键词： optimization Sensitivity Supply chain management Operations research

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Safe Pricing Mechanisms for Distributed Resource Allocation With Bandit Feedback

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2024年第4期11卷 2010-2021页

作者： Hutchinson, Spencer Turan, Berkay Alizadeh, Mahnoosh Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA

In societal-scale infrastructures, such as electric grids or transportation networks, pricing mechanisms are often used as a way to shape users' demand in order to lower operating costs and improve reliability. Existing approaches to pricing design for safety-critical networks often require that users are queried beforehand to negotiate prices, which has proven to be challenging to implement in the real world. To offer a more practical alternative, we develop learning-based pricing mechanisms that require no input from the users. These pricing mechanisms aim to maximize the utility of the users' consumption by gradually estimating the users' price response over a span of $T$ time steps (e.g., days) while ensuring that the infrastructure network's safety constraints that limit the users' demand are satisfied at all time steps. We propose two different algorithms for the two different scenarios when: the utility function is chosen by the central coordinator to achieve a social objective, and the utility function is defined by the price response under the assumption that the users are self-interested agents. We prove that both algorithms enjoy $\tilde{\mathcal {O}} (T<^>{2/3})$ regret with high probability. We then apply these algorithms to demand response pricing for the smart grid and numerically demonstrate their effectiveness.

关键词： Safety Resource management Pricing Vectors optimization Demand response Network systems Demand side management multi-armed bandit problem smart grid

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BIM-based multi-objective optimization of clash resolution: A NSGA-II approach

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JOURNAL OF BUILDING ENGINEERING 2024年 89卷

作者： Liu, Xinnan Zhao, Junxiang Yu, Yi Ji, Yingbo North China Univ Technol Sch Civil Engn Beijing 100144 Peoples R China China Commun Informat Technol Grp Co Ltd Beijing 100088 Peoples R China

With the wide application of building information modelling (BIM) in the Architectural, Engineering, and Construction (AEC) industry, holistic approaches based on the network theory to improve clash detection and resolution processes have received increasing attention. The previous studies using holistic approaches in the clash resolution mainly focus on single objective optimization. However, in engineering practice of BIM-based clash resolution, finding optimal solutions for a single-objective that meet the project's requirements is challenging. Moreover, optimization objectives need to be appropriately balanced because these objectives tend to conflict with each other, which leads to a typical multi-objective optimization problem. In this paper, by taking minimum number and minimum moving distance of components to be changed as optimization objectives, a second-generation non-dominated sorting genetic algorithm (NSGAII) approach to solve the multi-objective optimization problem is proposed based on component spatial relation network. Clash solutions are automatically generated under the spatial movement constraints and system priority constraints for clash components. The proposed method is validated by clash resolution of an underground parking lot in a residential project, and the results show that clash solutions generated by the proposed method is practical and acceptable. The proposed method is promising in facilitating the practical application of BIM-based automatic clash resolution.

关键词： BIM Component spatial relation network Clash resolution multi-objective optimization NSGA-II

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Vertical-Axis Wind Turbine Design Using Surrogate-assisted optimization with Physical Experiments In-loop 23

Vertical-Axis Wind Turbine Design Using Surrogate-assisted O...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Lette, Matthew Rahi, Kamrul Hasan Singh, Hemant Kumar Ray, Tapabrata Univ New South Wales Canberra ACT Australia Australian Dept Def Sydney NSW Australia

ISBN: (纸本)9798400701191

Most of the existing wind power comes from the use of traditional Horizontal-Axis Wind Turbines (HAWT), which typically require installation over vast countryside areas (in wind farms) due to considerations such as wake effects and noise. Recently, there has been an increasing interest to explore an alternative solution - VerticalAxis Wind Turbines (VAWT) - that may be more suitable for compact urban areas. In this paper, we conduct design optimization of twin-blade VAWTs by evaluating the candidate designs through direct small-scale prototyping and physical experiments in-loop. The problem is a practical example of expensive optimization where the number of evaluations affordable are severely limited. In addition to the conventional single-objective form (maximize rotational speed), we also solve a multi-objective version of the problem (maximize rotational speed and minimize mass). For conducting optimization, we leverage surrogate-assisted approaches that make use of both predicted mean and uncertainties in modeling for an efficient exploration of the design space. The experiments demonstrate that the approach is able to generate non-intuitive designs that are competitive or better than the baseline (classic twin-blade Savonius design) within a small evaluation budget. The study also strengthens the case for applying this approach for design optimization in general.

关键词： Vertical-axis wind turbine multi-objective optimization Experiment in-loop Expensive optimization

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Robust Congestion Control for Demand-Based optimization in Precoded multi-Beam High Throughput Satellite Communications

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IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第10期70卷 6918-6937页

作者： Van-Phuc Bui Trinh Van Chien Lagunas, Eva Grotz, Joel Chatzinotas, Symeon Ottersten, Bjorn Aalborg Univ Dept Elect Syst DK-9000 Aalborg Denmark Hanoi Univ Sci & Technol HUST Sch Informat & Commun Technol SoICT Hanoi 100000 Vietnam Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT L-1855 Luxembourg Luxembourg SES L-6815 Betzdorf Luxembourg

High-throughput satellite communication systems are growing in strategic importance thanks to their role in delivering broadband services to mobile platforms and residences and/or businesses in rural and remote regions globally. Although precoding has emerged as a prominent technique to meet ever-increasing user demands, there is a lack of studies dealing with congestion control. This paper enhances the performance of multi-beam high throughput geostationary satellite systems under congestion, where the users' quality of service (QoS) demands cannot be fully satisfied with limited resources. In particular, we propose congestion control strategies, relying on simple power control schemes. We formulate a multi-objective optimization framework balancing the system sum-rate and the number of users satisfying their QoS requirements. Next, we propose two novel approaches that effectively handle the proposed multi-objective optimization problem. The former is a model-based approach that relies on the weighted sum method to enrich the number of satisfied users by solving a series of the sum-rate optimization problems in an iterative manner. The latter is a data-driven approach that offers a low-cost solution by utilizing supervised learning and exploiting the optimization structures as continuous mappings. The proposed general framework is evaluated for different linear precoding techniques, for which the low computational complexity algorithms are designed. Numerical results manifest that our proposed framework effectively handles the congestion issue and brings superior improvements of rate satisfaction to many users than previous works. Furthermore, the proposed algorithms show low run-time and make them realistic for practical systems.

关键词： optimization Quality of service Throughput Satellites Resource management Precoding Neural networks multi-beam high throughput satellite communications quality of service requirements multi-objective optimization neural networks

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A survey on evolutionary computation for complex continuous optimization

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ARTIFICIAL INTELLIGENCE REVIEW 2022年第1期55卷 59-110页

作者： Zhan, Zhi-Hui Shi, Lin Tan, Kay Chen Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pazhou Lab Guangzhou 510330 Peoples R China Hong Kong Polytech Univ Dept Comp Hung Hom Hong Kong Peoples R China Hanyang Univ Ansan South Korea Chaoyang Univ Technol Taichung Taiwan

Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

关键词： Evolutionary computation (EC) Evolutionary algorithm (EA) Swarm intelligence (SI) Complex continuous optimization problems Large-scale optimization Dynamic optimization multi-modal optimization Many-objective optimization Constrained optimization Expensive optimization Function-oriented taxonomy

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