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A multi-objective optimization for preemptive identical parallel machines scheduling problem

COMPUTATIONAL & APPLIED MATHEMATICS 2017年第3期36卷 1367-1387页

作者： Aalaei, Amin Kayvanfar, Vahid Davoudpour, Hamid Amirkabir Univ Technol Tehran Polytech Dept Ind Engn & Management Syst 424 Hafez Ave Tehran 158754413 Iran

This research investigates a new notion in just-in-time philosophy on the identical parallel machines considering allowable job preemption with respect to a bi-objective approach. The work-in-process (WIP) is also allowed, since minimization of WIP is desirable in many industrial applications specifically those including perishable items. In this new notion, a new model is defined in which the earliness costs depend on the start times of the jobs. The goal of this study is to minimize two objectives simultaneously: (1) total weighted earliness and tardiness as well as holding cost of all jobs which are waiting to be processed as WIP costs and (2) number of jobs interruptions. In this context, two multi-objective meta-heuristic algorithms, i.e., the non-dominated sorting genetic algorithm II (NSGAII) and non-dominated ranking genetic algorithm (NRGA) are employed to solve such bi-objective problems. Three measurement factors are then employed to evaluate the algorithms performances. Computational results demonstrate that NRGA outperforms NSGAII in all small- and medium-to-large-sized sample-generated problems;however, intangibly.

关键词： Earliness and tardiness Just-in-time Identical parallel machines Preemption non-dominated sorting genetic algorithm

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Optimal control of novel fuel cell-based DVR using ANFISC-MOSSA to increase FRT capability of DFIG-wind turbine

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SOFT COMPUTING 2019年第15期23卷 6633-6655页

作者： Falehi, Ali Darvish Rafiee, Mansour Shahid Beheshti Univ Dept Elect Engn Tehran Iran

Although Doubly fed induction generator (DFIG)-based wind turbine has several significant advantages, any voltage fluctuation can severely disturb its normal and safe operation, i.e., it is characteristically very sensitive. Hence, fault ride-through is determined as a prominent benchmark for DFIG which must be enhanced by a voltage compensator. Dynamic voltage restorer (DVR), a voltage fluctuation mitigator, is connected in-series with distribution and sub-transmission lines so that DFIG can appropriately operate during any type of disturbance. In this paper, a new two switches boost converter (TSBC) coupled with quinary multilevel inverter (QMLI) is proposed which is fed by fuel cell to produce staircase sinusoidal voltage for DVR. The proposed step-up DC/DC presents two important advantages: low current ripple and low switch stress, while QMLI can create high-step staircase sinusoidal voltage without considering a number of switches. TSBC-QMLI-based DVR has been controlled by neuro fuzzy inference system controller (ANFISC). As the control problem is multi-objective and complex, Multi-Objective Salp Swarm algorithm (MOSSA) has been applied to optimally tune the ANFISC's parameters, and then compared with non-dominated sorting genetic algorithm II (NSGA-II). To verify and validate the compensation capability of proposed DVR, it has been thoroughly evaluated under different voltage disturbances. Eventually, the simulation results have validated the compensation capability of the suggested TSBC-QMLI-based DVR which is accurately controlled by MOSSA-based ANFISC to mitigate the balanced and unbalanced voltage sag and swell along with the flicker and harmonic voltage.

关键词： Fuel cell Wind turbine Dynamic voltage restorer Doubly fed induction generator Multi-Objective Salp Swarm algorithm non-dominated sorting genetic algorithm

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Multi-objective optimization of wire electrical discharge machining process using evolutionary computation method: Effect of cutting variation

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2015年第1期229卷 75-85页

作者： Golshan, Abolfazl Ghodsiyeh, Danial Izman, Sudin Univ Putra Malaysia Dept Mech Engn Fac Engn Serdang 43400 Malaysia Politecn Torino Dept Mech & Aerosp I-10129 Turin Italy Univ Teknol Malaysia Dept Mat Mfg & Ind Engn Fac Mech Engn Skudai Malaysia

This article focuses on comparing the performance of brass wire and zinc-coated brass wire that are widely used as the wire electrode in wire electrical discharge machining. To this end, an evolutionary computation method is presented based on non-dominated sorting genetic algorithm in order to find an optimization of rough cutting of the Ti-6Al-4V titanium alloy with the aid of response surface methodology modeling. This research examines the effects of three process parameters, namely, pulse on-time, pulse off-time, and peak current on the process outputs, that is, material removal rate, sparking gap, and white layer thickness. The obtained results indicated that zinc-coated wire was more predictable and it showed more reliable response in the experimental and modeling results. Additionally, the optimization results for both wires demonstrated the high performance of non-dominated sorting genetic algorithm approach to obtain the Pareto optimal set of solutions.

关键词： Wire electrical discharge machining brass wire zinc-coated brass wire response surface methodology non-dominated sorting genetic algorithm

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Opposition-based learning for competitive hub location: A bi-objective biogeography-based optimization algorithm

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KNOWLEDGE-BASED SYSTEMS 2017年 128卷 1-19页

作者： Niknamfar, Amir Hossein Niaki, Seyed Taghi Akhavan Niaki, Seyed Armin Akhavan Islamic Azad Univ Qazvin Branch Young Researchers & Elite Club Qazvin Iran Sharif Univ Technol Dept Ind Engn POB 11155-9414 Azadi Ave Tehran *** Iran West Virginia Univ Dept Stat Morgantown WV 26506 USA

This paper introduces a new hub-and-center transportation network problem for a new company competing against an operating company. The new company intends to locate p hubs and assign the center nodes to the located hubs in order to form origin-destination pairs. It desires not only to maximize the total captured flow in the market but also aims to minimize the total transportation cost. Three competition rules are established between the companies which must be abided. According to the competition rules, the new company can capture the full percentage of the traffic in each origin-destination pair if its transportation cost for each route is significantly less than of the competitor. If its transportation cost for each route is not significantly less than one of the competitors, only a certain percentage of the traffic can be captured. A bi-objective optimization model is proposed for the hub location, problem on hand under a competitive environment. As the problem is shown to be NP-hard, a novel meta-heuristic algorithm called multi-objective biogeography-based optimization is developed. As there is no benchmark in the literature, a popular non-dominated sorting algorithm is utilized to validate the results obtained. Moreover, to enhance the performance of the proposed Pareto-based algorithms, this paper intends to develop a binary opposition-based learning as a diversity mechanism for both algorithms. The algorithms are tuned to solve the problem, based on which their performances are compared, ranked, and analyzed statistically. Finally, the applicability of the proposed approach and the solution methodologies are demonstrated in three steps. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Competitive hub location Evolutionary computations Binary opposition-based learning Multi-objective biogeography-based optimization non-dominated sorting genetic algorithm

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Multiobjective Optimization for Vehicle Routing Optimization Problem in Low-Carbon Intelligent Transportation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第11期24卷 13161-13170页

作者： Yin, Nan Suzhou City Univ Dept Business Adm Suzhou 215104 Peoples R China Nanjing Univ Sci & Technol Sch Econ & Management Nanjing 210094 Peoples R China

The work aims to reduce the energy consumption and carbon emissions generated during the urban logistics transportation and distribution and make the actual path planning flexible. Based on Vehicle Routing Problem (VRP), the routing problem of distribution vehicles is optimized under satisfying customers' cargo demand and time requirements. Because non-dominated sorting genetic algorithm (NSGA-II) reduces the complexity of non-inferior sorting genetic algorithm and is characterized by fast running speed and good convergence, it is deeply improved. NSGA-II algorithm based on Multifactorial Evolutionary algorithm (MFEA) (M-NSGA-II) is proposed. In terms of the solution of the stability of the optimal values of four target functions, including distribution cost, customer satisfaction, fuel conservation, and carbon emission, the lowest distribution costs of M-NSGA-II algorithm in ten experiments were all lower than those of other three standard algorithms. The solution duration of M-NSGA-II algorithm was 85.2s and the corresponding average frontier value amounted to 20. The multi-objective path optimization model designed is of great value for reducing carbon emissions under satisfying customers' cargo demand and time requirements.

关键词： Carbon dioxide Optimization Costs Green products Vehicle routing Smart transportation genetic algorithms Intelligent transportation vehicle routing optimization multi-objective optimization green and low-carbon non-dominated sorting genetic algorithm

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Clusters of floor locations-allocation of stores to cross-docking warehouse considering satisfaction and space using MOGWO and NSGA-II algorithms

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2024年第1期36卷 315-342页

作者： Shoaee, Maryam Samouei, Parvaneh Bu Ali Sina Univ Fac Engn Dept Ind Engn Hamadan Iran

Optimal warehouse design is the most substantial issue for many companies, because of increasing their efficiency and productivity. Nowadays, a warehouse is just undefined as a site for goods storage and delivery but is taken into account as a unit interacting with other sectors. Recently, cross-docking warehouses have been widely used since they differ from traditional warehouses in terms of good deposition volume, storage and delivery times, mitigating procurement, exploitation of resources, and accelerating service pace. This study proposes a mathematical model for transit warehouse layout and design to minimize travel distances, downsize the free space of the warehouse and make retailers satisfied. Small-sized problems are solved by the augmented epsilon-constraint and the weighted sum methods, while NSGA-II and MOGWO algorithms are proposed for large-sized problems. Comparison between algorithms indicates higher efficiency of NSGA-II rather than MOGWO algorithm to solve cross-docking warehouse design for allocating stores to floor locations.

关键词： Cross-docking warehouse Layout non-dominated sorting genetic algorithm Multi-objective grey wolf optimizer Augmented epsilon-constraint Weighted sum method

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A simulation-based optimization approach for free distributed repairable multi-state availability-redundancy allocation problems

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2017年 157卷 177-191页

作者： Attar, Ahmad Raissi, Sadigh Khalili-Damghani, Kaveh Islamic Azad Univ Sch Ind Engn South Tehran Branch Tehran Iran

A simulation-based optimization (SBO) method is proposed to handle multi-objective joint availability redundancy allocation problem (JARAP). Here, there is no emphasis on probability distributions of time to failures and repair times for multi-state multi-component series-parallel configuration under active, cold and hot standby strategies. Under such conditions, estimation of availability is not a trivial task. First, an efficient computer simulation model is proposed to estimate the availability of the aforementioned system. Then, the estimated availability values are used in a repetitive manner as parameter of a two-objective joint availability redundancy allocation optimization model through SBO mechanism. The optimization model is then solved using two well-known multi-objective evolutionary computation algorithms, i.e., non-dominated sorting genetic algorithm (NSGA-II), and Strength Pareto Evolutionary algorithm (SPEA2). The proposed SBO approach is tested using non-exponential numerical example with multi-state repairable components. The results are presented and discussed through different demand scenarios under cold and hot standby strategies. Furthermore, performance of NSGA-II and SPEA2 are statistically compared regarding multi-objective accuracy, and diversity metrics.

关键词： Availability-redundancy allocation Simulation based optimization (SBO) Multi-state systems Distribution-free failure/repair time non-dominated sorting genetic algorithm Repairable systems

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An NSGA-II-based multiobjective approach for real-time routing selection in a flexible manufacturing system under uncertainty and reliability constraints

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2019年第9-12期100卷 2813-2829页

作者： Souier, Mehdi Dahane, Mohammed Maliki, Fouad Univ Tlemcen MELT PB 230 Tilimsen 13000 Algeria High Sch Management Tlemcen PB 1085 Tilimsen 13000 Algeria Univ Lorraine LGIPM F-57000 Metz France High Sch Appl Sci Tlemcen BP 165 Tilimsen 13000 Algeria

Routing flexibility is one of the most common types of flexibilities of manufacturing systems. It allows the system to continue producing given part types despite uncertainties. Its main purpose is to maintain a high level of performance so that the system can deal with disturbances (failures, maintenance actions,...). This type of flexibility occurs when there are alternative or redundant machine tools in the system. However, due to resource and alternative routing limitations, the scheduling problems in such systems can become very complex. Furthermore, though routing flexibility aims to enhance system responsiveness, it still depends on the reliability and availability of machines and individual components in a system. The present paper aims to investigate the scheduling problem in a flexible manufacturing system (FMS) with routing flexibility under uncertainties related to the random arrival of parts orders and machines failures, by considering reliability and maintenance constraints. The real-time decisions for part routing selection are made using a non-dominated sorting genetic algorithm (NSGA-II), by considering the workload, utilization level, and reliability of machines in a workstation, in order to minimize the deadlocks and maximize the overall system reliability. The simulation results obtained showed that, for an overloaded system, the proposed NSGA-II algorithm induces the best performance in terms of total profit, system productivity, and machines utilization.

关键词： Flexible manufacturing system Routing flexibility non-dominated sorting genetic algorithm Reliability Maintenance

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NSGA-II algorithm for multi-objective generation expansion planning problem

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ELECTRIC POWER SYSTEMS RESEARCH 2009年第4期79卷 622-628页

作者： Murugan, P. Kannan, S. Baskar, S. Arulmigu Kalasalingam Coll Engn Elect & Commun Engn Dept Krishnankoil 626190 Tamil Nadu India Thiagarajar Coll Engn Dept Elect Engn Madurai 625015 Tamil Nadu India

This paper presents an application of Elitist non-dominated sorting genetic algorithm version II (NSGA-II), to multi-objective generation expansion planning (GEP) problem. The GEP problem is considered as a two-objective problem. The first objective is the minimization of investment cost and the second objective is the minimization of outage cost (or maximization of reliability). To improve the performance of NSGA-II, two modifications are proposed. One modification is incorporation of Virtual Mapping Procedure (VMP), and the other is introduction of controlled elitism in NSGA-II. A synthetic test system having 5 types of candidate units is considered here for GEP for a 6-year planning horizon. The effectiveness of the proposed modifications is illustrated in detail. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Combinatorial optimization Generation expansion planning Multi-objective non-dominated sorting genetic algorithm (NSGA-II) Virtual Mapping Procedure

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PSS design for damping low-frequency oscillations in a multi-machine power system with penetration of renewable power generations

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IET RENEWABLE POWER GENERATION 2019年第1期13卷 116-127页

作者： Kahouli, Omar Jebali, Mariam Alshammari, Badr Abdallah, Hsan Hadj Natl Engn Sch Sfax Control & Energies Management CEM Lab Route Sokra Km 3-5BP 1173-3038 Sfax Tunisia Hail Univ Fac Engn POB 2440 Hail Saudi Arabia

This study focused on the use of the non-dominated sorting genetic algorithm (NSGA-II) to achieve an optimal power system stabiliser (PSS) parameters for a given operating point with a renewable source of energy so as to increase the system damping and guarantee enough stability margin. The parameters tuning was formulated using an eigenvalue-based multi-objective function. In recent years, the changeability and fluctuation of the wind power injected into the network have led to new challenges in small signal stability (SSS). These wind speed change and load demand change perturbations take place routinely and cause the variation of the operating conditions. However, as the conventional PSS is conceived for a fixed operating point in order to obtain the linearised transfer function model, it cannot yield good results when the operating range is too wide. To this end, the adaptive neuro-fuzzy inference system was proposed to estimate the stabiliser parameters in real time after a learning phase. The nine-bus Western System Coordinating Council and the obtained simulations results were assessed using Matlab/Simulink package. The validity of the proposed methodology was checked through the PSS parameters evolution simulation for daily load forecast curves and monthly wind speed prediction curves.

关键词： power system stability genetic algorithms eigenvalues and eigenfunctions power engineering computing wind power plants transfer functions damping fuzzy neural nets PSS design multimachine power system NSGA-II optimal power system stabiliser parameters system damping stability margin parameters tuning changeability wind power operating conditions conventional PSS fixed operating point linearised transfer function model operating range adaptive neuro-fuzzy inference system nine-bus Western System Coordinating Council PSS parameters evolution simulation monthly wind speed prediction curves low-frequency oscillations damping non-dominated sorting genetic algorithm small signal stability wind speed change renewable power generation penetration eigenvalue-based multiobjective function load demand change perturbations

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