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multi-mode resource-constrained project scheduling problem along with material ordering under time-of-use electricity tariffs and carbon taxes

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ANNALS OF OPERATIONS RESEARCH 2024年 1-45页

作者： Pouramin, Maryam Mirzazadeh, Abolfazl Davari-Ardakani, Hamed Mosadegh, Hadi Alajegerdi, Edris Kharazmi Univ Fac Engn Dept Ind Engn Tehran Iran Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran Univ Neyshabur Fac Sci Dept Phys Neyshabur Iran

The research highlights the potential of integrating energy management and the multi-Mode Resource-Constrained Project Scheduling Problem, which includes material ordering. It addresses the pressing concerns of escalating electricity consumption and greenhouse gas emissions by considering renewable energy resources, time-of-use (TOU) electricity tariffs, and carbon taxes. In this regard, a mixed integer linear programming model is developed. To solve the small-sized problem instances, epsilon-constraint method is used. However, since the problem under consideration is NP-hard and exact methods fail to provide solutions for large-sized instances within a reasonable timeframe, two multi-objective meta-heuristic algorithms, Non-dominated Sorting Genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO), are used as solution methods. Comparing results obtained by NSGA-II and MOPSO demonstrates the superiority of NSGA-II in the majority of performance metrics, regardless of the problem size. Through comprehensive sensitivity analysis of key model parameters, including TOU electricity tariffs and carbon tax rates, the research determines the optimal project duration and cost. Sensitivity analyses offer insights for sustainable project planning, highlighting the role of renewables in electricity consumption. Minimizing carbon taxes and proactive resource management underscore environmental responsibility, while TOU tariffs enhance sustainable energy practices. Overall, strategically integrating renewables in certain scenarios optimizes project execution, aligning with sustainability objectives for efficient and successful outcomes.

关键词： multi-mode resource constrained project scheduling problem Material ordering Electricity cost Carbon dioxide emission Non-dominated sorting genetic algorithm II multi-objective particle swarm optimization algorithm

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Presenting an agile supply chain mathematical model for COVID-19 (Corona) drugs using metaheuristic algorithms (case study: pharmaceutical industry)

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第3期30卷 6559-6572页

作者： Shayannia, Seyed Ahmad Islamic Azad Univ Dept Ind Management Firoozkooh Branch Firoozkooh Iran

This article focuses on the strategic issue of an agile supply chain. Different goals are considered in supply chain design. Today, the concept of sustainability in supply chains plays an important role in goal setting. In general, sustainability leads to less economic, social, and environmental risks, and attention to this type of sustainability covers many criteria in the literature. Therefore, in this study, optimization of the stability of a supply chain network has been considered. In addition, a lesser-known aspect of sustainability, the political aspect, has been added to the previous aspects. This paper assumes four levels, namely supplier, wholesaler, retailer, and customer. An optimal network structure is designed to consider economic, social, and environmental sustainability and the new measure of political sustainability. The design of this supply chain is done by defining a mathematical model that has several objective functions, including economic, political, and environmental goals. These objective functions select the relationships and links between the levels in a supply chain network. For this purpose, a directional graph corresponding to a supply chain is designed. Then, with the help of an ideal multi-objective planning problem, an optimal configuration is obtained, which is a subgraph of the main network graph. The target problem has been modeled for a holding company which is a supply chain for COVID-19 drugs. Then, problem is solved using multi-objective particle swarm optimization (MOPSO) metaheuristics algorithm and NSGAII multi-objective genetic algorithm. A comparison was carried out between these two methods, and the results indicated that MOPSO performed better. The value of the objective functions is calculated, and the best value for the considered functions is obtained. Also, the amount of drug supply has been obtained by two distributors. Also, the amount of medicine purchased by the customer from each pharmacy is determined.

关键词： Agile supply chain multi-objective particle swarm optimization algorithm COVID-19 Political goals Environment

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multi-Agent Consistent Formation Control Operation optimization for High-Speed Trains

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IEEE ACCESS 2023年 11卷 139201-139210页

作者： Pei, Huiqin Lan, Zhuyuan East China Jiaotong Univ Sch Elect & Automat Engn Nanchang 330013 Peoples R China

High-speed trains delay may happen due to the complicated and erratic operating circumstances of railway lines. In this paper, the problems of delayed recovery in high-speed train operations and the rapid acquisition of optimal trajectory for train offline operations are investigated. Firstly, taking on-time performance and energy saving as optimization objectives, an information communication topology model of train group is established. Then, a variable-parameter multi-objective particle swarm optimization algorithm is proposed to determine the best trajectory for train offline operation. The algorithm prevents decision variables from falling into a local optimum and effectively increases the speed of the overall search for an optimum. Secondly, consider the delay phenomenon in the dynamic process of online train operation, the idea of multi-agent consistent formation control is introduced, and an online train operation controller is designed to provide feedback regulation of the speed and position of trains deviating from the offline operation trajectory. The stability of train operation control system is demonstrated by Lyapunov's method. The controller can improve the immunity of disturbance and delay recovery for train operation. Finally, using actual data from the Wuhan to Changsha South line, the validity of the proposed theoretical results is further verified.

关键词： High-speed trains multi-objective particle swarm optimization algorithm consistent formation control operation optimization

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Research on distribution network optimal operation with a novel medium voltage photovoltaic power generation device with the soft open point function

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ENERGY REPORTS 2023年 9卷 1751-1763页

作者： Yu, Wenshan Li, Ming Zhang, Zhisheng You, Rui Qingdao Univ Coll Elect Engn 308 Ningxia Rd Qingdao 266071 Peoples R China Ocean Univ China Coll Engn 238 Songling Rd Qingdao 266100 Peoples R China

Soft open point (SOP) realizes the normal and flexible interconnection between distribution network feeders, leading to the improvement of the flexibility and rapidity of the distribution network control. However, drawbacks such as high cost and low utilization rate limit its application. A novel medium voltage photovoltaic power generation device with the SOP function is proposed in this paper. There are two grid-connected interfaces, and both of them adopt the cascaded H-bridge topology. A common DC-link is used for both H-bridge inverters belonging to the two grid-connected interfaces respectively. The proposed device eliminates the step-up transformers, and can be connected directly to the medium voltage distribution network. Therefore, advantages such as simple structure, low cost, low power loss and fault-tolerant operation capability are able to be obtained. The stability and flexibility of the distribution network operation are improved. The topology and power flow mode of the proposed medium voltage photovoltaic power generation device with the SOP function are analyzed, and the distribution network multi-objective optimal operation model with the proposed device is established. The proposed medium voltage photovoltaic power generation device with the SOP function is connected to the modified IEEE 33-node distribution network, and the network optimal operation with the device in terms of the power flow optimization and voltage improvement is analyzed.(c) 2023 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： Soft open point Photovoltaic power generation Cascaded H-bridge inverter multi-objective particle swarm optimization algorithm

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Design optimization of Permanent Magnet Eddy Current Coupler Based on an Intelligence algorithm

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Computers, Materials & Continua 2023年第11期77卷 1535-1555页

作者： Dazhi Wang Pengyi Pan Bowen Niu College of Information Science and Engineering Northeastern UniversityShenyang110819China

The permanent magnet eddy current coupler(PMEC)solves the problem of flexible connection and speed regulation between the motor and the load and is widely used in electrical transmission *** provides torque to the load and generates heat and losses,reducing its energy transfer *** issue has become an obstacle for PMEC to develop toward a higher *** paper aims to improve the overall performance of PMEC through multi-objective optimization ***,a PMEC modeling method based on the Levenberg-Marquardt back propagation(LMBP)neural network is proposed,aiming at the characteristics of the complex input-output relationship and the strong nonlinearity of ***,a novel competition mechanism-based multi-objective particle swarm optimization algorithm(NCMOPSO)is proposed to find the optimal structural parameters of *** search and mutation strategies are used to improve the original algorithm,which improves the shortcomings of multi-objective particle swarm optimization(MOPSO),which is too fast to converge into a global optimum,and balances the convergence and diversity of the *** order to verify the superiority and applicability of the proposed algorithm,it is compared with several popular multi-objective optimization *** them to the optimization model of PMEC,the results show that the proposed algorithm has better comprehensive ***,a finite element simulation model is established using the optimal structural parameters obtained by the proposed algorithm to verify the optimization *** with the prototype,the optimized PMEC has reduced eddy current losses by 1.7812 kW,increased output torque by 658.5 N·m,and decreased costs by 13%,improving energy transfer efficiency.

关键词： Competition mechanism Levenberg-Marquardt back propagation neural network multi-objective particle swarm optimization algorithm permanent magnet eddy current coupler

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Milling Parameters optimization of Al-Li Alloy Thin-Wall Workpieces Using Response Surface Methodology and particle swarm optimization

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Computer Modeling in Engineering & Sciences 2020年第9期124卷 937-952页

作者： Haitao Yue Chenguang Guo Qiang Li Lijuan Zhao Guangbo Hao School of Mechanical Engineering Liaoning Technical UniversityFuxin12300China School of Engineering University College CorkCorkT12 K8AFIreland

To improve the milling surface quality of the Al-Li alloy thin-wall workpieces and reduce the cutting energy *** research on the milling processing of AA2195 Al-Li alloy thin-wall workpieces based on Response Surface Methodology was carried *** single factor and interaction of milling parameters on surface roughness and specific cutting energy were analyzed,and the multi-objective optimization model was *** multiobjective particle swarm optimization algorithm introducing the Chaos Local Search algorithm and the adaptive inertial weight was applied to determine the optimal combination of milling *** was observed that surface roughness was mainly influenced by feed per tooth,and specific cutting energy was negatively correlated with feed per tooth,radial cutting depth and axial cutting depth,while cutting speed has a non-significant influence on specific cutting *** optimal combination of milling parameters with different priorities was *** experimental results showed that the maximum relative error of measured and predicted values was 8.05%,and the model had high reliability,which ensured the low surface roughness and cutting energy *** was of great guiding significance for the success of Al-Li alloy thin-wall milling with a high precision and energy efficiency.

关键词： Al-Li alloy thin-wall workpieces response surface methodology surface roughness specific cutting energy multi-objective particle swarm optimization algorithm

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Research on Fractional Order Fuzzy PID Control of the Pneumatic-hydraulic Upper Limb Rehabilitation Training System Based on PSO

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2022年第1期20卷 310-320页

作者： Wang, Hongyan Lu, Jinbo Univ Elect Sci & Technol China Sch Mech & Elect Engn Qingshuihe Campus 2006Xiyuan Ave Chengdu 611731 Peoples R China Southwest Petr Univ Sch Elect & Informat Engn Xindu Rd 8 Chengdu 610500 Peoples R China

In this paper, a new type of pneumatic-hydraulic upper limb rehabilitation training system is designed, and the system modeling and control method are studied. The 2DOF fuzzy fractional PID controller is proposed, which provides a new reference for improving the control accuracy of two degrees of freedom pneumatic system. Due to the increase of the regulating parameters of the fractional order controller, a multi-objective particle swarm optimization algorithm is designed to determine the parameters of the control system and optimize the determination process of the parameters. In order to verify the effectiveness of the control method, an experimental study was carried out by building a pneumatic-hydraulic upper limb rehabilitation training system platform. Compared with PID controller, the overshoot of the system controlled by fractional order fuzzy PID controller is reduced by 17.3%. The peak time is shortened about 11.7%, and the delay time of sine tracking signal is reduced about 10.6%. The results show that the control performance of two degrees of freedom fractional order fuzzy PID controller is better than other controllers.

关键词： Fractional order control fuzzy PID control multi-objective particle swarm optimization algorithm pneumatic-hydraulic upper limb rehabilitation training system

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Automatic Pricing and Replenishment Decision Based on WSO-BiLSTM and Nonlinear Programming 3

Automatic Pricing and Replenishment Decision Based on WSO-Bi...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Huang, Xiyi Yang, Bo Zhu, Mengyuan Nanjing Audit University Nanjing China

ISBN: (纸本)9798350380989

Supermarkets maximize their revenues by formulating appropriate replenishment and pricing strategies to satisfy both supply-side and demand-side conditions. This paper focuses on the restocking and pricing decision-making problem of vegetable commodities, which is solved based on objective planning analysis using simulated annealing algorithm and multi-objective particle swarm optimization algorithm. First of all, a wavelet analysis model is established, the wavelet variance diagram and quarterly trend diagram are drawn, and Spearman correlation analysis is used to solve the correlation between the sales volume of different categories or single products. Then, the fitting effects of multiple fitting models are compared, the number of independent variables is changed, and the fitting model with the best fit is selected. According to the cyclical characteristics of the total sales volume, the seasonal ARIMA model is selected to predict the total sales volume of each category in the next 7 days, and the predicted value is used as the upper limit of the total sales volume constraint for the subsequent target planning, taking into account the influence of other factors in real life. Finally, taking the total sales volume and cost-plus pricing as independent variables, while considering the influence of the attrition rate and the total number of saleable individual items limit. The objective function of revenue is established, and the order quantity of each individual item needs to satisfy the minimum display quantity as a constraint. The multivariate planning model is established, and the multi-objective particle swarm optimization algorithm is used for model solving. © 2024 IEEE.

关键词： goal planning multi-objective particle swarm optimization algorithm multivariate planning model simulated annealing algorithm wavelet analysis

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multi-objective optimization DESIGN OF CONDENSER IN MARINE NUCLEAR POWER SECONDARY LOOP SYSTEM

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HEAT TRANSFER RESEARCH 2022年第17期53卷 75-95页

作者： Zhao, Jiarui Li, Yanjun Shi, Changwei Zhang, Guolei Shi, Jianxin Harbin Engn Univ Coll Power & Energy Engn Harbin 150001 Peoples R China

Condenser is the key equipment in the nuclear power secondary loop system. Its structure size and performance parameters are important factors affecting the thermal efficiency and reasonable arrangement of nuclear power plant. According to the structure and heat transfer process of marine nuclear power condenser, the mathematical model of condenser is established in this paper, including thermodynamic calculation, structure design, strength calculation, resistance calculation, weight calculation, volume calculation, and thermal performance evaluation. Based on sensitivity analysis, the influence trend and degree of thermal and structural parameters on weight, volume, and entropy generation of condenser are discussed. The optimal design of condenser, aiming at the weight, volume, and entropy generation minimization while satisfying the structure and performance constraints, has been carried out with the multi-objective particle swarm optimization algorithm, the Pareto optimal solution set and Pareto front are obtained. The optimization results show that compared with the original design scheme, the condenser weight can be optimized up to 9.566%, the volume can be optimized up to 23.779%, and the entropy generation can be optimized up to 3.666%. The optimization results also prove the feasibility of the optimization design method from a theoretical point of view, which can provide reference for the practical engineering design and operation of marine nuclear power condenser.

关键词： secondary loop system condenser multi-objective particle swarm optimization algorithm multi-objective optimization

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Surgical rescheduling problem with emergency patients considering participants' dissatisfaction

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SOFT COMPUTING 2021年第16期25卷 10749-10769页

作者： Wang, Jian-Jun Miao, Hongru Xu, Ran Dalian Univ Technol Sch Econ & Management Dalian 116024 Peoples R China Georgia State Univ Robinson Coll Business Atlanta GA 30303 USA

Surgical rescheduling is necessary for adjusting initial schedules on surgery day after emergency demand is realized. While people-oriented medical service has been emphasized these years, the traditional rescheduling scheme which is only in pursuit of a great cost-related performance is no longer desirable since patients and medical staff are also highly involved in rescheduling and their preferences should not be ignored. In order to provide a satisfactory and people-centered rescheduling plan, this study considers the preferences of three involved participants (i.e. the operating room manager, medical staff, and elective patients) while designing a rescheduling plan. Based on prospect theory, we introduce three functions to evaluate three participants' dissatisfaction about rescheduling schemes in terms of their respective preferences. Then a multi-objective rescheduling model is established with multiple resource constraints, emergency lead-time target constraints, and the objective of minimizing the dissatisfaction of three participants caused by rescheduling. A hybrid particle swarm optimization (HPSO) algorithm with two improved strategies-an initial population construction strategy and a local search strategy, is then developed to solve the proposed problem. Several numerical experiments are carried out by leveraging data reported in existing case studies in conjunction with simulated data. The results demonstrate the effectiveness of two improved strategies and show that the proposed HPSO algorithm can provide better Pareto solutions for our problem in comparison with the basic non-dominated sorting genetic algorithm.

关键词： Surgical rescheduling Dissatisfaction function multi-objective model multi-objective particle swarm optimization algorithm

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