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PriEsT: an interactive decision support tool to estimate priorities from pairwise comparison judgments

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2015年第2期22卷 217-235页

作者： Siraj, Sajid Mikhailov, Ludmil Keane, John A. Univ Portsmouth Portsmouth Business Sch Portsmouth PO1 3DE Hants England COMSATS Inst Informat Technol Dept Elect Engn Wah Cantonment Pakistan Univ Manchester Manchester Business Sch Manchester M15 6PB Lancs England Univ Manchester Sch Comp Sci Manchester M13 9PL Lancs England

Pairwise comparison (PC) is a well-established method to assist decision makers (DMs) in estimating their preferences. This paper considers the rationale, design, and evaluation of an open-source priority estimation tool, PriEsT, which has been developed to offer new features related to the PC method. PriEsT is able to assist DMs in interactively identifying and revising their judgments based on different consistency measures and graphical aids. When inconsistency cannot be improved due to practical limitations, PriEsT offers a wide range of Pareto-optimal solutions based on multiobjective optimization, unlike other tools that offer only a single solution. DMs have the flexibility to select any of these nondominated solutions according to their requirements. The features of PriEsT have been demonstrated and evaluated through its application to a real-world case study: the selection of the most appropriate telecom infrastructure for rural areas. This case study using PriEsT has highlighted the presence of intransitive judgments in the acquired data, and the correction of these judgments has led to a different ranking of the available alternatives.

关键词： decision support systems multiple criteria decision analysis software multiple objective programming analysis of algorithms

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On the potential balance among compulsory education outcomes through econometric and multiobjective programming analysis

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2015年第2期241卷 527-540页

作者： Luque, M. Marcenaro-Gutierrez, O. D. Lopez-Agudo, L. A. Univ Malaga Dept Appl Econ Math E-29071 Malaga Spain Univ Malaga Dept Appl Econ Stat & Econometr E-29071 Malaga Spain

This paper sheds new light on the relationship between inputs and outputs in the framework of the educational production function. In particular, it is geared at gaining a better understanding of which factors may be affected in order to achieve an optimal educational output level. With this objective in mind, we analyze teacher-based assessments (actual marks) in three different subjects using a multiobjective schema. For much of the analysis we use data from a recent (2010) Survey - ESOC10, linked with the results from an educational assessment program conducted among 11 and 15 year-old students and with the administrative records on teacher-based scores. Following the statistical and econometric analysis of these data, they are used to build a multiobjective mixed integer model. A reference point approach is used to determine the profile of, potentially, the most "successful and balanced" students in terms of educational outcomes. This kind of methodology in multiobjective programming allows generating "very balanced" solutions in terms of the objective values (subjects). Finally, a sensitivity analysis is used to determine policies that can be carried out in order to improve the performance levels of primary and secondary education students. Particularly, policy makers should be more concerned with the need to promote some cultural habits - such as reading -, from both the students' and parents' side. Additionally, policy efforts should be focused on making the vocational pathways available to Spanish youth more appealing, with the aim of taking advantage of the particular skills of students not succeeding in the academic track. (C) 2014 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Education Students performance Reference point Econometric analysis

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Computational complexity of convoy movement planning problems

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MATHEMATICAL METHODS OF OPERATIONS RESEARCH 2015年第1期82卷 31-60页

作者： Gopalan, Ram Rutgers State Univ Sch Business Camden NJ 08102 USA

Convoy movement planning problems arise in a number of important logistical contexts, including military planning, railroad optimization and automated guided vehicle routing. In the convoy movement problem (CMP), a set of convoys, with specified origins and destinations, are to be routed with the objective of minimizing either makespan or total flow time, while observing a number of side constraints. This paper characterizes the computational complexity of several restricted classes of CMPs. The principal objective is to identify the most parsimonious set of problem features that make the CMP intractable. A polynomial-time algorithm is provided for the single criterion two-convoy movement problem. The performance of a simple prioritization-idling approximation algorithm is also analyzed for the K-convoy movement problem. Error bounds are developed for the makespan and flow time objectives.

关键词： Complexity theory Logistics multiple objective programming Convoy routing Approximation algorithms

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GP-DEMO: Differential Evolution for Multiobjective Optimization based on Gaussian Process models

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2015年第2期243卷 347-361页

作者： Mlakar, Miha Petelin, Dejan Tusar, Tea Filipic, Bogdan Jozef Stefan Inst SI-1000 Ljubljana Slovenia Jozef Stefan Int Postgrad Sch SI-1000 Ljubljana Slovenia

This paper proposes a novel surrogate-model-based multiobjective evolutionary algorithm called Differential Evolution for Multiobjective Optimization based on Gaussian Process models (GP-DEMO). The algorithm is based on the newly defined relations for comparing solutions under uncertainty. These relations minimize the possibility of wrongly performed comparisons of solutions due to inaccurate surrogate model approximations. The GP-DEMO algorithm was tested on several benchmark problems and two computationally expensive real-world problems. To be able to assess the results we compared them with another surrogate-model-based algorithm called Generational Evolution Control (GEC) and with the Differential Evolution for Multiobjective Optimization (DEMO). The quality of the results obtained with GP-DEMO was similar to the results obtained with DEMO, but with significantly fewer exactly evaluated solutions during the optimization process. The quality of the results obtained with GEC was lower compared to the quality gained with GP-DEMO and DEMO, mainly due to wrongly performed comparisons of the inaccurately approximated solutions. (C) 2014 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Evolutionary algorithms Surrogate models Gaussian Process modeling Probable Pareto dominance

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On the representation of the search region in multi-objective optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2015年第3期245卷 767-778页

作者： Klamroth, Kathrin Lacour, Renaud Vanderpooten, Daniel Berg Univ Wuppertal Dept Math & Comp Sci Wuppertal Germany Univ Paris 09 PSL LAMSADE UMR 7243 F-75016 Paris France

Given a finite set N of feasible points of a multi-objective optimization (MOO) problem, the search region corresponds to the part of the objective space containing all the points that are not dominated by any point of N, i.e. the part of the objective space which may contain further nondominated points. In this paper, we consider a representation of the search region by a set of tight local upper bounds (in the minimization case) that can be derived from the points of N. Local upper bounds play an important role in methods for generating or approximating the nondominated set of an MOO problem, yet few works in the field of MOO address their efficient incremental determination. We relate this issue to the state of the art in computational geometry and provide several equivalent definitions of local upper bounds that are meaningful in MOO. We discuss the complexity of this representation in arbitrary dimension, which yields an improved upper bound on the number of solver calls in epsilon-constraint-like methods to generate the nondominated set of a discrete MOO problem. We analyze and enhance a first incremental approach which operates by eliminating redundancies among local upper bounds. We also study some properties of local upper bounds, especially concerning the issue of redundant local upper bounds, that give rise to a new incremental approach which avoids such redundancies. Finally, the complexities of the incremental approaches are compared from the theoretical and empirical points of view. (C) 2015 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Search region Local upper bounds Generic solution approaches

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A novel multi-objective particle swarm optimization with multiple search strategies

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2015年第3期247卷 732-744页

作者： Lin, Qiuzhen Li, Jianqiang Du, Zhihua Chen, Jianyong Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China

Recently, multi-objective particle swarm optimization (MOPSO) has shown the effectiveness in solving multi-objective optimization problems (MOPs). However, most MOPSO algorithms only adopt a single search strategy to update the velocity of each particle, which may cause some difficulties when tackling complex MOPs. This paper proposes a novel MOPSO algorithm using multiple search strategies (MMOPSO), where decomposition approach is exploited for transforming MOPs into a set of aggregation problems and then each particle is assigned accordingly to optimize each aggregation problem. Two search strategies are designed to update the velocity of each particle, which is respectively beneficial for the acceleration of convergence speed and the keeping of population diversity. After that, all the non-dominated solutions visited by the particles are preserved in an external archive, where evolutionary search strategy is further performed to exchange useful information among them. These multiple search strategies enable MMOPSO to handle various kinds of MOPs very well. When compared with some MOPSO algorithms and two state-of-the-art evolutionary algorithms, simulation results show that MMOPSO performs better on most of test problems. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.

关键词： multiple objective programming Particle swarm optimization Evolutionary computation multiple search strategies

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Surrogate upper bound sets for bi-objective bi-dimensional binary knapsack problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2015年第2期244卷 417-433页

作者： Cerqueus, Audrey Przybylski, Anthony Gandibleux, Xavier Univ Nantes UFR Sci LINA UMR CNRS 6241 F-44322 Nantes 03 France Univ Nantes UFR Sci IRCCyN UMR CNRS 6597 F-44322 Nantes 03 France

The paper deals with the definition and the computation of surrogate upper bound sets for the bi-objective bi-dimensional binary knapsack problem. It introduces the Optimal Convex Surrogate Upper Bound set, which is the tightest possible definition based on the convex relaxation of the surrogate relaxation. Two exact algorithms are proposed: an enumerative algorithm and its improved version. This second algorithm results from an accurate analysis of the surrogate multipliers and the dominance relations between bound sets. Based on the improved exact algorithm, an approximated version is derived. The proposed algorithms are benchmarked using a dataset composed of three groups of numerical instances. The performances are assessed thanks to a comparative analysis where exact algorithms are compared between them, the approximated algorithm is confronted to an algorithm introduced in a recent research work. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Combinatorial optimization multiple objective programming Bi-dimensional binary knapsack problem Surrogate relaxation Bound sets

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A new algorithm for linearly constrained c-convex vector optimization with a supply chain network risk application

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2015年第2期247卷 359-365页

作者： Qu, Shaojian Goh, Mark Ji, Ying De Souza, Robert Shanghai Univ Sci & Technol Sch Business Shanghai 200093 Peoples R China Harbin Inst Technol Ctr Nat Sci Harbin Heilongjiang Peoples R China Natl Univ Singapore Logist Inst Asia Pacific Singapore 117548 Singapore Natl Univ Singapore NUS Business Sch Singapore 117548 Singapore

We study a class of vector optimization problems with a C-convex objective function under linear constraints. We extend the proximal point algorithm used in scalar optimization to vector optimization. We analyze both the global and local convergence results for the new algorithm. We then apply the proximal point algorithm to a supply chain network risk management problem under bi-criteria considerations. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.

关键词： multiple objective programming Pareto optimum C-convex Proximal point algorithm Supply chain network risk management

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Resource allocation in multi-class dynamic PERT networks with finite capacity

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2015年第3期247卷 879-894页

作者： Yaghoubi, Saeed Noori, Siamak Azaron, Amir Fynes, Brian Iran Univ Sci & Technol Sch Ind Engn Tehran Iran Istanbul Sehir Univ Dept Ind Engn Istanbul Turkey Univ Coll Dublin Michael Smurfit Grad Sch Business Dublin 2 Ireland Ryerson Univ Dept Mech & Ind Engn Toronto ON M5B 2K3 Canada

In this paper, the resource allocation problem in multi-class dynamic PERT networks with finite capacity of concurrent projects (COnstant Number of Projects In Process (CONPIP)) is studied. The dynamic PERT network is modeled as a queuing network, where new projects from different classes (types) are generated according to independent Poisson processes with different rates over the time horizon. Each activity of a project is performed at a devoted service station with one server located in a node of the network, whereas activity durations for different classes in each service station are independent and exponentially distributed random variables with different service rates. Indeed, the projects from different classes may be different in their precedence networks and also the durations of the activities. For modeling the multi-class dynamic PERT networks with CONPIP, we first consider every class separately and convert the queueing network of every class into a proper stochastic network. Then, by constructing a proper finite-state continuous-time Markov model, a system of differential equations is created to compute the project completion time distribution for any particular project. The problem is formulated as a multi-objective model with three objectives to optimally control the resources allocated to the service stations. Finally, we develop a simulated annealing (SA) algorithm to solve this multi-objective problem, using the goal attainment formulation. We also compare the SA results against the results of a discrete-time approximation of the original optimal control problem, to show the effectiveness of the proposed solution technique. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.

关键词： Project management Markov processes multiple objective programming Simulated annealing

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Multiobjective nondominated neighbor coevolutionary algorithm with elite population

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SOFT COMPUTING 2015年第5期19卷 1329-1349页

作者： Mu, Caihong Jiao, Licheng Liu, Yi Li, Yangyang Xidian Univ Int Res Ctr Intelligent Percept & Computat Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Shannxi Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

A nondominated neighbor coevolutionary algorithm (NNCA) with a novel coevolutionary mechanism is proposed for multiobjective optimization, where elite individuals are used to guide the search. All the nondominated individuals are divided into two subpopulations, namely, the elite population and the common population according to their crowding-distance values. The elite individual located in less-crowded region will have more chances to select more team members for its own team and thus this region can be explored more sufficiently. Therefore, the elite population will guide the search to the more promising and less-crowded region. Secondly, to avoid the 'search stagnation' situation which means that algorithms fail to find enough nondominated solutions, a size guarantee mechanism (SGM) is proposed for elite population by emigrating some dominated individuals to the elite population when necessary. The SGM can prevent the algorithm from searching around limited nondominated individuals and being trapped into the 'search stagnation' situation. In addition, several different kinds of crossover and mutation operator are used to generate offspring, which are benefits for the diversity property. Tests on 20 multiobjective optimization benchmark problems including five ZDT problems, five DTLZ problems and ten unconstrained CEC09 test problems show that NNCA is very competitive compared with seven the state-of-the-art multiobjective optimization algorithms.

关键词： multiple objective programming Evolutionary computations Coevolutionary algorithm Elite population

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