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Swarm intelligent based metaheuristics for a bi-objective flexible job shop integrated supply chain scheduling problems

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APPLIED SOFT COMPUTING 2022年第0期121卷

作者： Mahmud, Shahed Chakrabortty, Ripon K. Abbasi, Alireza Ryan, Michael J. Univ New South Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia Rajshahi Univ Engn & Technol Dept Ind & Prod Engn Rajshahi 6204 Bangladesh Capabil Associates Canberra Canberra ACT Australia

This study introduces a bi-objective integrated supply chain (SC) scheduling (SCS) model to deal with the challenges of providing highly customized and on-time delivery requirements at the least cost. To address these real-life challenges, the model integrates the supply portfolio into production scheduling with a customer-imposed delivery time window. A set of conflicting factors is considered for the SC in which the manufacturer is modeled using the flexible job shop (FJS) problem to include greater flexibility in process routing. Since the proposed SCS model extends the strongly NP-hard FJS problem, two new meta-heuristic algorithms are developed, enhancing the performance of the multi-objective particle swarm optimization (MOPSO). A Tabu search inspired search mechanism and a problem-specific mutation operator are designed in both algorithms. As inadequate archive diversity leads to premature convergence and unsatisfactory Pareto solutions in many cases, this work employs two practical approaches separately (i.e., crowding distance (CD) and niche count preservation of reference point (RP)) in the proposed MOPSOs, called CD-MOPSO and RP-MOPSO, respectively. For leader selection, CD-MOPSO uses the swarm distance-based Roulette wheel, whereas the concept of RP is proposed in RP-MOPSO to expedite convergence. The performances of the proposed algorithms are validated against four existing algorithms and assessed by utilizing several criteria after solving 45 artificial instances. The simulation results and statistical analysis demonstrate the supremacy of the RP-MOPSO which increases the flexibility for a decision-maker in providing a higher number of Pareto solutions and more diverse and regular frontiers within reasonable computational time. Finally, a sensitivity analysis of the model is conducted, and managerial insights are provided. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Supply chain scheduling Supplier selection Flexible job shop scheduling multi-objective optimization MOPSO

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Enhancing Robotics Online 3D Bin Packing: A Comparative Study of Conventional Heuristic and Deep Reinforcement Learning approaches 20

Enhancing Robotics Online 3D Bin Packing: A Comparative Stud...

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IEEE 20th International Conference on Automation Science and Engineering (CASE)

作者： Xiong, Heng Ding, Kai Ding, Wan Qiu, Xuchong Janschek, Klaus Xu, Jianfeng Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China BOSCH Corp Res Beijing Peoples R China Tech Univ Dresden Dresden Germany

ISBN: (纸本)9798350358513;9798350358520

Robotic object packing holds a wide array of practical applications across logistics and manufacturing sectors. The online 3D Bin Packing Problem (BPP) is a challenging task that involves online packing of three-dimensional boxes into a container while considering constraints and objectives. Unlike the offline version, where all boxes are known in advance, the online variant requires decisions about how to pack each box as it arrives, without prior knowledge of upcoming boxes. To maximize space utilization, our study explores two distinct strategies: conventional heuristics-based algorithms and a deep reinforcement learning (DRL)-based approach. For the heuristic strategy, we propose four heuristic criteria alongside two variants of multi-objective optimization (MOO) algorithms. Quantitative experiments reveal that MOO outperforms singleobjective approaches for online bin packing. In our DRL-based approach, we introduce a framework that leverages a candidate map that indicates the potentially feasible placements, ensuring a balanced exploration and exploitation in the considerable discrete action space. Experiments demonstrate the superior performance of our DRL-based approach compared to both DRL-based baseline methods and conventional approaches. Additionally, we discuss the limitations of DRL-based methods and offer practical recommendations for real-world applications.

关键词： Deep reinforcement learning

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A Systematic Review of optimization approaches for Integrated Energy System 4

A Systematic Review of Optimization Approaches for Integrate...

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4th Energy Conversion and Economics Annual Forum, ECE Forum 2024

作者： Li, Feng Hou, Hui Wang, Yingxiang Wang, Pingfan School of Automation Wuhan University of Technology Wuhan 430070 China Economics and Technology Research Institute State Grid Hubei Electric Power Company Wuhan 430077 China

ISBN: (纸本)9781837242627

Integrated energy system (IES) serves as a critical physical platform for advancing the clean energy transition and achieving carbon reduction goals. This paper systematically reviews the core issues in IES optimization, focusing on objective functions, constraints, and solution algorithms. First, the review outlines a multi-tier IES architecture. Next, it explores the formulation of objective functions in IES optimization, addressing multiple dimensions including economic, decarbonization, safety and loss, etc. It then systematically evaluates various types of constraints in IES, including operational and pricing constraints, among others. Finally, the paper summarizes solution algorithms for IES optimization, such as mathematical optimization, heuristic algorithms, and dynamic programming methods, etc. This review provides a comprehensive theoretical framework and practical basis for optimizing IES. © The Institution of Engineering & Technology 2024.

关键词： constraints integrated energy system objective functions optimization solution algorithms

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Large language model-based evolutionary optimizer: Reasoning with elitism

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NEUROCOMPUTING 2025年 622卷

作者： Brahmachary, Shuvayan Joshi, Subodh M. Panda, Aniruddha Koneripalli, Kaushik Sagotra, Arun Kumar Patel, Harshil Sharma, Ankush Jagtap, Ameya D. Kalyanaraman, Kaushic Shell India Markets Pvt Ltd Computat Sci Grp Chennai India Worcester Polytech Inst Aerosp Engn Dept Worcester MA 01609 USA

Large Language Models (LLMs) have demonstrated remarkable reasoning abilities, prompting interest in their application as black-box optimizers. This paper asserts that LLMs possess the capability for zero- shot optimization across diverse scenarios, including multi-objective and high-dimensional problems. We introduce a novel population-based method for numerical optimization using LLMs called Large Language- Model-Based Evolutionary Optimizer (LEO). Our hypothesis is supported through numerical examples, spanning benchmark and industrial engineering problems such as supersonic nozzle shape optimization, heat transfer, and windfarm layout optimization. We compare our method to several gradient-based and gradient-free optimization approaches. While LLMs yield comparable results to state-of-the-art methods, their imaginative nature and propensity to hallucinate demand careful handling. We provide practical guidelines for obtaining reliable answers from LLMs and discuss method limitations and potential research directions.

关键词： Large language models Evolutionary Optimizers multi-objective optimization Aerodynamic Design

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multi-objective topology optimization of pin-fin heat exchangers using spectral and finite-element methods

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STRUCTURAL AND multiDISCIPLINARY optimization 2021年第4期64卷 2075-2095页

作者： Ghasemi, Ali Elham, Ali Tech Univ Carolo Wilhelmina Braunschweig Inst Aircraft Design & Lightweight Struct Braunschweig Germany

Forced convective pin-fin heat exchangers, due to the high wet surface area per volume and the hindered thermal boundary layers, feature low thermal resistances. However, the considerable coolant pressure drop, particularly for densely packed fin arrays, imposes operational costs for pumping power supply. This paper develops a multi-objective topology optimization approach to optimize sink geometries in order to minimize thermal resistance and pressure loss, concurrently. In accordance to the pin-fin geometrical characteristics, a dedicated pseudo-3D conjugate heat transfer model is utilized, by assuming periodic flow and fin design pattern, to reasonably reduce the high cost of full-3D model optimization. For the solution of flow part, a pseudo-spectral scheme is used, which is intrinsically periodic and features a high spectral accuracy, and the finite element method for the non-periodic conjugate heat transfer model. Exact partial derivatives of the discrete solutions are obtained analytically by hand-differentiation. This task is rather convenient for the flow part, due to the simplicity of the pseudo-spectral implementation;however, the MATLAB symbolic toolbox is selectively utilized for the finite element code to ensure an error-free implementation. Finally, the sensitivities are computed directly or via a discrete adjoint method, for the flow and heat models, respectively. To examine the present approach, two approaches are used for optimization of a practical cooling task: constrained and unconstrained multi-objective formulations, where in all cases more emphasis is placed on thermal resistance minimization. A series of optimized heat sink geometries via constrained or unconstrained multi-objective optimizations are obtained to examine practical utility of the present approach. The optimized topologies demonstrated superior cooling performances at lower costs of pressure losses compared to conventional (circular) in-line and staggered fins, and confirmed th

关键词： Topology optimization Pin-fin sink multi-objective Spectral method Heat-exchanger optimization

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multi-objective reinforcement learning-based approach for pressurized water reactor optimization

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ANNALS OF NUCLEAR ENERGY 2024年 205卷

作者： Seurin, Paul Shirvan, Koroush MIT 77 Massachusetts Ave Cambridge MA 02139 USA

A novel method, the Pareto Envelope Augmented with Reinforcement Learning (PEARL), has been developed to address the challenges posed by multi-objective problems, particularly in the field of engineering where the evaluation of candidate solutions can be time-consuming. PEARL distinguishes itself from traditional policy-based multi-objective Reinforcement Learning methods by learning a single policy, eliminating the need for multiple neural networks to independently solve simpler sub-problems. Several versions inspired from deep learning and evolutionary techniques have been crafted, catering to both unconstrained and constrained problem domains. Curriculum Learning (CL) is harnessed to effectively manage constraints in these versions. PEARL's performance is first evaluated on classical multi-objective benchmarks. Additionally, it is tested on two practical PWR core Loading Pattern (LP) optimization problems to showcase its real-world applicability. The first problem involves optimizing the Cycle length (L-C) and the rod-integrated peaking factor (F-Delta h) as the primary objectives, while the second problem incorporates the mean average enrichment as an additional objective. Furthermore, PEARL addresses three types of constraints related to boron concentration (C-b), peak pin burnup (Bu-max), and peak pin power (F-q). The results are systematically compared against conventional approaches from stochastic optimization. Notably, PEARL, specifically the PEAL-NdS (crowding) variant, efficiently uncovers a Pareto front without necessitating additional efforts from the algorithm designer, as opposed to a single optimization with scaled objectives. It also outperforms the classical approach across multiple performance metrics, including the Hyper-volume. Future works will encompass a sensitivity analysis of hyper-parameters with statistical analysis to optimize the application of PEARL and extend it to more intricate problems.

关键词： PWR loading pattern optimization Reinforcement learning multi-objective optimization PEARL Curriculum learning

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Parametric optimization of friction stir welding process parameters of dissimilar welded joints using grey relational analysis and desirability function approach

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WORLD JOURNAL OF ENGINEERING 2023年第3期20卷 609-617页

作者： Nadikudi, Bhanodaya Kiran Babu Sreenidhi Inst Sci & Technol Hyderabad India

Purpose The main purpose of the present work is to study the multi response optimization of dissimilar friction stir welding (FSW) process parameters using Taguchi-based grey relational analysis and desirability function approach (DFA). Design/methodology/approach The welded sheets were fabricated as per Taguchi orthogonal array design. The effects of tool rotational speed, transverse speed and tool tilt angle process parameters on ultimate tensile strength and hardness were analyzed using grey relational analysis, and DFA and optimum parameters combination was determined. Findings The tensile strength and hardness values were evaluated from the welded joints. The optimum values of process parameters were estimated through grey relational analysis and DFA methods. Similar kind of optimum levels of process parameters were obtained through two optimization approaches as tool rotational speed of 1150 rpm, transverse speed of 24 mm/min and tool tilt angle of 2 degrees are the best process parameters combination for maximizing both the tensile strength and hardness. Through these studies, it was confirmed that grey relational analysis and DFA methods can be used to find the multi response optimum values of FSW process parameters. Research limitations/implications In the present study, the FSW is performed with L9 orthogonal array design with three process parameters such as tool rotational speed, transverse speed and tilt angle and three levels. practical implications Aluminium alloys are widely using in automotive and aerospace industries due to holding a high strength to weight property. Originality/value Very limited work had been carried out on multi objective optimization techniques such as grey relational analysis and DFA on friction stir welded joints made with dissimilar aluminium alloys sheets.

关键词： Aluminium alloys Process parameters Mechanical properties Grey relational analysis Desirability function analysis

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Exploring trade-offs in agro-ecological landscapes: Using a multi-objective land-use allocation model to support agroforestry research

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BASIC AND APPLIED ECOLOGY 2022年 64卷 103-119页

作者： Reith, Esther Gosling, Elizabeth Knoke, Thomas Paul, Carola Tech Univ Munich Inst Forest Management TUM Sch Life Sci Weihenstephan Hans Carl Von Carlowitz Pl 2 D-85354 Freising Weihenstephan Germany Univ Gottingen Dept Forest Econ & Sustainable Land Use Planning Busgenweg 1 D-37077 Gottingen Germany Ctr Biodivers & Sustainable Land Use Busgenweg 1 D-37077 Gottingen Germany

Finding the optimal land allocation for providing ecosystem services, conserving biodiversity and maintaining rural liveli-hoods is a key challenge of agricultural management and land-use planning. Agroforestry has been widely discussed as a sus-tainable land-use solution and as one strategy to improve the provision of multiple ecological and economic functions in agricultural landscapes. In this study, we use the backdrop of agroforestry research to evaluate a method from the multi-criteria decision analysis toolbox: robust multi-objective optimization. The key feature of this modelling approach is its capacity to integrate uncertain ecological and socio-economic data. We illustrate the optimization model with a case study from eastern Panama, showing how the model can bring together scientific and practical knowledge to provide potentially desirable land-scape compositions from the perspective of farmers, a public perspective, and a compromise solution. Example results of our case study show how to assess whether agroforestry is a desirable component in a landscape composition to satisfy multiple objectives of different interest groups. Furthermore, we use the model to demonstrate how different objectives influence the optimal area share and type of agroforestry. Due to its parsimonious nature, the model could be used as a starting point of an interactive co-learning process with decision-makers, researchers and other stakeholders. The model, however, is not yet suit-able for an exact prediction of future land-use dynamics, for questions of spatially explicit land-use configuration, studies going beyond the regional scale or for socio-economic interactions of agents. Therefore, we outline future research needs and recom-mendations for other types of models or hybrid approaches.(c) 2022 The Author(s). Published by Elsevier GmbH on behalf of Gesellschaft fur Okologie. This is an open access article under the CC BY license (http://***/licenses/by/4.0/

关键词： Agroforestry Decision-support tools Ecosystem services Integrated landscape management multi-objective optimization Sim-ulation models

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An Improved Self-Organizing Genetic Algorithm for Optimizing Container Transportation Routing Problems Under Times Windows

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Operations Research Forum 2025年第2期6卷 1-24页

作者： Ndikuriyo, Yves Zhang, Yinggui Fom, Dung Davou School of Traffic and Transportation Engineering Central South University Hunan Changsha 410075 China

The shortest path routing problem is a multi-objective optimization challenge that involves balancing often conflicting goals, such as minimizing transportation distance, cost, and time. To address such problems, hybrid methods using randomly generated weights have been employed to handle multiple objectives effectively. This research introduces a hybrid method, the self-organizing genetic algorithm (SOGA), enhanced by the integration of adaptive weight within its genetic algorithm loop and Dijkstra’'s algorithm in the initialization phase. Numerical experiments conducted on networks of up to 10,000 nodes demonstrate that the proposed path-based Dijkstra encoding and adaptive weight-enhanced SOGA outperform existing approaches in terms of robustness and computational efficiency. Compared to methods such as NSGA-II, SPEA, and traditional GA, the enhanced SOGA achieves a superior balance between solution quality and runtime performance. A case study on the East African Central Corridor further validates its practical applicability, achieving up to 16% cost savings and 20% distance reductions compared to benchmark solutions, albeit with slightly increased delivery times. Overall, this approach offers a promising framework for logistics planning, advancing the use of innovative, time-sensitive, and cost-effective routing strategies in multimodal transportation networks. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Container Dijkstra’s algorithm optimization Routing problem Self-organization genetic algorithm Time windows

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optimization of end-of-life product recycling supply chain using PSO algorithm and R language 27

Optimization of end-of-life product recycling supply chain u...

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27th World multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2023

作者： Ellili, Walid Marheel, Mahfoud Louati, Mahdi Loukil, Taïcir Moalla Computer Science Department University of Sfax Sfax3018 Tunisia

ISBN: (纸本)9781713880325

This article presents an optimization approach for the end-of-life product recycling supply chain using the Particle Swarm optimization (PSO) algorithm and the R programming language. The objective is to minimize the total cost associated with transportation, facility opening, and variable costs, while meeting the capacity constraints of collection, recycling, and landfill sites over multiple times. The PSO algorithm is utilized to find the optimal solution for the problem. The results demonstrate the effectiveness of the proposed approach in optimizing the end-of-life product recycling supply chain. The optimal solution yields a significant cost reduction compared to previous approaches. The article concludes with insights into the benefits and limitations of using the PSO algorithm and R language, and provides recommendations for future research in this area. Overall, this study contributes to the field of sustainable supply chain management by providing a practical and efficient solution for optimizing the end-of-life product recycling process. © 2023 Proceedings of World multi-Conference on Systemics, Cybernetics and Informatics, WMSCI. All rights reserved.

关键词： Optimal systems

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