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meta-heuristic algorithms for integrating manufacturing and supply chain functions

COMPUTERS & INDUSTRIAL ENGINEERING 2024年 192卷

作者： Canpolat, Onur Demir, Halil Ibrahim Erden, Caner Firat Univ Fac Engn Dept Software Engn Elazig Turkiye Sakarya Univ Fac Engn Dept Ind Engn Serdivan Sakarya Turkiye Sakarya Univ Appl Sci Fac Technol Dept Comp Engn Serdivan Sakarya Turkiye Sakarya Univ Appl Sci AI Res & Applicat Ctr Serdivan Sakarya Turkiye

This study proposes an integrated framework that incorporates essential manufacturing and supply chain functions. These functions encompass process planning, scheduling, due -date assignment, and delivery optimization. The objective of this integrated approach is to achieve multiple benefits, including balanced workload distribution, enhanced company performance, generation of more realistic planning schedules, and ultimately, the achievement of shorter due dates. As a result, the overall efficiency of operations is substantially improved, with approximately a 50 % increase over isolated function management. Additionally, the isolated integration of the delivery function within systems comprising three integrated functions has been found to improve efficiency by 18%. The study employs various heuristic techniques, including genetic algorithms, simulated annealing, random search, hybrid search, and evolutionary strategy, to assess the optimal solution method and rules for these functions. The Taguchi technique is employed to ascertain the optimal values for critical parameters, such as population size, mutation rate, crossover points, and random search rate. Among the solution methods investigated, genetic algorithms consistently yielded superior results Additionally, the weighted slack rule consistently exhibited notable effectiveness compared to other due -date assignment rules. Similarly, the savings algorithm outperformed other delivery optimization rules. However, it is important to note that among the scheduling rules evaluated, none has emerged as dominant.

关键词： Integrating manufacturing and supply chain functions Process planning Scheduling Due date assignment Delivery meta -heuristic algorithms

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Fuzzy logic-based energy management for isolated microgrid using meta-heuristic optimization algorithms

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APPLIED ENERGY 2023年 335卷

作者： Rodriguez, Mauricio Arcos-Aviles, Diego Martinez, Wilmar Univ Fuerzas Armadas ESPE Ctr Posgrad Maestria Invest Elect Mencio Automat 171-5-231B Sangolqui Sangolqui Ecuador Univ Fuerzas Armadas ESPE Dept Elect Grp Invest Propagac Control Elect & Networking PR 171-5-231B Sangolqui Sangolqui Ecuador Katholieke Univ Leuven Dept Elect Engn ESAT Agoralaan Ggebouw B Bus 8 B-3590 Diepenbeek Belgium EnergyVille Thor Pk 8310 B-3600 Genk Belgium

Rural areas have increased directly with the growth of the world population. Although, isolated rural areas do not have the energy infrastructure to supply essential services such as electricity. In Ecuador, as in the rest of the world, these areas of the rural population usually present access problems due to the topography of the place. This makes the construction of power lines to connect them to the Interconnected National Energy System unfeasible. In this regard, isolated microgrids have emerged as a great solution to cover the energy demands in these locations. However, an optimal implementation of isolated microgrids depends on several factors, such as geographical location, weather conditions, sizing, load demand, operating costs, and social impacts. Therefore, this study proposes the design of a new energy management system (EMS) for isolated microgrids comprising a photovoltaic system, diesel generator, and battery energy storage system (ESS). Since fuzzy logic control (FLC) has proven to be a powerful tool for dealing with the nonlinearities of a microgrid and the application of fuzzybased EMS for isolated microgrids is rarely reported in the literature, this study proposes the application of an FLC for the EMS's design of an isolated microgrid. The proposed fuzzy-based EMS uses generation and demand forecasts, enhances the operating time of diesel generators (DLG), and takes advantage of the available solar resource to supply the energy required by a community while preserving the ESS lifetime. An adjustment of the FLC parameters by Particle Swarm Optimization (PSO) and Cuckoo Search (CS) algorithms is performed to improve the behavior of the proposed EMS. Furthermore, a battery degradation model is applied to estimate the ESS State of Health (SOH). To highlight the advantages of the proposed approach, a case study in a specific community in Ecuador is presented. In this location, the proposed EMS is compared with an EMS without parameter adjustment deve

关键词： Isolated microgrid Energy management system Fuzzy control Renewable energy meta -heuristic algorithms

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Techno-economic analysis and optimal sizing of a battery-based and hydrogen-based standalone photovoltaic/wind hybrid system for rural electrification in Cameroon based on meta-heuristic techniques

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ENERGY CONVERSION AND MANAGEMENT 2023年 280卷

作者： Ngouleu, Clint Ameri Wankouo Kohole, Yemeli Wenceslas Fohagui, Fodoup Cyrille Vincelas Tchuen, Ghislain Univ Dschang Dept Energet Environm & Thermal Engn IUT FV Bandjoun UR ISIE POB 134 Bandjoun Cameroon Univ Dschang Fac Sci Dept Phys UR 2MSP POB 69 Dschang Cameroon Univ Bamenda Coll Technol Dept Elect & Elect Engn POB 39 Bambili Cameroon

In developing nations, particularly in African countries, where difficulties with rural electrification are highly severe, hybrid power systems appeared as a suitable method of distributing power to rural and off-grid locations. Cameroon, like other African countries, is facing a serious challenge of access to basic energy services for its population. The country's constraints present a global situation of less than 48% of the population having access to electrical energy. This situation is more frequent in rural areas. In this paper, a comparative assessment of meta-heuristics optimization techniques is conducted for optimal design of photovoltaic/wind/fuel cell and photovoltaic/wind/battery hybrid systems to constantly supply-three typical loads demand in Kousseri, Cameroun. For this purpose, four well-known meta-heuristics techniques namely artificial bee colony algorithm, cuckoo search algorithm, imperialist competitive algorithm and big bang-big crunch algorithm are implemented to solve the optimization problem, consisting of minimizing the net present cost while increasing the system reliability characterized by loss of power supply probability. Four others configurations namely photovoltaic/ battery, wind/battery, photovoltaic/fuel cell and wind/fuel cell are also evaluated and the outcomes are compared with the results of the photovoltaic/wind/fuel cell and photovoltaic/wind/battery systems with regards to the life-cycle cost and cost of energy. To determine the most affordable configurations, simulations and optimizations studies are conducted. Our findings revealed that, the cuckoo search algorithm performs better than other algorithms. The results also show that, photovoltaic/wind/battery standalone hybrid system is the more reliable and cost-effective configuration for satisfying the three typical electrical loads demand encoun-tered at Kousseri, Cameroon. The obtained optimal number of component and cost of energy of the photovoltaic/ wind/battery hybri

关键词： Hybrid systems meta -heuristic algorithms Optimal sizing Photovoltaic wind battery fuel cell Rural electrification

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Optimization based on performance of lungs in body: Lungs performance-based optimization (LPO)

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2024年 419卷

作者： Ghasemi, Mojtaba Zare, Mohsen Zahedi, Amir Trojovsky, Pavel Abualigah, Laith Trojovska, Eva Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Jahrom Univ Fac Engn Dept Elect Engn Jahrom Fras Iran Tarbiat Modares Univ Dept Elect & Comp Engn Tehran Iran Univ Hradec Kralove Fac Sci Dept Math Hradec Kralove 50003 Czech Republic Al al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Lebanese Amer Univ Dept Elect & Comp Engn Byblos 135053 Lebanon Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ MEU Res Unit Amman 11831 Jordan

The development of efficient optimization algorithms is crucial across various scientific disci-plines. As the complexity and diversity of optimization problems continue to grow, researchers seek faster and stronger algorithms capable of optimizing a wide range of functions. This paper introduces Lung performance-based optimization (LPO), a novel and efficient algorithm inspired by the regular and intelligent performance of lungs in the human body. LPO draws inspiration from the intricate mechanisms and adaptability of the respiratory system. The lungs exhibit remarkable efficiency in oxygen exchange, demonstrating a high level of optimization in their function. The forced oscillation technique measures air pressure and airflow rate to evaluate the respiratory system as an electrical impedance. The impedance curves have two distinct compo-nents, respiratory resistance (ZR) and respiratory reactance (ZX), which can be analyzed clinically and from an engineering perspective to gain insights into the respiratory system's workings. LPO aims to provide an innovative approach to solving complex optimization problems by emulating and harnessing this natural efficiency. To evaluate the effectiveness of LPO, experiments were conducted using the unconstrained optimization functions CEC2005 and CEC2014, as well as engineering design optimization problems. These problems were compared against numerous contemporary algorithms proposed in the literature. The results demonstrate that LPO excels in handling these optimization problems and exhibits the potential to tackle a wide range of modern optimization challenges. The findings highlight the power and effectiveness of LPO as a new optimization algorithm. With its inspiration rooted in the sophisticated performance of the lungs, LPO offers a unique perspective in the optimization landscape. Its ability to handle diverse optimization problems and its potential for application in various domains make LPO a promising algorithm for f

关键词： Global optimization Engineering design meta -heuristic algorithms Lung performance -based optimization (LPO)

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Designing a two-stage model for a sustainable closed-loop electric vehicle battery supply chain network: A scenario-based stochastic programming approach

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 190卷

作者： Saeedi, Mehran Parhazeh, Sina Tavakkoli-Moghaddam, Reza Khalili-Fard, Alireza Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Istinye Univ Res Ctr Performance & Prod Anal Istanbul Turkiye

Transportation is a fundamental requirement of modern life. Vehicles powered by fossil fuels are highly polluting. This study develops a two-stage stochastic programming model to establish a sustainable closed-loop supply chain for Electric Vehicle (EV) batteries. The model considers economic, environmental, and social criteria, including cost, energy consumption, carbon emissions, and job creation. The epsilon-constraint method and three multi-objective meta-heuristic algorithms are utilized to solve problems. Implementing this model in a case study of an EV battery supply chain aids managerial decision-making for optimal center establishment, flow determination, and inventory setting. Finally, essential parameters are analyzed, and several important managerial insights are prepared. The results suggest that investing in used battery collection significantly reduces costs and carbon emissions.

关键词： Sustainable closed -loop supply chain Electric vehicle battery Multi -objective optimization meta -heuristic algorithms Two -stage stochastic programming

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An integrated temporal and spatial synchronization for two-echelon vehicle routing problem in waste collection system

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JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION 2024年 40卷

作者： Rahmanifar, Golman Mohammadi, Mostafa Hajiaghaei-Keshteli, Mostafa Fusco, Gaetano Colombaroni, Chiara Sapienza Univ Rome Dept Civil Construct & Environm Engn Via Eudossiana 18 I-00184 Rome Italy Tecnol Monterrey Escuela Ingn & Ciencias Puebla Mexico

This paper presents a two -echelon vehicle routing problem (2E-VRP) with vehicle synchronization at meeting points for the reverse logistic network to collect waste in the urban area. Low -capacity vehicles are utilized to perform collection only in the inner part of the city because of restricted access and limited infrastructure to be expanded. While, high -capacity vehicles are used to transform waste from the border of the inner city to the recycling centers located outside of city. Vehicles of the first and second echelons visit each other at meeting points, which do not have storage capacity mainly because of environmental and social consequences of the transshipment of waste. The timing aspect plays a critical role at meeting points to ensure the reasonable sequence of operations. So, temporal synchronization, a critical aspect of 2E-VRP, is considered in this paper, as well as minimization of waiting time of vehicles in meeting points to avoid long dwelling time. Moreover, because the previous study of 2E-VRP has not dealt with space limitation of meeting points, this paper presents a 2E-VRP that incorporates temporal synchronization, waiting time, and temporal capacity simultaneously for the first time to address a more practical model. To solve the mentioned problem a set of capable and latest metaheuristics are proposed as well as the hybrid ones to probe their efficiency and reasonably construct high quality routes. Moreover, the impact of the temporal capacity is investigated compared to temporal synchronization without considering temporal capacity.

关键词： Two -echelon vehicle routing problem Temporal synchronization Temporal Capacity meta -heuristic algorithms

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A sustainable-circular citrus closed-loop supply chain configuration: Pareto-based algorithms

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023年 328卷 116892页

作者： Goodarzian, Fariba Ghasemi, Peiman Gonzalez, Ernesto DR. Santibanez Tirkolaee, Erfan Babaee Univ Seville Sch Engn Org Engn Grp Camino Descubrimientos S-N Seville 41092 Spain Univ Vienna Dept Business Decis & Analyt Kolingasse 14-16 A-1090 Vienna Austria Univ Talca Fac Ingn Talca1 Poniente1141 Talca Chile Istinye Univ Dept Ind Engn TR-34396 Istanbul Turkiye

Configuration of sustainable supply chains for agricultural products has been a well-known research field recently which is continuing to evolve and grow. It is a complex network design problem, and despite the abundant literature in the field, there are still few models offered to integrate social impacts and environmental effects to support network design decision-making to support the configuration of the citrus supply chain. In this work, the citrus supply chain design problem is investigated by integrating the production, distribution, in-ventory control, recycling and locational decisions in which the triple bottom lines of sustainability, as well as circularity strategy, are addressed. Accordingly, a novel multi-objective Mixed-Integer Linear Programming (MILP) model is proposed to formulate a multi-period multi-echelon problem to design the sustainable citrus Closed-Loop Supply Chain (CLSC) network. To solve the developed model, the epsilon-constraint approach is employed in small-sized problems. Furthermore, Strength Pareto Evolutionary Algorithm II (SPEA-II) and Pareto Envelope -based Selection Algorithm II (PESA-II) algorithms are used in medium-and large-sized problems. Taguchi design technique is then utilized to adjust the parameters of the algorithms efficiently. Three well-known assessment metrics and convergence analysis are regarded to test the efficiency of the suggested algorithms. The numerical results demonstrate that the SPEA-II algorithm has a superior efficiency over PESA-II. Moreover, to validate the applicability of the developed methodology, a real case study in Mazandaran/Iran is investigated with the help of a set of sensitivity analyses.

关键词： Citrus supply chain Closed -loop supply chain network Sustainability Circularity Mixed -integer linear programming meta -heuristic algorithms

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Short-term forecasting of streamflow by integrating machine learning methods combined with metaheuristic algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 245卷

作者： Jia, Faxian Zhu, Zijiang Dai, Weihuang Le, Van Vang Henan Univ Urban Construct Sch Management Pingdingshan 467000 Henan Peoples R China Guangdong Univ Foreign Studies South China Business Coll Sch Comp Sci Guangzhou 510545 Guangdong Peoples R China Guangdong Univ Foreign Studies Inst Intelligent Informat Proc South China Business Coll Guangzhou 510545 Guangdong Peoples R China Ho Chi Minh City Univ Transport Artificial Intelligent Transportat Ho Chi Minh City 700000 Vietnam

This paper aims to introduce an artificial intelligence approach to enhance short-term daily streamflow forecasts through the integration of machine learning (ML) techniques with meta-heuristic (MH) algorithms. To maximize performance, a comprehensive analysis involving the autocorrelation and partial autocorrelation functions of the observed streamflow was conducted to derive ten distinct input combinations, from which the most suitable set was selected for investigation. Subsequently, six distinct ML methods, namely, decision trees, support vector regression, long short-term memory, gated recurrent unit, multi-layer perceptron, and radial basis function (as the optimal input combination), were leveraged to predict one-day-ahead streamflow. The most promising outcomes from these six explored methods were synergistically amalgamated to enhance predictive precision. This amalgamation was then optimized by utilizing parameter values hyper-tuned by four different MH algorithms. The algorithms included the shuffled frog leaping algorithm, particle swarm optimization, ant colony optimization (ACO), and gray wolf optimization (GWO). In culmination, a case study centered around the "Wick" river in the UK was conducted to rigorously evaluate the performance of the proposed method, utilizing a range of statistical evaluation indices. The results underscored the efficacy of the proposed combined method in substantially refining streamflow predictions, exhibiting an impressive level of accuracy. Additionally, the outcomes emphasized that ACO and GWO stand out as the optimal algorithms for calibrating the parameters of the amalgamated model. For instance, the R2 values attained by the combined models optimized through the ACO and GWO algorithms were 0.69422 and 0.69444, respectively. These values notably surpass those observed in the initial models, substantiating the superiority of the applied optimization approaches.

关键词： Short-term streamflow forecast Machine learning models meta -heuristic algorithms

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Predicting the energy consumption in buildings using the optimized support vector regression model

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ENERGY 2023年 273卷

作者： Cai, Wei Wen, Xiaodong Li, Chaoen Shao, Jingjing Xu, Jianguo Ningbo Univ Technol Sch Civil & Transportat Engn Ningbo 315211 Zhejiang Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Shandong Peoples R China

One of the most significant axes of regional, national, and worldwide energy policy is energy efficiency in building design. In particular, the energy efficiency of HVAC systems is of paramount importance. They provide energy for both residential and commercial sectors. As a result, assessing building energy usage is a critical step in optimizing building energy consumption. The impact of eight input factors on the two output variables, heating and cooling loads, for residential structures was explored in this study. For this purpose, the SVR-supervised machine learning algorithm was used. Despite its advantages, such as predictive accuracy and robustness, this method suffers from the fact that there is no specific rule for fitting its parameters. Therefore, six meta-heuristic optimization algorithms were investigated, and the strongest algorithm for optimal parameter fitting for the SVR model was presented. The correlation and error parameters analysis showed that the hybrid model SVR-AEO has the best performance in simulating residential buildings' heating and cooling loads. Ac-cording to the obtained results, the value of R2 for the cooling and heating loads prediction in the training data is obtained to be 0.9975 and 0.99955, respectively.

关键词： Support vector machine Support vector regression meta -heuristic algorithms Building energy prediction Heating load Cooling load

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Optimization model for designing personalized tourism packages

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年 175卷

作者： Piya, Sujan Triki, Chefi Al Maimani, Abdulwahab Mokhtarzadeh, Mahdi Univ Sharjah Dept Ind Engn & Engn Management Sharjah U Arab Emirates Sultan Qaboos Univ Dept Mech & Ind Engn Seeb Oman Univ Kent Kent Business Sch Kent England Univ Salento Dept Engn Innovat Salento Italy Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

The tourism supply chain aims at satisfying the needs of the tourists based on their preferences. However, the preference of each tourist may be different. Some tourists prefer to optimize a single criterion, while others prefer to optimize conflicting multiple-criteria. The tourism service provider can hardly offer the tourists with the itinerary according to their precise preferences. This paper proposes a multi-objective optimization framework based on which tourists can generate itineraries according to their preferences. A mathematical model is pre-sented, which is multi-objective and NP-hard. Consequently, four meta-heuristic algorithms, namely none -dominated sorting genetic algorithm versions II (NSGA-II) and III (NSGA-III), multi objective grey wolf opti-mization, and multi objective imperialist competitive algorithm are developed. The proposed method helps the tourists to compare different combinations of activities and select the one that best suits their preferences. The model is tested on a small-scale real case pertaining to the Sultanat of Oman. Thereafter, the performances of the proposed algorithms were evaluated on large scale problems. The result shows that NSGAs outperformed other algorithms. NSGA-II outperformed its NSGA-III counterpart in small instances. Surprisingly, as the size of the problem increases, the efficiency of NSGA-II decreases while that of NSGA-III increases. In large instances, NSGA-III outperformed NSGA-II.

关键词： Tourism industry Tourist preferences Multi -objective optimization meta -heuristic algorithms

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