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multi-objective GROUPING GENETIC ALGORITHM FOR THE JOINT ORDER BATCHING, BATCH ASSIGNMENT, AND SEQUENCING problem

INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2022年第3期17卷 188-204页

作者： Alejandro Cano, Jose Cortes, Pablo Antonio Campo, Emiro Correa-Espinal, Alexander A. Univ Medellin Fac Econ & Adm Sci Medellin Colombia Univ Seville Escuela Tecn Super Ingn Seville Spain Politecn Colombiano Jaime Isaza Cadavid Medellin Colombia Univ Nacl Colombia Fac Minas Medellin Colombia

This article solves the order batching, batch assignment, and sequencing problem (JOBASP) given multiple objectives and heterogeneous picking vehicles in multi-parallel-aisle warehouse systems. A multi-objective grouping genetic algorithm (GGA) is developed to minimize total travel time and total tardiness by implementing an encoding scheme where a gene represents orders grouped in a batch and the assignment of the batch to a picking vehicle. Computer simulations show that the proposed algorithm performs 25.4% better than a first come, first served (FCFS) rule-based heuristic and 10.2% better than an earliest due date (EDD) rule-based heuristic. The proposed GGA provides significant savings of up to 46.8% and 28.4% on travel time and tardiness, respectively, for these benchmark heuristics. Therefore, this article introduces a GGA to solve the JOBASP with a reasonable computing time, making this approach interesting for warehouse operators using heterogeneous picking vehicles and addressing multiple objectives.

关键词： Order picking order batching batch assignment sequencing grouping genetic algorithms multi-objective problem joint order picking

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A multi-objective migrating birds optimization algorithm based on game theory for dynamic flexible job shop scheduling problem

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期227卷

作者： Wei, Lixin He, Jinxian Guo, Zeyin Hu, Ziyu Yanshan Univ Engn Res Ctr Minist Educ Intelligent Control Syst & Intelligent Qinhuangdao 066000 Hebei Peoples R China Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066000 Hebei Peoples R China

The occurrence of dynamic events such as machine breakdown during workshop production can make the original scheduling scheme infeasible. Therefore, this paper establishes a mathematical model for a multi-objective dynamic flexible job shop scheduling problem with machine breakdown , proposes a multi -objective migrating birds optimization algorithm based on game theory. Firstly, in order to solve the problem of difficult to determine the weight in weighted multi-objective optimization, this paper introduces game theory to balance the Pareto optimality , fairness between the two objectives of production efficiency and stability. When solving the solution of the game model, there may be no perfect Nash equilibrium solution, so a solution method that approximates the Nash equilibrium solution is designed. In the improved migrating algorithm, neighborhood operators based on path relinking and machine age are designed to improve the search ability. Based on the attributes of multi-objective problems, a multiple similarity measure method is designed to select and replace solutions. The experiment part proves the effectiveness of the game strategy in multi-objective optimality and fairness, and concludes that the algorithm has good performance by comparing with the advanced algorithms in recent years.

关键词： Dynamic flexible job shop scheduling multi-objective problem Game theory Migrating birds optimization

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Robust Emergency Medical System Design as a multi-objective Goal Programming problem 10th

Robust Emergency Medical System Design as a Multi-objective ...

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10th International Conference on Operations Research and Enterprise Systems (ICORES)

作者： Kvet, Marek Univ Zilina Fac Management Sci & Informat Univ 8215-1 Zilina 01026 Slovakia

ISBN: (纸本)9789897584855

The main goal of this paper is to introduce and compare three different mathematical modelling approaches to robust emergency medical service system design. The idea of system robustness follows from the necessity of making the system resistant to various detrimental events, which may unexpectedly occur in the associated transportation network and thus negatively affect service. Such viewpoint is important mainly in different kinds of public service systems including rescue services, in which time necessary for service delivery plays a very important role. While the standard method of robust system designing takes into account only the worst possible situation considering the set of detrimental scenarios, suggested modeling approaches compute a separate objective function value for each scenario and then a special constraint is added to the original mathematical model. This way, an epsilon-constraint principle to the problem solution is applied. In this paper, numerical experiments to study the performance characteristics of suggested solving methods accompany the theoretical explanation of all presented models.

关键词： Location problems Emergency Medical Service Robustness multi-objective problem Radial Formulation

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Personalized Exercise Group Assembly Using a Two Archive Evolutionary Algorithm

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第3期9卷 2572-2583页

作者： Sun, Yifei Cao, Yifei Wang, Ziang Hou, Sicheng Gao, Weifeng Zhan, Zhi-hui Shaanxi Normal Univ Sch Phys & Informat Technol Xian 710119 Peoples R China Xidian Univ Sch Math & Stat Xian 710126 Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China

Traditional exercise recommendation algorithms generate exercise groups according to the features of exercises for all students. However, as different students may have different knowledge proficiencies, this article focuses on the personalized exercises group assembly (PEGA) to select exercises for each student based on their knowledge proficiencies, which is formulated as a constrained multi-objective problem. In order to solve the constrained multi-objective PEGA problem efficiently, this paper proposes a two archives evolutionary algorithm (TAEA) with three novel designs. Firstly, as the number of exercises is very large in PEGA, the traditional binary-number encoding method will result in high consumption in both memory and computation. To this end, a new integer-number encoding (INE) method is designed for solution representation. It saves memory for exercise subset representation, speeds up evaluations, and generates solutions that satisfy some constraints. Secondly, based on the INE method, the TAEA adopts two archives name convergence-oriented archive (CA) and diversity-oriented archive (DA). The CA ensures the convergence, driving force, and feasibility of the solutions. The DA aims to provide as diverse a solution as possible, including exploration of infeasible regions. Thirdly, a classification-based offspring co-reproduction strategy is proposed to solve the issue of too much infeasible space exploration. Experimental results show that our INE method can help to reduce the running time and improve the optimization results. The effectiveness of the TAEA is demonstrated by comparing it with some recent existing algorithms.

关键词： Encoding Sun Recommender systems Maintenance engineering Convergence Standards Smoothing methods Mathematical models Assembly Vectors Convergence archive diversity archive evolutionary computation exercise recommendation multi-objective problem personalized exercise group assembly (PEGA)

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Modified γ-operator technique for intuitionistic fuzzy multi-objective nonlinear programming problems with application in agriculture

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 273卷

作者： Kaur, Shubhpreet Mahajan, Sumati Chauhan, Abhishek Deemed Univ Punjab Engn Coll Dept Math Chandigarh 160012 India

In recent years, intuitionistic fuzzy theory has gained significant attention for its ability to handle uncertainty through both membership and non-membership degrees. This paper presents a novel modification to gamma-operator technique by introducing distinct gamma-operators for membership and non-membership functions to tackle intuitionistic fuzzy environment. The proposed technique is rigorously validated through the proof of relevant theorems that demonstrate its capability to derive efficient solutions for multi-objective nonlinear programming problems, where all parameters are represented as triangular intuitionistic fuzzy numbers. To elucidate the proposed technique, an illustrative example is presented. Furthermore, a comparative study with existing techniques is conducted, which highlights the superior performance of the proposed method. Finally, an application in the agriculture sector demonstrates the practical relevance and effectiveness of the proposed method in real-world scenarios.

关键词： multi-objective problem gamma-operator Nonlinear programming Triangular intuitionistic fuzzy number Agricultural planning

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multi-objective RIME algorithm-based techno economic analysis for security constraints load dispatch and power flow including uncertainties model of hybrid power systems

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ENERGY REPORTS 2024年 11卷 4423-4451页

作者： Pandya, Sundaram B. Kalita, Kanak Jangir, Pradeep Cep, Robert Migdady, Hazem Chohan, Jasgurpreet Singh Abualigah, Laith Mallik, Saurav Shri KJ Polytech Dept Elect Engn Bharuch 392001 India Vel Tech Rangarajan Dr Sagunthala R&D Inst Science Dept Mech Engn Avadi 600062 India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai 602105 India VSB Tech Univ Ostrava Fac Mech Engn 17 Listopadu 2172-15 Ostrava 70800 Poruba Czech Republic Oman Coll Management & Technol CSMIS Dept Barka 320 Oman Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 India Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 India Al al Bayt Univ Prince Hussein Bin Abdullah Fac Informat Technol Comp Sci Dept Mafraq 25113 Jordan Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ MEU Res Unit Amman Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Univ Sains Malaysia Sch Comp Sci Pulau 11800 Pinang Malaysia Sunway Univ Malaysia Sch Engn & Technol Petaling Jaya 27500 Malaysia Harvard TH Chan Sch Publ Hlth Dept Environm Hlth Boston MA 02115 USA

In recent times, the landscape of power systems has undergone significant evolution, particularly with the integration of diverse renewable energy sources (RESs). This advancement presents an invaluable opportunity to enhance energy efficiency in the modern power grid, primarily by bolstering the role of stochastic RESs. The challenge lies in the optimal power flow (OPF), a multifaceted and non-linear optimization challenge that grows more complex with the inclusion of stochastic RESs that aims to optimize the allocation of power system resources to minimize the operational cost while maintaining the stability and security of the system. Addressing this, the current study introduces an innovative optimization approach, the multi-objective RIME (MORIME) algorithm. Drawing inspiration from the physical phenomenon of rime-ice, called the RIME, the MORIME seeks to effectively tackle OPF issues. This algorithm enhances solution accuracy by smartly dividing with nondominated sorting and crowding distance mechanism. The proposed OPF model incorporates three types of RESs: solar photovoltaic, wind and small-scale hydropower units. While uncertainties in wind speed and solar irradiation are managed through Monte Carlo simulations, the small hydro unit is considered a constant power source. The efficacy of the MORIME algorithm is tested on IEEE 30 bus systems and results indicate that the MORIME method identifies the optimal solution for the multi-objective OPF problem while satisfying the power system constraints, thereby proving its effectiveness and superiority over MOWOA, MOGWO, MOALO, MOMRFO and MOAGDE in terms of Hyper Volume (HV) and reciprocal of Pareto Sets Proximity (1/PSP) metrices. The MORIME source code is available at: https://***/kanak02/MORIME

关键词： multi-objective problem Nature-inspired algorithm RIME, Pareto front Optimal power flow Metaheuristics Renewable energy resources

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multi-objective optimal distributed generators integration using firefly algorithm with Fuzzy decision making

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ELECTRICAL ENGINEERING 2024年第5期106卷 6113-6130页

作者： Anbuchandran, S. Kalarathi, M. Suresh, S. Bhuvanesh, A. JCT Coll Engn & Technol Dept Elect & Elect Engn Coimbatore Tamil Nadu India Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi Tamil Nadu India Sri Eshwar Coll Engn Dept Elect & Elect Engn Coimbatore Tamil Nadu India PSN Coll Engn & Technol Dept Elect & Elect Engn Tirunelveli Tamil Nadu India

Power systems are being confronted with a number of ecological and financial difficulties, and distributed generators (DGs) will be an effective solution. However, inappropriate DGs incorporation may cause additional power stability issues and system losses. DGs that are strategically positioned could solve these issues. This study implements the multi-objective fuzzified firefly algorithm (MOFFA) to integrate optimally sized DGs with six different objective functions (OBFs) on a standard IEEE 33 bus system as well as a functional 62 bus Indian distribution system (IDS) of Tamil Nadu power sector. Three distinct scenarios of multi-objective optimization problems are addressed. In Scenario 1, the objective is to minimize power loss, bus voltage deviation, and cost simultaneously. In Scenario 2, the focus is on minimizing power loss, enhancing voltage stability index (VSI), and reducing toxic gasses outflow. Lastly, in Scenario 3, the goal is to minimize bus voltage deviation, cost, and lower average voltage total harmonic distortion THDv. The integration of DGs in the IEEE-33 bus test system resulted in remarkable improvements through the optimization process. Specifically, the power loss is reduced by approximately 65.64%, the cost decreased by about 29.58%, and the toxic discharge was lowered by approximately 66.69%. In the IDS-62 bus test system, the optimization results in a noteworthy reduction of power loss to 5.1% of the total load. These substantial reductions highlight the significant progress made, showcasing the effectiveness of the optimization process and the integration of DGs in reducing energy wastage.

关键词： Fuzzified firefly algorithm multi-objective problem Optimization DG integration IEEE-33

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A multi-objective Grey Wolf-Cuckoo Search algorithm applied to spatial truss design optimization

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APPLIED SOFT COMPUTING 2024年 155卷

作者： Vo, Nam Tang, Huy Lee, Jaehong Sejong Univ Deep Learning Architecture Res Ctr 209 Neungdong Ro Seoul South Korea

A novel hybrid algorithm called multi -objective Hybrid Grey Wolf Cuckoo Search (MOGWOCS) is developed for spatial truss designs in this study. A new simple yet efficient mechanism to select the best candidates is proposed. Furthermore, harmonic averaging is employed to be a replacement for conventional arithmetic mean for higher effectiveness. Additionally, the Levy flight in Cuckoo Search (CS) is utilized to increase efficiency in early searching and also reduce local entrapment possibility. For verification purposes, MOGWOCS is first performed on some mathematical functions and 11 CEC2020 mechanical problems. It is then examined on four large-scale truss design problems, in two of which multi -objective optimization is studied for the first time. To demonstrate the superiority of the proposed approach, five up-to-date algorithms, and various indicators are included for validation. It is found that MOGWOCS is able to produce solutions with higher optimality in terms of diversity and accuracy.

关键词： Structural design multi-objective problem Non-dominated sorting Crowding distance Grey wolf optimizer Cuckoo search

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multi-objective Hole-Making Sequence Optimization by Genetic Algorithm Based on Q-Learning

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第6期8卷 3793-3806页

作者： Zhang, Desong Chen, Yanjie Zhu, Guangyu Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350116 Peoples R China

As one of the most fundamental operations in mechanical production, hole-making plays a crucial role. However, existing hole-making sequence optimization models are not suitable for workshops with variable production parameters. To address this issue, a new model, named multi-objective multi-tool hole-making sequence optimization with precedence constraints (MO-MTpcHSO), is proposed in this paper. The model has two objectives: spindle travel distance and tool switching time. To solve MO-MTpcHSO, a customized Q-learning based genetic algorithm (QLGA) is proposed. The adaptive encoding method allows chromosomes to express feasible solutions, the population is considered as the agent, and the states are intervals of the diversity coefficient. Different insertion methods in the crossover operator are set as actions, and the reward is related to the diversity and values of objective functions of the population. The effectiveness of QLGA is validated by comparing it with other algorithms in practical workpieces. Moreover, the reasonability of actions and the necessity of the Q-learning framework in QLGA are validated.

关键词： Q-learning genetic algorithm multi-objective problem hole-making sequence optimization

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An Emotional-based Methodology to Detect Preferences in a Decision-making Process Applied to a Virtual Service Robot

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2024年第4期110卷 1-20页

作者： Alvarado-Gonzalez, Montserrat Jaimes, Antonio Lopez Univ Autonoma Metropolitana Dept Appl Math & Syst Ave Vasco Quiroga 4871 Mexico City 05348 Mexico

In a multi-objective problem, no single solution optimally satisfies all objectives. Thus, the challenge is to find a balance between conflicting objectives. The decision-making necessarily requires human intervention. The person responsible for selecting the most appropriate solution among all the trade-off solutions is the decision maker (DM). The DM seeks to approach only the solutions that best suit her/his preferences. Since there is plenty of specialized literature showing that emotions play a critical role in decision-making, we aim to incorporate them into the decision-making process. To elicit emotions that can be quantifiable, we propose the Emotional Assessment Method. The method presents a simulation of the objectives to be optimized that represents the consequences of each decision. Using this methodology, the decision maker assesses the emotions evoked by each presented simulation to guide his/her search for solutions that satisfy his/her preferences. As a case study, we aim to identify subjects' preferences towards robot behaviors. Seventy-two subjects with varying levels of familiarity with robots (divided into two datasets) participated in the experiments. We concluded that the method elicits subjects' emotions while observing the consequences of the robot's performance. Also, we found out that it is possible to identify subjects' preferences based on both the context and the emotions to select the robot's behavior.

关键词： Emotions towards robots Affective computing Service robots multi-objective problem

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