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 Annals of Operations ResearchJournal Prestige (SJR): 0.943 Citation Impact (citeScore): 2Number of Followers: 10      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1572-9338 - ISSN (Online) 0254-5330 Published by Springer-Verlag  [2351 journals]
• Managerial multiple objective optimization
• Authors: Ralph E. Steuer; José Rui Figueira; Hatem Masri
Pages: 1 - 2
PubDate: 2018-08-01
DOI: 10.1007/s10479-018-2937-5
Issue No: Vol. 267, No. 1-2 (2018)

• A modular capacitated multi-objective model for locating maritime search
and rescue vessels
• Authors: Amin Akbari; Ronald Pelot; H. A. Eiselt
Pages: 3 - 28
Abstract: This paper presents a mathematical multi-objective model to optimize the location-allocation of maritime search and rescue (SAR) vessels with regard to several criteria, including primary and backup coverage and mean access time. Atlantic Canada serves as the area of the study and the Canadian Coast Guard has provided the necessary datasets and information. A goal programming multi-objective model is developed to optimize the location and allocation of SAR vessels to potential future incidents in order to achieve greater level of responsiveness and coverage. Comparing the optimal solution found to the current arrangement of SAR vessels, shows a substantial improvement in terms of access time and coverage. The results of the study provide decision makers with valuable insights to make more informed strategic and tactical decisions for more efficient management of the SAR fleet.
PubDate: 2018-08-01
DOI: 10.1007/s10479-017-2593-1
Issue No: Vol. 267, No. 1-2 (2018)

• Multiobjective bi-level programming for shared inventory with emergency
and backorders
• Authors: Fouad Ben Abdelaziz; Sameh Mejri
Pages: 47 - 63
Abstract: The goal of this study is to model a shared inventory model as a bilevel programming problem. The model considers both emergency and backorders so decision makers have flexibility in meeting customer needs and extends the satisfaction level is higher than the simple model with no emergency and backorders. We focus on the interaction between decision makers in an uncertain environment. The considered situation consists in a large corporation that owns a warehouse and two concurrent chains. The company opted for shared inventory policy. The corporation is looking into minimizing its inventory and reducing the overtime for its personnel. The chains, independently, are trying to minimize their inventory costs. The chains are allowed to submit emergency and backorders to minimize their inventory cost while increasing their customers’ satisfaction. The warehouse plays the role of a leader and optimizes its objectives first then the two chains, as followers, try to satisfy their objectives independently. The problem is formulated into a decentralized bilevel programming problem where the leader has multiple objectives. We formulate the situation as a biobjective bilevel the mathematical program and we propose ways to solve it. . Our simulation, inspired from a real case, shows a cost reduction of up to 8.9 % compared to regular use of inventory model.
PubDate: 2018-08-01
DOI: 10.1007/s10479-016-2324-z
Issue No: Vol. 267, No. 1-2 (2018)

• Robustness of weighted goal programming models: an analytical measure and
its application to offshore wind-farm site selection in United Kingdom
• Authors: Mila Bravo; Dylan Jones; David Pla-Santamaria; Graham Wall
Pages: 65 - 79
Abstract: This paper proposes a method to measure robustness of weighted goal programming (WGP) models by focusing on random percentage changes in the set of observed technological coefficients that characterize the goal equations. The issue under consideration is to estimate the impact of the random percentage changes on the WGP deviations from the goal targets, the solution to the model before changes being kept equal. Normally distributed and independent percentage changes are assumed. As a result, a measure of robustness is obtained dependent on the parameters of the model, standard deviations of percentage changes, and the solution to the model before changes. A demonstration of the proposed robustness measure on an offshore wind-farm site location model from the literature is developed. The results indicate that robustness of proposed solution to the energy project is high. Conclusions are drawn as to the practicality and usage of the proposed model in comparison to other methodologies for handling uncertainty within the goal programming model.
PubDate: 2018-08-01
DOI: 10.1007/s10479-017-2437-z
Issue No: Vol. 267, No. 1-2 (2018)

• Normal regularity for the feasible set of semi-infinite multiobjective
optimization problems with applications
• Authors: Thai Doan Chuong; Do Sang Kim
Pages: 81 - 99
Abstract: We establish verifiable conditions for the feasible set of a nonsmooth semi-infinite multiobjective optimization problem to have the normal regularity (that is, the coincidence of the Fréchet normal cone and the limiting normal one) at a given point. In this way, both the Fréchet normal cone and the limiting normal one to the considered set are then computed via active constraint multipliers and limiting subdifferentials of the involved constraints. In order to achieve such goals, two classes of nonsmooth functions are introduced and exploited. Finally, the obtained results are applied to provide necessary optimality conditions for semi-infinite multiobjective optimization problems.
PubDate: 2018-08-01
DOI: 10.1007/s10479-016-2337-7
Issue No: Vol. 267, No. 1-2 (2018)

• A multiple objective methodology for sugarcane harvest management with
varying maturation periods
• Authors: Helenice de Oliveira Florentino; Chandra Irawan; Angelo Filho Aliano; Dylan F. Jones; Daniela Renata Cantane; Jonis Jecks Nervis
Pages: 153 - 177
Abstract: This paper addresses the management of a sugarcane harvest over a multi-year planning period. A methodology to assist the harvest planning of the sugarcane is proposed in order to improve the production of POL (a measure of the amount of sucrose contained in a sugar solution) and the quality of the raw material, considering the constraints imposed by the mill such as the demand per period. An extended goal programming model is proposed for optimizing the harvest plan of the sugarcane so the harvesting point is as close as possible to the ideal, considering the constrained nature of the problem. A genetic algorithm (GA) is developed to tackle the problem in order to solve realistically large problems within an appropriate computational time. A comparative analysis between the GA and an exact method for small instances is also given in order to validate the performance of the developed model and methods. Computational results for medium and large farm instances using GA are also presented in order to demonstrate the capability of the developed method. The computational results illustrate the trade-off between satisfying the conflicting goals of harvesting as closely as possible to the ideal and making optimum use of harvesting equipment with a minimum of movement between farms. They also demonstrate that, whilst harvesting plans for small scale farms can be generated by the exact method, a meta-heuristic GA method is currently required in order to devise plans for medium and large farms.
PubDate: 2018-08-01
DOI: 10.1007/s10479-017-2568-2
Issue No: Vol. 267, No. 1-2 (2018)

• A bi-objective approach to discrete cost-bottleneck location problems
• Authors: Sune Lauth Gadegaard; Andreas Klose; Lars Relund Nielsen
Pages: 179 - 201
Abstract: This paper considers a family of bi-objective discrete facility location problems with a cost objective and a bottleneck objective. A special case is, for instance, a bi-objective version of the (vertex) p-centdian problem. We show that bi-objective facility location problems of this type can be solved efficiently by means of an $$\varepsilon$$ -constraint method that solves at most $$(n-1)\cdot m$$ minisum problems, where n is the number of customer points and m the number of potential facility sites. Additionally, we compare the approach to a lexicographic $$\varepsilon$$ -constrained method that only returns efficient solutions and to a two-phase method relying on the perpendicular search method. We report extensive computational results obtained from several classes of facility location problems. The proposed algorithm compares very favorably to both the lexicographic $$\varepsilon$$ -constrained method and to the two phase method.
PubDate: 2018-08-01
DOI: 10.1007/s10479-016-2360-8
Issue No: Vol. 267, No. 1-2 (2018)

• A mixed-model multi-objective analysis of strategic supply chain decision
support in the Thai silk industry
• Authors: Natawat Jatuphatwarodom; Dylan F. Jones; Djamila Ouelhadj
Pages: 221 - 247
Abstract: This paper presents a methodology for combined usage of data envelopment analysis (DEA), analytical hierarchy process (AHP) and extended goal programming (EGP) in order to provide managerial decision support. The methodology allows the three techniques to be used in a coordinated manner to give an enhanced level of holistic decision support. DEA is first used in a descriptive sense in order to provide information regarding the efficiency of a set of units. The AHP is then used in order to determine the importance of criteria arising from decision problem(s) related to the improvement of unit efficiency. Finally, EGP is used in a prescriptive sense in order to select a set of specific actions for improving unit efficiency. Two specific multi-objective situations arising from the Thai Silk industry are used as case studies for the proposed methodology. These involve supplier selection and inventory management system management in the presence of multiple conflicting goals and objectives. In the case studies, DEA is used to provide efficiency estimates of current suppliers and processes. AHP is then used in order to determine the relative importance of criteria for supply chain efficiency improvement. Adaptations are made to an automated inconsistency reduction algorithm in order to resolve high levels of inconsistency found. The relation between decision maker confidence and consistency is investigated. Finally, an EGP model is built in order to suggest improvement actions to the supply chain processes. Results are given for a set of eight Thai silk manufacturers and conclusions are drawn.
PubDate: 2018-08-01
DOI: 10.1007/s10479-018-2774-6
Issue No: Vol. 267, No. 1-2 (2018)

• Doing good by doing well: a MCDM framework for evaluating corporate social
responsibility attractiveness
• Authors: Maria Teresa Lamata; Vicente Liern; Blanca Pérez-Gladish
Pages: 249 - 266
Abstract: Corporate social responsibility is a multidimensional concept with an imprecise nature. Evaluation of the degree of corporate social performance is one of the most discussed questions among academic researchers and practitioners. In this paper, we are concerned with devising an integrative overall indicator of corporate social performance. Fuzzy logic procedures appear as the adequate tools for the evaluation of corporate social responsibility, taking into account the multiple social responsibility dimensions and available information from different sources. The obtained fuzzy measure will be integrated into a general method for the evaluation of firms. This general framework will guide the investor in his investment decision process taking account of the available information and investor’s level of confidence in it. The definition of desirable and undesirable firms will depend on the investor’s preferences. Our proposal, based on a Fuzzy-AHP-TOPSIS approach, will allow us to rank firms based on how well they are doing good.
PubDate: 2018-08-01
DOI: 10.1007/s10479-016-2271-8
Issue No: Vol. 267, No. 1-2 (2018)

• A new efficiently encoded multiobjective algorithm for the solution of the
cardinality constrained portfolio optimization problem
• Authors: K. Liagkouras; K. Metaxiotis
Pages: 281 - 319
Abstract: This paper proposes a novel multiobjective evolutionary Algorithm (MOEA) for the solution of the cardinality constrained portfolio optimization problem (CCPOP). The proposed algorithm introduces an efficient encoding scheme specially designed for dealing with the difficulties of the CCPOP. Also, the proposed algorithm incorporates a new mutation and recombination operator tailor-made to work well with the new encoding scheme. Datasets from seven different stock markets are utilized for testing the efficiency of the proposed approach. In particular, the performance of the proposed efficiently encoded multiobjective portfolio optimization solver (EEMPOS) is assessed in comparison with two well-known MOEAs, namely NSGAII and MOEA/D. The experimental results indicate that the proposed EEMPOS outperforms the two other MOEAs for all examined performance metrics when is applied to the solution of the CCPOP for a fraction of time required by the other techniques.
PubDate: 2018-08-01
DOI: 10.1007/s10479-016-2377-z
Issue No: Vol. 267, No. 1-2 (2018)

• Portfolio selection problem: a review of deterministic and stochastic
multiple objective programming models
• Authors: Meryem Masmoudi; Fouad Ben Abdelaziz
Pages: 335 - 352
Abstract: The literature on portfolio selection mostly concentrates on computational analysis rather than on modelling efforts. In response, this paper provides a comprehensive literature review of multiple objective deterministic and stochastic programming models for the portfolio selection problem. First, we summarize different concepts related to portfolio selection theory, including pricing models and portfolio risk measures. Second, we report the mathematical models that are generally used to solve deterministic and stochastic multiple objective programming problems. Finally, we present how these models can be used to solve the portfolio selection problem.
PubDate: 2018-08-01
DOI: 10.1007/s10479-017-2466-7
Issue No: Vol. 267, No. 1-2 (2018)

• Exact and approximate approaches for the Pareto front generation of the
single path multicommodity flow problem
• Authors: Hela Masri; Saoussen Krichen
Pages: 353 - 377
Abstract: A major problem in communication networks is how to efficiently define the routing paths and to allocate bandwidths in order to satisfy a given collection of transmission requests. In this paper, we study this routing problem by modeling it as a bi-objective single path multicommodity flow problem (SMCFP). Two conflicting objective functions are simultaneously optimized: the delay and reliability of the generated paths. To tackle the complexity of this problem (NP-hard), most of the existing studies proposed approximate methods and metaheuristics as solution approaches. In this paper, we propose to adapt the augmented $$\epsilon$$ -constraint method in order to solve small sized instances of the bi-SMCFP. For large scale problems, we develop three metaheuristics: a multiobjective multi-operator genetic algorithm, an adaptive multiobjective variable neighborhood search and new hybrid method combining the $$\epsilon$$ -constraint with the evolutionary metaheuristic. The idea of the hybridization schema is to first use the metaheuristic to generate a good approximation of the Pareto front, then to enhance the quality of the solutions using the $$\epsilon$$ -constraint method to push them toward the exact Pareto front. An intelligent decomposition scheme is used to reduce the size of the search space before applying the exact method. Computational results demonstrate the efficiency of the proposed hybrid algorithm using instances derived from real network topology and other randomly generated instances.
PubDate: 2018-08-01
DOI: 10.1007/s10479-017-2667-0
Issue No: Vol. 267, No. 1-2 (2018)

• Inverse multiple criteria sorting problem
• Authors: Vincent Mousseau; Özgür Özpeynirci; Selin Özpeynirci
Pages: 379 - 412
Abstract: Multiple criteria sorting problem is to assign objects evaluated with multiple criteria to one of the predefined ordered classes. In this study, we consider the inverse multiple criteria sorting problem (IMCSP), in which it is possible to perform actions which have an impact of objects evaluations, hence on the objects classification. IMCSP aims at determining which action(s) to implement so as to provide guaranties on objects classification. Each action has a corresponding cost and impact on the evaluations of objects on each criterion. In this paper we study IMCSP for three different sorting methods: linear, UTADIS and MR-Sort. We consider two levels of information; (i) the sorting method parameters are known explicitly (simple version), and (ii) assignment examples restrict the set of compatible parameters (robust version). We study two types of problems; first, finding the least costly set of actions that guarantees the objects assignment to desired classes, and second, improving the assignment of objects under a limited budget. For each case, we develop a resolution method based on mathematical programming models. Extensive computational experiments on randomly generated instances show the performance and applicability of the approach.
PubDate: 2018-08-01
DOI: 10.1007/s10479-017-2420-8
Issue No: Vol. 267, No. 1-2 (2018)

• A branch-and-cut technique to solve multiobjective integer quadratic
programming problems
• Authors: Fatma Zohra Ouaïl; Mohamed El-Amine Chergui
Pages: 431 - 446
Abstract: This article proposes an exact method to solve the integer programming problem featuring several convex quadratic functions to be minimized (henceforth denoted by MOIQP). The proposed algorithm is a branch and bound based technique suitable for MOIQP problems to generate the set of all efficient solutions. The features of the method are as follows. First, the branch and bound technique allows solving the relaxed problem according to any linear function and progressively generates integer solutions. Then, the efficient cut proposed reduces the search area by truncating domains containing non efficient solutions without having to enumerate them. Finally, at each node of the tree search, three fathoming rules are used to enhance the speed of the procedure. Computational experiments are presented in order to analyze the performance of the algorithm.
PubDate: 2018-08-01
DOI: 10.1007/s10479-017-2698-6
Issue No: Vol. 267, No. 1-2 (2018)

• An interactive algorithm for multiple criteria constrained sorting problem
• Authors: Selin Özpeynirci; Özgür Özpeynirci; Vincent Mousseau
Pages: 447 - 466
Abstract: In this study, we consider the multiple criteria constrained sorting problem and propose an interactive algorithm. The problem is to assign alternatives evaluated on multiple criteria to sorted categories where categories may have size restrictions. The proposed interactive algorithm determines the eligible categories for each alternative and the alternatives with a single eligible category are assigned to the corresponding categories. In case of no such alternative exists, the algorithm asks the DM to assign an alternative to a category and proceeds. The algorithm has the capability to detect and resolve any inconsistencies that may arise during the iterations. We implement the algorithm on two different real life problems considering two underlying sorting methods, MR-Sort and UTADIS and present the computational results.
PubDate: 2018-08-01
DOI: 10.1007/s10479-017-2418-2
Issue No: Vol. 267, No. 1-2 (2018)

• On outperforming social-screening-indexing by multiple-objective portfolio
selection
• Authors: Yue Qi
Pages: 493 - 513
Abstract: Socially responsible investment has been rapidly growing over the past two decades and is typically fulfilled by screening and indexing. Recently, scholars propose multiple-objective portfolio selection for corporate social responsibility (CSR). The proposal raises the question whether multiple-objective portfolio selection can outperform screening and indexing. The question is not fully answered although researchers have made some encouraging trial. By formulating multiple-objective portfolio selection for CSR, I propose a theorem to demonstrate that investors can outperform screening and indexing in expected CSR with identical or better expected return and with identical variance, and can outperform screening and indexing in expected return with identical or better expected CSR and with identical variance. I empirically test the outperformance by component stocks of Dow Jones Industrial Average and report the results.
PubDate: 2018-08-01
DOI: 10.1007/s10479-018-2921-0
Issue No: Vol. 267, No. 1-2 (2018)

• A multi-objective approach to the cash management problem
• Authors: Francisco Salas-Molina; David Pla-Santamaria; Juan A. Rodriguez-Aguilar
Pages: 515 - 529
Abstract: Cash management is concerned with optimizing costs of short-term cash policies of a company. Different optimization models have been proposed in the literature whose focus has been only placed on a single objective, namely, on minimizing costs. However, cash managers may also be interested in risk associated to cash policies. In this paper, we propose a multi-objective cash management model based on compromise programming that allows cash managers to select the best policies, in terms of cost and risk, according to their risk preferences. The model is illustrated through several examples using real data from an industrial company, alternative cost scenarios and two different measures of risk. As a result, we provide cash managers with a new tool to allow them deciding on the level of risk to take in daily decision-making.
PubDate: 2018-08-01
DOI: 10.1007/s10479-016-2359-1
Issue No: Vol. 267, No. 1-2 (2018)

• Third party logistics (3PL) selection for cold chain management: a fuzzy
AHP and fuzzy TOPSIS approach
• Authors: Rajesh Kr. Singh; Angappa Gunasekaran; Pravin Kumar
Pages: 531 - 553
Abstract: Managing value chain of perishable food items or pharmaceutical drugs is known as cold chain management. In India, approximately 30% fruits and vegetables get wasted due to lack of effective cold chain management. Logistic providers play a crucial role in making cold chains more effective. Based on literature review, ten criteria are selected for the third party logistics (3PL) selection process. Some of these criteria are transportation and warehousing cost, logistic infrastructure and warehousing facilities, customer service and reliability, network management, etc. This study illustrates a hybrid approach for selection of 3 PL for cold chain management under fuzzy environment. A hybrid model of Fuzzy AHP and Fuzzy TOPSIS is proposed in this paper for the selection of an appropriate 3PL in order to outsource logistics activities of perishable products. Fuzzy AHP is used to rank different criteria for 3PL selection, then Fuzzy TOPSIS is used to select the best 3 PL based on performance. The results imply that logistic providers should focus on practices such as automation of processes and innovation in cold chain processes to become more competitive.
PubDate: 2018-08-01
DOI: 10.1007/s10479-017-2591-3
Issue No: Vol. 267, No. 1-2 (2018)

• The efficiency of mutual funds
• Authors: Javier Vidal-García; Marta Vidal; Sabri Boubaker; Majdi Hassan
Pages: 555 - 584
Abstract: This paper analyzes the short-term market efficiency of the mutual fund industry around the world. Using a unique database of worldwide domestic equity funds, it employs a parametric (regression model) and non-parametric (data envelopment analysis (DEA) model) approaches to establish a relation between cost (expense ratio, turnover, loads, and risk) and benefit (return) of mutual funds. The empirical results of the parametric approach show a statistically significant negative relationship between expenses and risk-adjusted performance across countries. When we reexamine this relationship using a non-parametric approach, we show, in contrast to our previous result, a positive relationship between expenses and risk-adjusted performance. Thus, using the DEA methodology, we find strong evidence that equity mutual funds around the world are approximately mean–variance efficient.
PubDate: 2018-08-01
DOI: 10.1007/s10479-017-2429-z
Issue No: Vol. 267, No. 1-2 (2018)

• Multiobjective portfolio optimization: bridging mathematical theory with
asset management practice
• Authors: Panos Xidonas; Christis Hassapis; George Mavrotas; Christos Staikouras; Constantin Zopounidis
Pages: 585 - 606
Abstract: We attempt to establish an integrated portfolio optimization business framework, in order to bridge the underlying gap between the complex mathematical theory of multiobjective mathematical programming and asset management practice. Our aim is to assist practitioners and portfolio managers in formulating successful investment strategies, by providing them with an effective decision support tool. In particular, we propose a multiobjective portfolio model, able to support the simultaneous optimization of multiple investment objectives. We also manage to integrate a set of sophisticated real-world non-convex investment policy limitations, such as the cardinality constraints, the buy-in thresholds, the transaction costs, along with particular normative rules. The underlying investment management rationale of the proposed managerial protocol is displayed through an illustrative business flowchart, while we also provide an analytical step-by-step portfolio management business routine. The validity of the model is verified through an extended empirical testing application on the Eurostoxx 50. According to the results, a sufficient number of efficient or Pareto optimal portfolios produced by the model, appear to possess superior out-of-sample returns with respect to the underlying benchmark.
PubDate: 2018-08-01
DOI: 10.1007/s10479-016-2346-6
Issue No: Vol. 267, No. 1-2 (2018)

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