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Towards sustainable scheduling of unrelated parallel batch processors: A multiobjective approach with triple bottom line, classical and data-driven robust optimization

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COMPUTERS & OPERATIONS RESEARCH 2025年 173卷

作者： Fallahi, Ali Bani, Erfan Amani Varmazyar, Mohsen Sharif Univ Technol Dept Ind Engn Tehran *** Iran

Sustainable scheduling has become a critical aspect of modern industrial practices, requiring the integration of economic, environmental, and social dimensions. This research introduces an integrated problem for scheduling unrelated batch processors, aiming to optimize total cost, energy consumption, and social benefit through a multiobjective mixed-integer linear mathematical programming model. The study addresses the uncertainty in job processing time using robust optimization to enhance the model's reliability. A three-stage solution methodology is proposed to solve the problem. First, robust optimization approaches are suggested to handle job processing time uncertainty. To this end, classical and kernel learning data-driven robust approaches are employed for uncertainty handling incases of interval-bounded and distributional asymmetry uncertainties. Then, the global criterion multiobjective method is presented to resolve goal conflicts. To tackle the NP-hard complexity, three efficient multiobjective metaheuristic algorithms, non-dominated sorting genetic algorithm-II, multiobjective particle swarm optimization, and multiobjective grey wolf optimizer, are designed. The developed model and methodologies are extensively evaluated through numerical experiments. Results demonstrate the efficiency of the current framework against the literature model in solving the studied problem. Also, the robust models can properly handle the problem's uncertainty regarding the assumptions of studied cases. The global criterion method's performance is acceptable for small instances, while metaheuristics excel in solving larger problems. Based on the assumptions of the studied robust cases, the developed framework is investigated for two case studies from poultry production and glass ceramization real-world industries to illustrate its applicability further.

关键词： Production scheduling Multiobjective optimization Robust optimization Machine learning evolutionary algorithms

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Embedding a Repair Operator in evolutionary Single and Multi-objective algorithms - An Exploitation-Exploration Perspective 11th

Embedding a Repair Operator in Evolutionary Single and Multi...

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11th International Conference on evolutionary Multi-Criterion Optimization, EMO 2021

作者： Deb, Kalyanmoy Mittal, Sukrit Saxena, Dhish Kumar Goodman, Erik D. BEACON NSF Center and Michigan State University East Lansing United States Indian Institute of Technology Roorkee Roorkee India

ISBN: (纸本)9783030720612

evolutionary algorithms (EAs) are population-based search and optimization methods whose efficacy strongly depends on a fine balance between exploitation caused mainly by its selection operators and exploration introduced mainly by its variation (crossover and mutation) operators. An attempt to improve an EA’s performance by simply adding a new and apparently promising operator may turn counter-productive, as it may trigger an imbalance between the exploitation-exploration trade-off. This necessitates a proper understanding of mechanisms to restore the balance while accommodating a new operator. In this paper, we introduce a new repair operator based on an AI-based mapping between past and current good population members to improve an EA’s convergence properties. This operator is applied to problems with different characteristics, including single-objective (with single and multiple global optima) and multi-objective problems. The focus in this paper is to highlight the importance of restoring the exploitation-exploration balance, when a new operator is introduced. We show how different combinations of problems and EA characteristics pose different opportunities for restoration of this balance, enabling the repair-based EAs/EMOs to outperform the original EAs/EMOs in most cases. © 2021, Springer Nature Switzerland AG.

关键词： evolutionary algorithms

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Totipotent neural controllers for modular soft robots: Achieving specialization in body-brain co-evolution through Hebbian learning

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NEUROCOMPUTING 2025年 614卷

作者： Ferigo, Andrea Iacca, Giovanni Medvet, Eric Nadizar, Giorgia Univ Trento Dept Informat Engn & Comp Sci Trento Italy Univ Trieste Dept Engn & Architecture Trieste Italy Univ Trieste Dept Math & Geosci Trieste Italy

Multi-cellular organisms typically originate from a single cell, the zygote, that then develops into a multitude of structurally and functionally specialized cells. The potential of generating all the specialized cells that make up an organism is referred to as cellular "totipotency", a concept introduced by the German plant physiologist Haberlandt in the early 1900s. In an attempt to reproduce this mechanism in synthetic organisms, we present a model based on a kind of modular robot called Voxel-based Soft Robot (VSR), where both the body, i.e ., the arrangement of voxels, and the brain, i.e ., the Artificial Neural Network (ANN) controlling each module, are subject to an evolutionary process aimed at optimizing the locomotion capabilities of the robot. In an analogy between totipotent cells and totipotent ANN-controlled modules, we then include in our model an additional level of adaptation provided by Hebbian learning, which allows the ANNs to adapt their weights during the execution of the locomotion task. Our in silico experiments reveal two main findings. Firstly, we confirm the common intuition that Hebbian plasticity effectively allows better performance and adaptation. Secondly and more importantly, we verify for the first time that the performance improvements yielded by plasticity are in essence due to a form of specialization at the level of single modules (and their associated ANNs): thanks to plasticity, modules specialize to react indifferent ways to the same set of stimuli, i.e ., they become functionally and behaviorally different even though their ANNs are initialized in the same way. This mechanism, which can be seen as a form of totipotency at the level of ANNs, can have, in our view, profound implications in various areas of Artificial Intelligence (AI) and applications thereof, such as modular robotics and multi-agent systems.

关键词： Hebbian learning Neural plasticity Voxel-based Soft Robots evolutionary algorithms

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Multi-objective optimization of work package scheme problem to minimize project carbon emissions and cost

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 200卷

作者： Zhang, Yaning Li, Xiao Teng, Yue Shen, Geoffrey Q. P. Bai, Sijun Hong Kong Polytech Univ Dept Bldg & Real Estate Hong Kong Peoples R China Northwestern Polytech Univ Sch Management Xian Peoples R China Univ Hong Kong Dept Civil Engn Hong Kong Peoples R China

The construction industry accounts for around 30% of global energy consumption and 33% of CO2 emissions. For the carbon neutrality initiative, reducing carbon emissions from construction projects become a critical objective for project success. However, a dilemma arises in balancing carbon emissions and project cost, particularly during the work package-based project planning phase. To address this issue, this article presents a novel multiobjective optimization model for the work package scheme problem, aimed at minimizing both project carbon emissions and cost. Multi-objective evolutionary algorithms (EAs) are developed to solve the model. Firstly, a multi-objective Mixed-Integer Programming (MIP) model is developed to establish the functional relation between work package attributes (duration and work content) and optimization objectives (carbon emissions and cost). Secondly, two multi-objective optimization EAs, NSGA-II and SPEA2, are developed to obtain the Pareto frontier. The experimental results indicate that NSGA-II and SPEA2 exhibit superior trade-off capabilities compared to the Gurobi and the state-of-the-art heuristic algorithm. Compared to Gurobi, the proposed EAs achieve an approximately 68% reduction in carbon emissions, accompanied by about an 11% cost increase. Compared to the heuristic algorithm, the EAs achieve around 10% reductions in carbon emissions with an approximately 5% cost increase. Additionally, sensitivity analysis conducted on a project instance dataset demonstrates the robustness of the proposed model and algorithms. This article paves the way for achieving lowcarbon and sustainable construction project management in the context of carbon neutrality.

关键词： Multi-objective optimization Project planning Work packages Carbon emissions evolutionary algorithms

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Online Optimization of Curriculum Learning Schedules using evolutionary Optimization 6

Online Optimization of Curriculum Learning Schedules using E...

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6th Annual IEEE Conference on Games (CoG)

作者： Jiwatode, Mohit Schlecht, Leon Dockhorn, Alexander Leibniz Univ Hannover Inst Informat Proc Hannover Germany

ISBN: (纸本)9798350350685;9798350350678

We propose RHEA CL, which combines Curriculum Learning (CL) with Rolling Horizon evolutionary algorithms (RHEA) to automatically produce effective curricula during the training of a reinforcement learning agent. RHEA CL optimizes a population of curricula, using an evolutionary algorithm, and selects the best-performing curriculum as the starting point for the next training epoch. Performance evaluations are conducted after every curriculum step in all environments. We evaluate the algorithm on the DoorKey and DynamicObstacles environments within the Minigrid framework. It demonstrates adaptability and consistent improvement, particularly in the early stages, while reaching a stable performance later that is capable of outperforming other curriculum learners. In comparison to other curriculum schedules, RHEA CL has shown to yield performance improvements for the final Reinforcement learning (RL) agent at the cost of additional evaluation during training.

关键词： Curriculum Learning Reinforcement Learning evolutionary algorithms Rolling Horizon algorithms

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Emergency medical supplies scheduling during public health emergencies: algorithm design based on AI techniques

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年第2期63卷 628-650页

作者： Xia, Huosong Sun, Zelin Wang, Yuan Zhang, Justin Zuopeng Kamal, Muhammad Mustafa Jasimuddin, Sajjad M. Islam, Nazrul Wuhan Text Univ Sch Management Wuhan Peoples R China Res Ctr Enterprise Decis Support Key Res Inst Humanities & Social Sci Univ Hubei Pr Wuhan Peoples R China Wuhan Text Univ Res Inst Management & Econ Wuhan Peoples R China Univ North Florida Coggin Coll Business Dept Management Jacksonville FL USA Coventry Univ Sch Strategy & Leadership Coventry England Kedge Business Sch Marseille France Univ East London Royal Docks Sch Business & Law London England

Based on AI technology, this study proposes a novel large-scale emergency medical supplies scheduling (EMSS) algorithm to address the issues of low turnover efficiency of medical supplies and unbalanced supply and demand point scheduling in public health emergencies. We construct a fairness index using an improved Gini coefficient by considering the demand for emergency medical supplies (EMS), actual distribution, and the degree of emergency at disaster sites. We developed a bi-objective optimisation model with a minimum Gini index and scheduling time. We employ a heterogeneous ant colony algorithm to solve the Pareto boundary based on reinforcement learning. A reinforcement learning mechanism is introduced to update and exchange pheromones among populations, with reward factors set to adjust pheromones and improve algorithm convergence speed. The effectiveness of the algorithm for a large EMSS problem is verified by comparing its comprehensive performance against a super-large capacity evaluation index. Results demonstrate the algorithm's effectiveness in reducing convergence time and facilitating escape from local optima in EMSS problems. The algorithm addresses the issue of demand differences at each disaster point affecting fair distribution. This study optimises early-stage EMSS schemes for public health events to minimise losses and casualties while mitigating emotional distress among disaster victims.

关键词： Scheduling public health emergency medical supply heuristics evolutionary algorithms artificial intelligence

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A generalized bilevel optimization model for large-scale task scheduling in multiple agile earth observation satellites

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KNOWLEDGE-BASED SYSTEMS 2025年 309卷

作者： Chen, Jiawei Wang, Feiran Chen, Yingguo He, Lei Du, Yonghao Wu, Jian Chen, Yingwu Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Xidian Univ Hangzhou Res Inst Hangzhou 310000 Peoples R China

Scheduling large-scale tasks within a multi-agile earth observation satellite system poses a formidable challenge. Given the complexity of this optimization problem, the responding solving strategy should exhibit both efficiency and effectiveness in terms of computational time and solution quality. The current strategies can broadly be categorized into all-in-one and two-stage methodologies. The latter, more conducive to real- world scenarios, owing to reducing the problem complexity and practical operational flexibility, dissects this challenge into task allocation and single-satellite scheduling. However, existing two-stage strategies still consider the objectives and constraints at the equivalent level and only adapt to specific algorithms. In this way, the pivotal issues concerning the solution complexity and strategy compatibility remain fundamentally unaddressed. To address this limitation, we proposed a generalized bilevel optimization model called Question- and-Answer model, which establishes two distinct optimization model with different contains and objectives. In this model, the upper questions are highly indispensable in the response from the lower level and constraints are considered separately at two stages. To ascertain the generalization of this framework, we conduct a comprehensive range of experiments employing various proposed strategies and algorithms in two stages. Diverse algorithms ranging from heuristic principles, and evolutionary strategies, to reinforcement learning can be seamlessly combined and integrated within this framework. The results demonstrate that the scale of scenarios does not affect the effectiveness of any amalgamated algorithms within this framework. Furthermore, the transition of upper questions according to the lower answers indeed improves the objectives but concurrently intensify the computational time.

关键词： Large-scale scheduling evolutionary algorithms Reinforcement learning Bilevel optimization

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Preventing Premature Convergence and Proving the Optimality in evolutionary algorithms 11

Preventing Premature Convergence and Proving the Optimality ...

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11th Biennial International Conference on Artificial Evolution (EA)

作者： Vanaret, Charlie Gotteland, Jean-Baptiste Durand, Nicolas Alliot, Jean-Marc Ecole Natl Aviat Civile Lab Math Appl Informat & Automat Aerien Toulouse France Inst Rech Informat Toulouse Toulouse France

ISBN: (纸本)9783319116839;9783319116822

evolutionary algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their exponential complexity and their inability to quickly compute a good approximation of the global minimum. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a branch and bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and is highly competitive with both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality.

关键词： evolutionary algorithms

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Chaotic dynamics in an overlapping generations model: Forecasting and regularization

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CHAOS SOLITONS & FRACTALS 2025年 196卷

作者： Alexeeva, Tatyana A. Kuznetsov, Nikolay V. Mokaev, Timur N. Zelinka, Ivan HSE Univ Sch Comp Sci Phys & Technol Kantemirovskaya ul 3 St Petersburg 194100 Russia St Petersburg Univ Fac Math & Mech St Petersburg 198504 Russia RAS Inst Problems Mech Engn VO Bolshoj pr 61 St Petersburg 199178 Russia VSB TUO Fac Elect Engn & Comp Sci Dept Comp Sci 17 listopadu 2172-15 Ostrava 70800 Czech Republic VSB TUO IT4Innovat Natl Supercomp Ctr Ostrava 70800 Czech Republic

Irregular dynamics (especially chaotic) is often undesirable in economics because it presents challenges for predicting and controlling the behavior of economic agents. In this paper, we used an overlapping generations (OLG) model with a control function in the form of government spending as an example, to demonstrate an effective approach to forecasting and regulating chaotic dynamics based on a combination of classical control methods and artificial intelligence algorithms. We showed that in the absence of control variables, both regular and irregular (including chaotic) behavior could be observed in the model. In the case of irregular dynamics, a small control action introduced in the model allows modifying the behavior of economic agents and switching their dynamics from irregular to regular mode. We used control synthesis by the Pyragas method to solve the problem of regularizing the irregular behavior and stabilizing unstable periodic orbits (UPOs) embedded in the chaotic attractor of the model. To maximize the basin of attraction of stabilized UPOs, we used several types of evolutionary algorithms (EAs). We compared the results obtained by applying these EAs in numerical experiments and verified the outcomes by numerical simulation. The proposed approach allows us to improve the forecasting of dynamics in the OLG model and make agents' expectations more predictable.

关键词： Overlapping generations (OLG) model Forecasting Control Chaos Artificial intelligence evolutionary algorithms

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A weighted knowledge extraction strategy for dynamic multi-objective optimization

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SWARM AND evolutionary COMPUTATION 2025年 92卷

作者： Xie, Yingbo Qiao, Junfei Wang, Ding Henan Polytech Univ Sch Elect Engn & Automat Henan Key Lab Intelligent Detect & Control Coal Mi Jiaozuo 454003 Peoples R China Beijing Univ Technol Sch Informat Sci & Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China

Multi-objective evolutionary algorithms suffer from performance degradation when solving dynamic multi- objective optimization problems (DMOPs) with a new conditional configuration from scratch, which motivates the research on knowledge extraction. However, most knowledge extraction strategies only focus on obtaining effective information from a single knowledge source, while ignoring the useful information from other knowledge sources with similar properties. Motivated by this, a weighted multi-source knowledge extraction strategy-based dynamic multiobjective evolutionary algorithm is proposed. First, a similarity criterion based on angle information is constructed to quantify similarity between different source domains and the target domain. Second, a knowledge extraction technique is developed to select a specific number of individuals from each source domain using a distance metric. Third, a generation strategy based on dynamic weighting mechanism is proposed, which generates a certain number of individuals and merges these individuals into the initial population within the new environment. Finally, the comprehensive experiments are conducted on public DMOP benchmarks and demonstrate the devised method significantly outperforms the state-of-the-art competing algorithms.

关键词： Dynamic multiobjective optimization Change response evolutionary algorithms evolutionary environment

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