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A machine cell formation algorithm for simultaneously minimising machine workload imbalances and inter-cell part movements

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2005年第3期26卷 268-275页

作者： Kim, CO Baek, JG Jun, J Yonsei Univ Dept Informat & Ind Engn Seoul 120749 South Korea Induk Inst Technol Dept Ind Syst Engn Seoul 139749 South Korea Korea Univ Res Inst Informat & Commun Technol Seoul 136701 South Korea

This paper considers a multi-objective machine cell problem, in which part types have several alternative part routings and the expected annual demand of each part type is known. This problem is characterised as optimally determining part type (routing) sets and corresponding machine cells such that total inter-cell part movements and total machine workload imbalances are simultaneously minimised. Due to the complexity of the problem, a two-stage heuristic algorithm is proposed, and computational experiments were conducted to verify the performance of the algorithm.

关键词： machine cell formation problem multi-objective function machine workload imbalances inter-cell part movements alternative part routings two-stage heuristic algorithm

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Solving dynamic distribution network reconfiguration using deep reinforcement learning

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ELECTRICAL ENGINEERING 2022年第3期104卷 1487-1501页

作者： Kundacina, Ognjen B. Vidovic, Predrag M. Petkovic, Milan R. Univ Novi Sad Fac Tech Sci Novi Sad Serbia Schneider Elect DMS NS Novi Sad Serbia

Distribution network reconfiguration, as a part of the distribution management system, plays an important role in increasing the energy efficiency of the distribution network by coordinating the operations of the switches in the distribution network. Dynamic distribution network reconfiguration (DDNR), enabled by the sufficient number of remote switching devices in the distribution network, attempts to find the optimal topologies of the distribution network over the specified time interval. This paper proposes data-driven DDNR based on deep reinforcement learning (DRL). DRL-based DDNR controller aims to minimize the objective function, i.e. active energy losses and the cost of switching manipulations while satisfying the constraints. The following constraints are considered: allowed bus voltages, allowed line apparent powers, a radial network configuration with all buses being supplied, and the maximal allowed number of switching operations. This optimization problem is modelled as a Markov decision process by defining the possible states and actions of the DDNR agent (controller) and rewards that lead the agent to minimize the objective function while satisfying the constraints. Switching operation constraints are modelled by modifying the action space definition instead of including the additional penalty term in the reward function, to increase the computational efficiency. The proposed algorithm was tested on three test examples: small benchmark network, real-life large-scale test system and IEEE 33-bus radial system, and the results confirmed the robustness and scalability of the proposed algorithm.

关键词： Distribution network Network reconfiguration Deep reinforcement learning Active energy loss and switching operations minimization multi-objective function

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Adaptive fuzzy-based node communication performance prediction with hybrid heuristic Cluster Head selection framework in WSN using enhanced K-means clustering mechanism

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JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS 2024年第3期16卷 309-335页

作者： Ayyappan, Asha Arunachalam, Rajesh Kumar, Manivel Lenin Rajalakshmi Engn Coll Dept Elect & Commun Engn Mevalurkuppam 602105 Tamil Nadu India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Elect & Commun Engn Chennai 602105 Tamil Nadu India Vignan Inst Technol & Sci Dept Elect & Commun Engn Hyderabad 508284 Telangana India

The "Wireless Sensor Networks (WSN)" has gained a lot of interest among research scholars and has been utilized in various advanced applications in distinct fields. Along with the load balancing techniques, the clustering scheme also prolongs the network's overall lifespan. The "Cluster Head (CH)" performs the task of load balancing between the nodes in the "Clustering algorithm";hence, the CH selection procedure is regarded as a critical task in the case of the clustering algorithms. Depending on the CH selection and cluster nodes, the rate of energy consumed by these CHs will be reduced in the wireless sensor. CH selection is a promising solution for the transmission of information within various parameters. Thus, CH selection leads to an increase in the duration of the system and a reduction in the energy utilization by the nodes. Therefore, an "optimization- based CH selection" mechanism in WSN is developed in this paper along with an enhanced node communication performance prediction strategy to provide better communication between the "Sensor Nodes (SNs)" with limited energy expenditure. The node's communication performance is predicted using the Adaptive Fuzzy, in which metrics such as bit rate, latency, throughput, loss, and packet delivery ratio are specified as the input to the network. Here, the parameters within the fuzzy network are tuned with the help of the recommended "Hybrid Position of Heap and African Buffalo Optimization (HP-HABO)". Then, to perform efficient node clustering, the "Optimal K-Means Clustering (OKMC)" approach is executed and the CHs are formed using the developed HP-HABO. The objective function depends on the constraints like energy, distance, and predicted communication performance attained by forming these CHs. The performance of the developed CH selection mechanism is verified by analyzing the experimental outcome of the proposed technique with different optimization algorithms and previous works concerning the objective constra

关键词： Wireless sensor network node communication performance prediction Hybrid Position of Heap and African Buffalo Optimization adaptive fuzzy Optimal K-Means Clustering multi-objective function

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Safety-based path finding in urban areas for older drivers and bicyclists

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2014年第Nov.期48卷 143-157页

作者： Chandra, Shailesh Texas A&M Transportat Inst El Paso TX 79902 USA

This paper presents a safety-based path finding methodology for older drivers and bicyclists in an urban area. The paths are estimated based on costs consisting of both safety and travel time. Safety is evaluated against potential risk of a crash involving an older driver (or a bicyclist) with other vehicles present on the road. To accomplish this, simple formulations are developed for safety indicators of streets and intersections, which are actually generic irrespective of the type of road user. Traffic attributes such as speed and density, driver attributes such as perception-reaction time and street attributes of length and tire-to-road friction coefficient are taken into account in building the safety indicators. Thus, the safety indicators do not necessarily require historical crash data which may or may not be available during path finding. Subsequently, a multi-objective shortest path algorithm is presented that identifies the best path (the non-inferior path) from amongst a set of selected safest paths with due considerations to travel time incurred on each. A simple application example of the proposed methodology is demonstrated on an existing street network system from the City of College Station, Texas. The contributions of this research are twofold - first, the safety indicators can be used by planners in determining high crash potential sites - streets and/or intersections - and second, the safety-based path finding methodology developed in this paper can be integrated with modern day route planning devices and tools in guiding older drivers and bicyclists within an Intelligent Transportation Systems framework. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Older drivers Bicycle path Safe path Safety indicator multi-objective function

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Drilling process optimization for graphite/bismaleimide-titanium alloy stacks

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COMPOSITE STRUCTURES 2004年第1期63卷 101-114页

作者： Kim, D Ramulu, M Univ Washington Dept Mech Engn Seattle WA 98195 USA Washington State Univ Sch Mech & Mat Engn Vancouver WA 98686 USA

In this study, the drilling process of graphite/bismaleimide-titanium alloy (Gi-/Bi-Ti) stacks was optimized in terms of machined hole quality and machining cost. The drilling experiments were conducted by using two different cutter materials, HSS-Co and carbide. Drilled hole quality parameters include surface texture, titanium burrs, hole diameter, cylindricity, and roundness deviation. Machining cost was estimated through drill wear experimentations. A multiple objective linear program was used to optimize drilling feed and speed not only to maximize each hole quality parameter to the greatest extent possible but also to minimize machining cost. Optimum process conditions for achieving desired hole quality and process cost were found to be a combination of low feed and low speed when using carbide drills, and high feed and low speed in drilling with HSS-Co drills. The sensitivity of weighting factors on the feasible process conditions depends on the optimal point position and distribution of each objective. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： composite-titanium stacks drilling process optimization tool wear hole quality machining cost multi-objective function

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Optimal allocation of remote control switches in radial distribution network for reliability improvement

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AIN SHAMS ENGINEERING JOURNAL 2018年第3期9卷 403-414页

作者： Ray, Saheli Bhattacharjee, Subhadeep Bhattacharya, Aniruddha Natl Inst Technol Agartala Dept Elect Engn Agartala 799046 Tripura India

This paper presents differential search algorithm in order to solve reliability optimization problem of radial distribution network. Remote control switches have been optimally allocated to improve reliability at a compromised cost. A multi-objective problem has been formulated and solved using differential search algorithm. The test systems considered in this paper are an 8 bus radial distribution network and a 33 bus radial distribution network. Simulation results obtained using differential search algorithm when applied to the test cases, have been compared with those obtained by particle swarm optimization. Differential search algorithm has been found to provide superior results as compared to particle swarm optimization. (C) 2016 Production and hosting by Elsevier B.V. on behalf of Ain Shams University.

关键词： Differential search algorithm multi-objective function Radial distribution system Remote control switches Repair time Restoration time

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An improved ant colony optimisation algorithm for three-tier supply chain scheduling based on networked manufacturing

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2013年第13期51卷 3945-3962页

作者： Tang, Liang Jing, Ke He, Jie Shenyang Aerosp Univ Dept Ind Engn Shenyang Peoples R China Southeast Univ Sch Transportat Nanjing Jiangsu Peoples R China Shenyang Aerosp Univ Sch Econ & Management Shenyang Peoples R China

This paper presents a study on supply chain scheduling from the perspective of networked manufacturing (NM). According to feature analysis of supply chain scheduling based on NM, we comprehensively consider the combined benefits of cost, time, and satisfaction level for customised services. In order to derive a scheduling strategy among supply chain members based on NM, we formulate a three-tier supply chain scheduling model composed of manufacturer, collaborative design enterprise and customer. Three objective functions - time function, cost function and delay punishment function - are employed for model development. We also take into account multi-objective optimisation under the constraint of product capacity. By using an improved ant colony optimisation algorithm, we add different pheromone concentrations to selected nodes that are obtained from feasible solutions and we confine pheromone concentrations within the minimum value (min) and the maximum value (max), thus obtaining optimal results. The results obtained by applying the proposed algorithm to a real-life example show that the presented scheduling optimisation algorithm has better convergence, efficiency, and stability than conventional ant colony optimisation. In addition, by comparing with other methods, the output results indicate that the proposed algorithm also has better solutions.

关键词： supply chain scheduling improved ant colony optimisation (IM-ACO) networked manufacturing (NM) multi-objective function

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Genetic Algorithms for multi-objective Optimization: Application to a multi-reservoir System in the Chi River Basin, Thailand

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WATER RESOURCES MANAGEMENT 2013年第12期27卷 4369-4378页

作者： Kumphon, Bungon Mahasarakham Univ Fac Sci Dept Math Maha Sarakham Thailand

The multi-objective genetic algorithm is applied to determine the optimal operation of a multi-reservoir system in the Chi River Basin, Thailand. Two competing objective functions are considered;dam release and dam storage. The predicted values for the release and storage needed are mostly lower than in current established management practice.

关键词： multi-objective function Genetic algorithms Optimization Pareto-optimal Evolutionary computation

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Adaptive differential evolution algorithm for efficient reactive power management

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APPLIED SOFT COMPUTING 2017年 53卷 336-351页

作者： Sakr, Walaa S. EL-Sehiemy, Ragab A. Azmy, Ahmed M. Kafrelsheikh Univ Dept Elect Engn Fac Engn Kafrelsheikh Egypt Tanta Univ Fac Engn Elec Power & Machines Engn Dept Tanta Egypt

This paper introduces a proposed procedure to solve the optimal reactive power management (ORPM) problem based on a multi-objective function using a modified differential evolution algorithm (MDEA). The proposed MDEA is investigated in order to enhance the voltage profile as well as to reduce the active power losses by solving the ORPM problem. The ORPM objective function aims to minimize transmission power losses and voltage deviation considering the system constraints. The MDEA aims to enhance the convergence characteristic of the differential evolution algorithm through updating the self-adaptive scaling factor, which can exchange information dynamically every generation. The scaling factor dynamically adopts the global and local searches to efficiently eliminate trapping in local optima. In addition, a strategy is developed to update the penalty factor for alleviating the effects of various system constraints. Numerical applications of different case studies are carried out on three standard IEEE systems, i.e., 14 bus, 30-bus and 57-bus test systems. Also, the proposed procedure is applied on Western Delta Network, which is a real part of the Egyptian main grid system. The flexibility of synchronous machines to provide controllable reactive power is proven with less dependency on the discrete reactive power controllers, such as installing the switchable devices and variations of tap changers. The obtained results show the effectiveness of the proposed enhanced optimization algorithm as an advanced optimization technique that was successively implemented with good performance characteristics. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Adaptive penalty factor Modified differential evolution algorithm multi-objective function Optimal reactive power management

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Application of Bacteria Foraging Algorithm for the Design Optimization of multi-objective Yagi-Uda Array

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2011年第1期21卷 25-35页

作者： Mangaraj, B. B. Misra, I. S. Sanyal, S. K. Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 India Veer Surendrasai Univ Technol Dept Elect & Telecommun Engn Burla 768018 Sambalpur India

The design of antenna array with desirable multiple performance parameters such as directivity, input impedance, beam width, and side-lobe level using any optimization algorithm is a highly challenging task. Bacteria Foraging Algorithm (BFA), as reported by electrical engineers, is the most robust and efficient algorithm in comparison with other presently available algorithms for global optimization of multi-objective, multi-parameter design problems. The objective of this article is to apply this new optimization technique, BFA, in the design of Yagi-Uda array for multi-objective design parameters. We optimize length and spacing for 6 and 15 elements array to achieve higher directivity, pertinent input impedance, minimum 3-dB beam width, and maximum front to back ratio both in the E and H planes of the array. At first, we develop a Method of Moments code in MATLAB environment for the Yagi-Uda array structure for obtaining the above design parameters and then coupled with the BFA for the evaluation of the optimized design parameters. Detail simulation results are included to confirm the design criteria. (C) 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE 21:25-35, 2011.

关键词： bacteria foraging algorithm fitness function moment method multi-objective function Yagi-Uda array

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