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Research on selection strategy of machining equipment in cloud manufacturing

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2014年第9-12期71卷 1549-1563页

作者： Wang, Shi-long Guo, Liang Kang, Ling Li, Chang-song Li, Xiao-yong Stephane, Yossanguem Madjinoudji Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China

Cloud manufacturing (CM) is a new type of networked manufacturing model, which is proposed in 2010. Optimization technology is one of the key techniques for CM operation, which are used for the efficient integration of manufacturing resources. In all kinds of manufacturing resources, the machining equipment is one of the most important resources. Using optimization techniques to achieve optimal selection of machining equipment is rarely studied in the CM. In order to handle the optimization selection of machining equipment in CM, comparing with the existing resources optimal configuration, an optimal selection strategy is introduced for the machining equipment in CM. In the selection strategy, first, a multiple objective and binary integer programming model is proposed to describe the optimal selection of machining equipment in CM. Second, after analyzing the mathematical model and the real-world problem of the machining equipment selection in CM, the priority method is adopted to convert the multiple-objective problem into a single-objective problem. Third, an improved particle swarm optimization (IPSO) algorithm based on a novel encoding scheme and fitness function is presented to solve the single-objective mathematical model. Finally, the simulation experiments verify the effectiveness of the IPSO algorithm and show that the selection strategy is more objective and effective to help the client select the machining equipment in the CM than current resources optimization model. This research provides a theoretical support for the development of CM.

关键词： Cloud manufacturing Machining equipment selection Particle swarm optimization multiple objective programming Binary integer programming

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A normal boundary intersection approach to multiresponse robust optimization of the surface roughness in end milling process with combined arrays

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PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY 2014年第3期38卷 628-638页

作者： Brito, T. G. Paiva, A. P. Ferreira, J. R. Gomes, J. H. F. Balestrassi, P. P. Univ Fed Itajuba Inst Ind Engn BR-37500903 Itajuba MG Brazil

Robust parameter design (RPD) has recently been applied in modern industries in a large deal of processes. This technique is occasionally employed as a multiobjective optimization approach using weighted sums as a trade-off strategy;in such cases, however, a considerable number of gaps have arisen. In this paper, the use of normal boundary intersection (NBI) method coupled with mean-squared error (MSE) functions is proposed. This approach is capable of generating equispaced Pareto frontiers for a bi-objective robust design model, independent of the relative scales of the objective functions. To verify the adequacy of this proposal, a central composite design (CCD) is developed with combined arrays for the AISI 1045 steel end milling process. In this case study, a CCD with three noise factors and four control factors are used to create the mean and variance equations for MSE of two quality characteristics. The numerical results indicate the NBI-MSE approach is capable of generating a convex and equispaced Pareto frontier to MSE functions of surface roughness, thus nullifying the drawbacks of weighted sums. Moreover, the results show that the achieved optimum lessens the sensitivity of the end milling process to the variability transmitted by the noise factors. (C) 2014 Elsevier Inc. All rights reserved.

关键词： multiple objective programming Robust parameter design (RPD) Normal boundary intersection (NBI) End milling process Surface roughness

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A multivariate robust parameter optimization approach based on Principal Component Analysis with combined arrays

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COMPUTERS & INDUSTRIAL ENGINEERING 2014年第1期74卷 186-198页

作者： de Paiva, Anderson Paulo Gomes, Jose Henrique F. Peruchi, Rogerio Santana Leme, Rafael Coradi Balestrassi, Pedro Paulo Univ Fed Itajuba Inst Ind Engn & Management BR-37500188 Itajuba MG Brazil

Today's modern industries have found a wide array of applications for optimization methods based on modeling with Robust Parameter Designs (RPD). Methods of carrying out RPD have thus multiplied. However, little attention has been given to the multiobjective optimization of correlated multiple responses using response surface with combined arrays. Considering this gap, this paper presents a multiobjective hybrid approach combining response surface methodology (RSM) with Principal Component Analysis (PCA) to study a multi-response dataset with an embedded noise factor, using a DOE combined array. How this approach differs from the most common approaches to RPD is that it derives the mean and variance equations using the propagation of error principle (POE). This comes from a control-noise response surface equation written with the most significant principal component scores that can be used to replace the original correlated dataset. Besides the dimensional reduction, this multiobjective programming approach has the benefit of considering the correlation among the multiple responses while generating convex Pareto frontiers to mean square error (MSE) functions. To demonstrate the procedure of the proposed approach, we used a bivariate case of AISI 52100 hardened steel turning employing wiper mixed ceramic tools. Theoretical and experimental results are convergent and confirm the effectiveness of the proposed approach. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： multiple objective programming Robust Parameter Design (RPD) Multivariate Mean Square Error (MMSE) Principal Component Analysis (PCA)

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A modified augmented max min model for weighted fuzzy goal programming

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2014年第1期27卷 339-350页

作者： Arikan, Feyzan Gazi Univ Dept Ind Engn TR-06570 Maltepe Turkey

Since Narasimhan's first application of Fuzzy Set Theory to Goal programming (GP) in 1980, much research into fuzzy GP has been carried out. In fuzzy GP studies, although several researchers remained loyal to using the traditional GP representation, the majority have followed the fuzzy programming approach. Among fuzzy-programming-based studies, only the studies of Tiwari et al. and Lin seem applicable when the objectives have relative importance. However, both have disadvantages. Because the former model uses the add operator, some of the objectives may not be preferred at the optimal solution even they have heavy weights. The latter model is based on the max-min approach, which does not guarantee a non-dominated solution. In this study, a weighted Fuzzy GP model is presented to overcome these disadvantages, along with the shortcomings of other existing models. The model is modified from Lai and Hwang's augmented max-min model, which is guaranteed to reach a non-dominated solution. The superiority of the model over the existing approaches is demonstrated using numerical examples chosen from the literature.

关键词： multiple objective programming fuzzy sets fuzzy goal programming non-dominated solution relative weights

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Implementation aspects of interactive multiobjective optimization for modeling environments: the case of GAMS-NIMBUS

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2014年第3期58卷 757-779页

作者： Ojalehto, Vesa Miettinen, Kaisa Laukkanen, Timo Univ Jyvaskyla Dept Math Informat Technol FI-40014 Jyvaskyla Finland Aalto Univ Sch Engn Dept Energy Technol FI-00076 Aalto Finland

Interactive multiobjective optimization methods have provided promising results in the literature but still their implementations are rare. Here we introduce a core structure of interactive methods to enable their convenient implementation. We also demonstrate how this core structure can be applied when implementing an interactive method using a modeling environment. Many modeling environments contain tools for single objective optimization but not for interactive multiobjective optimization. Furthermore, as a concrete example, we present GAMS-NIMBUS Tool which is an implementation of the classification-based NIMBUS method for the GAMS modeling environment. So far, interactive methods have not been available in the GAMS environment, but with the GAMS-NIMBUS Tool we open up the possibility of solving multiobjective optimization problems modeled in the GAMS modeling environment. Finally, we give some examples of the benefits of applying an interactive method by using the GAMS-NIMBUS Tool for solving multiobjective optimization problems modeled in the GAMS environment.

关键词： multiple objective programming Interactive methods NIMBUS method Modeling languages Pareto optimality

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A two stage fuzzy piecewise logistic model for penetration forecasting

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APPLIED SOFT COMPUTING 2014年 21卷 149-158页

作者： Tseng, Fang-Mei Yu, Jing-Rung Yuan Ze Univ Coll Management Taoyuan 320 Taiwan Natl Chi Nan Univ Dept Informat Management Puli Nantou 545 Taiwan

It is undeniably crucial for a firm to be able to make a forecast regarding the sales volume of new products. However, the current economic environments invariably have uncertain factors and rapid fluctuations where decision makers must draw conclusions from minimal data. Previous studies combine scenario analysis and technology substitution models to forecast the market share of multigenerational technologies. However, a technology substitution model based on a logistic curve will not always fit the S curve well. Therefore, based on historical data and the data forecast by both the Scenario and Delphi methods, a two stage fuzzy piecewise logistic growth model with multiple objective programming is proposed herein. The piecewise concept is adopted in order to reflect the market impact of a new product such that it can be possible to determine the effective length of sales forecasting intervals even when handling a large variation in data or small size data. In order to demonstrate the model's performance, two cases in the Television and Telecommunication industries are treated using the proposed method and the technology substitution model or the Norton and Bass diffusion model. A comparison of the results shows that the proposed model outperforms the technology substitution model and the Norton and Bass diffusion model. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Fuzzy piecewise regression Logistic model multiple objective programming Delphi method

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A multi-objective integrated facility location-hardening model: Analyzing the pre- and post-disruption tradeoff

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2014年第1期237卷 257-270页

作者： Medal, Hugh R. Pohl, Edward A. Rossetti, Manuel D. Mississippi State Univ Dept Ind & Syst Engn Starkville MS 39762 USA Univ Arkansas Dept Ind Engn Fayetteville AR 72701 USA

Two methods of reducing the risk of disruptions to distribution systems are (1) strategically locating facilities to mitigate against disruptions and (2) hardening facilities. These two activities have been treated separately in most of the academic literature. This article integrates facility location and facility hardening decisions by studying the minimax facility location and hardening problem (MFLHP), which seeks to minimize the maximum distance from a demand point to its closest located facility after facility disruptions. The formulation assumes that the decision maker is risk averse and thus interested in mitigating against the facility disruption scenario with the largest consequence, an objective that is appropriate for modeling facility interdiction. By taking advantage of the MELHP's structure, a natural three-stage formulation is reformulated as a single-stage mixed-integer program (MIP). Rather than solving the MIP directly, the MFLHP can be decomposed into sub-problems and solved using a binary search algorithm. This binary search algorithm is the basis for a multi-objective algorithm, which computes the Pareto-efficient set for the pre- and post-disruption maximum distance. The multi-objective algorithm is illustrated in a numerical example, and experimental results are presented that analyze the tradeoff between objectives. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Location Interdiction multiple objective programming Catastrophe planning and management Discrete optimization

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Locating series FACTS devices for multi-objective congestion management improving voltage and transient stability

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2014年第2期236卷 763-773页

作者： Esmaili, Masoud Shayanfar, Heidar Ali Moslemi, Ramin Islamic Azad Univ Dept Elect Engn West Tehran Branch Tehran Iran Iran Univ Sci & Technol Ctr Excellence Power Syst Automat & Operat Tehran Iran

Transmission congestion management is a vital task in electricity markets. Series FACTS devices can be used as effective tools to relieve congestion mostly employing Optimal Power Flow based methods, in which total cost as the objective function is minimized. However, power system stability may be deteriorated after relieving congestion using traditional methods leading to a vulnerable power system against disturbances. In this paper, a multi-objective framework is proposed for congestion management where three competing objective functions including total operating cost, voltage and transient stability margins are simultaneously optimized. This leads to an economical and robust operating point where enough levels of voltage and transient security are included. The proposed method optimally locates and sizes series FACTS devices on the most congested branches determined by a priority list based on Locational Marginal Prices. Individual sets of Pareto solutions, resulted from solving multi-objective congestion management for each location of FACTS devices, are merged together to create the comprehensive Pareto set. Results of testing the proposed method on the well-known New-England test system are discussed in details and confirm efficiency of the proposed method. (C) 2014 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Congestion management FACTS devices Electricity markets Power system stability margin

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A study of interval metric and its application in multi-objective optimization with interval objectives

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COMPUTERS & INDUSTRIAL ENGINEERING 2014年第1期74卷 169-178页

作者： Bhunia, Asoke Kumar Samanta, Subhra Sankha Univ Burdwan Dept Math Burdwan 713104 W Bengal India

The aim of this paper is to discuss the optimality of interval multi-objective optimization problems with the help of different interval metric. For this purpose, we have proposed the new definitions of interval order relations by modifying the existing definitions and also modified different definitions of interval mathematics. Using the definitions of interval order relations and interval metric, the multi-objective optimization problem is converted into single objective optimization problem by different techniques. Then the corresponding problems have been solved by hybrid Tournament Genetic Algorithm with whole arithmetic crossover and double mutation (combination of non-uniform and boundary mutations). To illustrate the methodology, five numerical examples have been solved and the computational results have been compared. Finally, to test the efficiency of the proposed hybrid Tournament Genetic Algorithm, sensitivity analyses have been carried out graphically with respect to genetic algorithm parameters. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： multiple objective programming Interval objective Interval order relations Pareto optimality Tournament Genetic Algorithm

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Distributed localized bi-objective search

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2014年第3期239卷 731-743页

作者： Derbel, Bilel Humeauc, Jeremie Liefooghe, Arnaud Verel, Sebastien Univ Lille 1 CNRS LIFL F-59655 Villeneuve Dascq France Inria Lille Nord Europe F-59650 Villeneuve Dascq France Ecole Mines Douai Dept IA F-59508 Douai France Univ Littoral Cote Dopale LISIC F-62228 Calais France

We propose a new distributed heuristic for approximating the Pareto set of bi-objective optimization problems. Our approach is at the crossroads of parallel cooperative computation, objective space decomposition, and adaptive search. Given a number of computing nodes, we self-coordinate them locally, in order to cooperatively search different regions of the Pareto front. This offers a trade-off between a fully independent approach, where each node would operate independently of the others, and a fully centralized approach, where a global knowledge of the entire population is required at every step. More specifically, the population of solutions is structured and mapped into computing nodes. As local information, every node uses only the positions of its neighbors in the objective space and evolves its local solution based on what we term a 'localized fitness function'. This has the effect of making the distributed search evolve, over all nodes, to a high quality approximation set, with minimum communications. We deploy our distributed algorithm using a computer cluster of hundreds of cores and study its properties and performance on rho MNK-landscapes. Through extensive large-scale experiments, our approach is shown to be very effective in terms of approximation quality, computational time and scalability. (C) 2014 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Combinatorial optimization Parallel and distributed computing Evolutionary computation

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