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Publisher: Springer-Verlag   (Total: 2341 journals)

 Annals of Operations Research   [SJR: 1.186]   [H-I: 78]   [8 followers]  Follow         Hybrid journal (It can contain Open Access articles)    ISSN (Print) 1572-9338 - ISSN (Online) 0254-5330    Published by Springer-Verlag  [2341 journals]
• Preface: Multiple criteria optimization and goal programming in science,
engineering, and social sciences
• Authors: Davide La Torre
Pages: 1 - 5
PubDate: 2017-04-01
DOI: 10.1007/s10479-017-2443-1
Issue No: Vol. 251, No. 1-2 (2017)

• Nondifferentiable minimax programming problems with applications
• Authors: Thai Doan Chuong; Do Sang Kim
Pages: 73 - 87
Abstract: Abstract This paper is devoted to the study of optimality conditions and duality in nondifferentiable minimax programming problems and applications. Employing some advanced tools of variational analysis and generalized differentiation, we establish new necessary conditions for optimal solutions of a minimax programming problem involving inequality and equality constraints. Sufficient conditions for the existence of such solutions to the considered problem are also obtained by way of $$L$$ -invex-infine functions. We state a dual problem to the primal one and explore weak, strong and converse duality relations between them. In addition, some of these results are applied to a nondifferentiable multiobjective optimization problem.
PubDate: 2017-04-01
DOI: 10.1007/s10479-015-1843-3
Issue No: Vol. 251, No. 1-2 (2017)

• A chance constrained recourse approach for the portfolio selection problem
• Authors: Meryem Masmoudi; Fouad Ben Abdelaziz
Pages: 243 - 254
Abstract: Abstract This paper deals with the stochastic portfolio selection problem when the loss in the portfolio return is considered as a recourse cost. We suppose that the investor would penalize infeasible solutions for uncertain constraints with the most probable highest recourse cost rather than with the expected recourse cost as in the traditional recourse approach. This novel approach which is mixed with a goal programming approach is used to solve a multi-objective stochastic portfolio selection model. We illustrate the paper results by an empirical example using the weekly returns of the Standard & Poor’s 100 securities between January 2001 and November 2011.
PubDate: 2017-04-01
DOI: 10.1007/s10479-015-1844-2
Issue No: Vol. 251, No. 1-2 (2017)

• The conservative Kalai–Smorodinsky solution for multiple scenario
bargaining
• Authors: L. Monroy; V. Rubiales; A. M. Mármol
Pages: 285 - 299
Abstract: Abstract In this paper we address two-person bargaining problems under uncertainty where several states of nature or future scenarios are considered. We propose a solution concept based on the distance to a utopia minimum outcome vector, which guarantees conservative levels of achievement for the agents. We also provide an axiomatic characterization for a significant class of these bargaining problems. An extension of the classic model of firm-union negotiation, which includes situations where uncertainty about the consequences of the agreements have to be taken into account, is analyzed in this framework.
PubDate: 2017-04-01
DOI: 10.1007/s10479-015-1894-5
Issue No: Vol. 251, No. 1-2 (2017)

• A fuzzy multi-criteria approach for robust operating room schedules
• Authors: Sebastian Rachuba; Brigitte Werners
Pages: 325 - 350
Abstract: Abstract Operating room schedules are regularly influenced by uncertain demands such as unknown surgery durations or randomly arriving emergency patients. The performance of these schedules depends on the information available about these uncertainties when designing the schedules. We focus on an offline operational planning level which assigns patients to days and rooms without focusing on the intra-day sequence. A sufficient amount of time per day is to be reserved for elective and emergency surgeries. At the same time we observe that the performance of a particular schedule influences several stakeholders’ interests. We therefore combine the aspects of uncertain planning parameters and multiple stakeholders’ interests and investigate the performance of schedules for operating rooms using a dedicated robust multi-criteria optimisation approach. We compute a robust compromise schedule focusing on stochastic surgery times and different objectives and simultaneously reserve time windows dedicated to randomly arriving emergency demand. In order to evaluate the schedule’s quality, we perform an extensive simulation study and demonstrate to what extent each robust schedule achieves the mentioned goals. In a second step, we perform a sensitivity analysis in order to investigate how significant changes in assumptions about the stochastic model parameters affect the level of achievement of the different objectives.
PubDate: 2017-04-01
DOI: 10.1007/s10479-015-1926-1
Issue No: Vol. 251, No. 1-2 (2017)

• An extension on super slacks-based measure DEA approach
• Authors: Ya Chen; Yongjun Li; Liang Liang; Huaqing Wu
Abstract: Abstract In order to break the tie of efficient decision-making units, super-efficiency data envelopment analysis is proposed to fully discriminate them. Recently, a slacks-based version of the super slacks-based measure (S-SBM) is developed and a novel two-stage approach is proposed to calculate both super-efficiency score by the S-SBM model and efficiency score by the slacks-based measure model. In this paper, we extend the approach to consider continuity of efficiency scores. We illustrate the discontinuity of efficiency measure, and define a continuous slacks-based measure which is proved continuous and directly calculated. An interesting efficiency zone category is also provided. In addition, this paper investigates the relationship among the super-efficiency measures of the proposed approach and some existing approaches under variable returns to scale.
PubDate: 2017-04-19
DOI: 10.1007/s10479-017-2495-2

• Decomposition method for oligopolistic competitive models with common
environmental regulation
• Authors: E. Allevi; A. Gnudi; I. V. Konnov; G. Oggioni
Abstract: Abstract Global climate change has encouraged international and regional adoption of environmental policies aiming at reducing the generation of greenhouse gas emissions. Europe has taken the leadership in environmental regulations by introducing the European-Union Emissions Trading System (EU-ETS) in 2005 and other policies to mitigate carbon emissions and increase the efficiency of production processes. These environmental policies have significantly affected the production choices of the European energy and industrial sectors. In this paper, we consider a market where a set of players (firms) produce different commodities under a common environmental regulation that limits their emissions. Due to these environmental restrictions, the problem is treated as a generalized non-cooperative game where players have joint (environmental) constraints caused by the common and compulsory emission regulation. The problem is to find a natural mechanism for attaining the corresponding generalized equilibrium state. We suggest a share allocation method, which yields a suitable decomposition type procedure and replaces the initial problem with a sequence of non-cooperative games on Cartesian product sets. We also show that its implementation can be simplified essentially after the application of a regularized penalty method. In the case study, we take inspiration from the EU-ETS and we introduce an environmental regulation that restricts the carbon emissions of firms representing the energy, cement, and steel sectors respectively in Germany, France, Italy, and Spain. Our results confirm the important role played by energy sector in reducing carbon emissions.
PubDate: 2017-04-18
DOI: 10.1007/s10479-017-2494-3

• Reverse auctions with regret-anticipated bidders
• Authors: Xiaohu Qian; Shu-Cherng Fang; Min Huang; Qi An; Xingwei Wang
Abstract: Abstract Suppliers may experience emotional/behavioral consequences of anticipated regrets that consist of winner and loser regrets in first- and second-price sealed-bid reverse auctions. Constructing mathematical models that incorporate regret theory to derive closed-form solutions of regret-anticipated suppliers’ bid decisions, this paper theoretically examines the effects of anticipated regrets on suppliers’ bid prices, buyer’s expected procurement cost and auction format decision. Comparing with the no regret scenario, we find that winner regret has adverse effects on the buyer’s expected procurement cost in first-price sealed-bid reverse auctions with regret-anticipated suppliers. To mitigate the adverse effects, we propose using the reserve price strategy for the buyer with theoretical analysis and numerical supports. An interesting analysis reveals that as the number of suppliers increases, the optimal reserve price increases or decreases depending on the degree of winner regret is lower or higher than that of loser regret. Also, the classical revenue equivalence theorem no longer holds when the degree of winner regret differs from that of loser regret.
PubDate: 2017-04-11
DOI: 10.1007/s10479-017-2475-6

• Improving hospital layout planning through clinical pathway mining
• Authors: Ines Verena Arnolds; Daniel Gartner
Abstract: Abstract Clinical pathways (CPs) are standardized, typically evidence-based health care processes. They define the set and sequence of procedures such as diagnostics, surgical and therapy activities applied to patients. This study examines the value of data-driven CP mining for strategic healthcare management. When assigning specialties to locations within hospitals—for new hospital buildings or reconstruction works—the future CPs should be known to effectively minimize distances traveled by patients. The challenge is to dovetail the prediction of uncertain CPs with hospital layout planning. We approach this problem in three stages: In the first stage, we extend a machine learning algorithm based on probabilistic finite state automata (PFSA) to learn significant CPs from data captured in hospital information systems. In that stage, each significant CP is associated with a transition probability. A unique feature of our approach is that we can generalize the data and include those CPs which have not been observed in the data but which are likely to be followed by future patients according to the pathway probabilities obtained from the PFSA. At the same time, rare and non-significant CPs are filtered out. In the second stage, we present a mathematical model that allows us to perform hospital layout planning decisions based on the CPs, their probabilities and expert knowledge. In the third stage, we evaluate our approach based on different performance measures. Our case study results based on real-world hospital data reveal that using our CP mining approach, distances traveled by patients can be reduced substantially as compared to using a baseline method. In a second case study, when using our approach for reconstructing a hospital and incorporating expert knowledge into the planning, existing layouts can be improved.
PubDate: 2017-04-09
DOI: 10.1007/s10479-017-2485-4

• Risk minimization in multi-factor portfolios: What is the best
strategy?
• Authors: Philipp J. Kremer; Andreea Talmaciu; Sandra Paterlini
Abstract: Abstract Exposures to risk factors, as opposed to individual securities or bonds, can lead to an ex-ante improved risk management and a more transparent and cheaper way of developing active asset allocation strategies. This paper provides an extensive analysis of eight state-of-the-art risk-minimization schemes and compares risk factor performance in a conditional performance analysis, contrasting good and bad states of the economy. The investment universe spans a total of 25 risk factors, including size, momentum, value, high profitability and low investments, from five non-overlapping regions (i.e., USA, UK, Japan, Developed Europe ex. UK and, Asia ex. Japan). Considering as investment period the interval from May 2004 to June 2015, our results show that each single factor yields positive premia in exchange for risk, which can lead to considerable underperformance and extensive recovery periods during times of crisis. The best factor investments can be found in Asia ex. Japan and the US. However, risk factor based portfolio construction across the various regions enables the investor to exploit low correlation structures, reducing the overall volatility, as well as tail- and extreme risk measures. Finally, the empirical results point towards the long-only global minimum variance portfolio, as the best risk minimization strategy.
PubDate: 2017-04-07
DOI: 10.1007/s10479-017-2467-6

• Bridging the research-practice gap in disaster relief: using the IFRC Code
of Conduct to develop an aid model
• Authors: John B. Coles; Jing Zhang; Jun Zhuang
Abstract: Abstract Bridging the gap between research and practice has been a recognized problem in many fields, and has been especially noticeable in the field of disaster relief. As the number and impact of disasters have increased, there has been a jump in interest from the research community in an attempt to provide tools and solutions for some of the challenges in the field. The International Federation of Red Cross and Red Crescent Societies (IFRC) Code of Conduct (CoC) for Disaster Operations provides a qualitative set of guidelines that is an excellent building block for operational theory, but is insufficiently rigorous in guiding quantitative decision making. In this paper, we review the CoC, exploring each of the ten core principles and identifying three significant operational trade-offs. We then propose a model framework that can be implemented as a stand-alone model, or can be used as a foundation for other quantitative aid allocation models. Finally, we provide an example of how the proposed model could be used to guide decision making in a Microsoft Excel $$^{{\textregistered }}$$ environment using CoinOR’s OpenSolver $$^{\textregistered }$$ . New insights in the field of aid disbursement are provided by examining the challenges of financial management and investment as dictated by the CoC. This paper fills a unique gap in the literature by addressing the issue of financial allocation as guided by a qualitative standard used by the disaster relief community, and serves as a complement to the work in the field of humanitarian logistics.
PubDate: 2017-04-06
DOI: 10.1007/s10479-017-2488-1

• A service model for nutrition supplement prediction based on Fuzzy Bayes
model using bigdata in livestock
• Authors: Saraswathi Sivamani; Jongsun Choi; Yongyun Cho
Abstract: Abstract The paper proposes a novel method in the decision support system for the nutritional management of livestock using the Bayesian model based on fuzzy rules. The objective is to analysis the decision based on fuzzy rules over the nutrition management that helps to improve the health of the livestock. Bayesian logic mainly focuses on the probabilities of the food intake with respect to the Food Intake Amount, Cow Stage and weight of the livestock. The conditional probability of the Bayesian reasoning is introduced along with the fuzzy rule, to determine the health status of the livestock. The fuzzy logic technique helps to decide on the decision system, when there are more than one dependencies. In this paper, the total digestible nutrient of the cow is determined over the period of time to get the rate of probability, and the fuzzy rule is applied to determine the health status of the cow, to predict the nutritional intake in the livestock.
PubDate: 2017-04-04
DOI: 10.1007/s10479-017-2490-7

• Two-warehouse inventory model for non-instantaneous deteriorating items
with stock-dependent demand and inflation using particle swarm
optimization
• Authors: Sunil Tiwari; Chandra K. Jaggi; Asoke Kumar Bhunia; Ali Akbar Shaikh; Mark Goh
Abstract: Abstract We investigate a two-warehouse inventory model for non-instantaneous deteriorating items with partial backlogging and stock-dependent demand under inflationary conditions. Shortages are allowed. The backlogging rate is variable and depends on the waiting time for the next replenishment. This paper seeks to determine an optimal replenishment policy that minimizes the present value of the total cost per unit time. The necessary and sufficient conditions for the existence and uniqueness of the optimal solution are found. The corresponding problems are formulated and solved with particle swarm optimization. Numerical experimentation and post-optimality analysis are conducted.
PubDate: 2017-04-04
DOI: 10.1007/s10479-017-2492-5

• Single-machine and parallel-machine serial-batching scheduling problems
with position-based learning effect and linear setup time
• Authors: Jun Pei; Bayi Cheng; Xinbao Liu; Panos M. Pardalos; Min Kong
Abstract: Abstract This paper introduces the serial batching scheduling problems with position-based learning effect, where the actual job processing time is a function of its position. Two scheduling problems respectively for single-machine and parallel-machine are studied, and in each problem the objectives of minimizing maximum earliness and total number of tardy jobs are both considered respectively. In the proposed scheduling models, all jobs are first partitioned into serial batches, and then all batches are processed on the serial-batching machine. We take some practical production features into consideration, i.e., setup time before processing each batch increases with the time, regarded as time-dependent setup time, and we formalize it as a linear function of its starting time. Under the single-machine scheduling setting, structural properties are derived for the problems with the objectives of minimizing maximum earliness and number of tardy jobs respectively, based on which optimization algorithms are developed to solve them. Under the parallel-machine scheduling setting, a hybrid VNS–GSA algorithm combining variable neighborhood search (VNS) and gravitational search algorithm (GSA) is proposed to solve the problems with these two objectives respectively, and the effectiveness and efficiency of the proposed VNS–GSA are demonstrated and compared with the algorithms of GSA, VNS, and simulated annealing (SA). This paper demonstrates that the consideration of different objectives leads to various optimal decisions on jobs assignment, jobs batching, and batches sequencing, which generates a new insight to investigate batching scheduling problems with learning effect under single-machine and parallel-machine settings.
PubDate: 2017-04-04
DOI: 10.1007/s10479-017-2481-8

• Predicting pediatric clinic no-shows: a decision analytic framework using
elastic net and Bayesian belief network
• Authors: Kazim Topuz; Hasmet Uner; Asil Oztekin; Mehmet Bayram Yildirim
Abstract: Abstract No-shows are becoming a major problem in primary care facilities, creating additional costs for the facility while adversely affecting the quality of patient care. Accurately predicting no-shows plays an important role in the overbooking strategy. In this study, a hybrid probabilistic prediction framework based on the elastic net (EN) variable-selection methodology integrated with probabilistic Bayesian Belief Network (BBN) is proposed. The study predicts the “no-show probability of the patient(s)” using demographics, socioeconomic status, current appointment information, and appointment attendance history of the patient and the family. The proposed framework is validated using ten years of local pediatric clinic data. It is shown that this EN-based BBN framework is a comparable prediction methodology regarding the best approaches found in the literature. More importantly, this methodology provides novel information on the interrelations of predictors and the conditional probability of predicting “no-shows.” The output of the model can be applied to the appointment scheduling system for a robust overbooking strategy.
PubDate: 2017-04-04
DOI: 10.1007/s10479-017-2489-0

• Multiproduct price optimization under the multilevel nested logit model
• Authors: Hai Jiang; Rui Chen; He Sun
Abstract: Abstract We study the multiproduct price optimization problem under the multilevel nested logit model, which includes the multinomial logit and the two-level nested logit models as special cases. When the price sensitivities are identical within each primary nest, that is, within each nest at level 1, we prove that the profit function is concave with respect to the market share variables. We proceed to show that the markup, defined as price minus cost, is constant across products within each primary nest, and that the adjusted markup, defined as price minus cost minus the reciprocal of the product between the scale parameter of the root nest and the price-sensitivity parameter of the primary nest, is constant across primary nests at optimality. This allows us to reduce the multidimensional pricing problem to an equivalent single-variable maximization problem involving a unimodal function. Based on these findings, we investigate the oligopolistic game and characterize the Nash equilibrium. We also develop a dimension reduction technique which can simplify price optimization problems with flexible price-sensitivity structures.
PubDate: 2017-04-03
DOI: 10.1007/s10479-017-2478-3

• Resource deployment and donation allocation for epidemic outbreaks
• Authors: Azrah Anparasan; Miguel Lejeune
Abstract: Abstract Non-profit organizations play a central role in responding to the devastating consequences of epidemic outbreaks in developing economies. We propose an epidemic response model in resource-limited countries that determines the number, size, and location of treatment facilities, deploys critical medical staff, locates ambulances to triage points, and organizes the transportation of severely ill patients to treatment facilities. The model is based on the 2010 cholera outbreak in Haiti and is general enough to be used for similar epidemic outbreaks. The model enables not-for-profit decision-makers to assess health care triage capabilities, transportation needs, and requirements for medical personnel staffing and deployment. We propose an algorithmic procedure using hierarchical constraints and valid inequalities that reduce the solution time by one order of magnitude. Additionally, we propose a framework that can be used to optimally allocate a donation, to determine a list of priorities for earmarked donations, and to perform a cost-benefit analysis of an intervention strategy financed by a donation. The model is formulated as a large integer problem with many symmetries. An extended analysis based on the 2010 cholera outbreak in Haiti provides insights about: the criticality of the resources, the implementation of a balanced response strategy, the optimal allocation of resources in terms of the severity of the attack rate, and the benefits of the proposed response approach with respect to other intervention strategies.
PubDate: 2017-04-03
DOI: 10.1007/s10479-016-2392-0

• Integer quadratic fractional programming problems with bounded variables
• Authors: Ekta Jain; Kalpana Dahiya; Vanita Verma
Abstract: Abstract This paper develops an algorithm for solving quadratic fractional integer programming problems with bounded variables (QFIPBV). The method provides complete ranking and scanning of the integer feasible solutions of QFIPBV by establishing the existence of a linear or a linear fractional function, which acts as a lower bound on the values of the objective function of QFIPBV over the entire feasible set. The method involves ranking and scanning of the set of optimal integer feasible solutions of the linear or linear fractional program so constructed which requires introduction of various cuts at intermediate steps, for which, a new technique has been developed in the current paper. Numerical examples are included in support of the theory.
PubDate: 2017-04-03
DOI: 10.1007/s10479-017-2484-5

• Optimal decision for the market graph identification problem in a sign
similarity network
• Authors: V. A. Kalyagin; A. P. Koldanov; P. A. Koldanov; P. M. Pardalos
Abstract: Abstract Research into the market graph is attracting increasing attention in stock market analysis. One of the important problems connected with the market graph is its identification from observations. The standard way of identifying the market graph is to use a simple procedure based on statistical estimations of Pearson correlations between pairs of stocks. Recently a new class of statistical procedures for market graph identification was introduced and the optimality of these procedures in the Pearson correlation Gaussian network was proved. However, the procedures obtained have a high reliability only for Gaussian multivariate distributions of stock attributes. One of the ways to correct this problem is to consider different networks generated by different measures of pairwise similarity of stocks. A new and promising model in this context is the sign similarity network. In this paper the market graph identification problem in the sign similarity network is reviewed. A new class of statistical procedures for the market graph identification is introduced and the optimality of these procedures is proved. Numerical experiments reveal an essential difference in the quality between optimal procedures in sign similarity and Pearson correlation networks. In particular, it is observed that the quality of the optimal identification procedure in the sign similarity network is not sensitive to the assumptions on the distribution of stock attributes.
PubDate: 2017-04-03
DOI: 10.1007/s10479-017-2491-6

• Extraction dependence structure of distorted copulas via a measure of
dependence
• Authors: Hien Duy Tran; Uyen Hoang Pham; Sel Ly; T. Vo-Duy
Abstract: Abstract Copulas are one of the most powerful tools in modeling dependence structure of multivariate variables. In Tran et al. (Integrated uncertainty in knowledge modelling and decision making. Springer, Berlin, pp 126–137, 2015), we have constructed a new measure of dependence, $$\lambda (C),$$ based on Sobolev norm for copula C which can be used to characterize comonotonicity, countermonotonicity and independence of random vectors. This paper aims to use the measure $$\lambda (C)$$ to study how dependence structure of a distorted copula after being transformed by a distortion function is changed. Firstly, we propose two methods to estimate the measure $$\lambda (C)$$ , one for known copula C using conditional copula-based Monte Carlo simulation and the latter for unknown copula dealing with empirical data. Thereafter, PH-transform $$g_{ PH }$$ of extreme value copulas and Wang’s transform $$g_\gamma$$ of normal and product copula are studied, and we observe their dependence behaviors changing through variability of the measure $$\lambda (C)$$ . Our results show that dependence structure of distorted copulas is subject to comonotonicity as increasing the parametric $$\gamma$$ .
PubDate: 2017-04-01
DOI: 10.1007/s10479-017-2487-2

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