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Multi-objective simultaneous stowage and load planning for a container ship with container rehandle in yard stacks

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2006年第2期171卷 373-389页

作者： Imai, A Sasaki, K Nishimura, E Papadimitriou, S Kobe Univ Fac Maritime Sci Kobe Hyogo 6580022 Japan World Maritime Univ S-20124 Malmo Sweden Univ Piraeus Dept Maritime Studies GR-18532 Piraeus Greece

The efficiency of a maritime container terminal primarily depends on the smooth and orderly process of handling containers, especially during the ship's loading process. The stowage and associated loading plans are mainly determined by two criteria: ship stability and the minimum number of container rehandles required. The latter is based on the fact that most container ships have a cellular structure and that export containers are piled up in a yard. These two basic criteria are often in conflict. This paper is concerned with the ship's container stowage and loading plans that satisfy these two criteria. The GM, list and trim are taken into account for the stability measurements. The problem is formulated as a multi-objective integer programming. In order to obtain a set of noninferior solutions of the problem, the weighting method is employed. A wide variety of numerical experiments demonstrated that solutions by this formulation are useful and applicable in practice. (c) 2004 Elsevier B.V. All rights reserved.

关键词： logistics multiple objective programming genetic algorithms heuristics containership handling

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An integrated approach for deriving priorities in analytic network process

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第3期180卷 1427-1432页

作者： Yu, Jing-Rung Cheng, Sheu-Ji Natl Chi Nan Univ Dept Informat Management Puli 545 Nantou Taiwan Natl WhaNan Commercial High Sch Dept Data Proc Chiayi 6000 Taiwan

A multiple objective programming approach for the analytic network process (ANP) is proposed to obtain all local priorities for crisp or interval judgments at one time, even in an inconsistent situation. The weakness of the ANP and fuzzy ANP (FANP) is that the complexity of generating priorities is equal to the number of comparison matrices. In the proposed approach, all sets of crisp priorities for each pairwise comparison matrix can be obtained directly. Moreover, from the outcomes of three examples, the power to reach a limiting supermatrix is less than or equal to the power of the FANP. Thus, the proposed approach can be regarded as an efficient alternative of the fuzzy ANP. (c) 2006 Elsevier B.V. All rights reserved.

关键词： analytic network process fuzzy preference programming two-phase approach multiple objective programming

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ISTMO: An interval reference point-based method for stochastic multiobjective programming problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2009年第1期197卷 25-35页

作者： Munoz, Maria M. Ruiz, Francisco Univ Malaga Dept Appl Econ Math Sch Business E-29071 Malaga Spain

In this paper, we present an interactive algorithm (ISTMO) for stochastic multiobjective problems with continuous random variables. This method combines the concept of probability efficiency for stochastic problems with the reference point philosophy for deterministic multiobjective problems. The decision maker expresses her/his references by dividing the variation range of each objective into intervals, and by setting the desired probability for each objective to achieve values belonging to each interval. These intervals may also be redefined during the process. This interactive procedure helps the decision maker to understand the stochastic nature of the problem, to discover the risk level (s)he is willing to assume for each objective, and to learn about the trade-offs among the objectives. (C) 2008 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Stochastic programming Probability efficiency Interactive methods Reference point

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INTEGRATED PRODUCTION-DISTRIBUTION PLANNING OPTIMIZATION USING NEUTROSOPHIC programming

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2022年第2期29卷 174-191页

作者： Badhotiya, Gaurav Kumar Soni, Gunjan Nepal, Bimal Mittal, Murari Lal Graph Era Deemed Univ Dept Mech Engn Dehra Dun Uttarakhand India Malaviya Natl Inst Technol Jaipur Dept Mech Engn Jaipur Rajasthan India Texas A&M Univ Engn Technol & Ind Distribut College Stn TX 77843 USA

Consideration and management of uncertainties are critical in effective decision-making in production-distribution planning problems. Further, these decisions often involve multiple objectives which are conflicting in nature. More importantly, the decisions represented by these objective functions are often based on imprecise or uncertain data either due to unavailability or lack of objectivity of information, therefore cannot be solved by classical deterministic modeling techniques. To that end, this paper presents a neutrosophic programming-based approach to solve an integrated production-distribution planning problem in a two-echelon supply chain by considering uncertainties and indeterminateness in the data. The problem is formulated as a tri-objective mixed-integer linear programming model considering important features of production distribution planning decisions. The three objectives considered are to minimize: total cost, delivery time, and backorder level. These objectives are represented by membership functions of the neutrosophic set, i.e., truth, indeterminacy, and falsity. The efficacy of the proposed methodology is illustrated by considering problem instances inspired by a real-world case in the automotive industry. A Pareto optimality test performed on the proposed neutrosophic model shows the existence of a strong optimal solution of the proposed neutrosophic model.

关键词： Information indeterminacy Production and distribution planning Neutrosophic sets multiple objective programming production planning Uncertainty

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A possibilistic optimization over an integer efficient set within a fuzzy environment

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RAIRO-OPERATIONS RESEARCH 2020年第5期54卷 1437-1452页

作者： Menni, Assia Chaabane, Djamal USTHB Dept Operat Res Fac Math AMCD RO Lab Bp 32 El Alia Algiers 16121 Algeria

Optimizing a linear function over the efficient set of a multiple objective Integer Linear programming (MOILP) problem is known as a difficult problem to deal with, since a discrete efficient set is generally not convex and not explicitly known. Such problem becomes more and more difficult when parameters are defined with uncertainty. In this work, we deal with problems of this type for which parameters are imprecise and are assumed to be trapezoidal fuzzy numbers. The method is based on possibility and necessity measures introduced in the literature by D. Dubois and H. Prade.

关键词： multiple objective programming discrete optimization Fuzzy parameters possibility measure necessity measure

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Research on selection strategy of machining equipment in cloud manufacturing

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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 multi-objective lead time control problem in multi-stage assembly systems using genetic algorithms

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第1期180卷 292-308页

作者： Perkgoz, Cahit Azaron, Amir Katagiri, Hideki Kato, Kosuke Sakawa, Masatoshi Natl Univ Ireland Univ Coll Cork Dept Comp Sci Cork Constraint Computat Ctr Cork Ireland Hiroshima Univ Grad Sch Engn Dept Artificial Complex Syst Engn Hiroshima 7398527 Japan

in this paper, we develop a multi-objective model to optimally control the lead time of a multi-stage assembly system, using genetic algorithms. The multi-stage assembly system is modelled as an open queueing network. It is assumed that the product order arrives according to a Poisson process. In each service station, there is either one or infinite number of servers (machines) with exponentially distributed processing time, in which the service rate (capacity) is controllable. The optimal service control is decided at the beginning of the time horizon. The transport times between the service stations are independent random variables with generalized Erlang distributions. The problem is formulated as a multi-objective optimal control problem that involves four conflicting objective functions. The objective functions are the total operating costs of the system per period (to be minimized), the average lead time (min), the variance of the lead time (min) and the probability that the manufacturing lead time does not exceed a certain threshold (max). Finally, we apply a genetic algorithm with double strings using continuous relaxation based on reference solution updating (GADSCRRSU) to solve this multiobjective problem, using goal attainment formulation. The results are also compared against the results of a discrete-time approximation technique to show the efficiency of the proposed genetic algorithm approach. (c) 2006 Elsevier B.V. All rights reserved.

关键词： queueing genetic algorithms multiple objective programming production

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An empirical study on similarity-based mating for evolutionary multiobjective combinatorial optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2008年第1期188卷 57-75页

作者： Ishibuchi, Hisao Narukawa, Kaname Tsukamoto, Noritaka Nojima, Yusuke Osaka Prefecture Univ Dept Comp Sci & Intelligent Syst Naka Ku Osaka 5998531 Japan

We have already proposed a similarity-based mating scheme to recombine extreme and similar parents for evolutionary multiobjective optimization. In this paper, we examine the effect of the similarity-based mating scheme on the performance of evolutionary multiobjective optimization (EMO) algorithms. First we examine which is better between recombining similar or dissimilar parents. Next we examine the effect of biasing selection probabilities toward extreme solutions that are dissimilar from other solutions in each population. Then we examine the effect of dynamically changing the strength of this bias during the execution of EMO algorithms. Computational experiments are performed on a wide variety of test problems for multiobjective combinatorial optimization. Experimental results show that the performance of EMO algorithms can be improved by the similarity-based mating scheme for many test problems. (C) 2007 Elsevier B.V. All rights reserved.

关键词： multiple objective programming combinatorial optimization evolutionary computation genetic algorithms

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Bi-objective optimization for low-carbon product family design

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2016年 41卷 53-65页

作者： Wang, Qi Tang, Dunbing Yin, Leilei Salido, Miguel A. Giret, Adriana Xu, Yuchun Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Jiangsu Peoples R China Univ Politecn Valencia Dept Sistemas Informat & Comp Camino de Vera S-N Valencia 46071 Spain Cranfield Univ Sch Appl Sci Cranfield MK43 0AL Beds England

Consumers, industry, and government entities are becoming increasingly concerned about the issue of global warming. With this in mind, manufacturers have begun to develop products with consideration of low-carbon. In recent years, many companies are utilizing product families to satisfy various customer needs with lower costs. However, little research has been conducted on the development of a product family that considers environmental factors. In this paper, a low-carbon product family design that integrates environmental concerns is proposed. To this end, a new method of platform planning is investigated with considerations of cost and greenhouse gas (GHG) emission of a product family simultaneously. In this research, a lowcarbon product family design problem is described at first, and then a GHG emission model of product family is established. Furthermore, to support lowcarbon product family design, an optimization method is applied to make a significant trade-off between cost and GHG emission to implement a feasible platform planning. Finally, the effectiveness of the proposed method is illustrated through a case study. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： multiple objective programming Low carbon design Platform planning Product family design

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Decision support for disaster relief: Coordinating spontaneous volunteers

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第2期299卷 690-705页

作者： Sperling, Martina Schryen, Guido Paderborn Univ Dept Management Informat Syst Warburger Str 100 D-33098 Paderborn Germany

In the aftermath of large-scale disasters, the exploitation of often up to thousands of spontaneous vol-unteers is crucial to meet the need for surge capacity which cannot be met by official responders. How-ever, the coordination of spontaneous volunteers differs in several regards from that of professional and paid relief workers. Based on empirical requirements identified in interviews with the manager of a pro-fessional fire department, we suggest a multi-objective mixed-integer linear optimization problem with lexicographically ordered objective functions, which we refer to as spontaneous volunteer coordination problem (SVCP). Acknowledging that disaster situations are unavoidably linked to uncertainty, we con-sider uncertainty with a sequence of (deterministic) SVCP instances, where each instance depends on the solutions of previous SVCP instances. We conduct comprehensive computational experiments based on real-world data of a flood disaster that the fire department faced. From our computational results, we derive detailed implications for the fire department on how to use our decision support model. We also derive recommendations for all relief organizations which aim at adopting or adapting our model for the coordination of spontaneous volunteers in a broad set of disasters. Our implications include several rec-ommendations for relief organizations in terms of performing extensive computational tests in order to parameterize and instantiate the generic model before its use during the disaster response phase;thereby we also address tasks to be executed during the preparedness phase of a disaster. (c) 2021 Elsevier B.V. All rights reserved.

关键词： OR in disaster relief Coordination of spontaneous volunteers Decision support systems multiple objective programming Uncertainty modeling

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