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Design of reconfigurable flow lines using MOPSO and maximum deviation theory

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第5-8期84卷 1587-1600页

作者： Goyal, Kapil Kumar Jain, Pramod Kumar MM Univ Mullana Ambala India IIT Roorkee Mech & Ind Engn Dept Roorkee 247667 Uttar Pradesh India

Reconfigurable manufacturing system (RMS) is a state-of-the-art technology, which offers the exact capacity and functionality needed, through rapid and cost-effective reconfigurations in its modular structure. The reconfigurable machine tools (RMTs) can perform a variety of operations in its existing configuration and can further be reconfigured to change the operational capabilities. Thus, the selection of a machine configuration plays a vital role not only in the cost-effectiveness but in the future reconfiguration effort requirements. In the present study, authors have attempted a multiple-objective optimization of RMS flow line configuration, taking the cost, machine utilization, operational capability, and configuration convertibility as the conflicting objectives. The cost and machine utilization represent the economic justification while the operational capability and the configuration convertibility portray the ability of rapid and cost-effective reconfiguration ability. The emphasis of this research is to design the economic RMS configuration, which can rapidly and cost-effectively respond to the volatile market conditions. multiple-objective particle swarm optimization (MOPSO) has been implemented to handle the discrete and discontinuous search space. The large number of Pareto frontiers is further ranked to avoid subjectiveness and imprecision in the decision making, based on the composite scores obtained through maximum deviation theory. A numerical illustration is presented to illustrate the developed methodology of reconfigurable flow line configuration design.

关键词： Reconfigurable manufacturing systems (RMSs) Reconfigurable machine tool (RMT) Machine selection multiple-objective optimization multiple-objective particle swarmoptimization (MOPSO) Maximum deviation method

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OPTIMIZING 3-objective PORTFOLIO SELECTION WITH EQUALITY CONSTRAINTS AND ANALYZING THE EFFECT OF VARYING CONSTRAINTS ON THE EFFICIENT SETS

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JOURNAL OF INDUSTRIAL AND MANAGEMENT optimization 2021年第4期17卷 1531-1556页

作者： Qi, Yue Li, Xiaolin Zhang, Su China Acad Corp Governance 94 Weijin Rd Tianjin 300071 Peoples R China Nankai Univ Dept Financial Management Business Sch 94 Weijin Rd Tianjin 300071 Peoples R China

Markowitz proposes portfolio selection as a 2-objective model and emphasizes computing (whole) efficient sets and nondominated sets. Computing the sets has long been a topic in multiple-objective optimization. Researchers have gradually recognized other criteria in addition to variance and expected return. To formulate the additional criteria, researchers propose multiple-objective portfolio selection. However, computing the corresponding efficient set and nondominated set is not fully achieved. Moreover, discovering the sets' properties and utilizing the properties remain typically unanswered. In this paper, we extend Sharpe's and Merton's model by adding a general linear objective and imposing equality constraints. To optimize the model, we analytically derive the minimum-variance surface (defined later), prove it as a nondegenerate paraboloid, and prove the nondominated set as a paraboloidal segment. We also analytically derive the efficient set and prove it as a 2-dimensional translated cone. We then prove that the set subsumes the efficient set of the corresponding traditional model, so the efficient set expands as the general linear objective is added. Furthermore, constraints can be changed or added. We utilize the translated-cone properties and readily compute the changing constraints' effect on the efficient sets by formulae or linear-equation systems.

关键词： multiple-objective optimization multiple-objective portfolio selection efficient set nondominated set paraboloid constraint

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AN INTERACTIVE METHOD FOR multiple-objective MATHEMATICAL-PROGRAMMING PROBLEMS

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JOURNAL OF optimization THEORY AND APPLICATIONS 1991年第3期68卷 539-561页

作者： SHIN, WS RAVINDRAN, A UNIV OKLAHOMA IND ENGNNORMANOK 73019

An interactive method is developed for solving the general nonlinear multiple objective mathematical programming problems. The method asks the decision maker to provide partial information (local tradeoff ratios) about his utility (preference) function at each iteration. Using the information, the method generates an efficient solution and presents it to the decision maker. In so doing, the best compromise solution is sought in a finite number of iterations. This method differs from the existing feasible direction methods in that (i) it allows the decision maker to consider only efficient solutions throughout, (ii) the requirement of line search is optional, and (iii) it solves the problems with linear objective functions and linear utility function in one iteration. Using various problems selected from the literature, five line search variations of the method are tested and compared to one another. The nonexisting decision maker is simulated using three different recognition levels, and their impact on the method is also investigated.

关键词： multiple-objective optimization MATHEMATICAL PROGRAMMING INTERACTIVE METHODS TRADEOFF CUTTING PLANE

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Parametrically computing efficient frontiers and reanalyzing efficiency-diversification discrepancies and naive diversification

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INFOR 2019年第3期57卷 430-453页

作者： Qi, Yue Zhang, Yushu Ma, Siyuan Nankai Univ China Acad Corp Governance 94 Weijin Rd Tianjin 300071 Peoples R China Nankai Univ Dept Financial Management Business Sch 94 Weijin Rd Tianjin 300071 Peoples R China Nankai Univ Collaborat Innovat Ctr China Econ 94 Weijin Rd Tianjin 300071 Peoples R China Stevens Inst Technol Sch Business 1 Castle Point Terrace Hoboken NJ USA

Portfolio selection is recognized as the birth-place of modern finance. Weighted-sums methods or e-constraint methods are normally utilized for portfolio optimization, but the results are only approximations of efficient frontiers. One concern of portfolio selection is efficiency-diversification discrepancies that efficient frontiers lack diversification. Scholars typically analyze the discrepancies by using weighted-sums methods or e-constraint methods, studying only a specific portfolio, and utilizing small-scale portfolio selection. Some scholars find that portfolio selection is not consistently better than naive diversification. We utilize parametric quadratic programming, exhaustively sample US stocks, build batches of 5-stock problems up to 1800-stock problems, obtain the structure of (whole) efficient frontiers, and propose new diversification measures on the basis of the structure. We find that (1) setting upper bounds can be more effective in changing diversification status than setting right-hand sides or setting the numbers of constraints can, (2) portfolio selection can substantially outperform naive diversification at least in sample so the cost of naive diversification can be prohibitive, and (3) efficiency-diversification discrepancies can arise due to efficient frontiers' nature of having relatively small numbers of stocks and cannot be easily reconciled.

关键词： multiple-objective optimization portfolio optimization parametric quadratic programming efficient set diversification

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multiple-objective based model predictive control of pulse jet fabric filters

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2000年第A4期78卷 581-589页

作者： Ju, J Chiu, MS Tien, C Natl Univ Singapore Dept Chem & Environm Engn Singapore 119260 Singapore

With the increasingly stringent emissions regulations of fine particulates and air toxics, pulse jet fabric filters have become an attractive particulate collection option for utilities. Despite their wide application, the present control algorithm used in fabric filtration systems can best be described as rudimentary. In this paper, a model predictive control (MPC) technique is employed owing to its distinct advantages. Unlike its conventional counterpart, the proposed MPC algorithm takes the economic factor into consideration, which is formulated in a multiple-objective optimization framework. To avoid the local optimum, a global optimization technique is incorporated into the proposed MPC design. Simulation results show that the proposed multiple-objective optimization based MPC design method is especially suitable to the pulse jet fabric filtration process, where the set point change and process disturbance occur frequently.

关键词： model predictive control pulse jet fabric filter multiple-objective optimization

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ON THE UTOPIAN APPROACH TO THE MULTIobjective LQ PROBLEM

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OPTIMAL CONTROL APPLICATIONS & METHODS 1993年第2期14卷 111-124页

作者： DENICOLAO, G LOCATELLI, A POLITECN MILAN DIPARTIMENTO ELETTRONVIA PONZIO 34-5I-20133 MILANITALY POLITECN MILAN DIPARTIMENTO ELETTRONCNRCTR TEORIA SISTEMII-20133 MILANITALY

The infinite horizon, multiobjective linear quadratic control problem for continuous time systems is considered. Following a utopian approach, the optimal solution is defined as the solution that minimizes the distance from the utopian point in the cost space. It is shown that under standard stabilizability and detectability assumptions the optimal solution always exists. The solution coincides with the solution of a scalar LQ problem in which the cost matrices are given by a suitably weighted sum of their counterparts in the individual criteria. By exploiting the characterization and properties of the optimal solution, it is shown that the problem can be efficiently solved by means of a Newton-type algorithm.

关键词： multiple-objective optimization LINEAR QUADRATIC CONTROL PARETO OPTIMALITY UTOPIAN POINT METHOD

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Do multiple-objective metaheuristics deliver on their promises? A computational experiment on the set-covering problem

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2003年第2期7卷 133-143页

作者： Jaszkiewicz, A Poznan Univ Tech Inst Comp Sci PL-60965 Poznan Poland

In this paper, we compare the computational efficiency of three state-of-the-art multiple-objective metaheuristics (MOMHs) and their single-objective counterparts on the multipleobjective set-covering problem (MOSCP). We use a methodology that allows consistent evaluation of the quality of approximately Pareto-optimal solutions generated by of both MOMHs and single-objective metaheuristics (SOMHs). Specifically, we use the average value of the scalarizing functions over a representative sample of weight vectors. Then, we compare computational efforts needed to generate solutions of approximately the same quality by the two kinds of methods. In the computational experiment, we use two SORMs-the evolutionary algorithm (EA) and the memetic algorithm (MA), and three MOMHs-controlled elitist nondominated sorting genetic algorithm, the strength Pareto EA, and the Pareto MA. The methods are compared on instances of the MOSCP with 2,3, and 4 objectives, 20,40,80 and 200 rows, and 200,400,800 and 1000 columns. The results of the experiment indicate good computational efficiency of the multiple-objective metaheuristics in comparison to their single-objective counterparts.

关键词： metaheuristics multiple-objective optimization set-covering problem

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A heuristic relief transportation planning algorithm for emergency supply chain management

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2010年第7期87卷 1638-1664页

作者： Chern, C. C. Chen, Y. L. Kung, L. C. Natl Taiwan Univ Dept Informat Management Taipei 106 Taiwan

Emergency supply chain operations have to fulfil all the demands in a very short period of time, using the limited resources at its disposal. Mixed integer programming (MIP) is a popular method to solve emergency supply chain planning problems. However, as such problem increases in complexity, the MIP model becomes insolvable due to the time and computer resources it requires. This study proposes a heuristic algorithm, called the Emergency Relief Transportation Planning Algorithm (ERTPA). ERTPA will group and sort demands according to the required products, the imposed due dates, the possible shared capacities, and the distances from the demand nodes to the depots. Then, ERTPA plans the demands individually, using a shortest travelling-time tree and a minimum cost production tree. To show the effectiveness and efficiency of the heuristic algorithm, a prototype was constructed and tested, using complexity and computational analyses.

关键词： emergency response system emergency relief transportation heuristic algorithm emergency supply chain management multiple-objective optimization

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A Pareto-discrete hummingbird algorithm for partial sequence-dependent disassembly line balancing problem considering tool requirements

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JOURNAL OF MANUFACTURING SYSTEMS 2021年 60卷 406-428页

作者： Yin, Tao Zhang, Zeqiang Jiang, Jin Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Technol & Equipment Rail Transit Operat & Mainten Chengdu 610031 Peoples R China

To reduce the disassembly costs to enterprises and improve the disassembly efficiency of waste products, this study proposed a partial sequence-dependent disassembly line balancing problem (PSD-DLBP) and established a multi-objective mathematical model to simultaneously minimize the number of workstations, total disassembly time, idle balance index and the number of disassembly tools. Then, a Pareto-discrete hummingbird algorithm (PDHA) was proposed to address PSD-DLBP effectively. The PDHA includes two stages: self-searching stage and information-interacting stage. With these two stages, the exploration and exploitation abilities of PDHA can be balanced. Later, the effectiveness and superiority of the PDHA were verified by comparing it with the other four algorithms for two different-scale examples. Finally, the model and PDHA were applied to the optimization of a partial sequence-dependent disassembly line of waste laptops. The optimization results show that the partial disassembly can make the line smoother and the utilization efficiency of workstations higher than full disassembly, and PDHA is superior in solving the PSD-DLBP.

关键词： Sequence-dependent disassembly line Partial disassembly Pareto-discrete hummingbird algorithm Tool requirements multiple-objective optimization

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Designing a sustainable supply chain network integrated with vehicle routing: A comparison of hybrid swarm intelligence metaheuristics

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COMPUTERS & OPERATIONS RESEARCH 2019年 110卷 220-235页

作者： Govindan, Kannan Jafarian, Ahmad Nourbakhsh, Vahid Univ Southern Denmark Ctr Sustainable Supply Chain Engn Dept Technol & Innovat Odense Denmark ATU Fac Management & Accounting Tehran Iran Univ Calif Irvine Paul Merage Sch Business Irvine CA 92697 USA

Recently, a growing concern with sustainability has become a consideration in business operations. However, there is a lack of mathematical models that quantify environmental effects and, in particular, social impacts of supply chains because of the inherently subjective nature of these aspects. To fill this gap, this paper models a distribution network in which the triple bottom lines of sustainability are captured. Different impacts of the network on the stakeholders, including company owners, workers, consumers and society, are considered as whole. In the current model, a multi-product vehicle routing problem with time windows (MPVRPTW) as an operational decision is integrated with strategic decisions related to the network design. To solve this model, three hybrid swarm intelligence techniques (particle swarm optimization (PSO), electromagnetism mechanism algorithm (EMA), and artificial bee colony (ABC)) are proposed, and each is hybridized with variable neighborhood search (VNS) are proposed. Because metaheuristic methods are sensitive to input parameters, response surface methodology (RSM) with the multi-objective decision making (MODM) approach is applied for tuning the parameters. The proposed approaches are compared with the hybrid of genetic algorithm (GA) and VNS as the benchmark algorithm. A fair comparison is conducted by employing six metrics to evaluate the quality of the Pareto frontier obtained by the algorithms on the test problems. According to the results, the predominance of EMA is enhanced by VNS local search. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Sustainable supply chain network Environmental management Corporate social responsibility multiple-objective optimization Vehicle routing problem Swarm intelligence algorithms

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