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Multiobjective model for solving resource-leveling problem with discounted cash flows

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2018年第6期25卷 2009-2030页

作者： Abadi, Niloofar Nikoofal Sahl Bagheri, Mohsen Assadi, Mohammad Sadjad Univ Technol Dept Ind Engn Mashhad Iran

Nowadays, executers are struggling to improve the economic and scheduling situation of projects. Construction scheduling techniques often produce schedules that cause undesirable resource fluctuations that are inefficient and costly to implement on site. The objective of the resource-leveling problem is to reduce resource fluctuation related costs (hiring and firing costs) without violating the project deadline. In this article, minimizing the discounted costs of resource fluctuations and minimizing the project makespan are considered in a multiobjective model. The problem is formulated as an integer nonlinear programming model, and since the optimization problem is NP-hard, we propose multiobjective evolutionary algorithms, namely nondominated sorting genetic algorithm-ii (NSGA-ii), strength Pareto evolutionary algorithm-ii (SPEA-ii), and multiobjective particle swarm optimization (MOPSO) to solve our suggested model. To evaluate the performance of the algorithms, experimental performance analysis on various instances is presented. Furthermore, in order to study the performance of these algorithms, three criteria are proposed and compared with each other to demonstrate the strengths of each applied algorithm. To validate the results obtained for the suggested model, we compared the results of the first objective function with a well-tuned genetic algorithm and differential algorithm, and we also compared the makespan results with one of the popular algorithms for the resource constraints project scheduling problem. Finally, we can observe that the NSGA-ii algorithm presents better solutions than the other two algorithms on average.

关键词： scheduling resource leveling net present value serial scheduling scheme nondominated sorting genetic algorithm-ii strength Pareto evolutionary algorithm-ii multiobjective particle swarm optimization

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Lifetime maximisation of disjoint wireless sensor networks using multiobjective genetic algorithm

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IET WIRELESS SENSOR SYSTEMS 2018年第5期8卷 200-207页

作者： El-Sherif, Mohamed Fahmy, Yasmine Kamal, Hanan Cairo Univ Fac Engn Elect & Commun Dept Giza Egypt

One of the lifetime maximisation methods for wireless sensor network (WSN) depends on organising the dense sensors into groups which can work in a cooperative sequential manner. Each group contains a subset of sensors that cover all the monitored area and is called a complete cover or simply a cover. Increasing the number of organised covers and maximising the covers lifetime enable longer network lifetime. Here, the authors investigate the WSN lifetime problem as a two-objective optimisation problem. The first objective is to find the maximum number of covers. The second objective considers the problem of wasted energy. Minimising the wasted energy in the critical sensors is achieved by defining a difference factor (DF). The DF is an indication of the difference between the critical sensor lifetime and the cover lifetime. This second objective is compared with other choices in the literature such as minimising the overlapping and minimising the variance. This optimisation problem is addressed using non-dominated sorting genetic algorithm-ii (NSGA-ii). Simulation results are conducted for the network lifetime when using one-objective and different two-objective optimisation problem. The choice of DF as the second objective is proved to overcome drawbacks of other second objectives choices.

关键词： wireless sensor networks genetic algorithms telecommunication network reliability energy measurement minimisation DF two-objective optimisation problem multiobjective genetic algorithm lifetime maximisation methods wireless sensor network WSN lifetime problem nondominated sorting genetic algorithm-ii difference factor NSGA-ii

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Consideration of small signal stability in multi-objective DS reconfiguration in the presence of distributed generation

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IET GENERATION TRANSMISSION & DISTRIBUTION 2017年第1期11卷 236-245页

作者： Shukla, Jyoti Das, Biswarup Pant, Vinay Indian Inst Technol Dept Elect Engn Roorkee Uttar Pradesh India

This study presents a methodology for distribution system (DS) reconfiguration in the presence of distributed generations with objectives of minimising real power loss, switching operations as well as maximising the voltage stability margin while maintaining the constraints of bus voltage, branch current carrying capacity and radiality of DS. Furthermore, small signal stability of the system has also been considered in the formulated reconfiguration problem. To obtain the pareto-optimal solutions of this constrained multi-objective optimisation problem, knee point-driven evolutionary algorithm, is applied. In contrast to the non-dominated sorting genetic algorithm-ii (NSGA-ii)-based approach, preference is given to the knee points among non-dominated solutions in selection and tournament mating. Therefore, it maintains better balance between the convergence of the method and the diversity in the population. The method has been tested on IEEE 33-bus, 69-bus and 119-bus radial DSs to demonstrate its feasibility and effectiveness. The obtained results have also been compared with those obtained by the multi-objective NSGA-ii-based method.

关键词： distributed power generation power system stability Pareto optimisation genetic algorithms distribution networks small signal stability multiobjective DS reconfiguration distributed generation distribution system real power loss minimization switching operation voltage stability margin maximization branch current carrying capacity formulated reconfiguration problem Pareto-optimal solution constrained multiobjective optimisation problem knee point-driven 20 evolutionary algorithm nondominated sorting genetic algorithm-ii IEEE 33-bus radial DS 69-bus radial DS 119-bus radial DS multiobjective NSGA-ii-based method

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Parametric study and multi-objective optimization in single-point incremental forming of extra deep drawing steel sheets

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2016年第5期230卷 825-837页

作者： Kurra, Suresh Nasih, H. R. Regalla, Srinivasaprakash Gupta, Amit Kumar BITS Pilani Hyderabad Campus Dept Mech Engn Hyderabad 500078 Andhra Prades India

Single-point incremental forming is the most economical process to make the sheet metal prototypes and low volume production without any dedicated dies and simple tooling. The surface finish of the parts produced in this process gets affected by various process parameters. To get the proper quality of parts for functional applications, it is important to understand the effect of various process parameters on part quality. Another drawback with this process is long processing time, which also gets affected by different process parameters. Thus, the first objective of this article is to study the effect of various process parameters on surface roughness and manufacturing time. Second objective is to carry out the multi-objective optimization to get optimum process parameters. For this, detailed experiments are conducted using Box-Behnken design. The effects of step depth, tool diameter, wall angle, feed rate and lubricant type on surface roughness and processing time have been investigated. Based on experimental data, mathematical models have been developed for both the response variables, namely, surface roughness and manufacturing time. Since the response variables are mutually exclusive in nature, multi-objective optimization algorithm (non-dominated sorting genetic algorithm-ii) has been used to get optimum process parameters. The Pareto front obtained from this algorithm helps the manufacturing engineer to select optimum process parameters in single-point incremental forming process.

关键词： Incremental forming Box-Behnken design surface roughness manufacturing time multi-objective optimization nondominated sorting genetic algorithm-ii

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Dynamic economic emission dispatch using nondominated sorting genetic algorithm-ii

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2008年第2期30卷 140-149页

作者： Basu, M. Jadavpur Univ Dept Power Engn Kolkata 700098 India

This paper presents nondominated sorting genetic algorithm-ii for dynamic economic emission dispatch problem. This problem is formulated as a nonlinear constrained multi-objective optimization problem. nondominated sorting genetic algorithm-ii is proposed to handle dynamic economic emission dispatch problem as a true multi-objective optimization problem with competing and noncommensurable objectives. The proposed approach has a good performance in finding a diverse set of solutions and in converging near the true pareto-optimal set. Numerical results for a sample test system have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed pareto-optimal solutions of dynamic economic emission dispatch problem in one single run. The comparison with the classical technique demonstrates the superiority of the proposed algorithm. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： dynamic economic dispatch dynamic economic emission dispatch multi-objective optimization nondominated sorting genetic algorithm-ii real-coded genetic algorithm

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A hybrid approach for multi-weapon production planning with large-dimensional multi-objective in defense manufacturing

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2014年第2期228卷 302-316页

作者： Zhou, Yu Jiang, Jiang Yang, Zhen-yu Tan, Yue-jin Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China

Multi-weapon production planning contains multi-objective combinatorial optimization and decision-making problems with the NP-hard and large-dimensional natures, which are difficult to be attacked by one single technique successfully. A four-stage hybrid approach is proposed to solve this problem. In the first stage, the multi-weapon production planning problem is formulated with 2N (N > 5) objectives based on operational capability requirements and expected downside risk measure. In the second stage, the formulation addressed is converted into a bi-objective optimization model using goal programming. In the third stage, an algorithm DENS based on differential evolution and nondominated sorting genetic algorithm-ii is developed to obtain the Pareto set. Finally, the multiple attribute decision-making method technique for order preference by similarity to ideal solution is employed to acquire the compromise solution from the Pareto set. A case study is given to demonstrate the effectiveness of the proposed approach. The concrete advantages of goal programming, DENS, and technique for order preference by similarity to ideal solution are also validated in this case. This approach can support the weapon production planning in defense manufacturing and is also applicable to solve the multi-level and multi-objective problem in other manufacturing fields.

关键词： Production planning multi-objective goal programming differential evolution nondominated sorting genetic algorithm-ii technique for order preference by similarity to ideal solution

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Multi-Objective Clustering Optimization for Multi-Channel Cooperative Sensing in CRNs

Multi-Objective Clustering Optimization for Multi-Channel Co...

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IEEE Global Communications Conference (GLOBECOM)

作者： Celik, Abdulkadir Kamal, Ahmed E. Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA

ISBN: (纸本)9781479935123

Cooperative spectrum sensing (CSS) has been extensively studied in the literature to mitigate the weakness of spectrum sensing against hostile propagation phenomenon. Especially for large networks, clustered CSS is preferred to alleviate the energy efficiency, delay and overhead problems. In this study, reporting and sensing channels are first modeled with the consideration of path loss and fading. Then, CSS is divided into three phases: 1) In sensing phase, optimal sensing time is obtained for each local user subject to local detection and false alarm probability thresholds, 2) In reporting phase, adopting Dijkstra's algorithm, multi-hop paths with the maximum success rate and cluster head (CH) selection which gives the mimimum total error rate within each cluster is computed, and 3) In decision phase, collecting independent but unidentically distributed (i.u.d.) member decisions, the CH decides on channel occupancy based on an optimal voting rule for i.u.d. reports. Next, following the phases above, a multi-objective clustering optimization (MOCO) is formulated to select SUs into cluster seeking energy and throughput efficiency goals subject to global detection and false alarm probability constraints. Finally, the Non-dominated sorting genetic algorithm-ii (NSGA-ii) is employed to solve MOCO. Results based on our approach are presented and the merits of this approach are demonstrated.

关键词： cognitive radio cooperative communication genetic algorithms probability radio spectrum management CRN CSS Dijkstra algorithm MOCO NSGA-ii channel occupancy cluster head selection cluster seeking energy cooperative spectrum sensing decision phase false alarm probability thresholds local detection local user maximum success rate multichannel cooperative sensing multiobjective clustering optimization nondominated sorting genetic algorithm-ii optimal sensing time optimal voting rule reporting phase sensing phase throughput efficiency Cascading style sheets Clustering algorithms Fading Sensors Signal to noise ratio Throughput Wireless communication

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Optimal Multivariable Two-Degree-of-Freedom Control of Electric Wheelchair Using Non-dominated sorting genetic algorithm-ii

Optimal Multivariable Two-Degree-of-Freedom Control of Elect...

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IEEE International Conference on Control Applications (CCA)

作者： Saadatzi, Mohammad Nasser Poshtan, Javad Saadatzi, Mohammad Hossein Iran Univ Sci & Technol Tehran Iran KN Toosi Univ Technol Tehran Iran

ISBN: (纸本)9781457710636

Electric wheelchairs (EW) experience various terrains surfaces and slopes as well as occupants with diverse weights. This, in turn, imparts a substantial amount of perturbation to the EW dynamics. In this paper we make use of a two-degree-of-freedom control architecture called disturbance observer (DOB) which reduces sensitivity to model uncertainties while enhancing rejection of disturbances which occur when entering slopes. The feedback loop which is designed via characteristic loci method (CLM) is then augmented with a DOB containing a parameterized low-pass filter. According to the disturbance rejection, sensitivity reduction, and noise rejection of the whole controller, three performance indices are defined which enable us to pick the filter's optimal parameters using a multi-objective optimization (MOO) approach called non-dominated sorting genetic algorithm-ii (NSGA-ii). Finally, experimental results show desirable improvement in stiffness and disturbance rejection of the proposed controller as well as its robust stability.

关键词： Approximation methods Brushless DC motors Control systems Low pass filters Noise Uncertainty characteristic loci method disturbance observer disturbance rejection elderly patients electric vehicles electric wheelchair feedback feedback loop genetic algorithms handicapped aids low-pass filters medical control systems mobile robots mobility-impaired patients multiobjective optimization multivariable control systems noise rejection nondominated sorting genetic algorithm-ii observers optimal control optimal multivariable two-degree-of-freedom control parameterized low-pass filter robust control robust stability sensitivity reduction wheelchairs

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Non-dominated sorting genetic algorithm-ii for robust multi-objective optimal reactive power dispatch

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IET GENERATION TRANSMISSION & DISTRIBUTION 2010年第9期4卷 1000-1008页

作者： Zhihuan, L. Yinhong, L. Xianzhong, D. Huazhong Univ Sci & Technol Hubei Elect Power Secur & High Efficiency Key Lab Wuhan 430074 Hubei Peoples R China Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30318 USA

The concept of robust optimal solution is incorporated into multi-objective optimal reactive power dispatch (MORPD) for the consideration of uncertain load perturbations during system operations. Robust MORPD searches for solutions that are immune to parameter drifts and load changes. It uses information of load-increase directions to promote the stability of optimal solutions in the presence of load perturbations. Non-dominated sorting genetic algorithm-ii (NSGA-ii) is adopted to search for the robust Pareto solutions on a standard IEEE 118-bus system. The simulation validated the effectiveness of NSGA-ii for robust MORPD. NSGA-ii obtained Pareto solutions over the trade-off surface. The experimental results also indicated that the robust Pareto solutions are comparatively less sensitive to load perturbations in their neighbourhoods and can maintain their objective values against uncertain load perturbations. Robust MORPD can provide optimal solutions with a higher degree of stability in the face of perturbations and can be more practical in reactive power optimisation of the real-time operation systems.

关键词： robust Pareto solutions power generation dispatch robust multiobjective optimal reactive power dispatch power system security IEEE 118-bus system uncertain load perturbations NSGA-ii Power system control reactive power genetic algorithms nondominated sorting genetic algorithm-ii sorting Combinatorial mathematics Power system management, operation and economics reactive power optimisation Optimisation techniques Pareto optimisation

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Multiobjective Evolutionary algorithm with Risk Minimization Applied to a Fleet Mix Problem

Multiobjective Evolutionary Algorithm with Risk Minimization...

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2010 IEEE World Congress on Computational Intelligence

作者： Willick, Kyle Wesolkowski, Slawomir Mazurek, Michael Canadian Forces Aerosp Warfare Ctr OR Team Ottawa ON Canada DRDC CORA Ottawa ON Canada

ISBN: (纸本)9781424481262

We apply the non-dominated sorting genetic algorithm-ii (NSGA-ii) to a multi-objective fleet-mix problem for risk mitigation. The Stochastic Fleet Estimation (SaFE) model, a Monte Carlo-based model, is used to determine average annual requirements which a fleet must meet. We search for Pareto-optimal combinations of platform-to-task assignments that can be used to complete SaFE generated scenarios. Solutions are evaluated using three objectives, with a goal of minimizing fleet cost, total task duration, and the risk that a solution will not be able to accomplish future scenarios. Optimization over all three objectives allowed for exploration of configurations which were low cost and low risk, a region not explored by prior experiments without the risk objective.

关键词： Monte Carlo based model Monte Carlo methods Pareto optimal combination Pareto optimisation evolutionary computation fleet mix problem genetic algorithms multiobjective evolutionary algorithm nondominated sorting genetic algorithm-ii platform to task assignment risk analysis risk minimization stochastic fleet estimation stochastic processes transportation

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