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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.

In this paper, we introduce a new class of higher-order cone-convex, \((K_1, K_2)\)-pseudoconvex and quasiconvex functions which encapsulates several already known functions. Higher-order sufficient optimality conditions have been established for a vector optimization problem over cones by using these functions, under weaker conditions on multipliers as compared to other papers in this domain. Wolfe type and Mond–Weir type higher-order duals are formulated and corresponding duality results are established. A number of previously studied problems appear as special cases of our primal-dual models. In case of nonlinear programming problem, our higher-order duals reduce to the corresponding higher-order duals given by Mangasarian (J Math Anal Appl 51:607–620, 1975) and Mond and Zhang (Generalized convexity, generalized monotonicity: recent results. Kluwer, Dordrecht, pp 357–372, 1998).

This paper proposes an investor-friendly random fuzzy portfolio selection model considering both robustness and adjustment of future asset returns derived from investor’s forecasts for financial tendency using a fuzzy inference method. It is important to predict the price or the return of each asset appropriately considering current market trends in portfolio optimization. In this paper, a standard multi-factor model, namely Arbitrage Pricing Theory (APT), is introduced as an asset pricing model. In addition, in order to extend standard APT by integrating important rules of current markets trends derived from technical analysis and fundamental analysis, each factor of APT is assumed to be a random fuzzy variable whose mean is adjusted by the fuzzy reasoning method, particularly product–sum-gravity method. Furthermore, it is also important for the investor to reduce the worst case of the total loss in terms of risk-averse. Therefore, worst-case conditional Value-at-Risk, which is a robust programming approach without assuming some specific random distribution, is considered. Since the proposed model is formulated as a biobjective programming problem both minimizing the value of worst-case conditional Value-at-Risk and maximizing the total expected return, it is equivalently transformed into the deterministic nonlinear programming problem using the satisficing trade-off method, and the efficient algorithm to obtain the optimal portfolio is developed. By solving our proposed model, the investor can obtain the risk-averse optimal portfolio with the large total return complying with current market trends.

Management of disaster relief operations is a complex task involving pre-disaster, during disaster and post-disaster operations. It requires the involvement and coordination of multiple actors and processes. Traditionally, operations research applications in general and specifically for disaster relief have been made, largely, on quantitative and analytical front. There is a lack of qualitative and interpretive approaches in operations research applications. This paper uses an interpretive method, SAP-LAP (situation, actor, process, learning, action, performance) framework and linkages, in the context of disaster management. It enables to develop a theoretical framework for disaster management answering the fundamental questions of theory building. The paper first provides a selective review of disaster management and identifies the gap in building theoretical framework for the same. It then describes the methodology in terms of SAP-LAP framework and linkages with a broad appreciation of its application in operations as well as strategic management. This methodology is applied to develop a theoretical framework for disaster management. The paper finally discusses both the theoretical and practical implications of the proposed framework and concludes with future directions of research.

This paper develops an EOQ inventory model that considers the demand rate as a function of stock and selling price. Shortages are permitted and two cases are studied: (i) complete backordering and (ii) partial backordering. The inventory model is for a deteriorating seasonal product. The product’s deterioration rate is controlled by investing in the preservation technology. The main purpose of the inventory model is to determine the optimum selling price, ordering frequency and preservation technology investment that maximizes the total profit. Additionally, the paper proves that the total profit is a concave function of selling price, ordering frequency and preservation technology investment. Therefore, a simple algorithm is proposed to obtain the optimal values for the decision variables. Several numerical examples are solved and studied along with a sensitivity analysis.

Recently, financial crisis occurred frequently, such as Asian crisis, sub-prime loan crisis and so on. And, financial contagion behavior analysis is an important subject in the field of international finance. This paper analyzes the financial contagion behavior of US subprime crisis and describes the co-movement behavior between US stock index return and the stock index returns of tested countries by complex network approach. The co-movement modes are defined by a coarse graining procedure, and then by weighted complex network models and evolutionary models transformation characteristics between the modes are analyzed. In order to analyze the sub-prime loan crisis contagion from US to other countries, we selected three main stock market indexes (China, Japan and Germany), S&P 500 index to present an empirical analysis, and the stock market index data is from 2006.1.1 to 2009.3.31. To analyze the contagion effect of the sub-prime crisis, the data is divided into three segments to be analyzed based on complex network approach. The empirical result shows that the co-movement modes of the S&P 500 index and the stock indexes of other countries are clustered around a few critical modes during the evolution. The co-movement modes have the characteristic of grouping, and the conversion of the co-movement modes requires an average of 3–5 days. This paper confirms the sub-prime crisis contagion behavior by comparing the parameters including degree, clustering coefficient and betweenness centrality in three stages. This research may provide further information between S&P 500 index and other stock indexes for the co-movement. More importantly, a new approach is provided for testing the financial contagion effect in this paper.

Environmental disaster and rapid climate change cautioned government bodies and environmental protection agencies, which resulted in legislations forcing business organizations to restrict their carbon emissions to make their supply chain a disaster resilient supply chain. In supply chain, carbon emissions are seen right away from the procurement of raw materials till the distribution of finished goods through logistics. Hence, there is strong need to fix carbon emissions and to make existing supply chain an environmentally sustainable supply chain to gain environmental disaster relief. This paper primarily focuses on minimizing carbon emissions in the raw material procurement and its logistics using a cap-and-trade method. In the raw material procurement and logistics, the carbon emission is caused during the ordering, transporting and holding the procured items from various suppliers. The paper aims to model sustainability-resilience link at the supply chain design level through the procurement and logistics of raw material which is considered as the primary stage of any supply chain. A dynamic non-linear mixed integer model featuring an environmental sustainability through cap-and-trade method of carbon emission is proposed that can be used to design sustainable procurement logistics for disaster resilient supply chain management. The proposed model in the paper is referred as SPL_DRSCM (sustainable procurement and logistics for disaster resilient supply chain management) and is validated through illustrations. Important useful managerial and practical insights are obtained from set of five different deterministic data sets. Proposed model for SPL_DRSCM MINLP shows significant cost saving while optimizing procurement and its logistics under carbon emission constraint. Comparative analysis is conducted and detailed numerical results are presented.

A Bayesian network is a promising probabilistic model to represent causal relations between nodes (random variables). One of the major research issue in a Bayesian network is how to infer causal relations from a dataset by constructing better heuristic learning algorithms. Many kinds of approaches were so far introduced, and estimation of distribution algorithms (EDAs) are one of the promising causal discovery algorithms. However, the performance of EDAs is considerably dependent on the quality of the first population because new individuals are reproduced from the previous populations. In this paper, we introduce a new initialization method for EDAs that extracts promising candidate causal relations based on causal scores. Then, we used the promising relations to construct a better first population and to reproduce better individuals until the learning algorithm is terminated. Experimental results show that EDAs infer a more number of correct causal relations when promising relations were used in EDA based structure learning. It means that the performance of EDAs can be improved by providing better local search space, and it was the promising relations in this paper.

Bond immunization is an important topic in portfolio management. This paper presents a scenario based optimization framework for solving a cash flow matching problem. In this problem, the time horizon of the cash flow generated by the liability is longer than the maturities of the available bonds, and the interest rates are uncertain. Bond purchase decisions are made each period to generate cash flows to cover the obligations due in the future. We use buffered probability of exceedance (bPOE) and conditional value-at-risk (CVaR) to control for the risk of shortfalls. The initial cost of the hedging portfolio of bonds is minimized and optimal positions in bonds are calculated at all time periods. We also study the methodology when solving the optimization problem to minimize bPOE instead of CVaR, which has important practical relevance. The methodology we present in this paper is quite general and can be extended to other financial optimization problems. We use portfolio safeguard optimization package to solve the optimization problems.

In this paper, a deterministic inventory control model with deterioration is developed. Here, the deterioration rate follows stochastic deterioration, especially Weibull distribution deterioration. A time-dependent demand approach is introduced to show the applicability of our proposed model and to be up-to-date with respect to time. The main purpose of the paper is to investigate the optimal retailer's replenishment decisions for deteriorating items including time-dependent demand for demonstrating more practical circumstances within economic-order quantity frameworks. Keeping in mind the criterion of modern era, we consider that the holding cost is totally dependent on time, and shortages are allowed for this model. Subject to the formulated model, we minimize the total inventory cost. The mathematical model is explored by numerical examples to validate the proposed model. A sensitivity analysis of the optimal solution with regard to important parameters is also carried out to elaborate the quality, e.g., stability, of our result and to possibly modify our model. The paper ends with a conclusion and an outlook to future studies.

For solving a sequential decision-making problem in a non-Markovian domain, standard dynamic programming (DP) requires a complete mathematical model; hence, a totally model-based approach. By contrast, this paper describes a totally model-free approach by actor-critic reinforcement learning with recurrent neural networks. The recurrent connections (or context units) in neural networks act as an implicit form of internal state (i.e., history memory) for developing sensitivity to hidden non-Markovian dependencies, rendering the process Markovian implicitly and automatically in a totally model-free fashion. That is, the model-free recurrent-network agent neither learns transitional probabilities and associated rewards, nor by how much the state space should be enlarged so that the Markov property holds. For concreteness, we illustrate time-lagged path problems, in which our learning agent is expected to learn a best (history-dependent) policy that maximizes the total return, the sum of one-step transitional rewards plus special “bonus” values dependent on prior transitions or decisions. Since we can obtain an optimal solution by model-based DP, this is an excellent test on the learning agent for understanding its model-free learning behavior. Such actor-critic recurrent-network learning might constitute a mechanism which animal brains use when experientially acquiring skilled action. Given a concrete non-Markovian problem example, the goal of this paper is to show the conceptual merit of totally model-free learning with actor-critic recurrent networks, compared with classical DP (and other model-building procedures), rather than pursue a best recurrent-network learning strategy.

In this paper we will experimentally prove that after recalculating the motion capture (MoCap) data to position-invariant representation it can be directly used by classifier to successfully recognize various actions types. The assumption on classifier is that it is capable to deal with objects that are described by hundreds of numeric values. The second novelty of this paper is application of neural network trained with the parallel stochastic gradient descent, Random Forests and Support Vector Machine with Gaussian radial basis kernel to perform classification task on gym exercises and karate techniques MoCap datasets. We have tested our approach on two datasets using k-fold cross-validation method. Depending of the dataset we have obtained averaged recognition rate from 100 to 97 %. Our results presented in this work give very important hints for developing similar actions recognition systems because proposed features selection and classification setup seems to guarantee high efficiency and effectiveness.

Interpretive structural modelling (ISM) has been further interpreted in the form of total interpretive structural modelling (TISM). These are graphical models that represent the hierarchical relationships and help in better and precise conceptualization and theory building. ISM only interprets the nodes in a digraph, but TISM interprets both nodes and links. The errors observed in applications of ISM and TISM reported in past have acted as motivation for this paper to provide checks and guidelines for correctness of total interpretive structural models. The paper first gives an overview of past applications of TISM. The process of TISM is first outlined and then the guidelines and thumb rules are provided to check the correctness of TISM at each step. Some typical errors in TISM models and their modifications are discussed to help future modellers to translate their ill-structured mental models into sound theoretical models. A discussion on usefulness of TISM for big data analytics for theory building is provided and future directions of research are outlined.

Development and application of the data envelopment analysis (DEA) method, have been the subject of numerous reviews. In this paper, we consider the papers that apply DEA methods specifically to financial services, or which use financial services data to experiment with a newly introduced DEA model. We examine 620 papers published in journals indexed in the Web of Science database, from 1985 to April 2016. We analyse the sample applying citations network analysis. This paper investigates the DEA method and its applications in financial services. We analyse the diffusion of DEA in three sub-samples: (1) banking groups, (2) money market funds, and (3) insurance groups by identifying the main paths, that is, the main flows of the ideas underlying each area of research. This allows us to highlight the main approaches, models and efficiency types used in each research areas. No unique methodological preference emerges within these areas. Innovations in the DEA methodologies (network models, slacks based models, directional distance models and Nash bargaining game) clearly dominate recent research. For each subsample, we describe the geographical distribution of these studies, and provide some basic statistics related to the most active journals and scholars.

This paper explores the study of multi-choice multi-objective transportation problem (MCMTP) under the light of conic scalarizing function. MCMTP is a multi-objective transportation problem (MOTP) where the parameters such as cost, demand and supply are treated as multi-choice parameters. A general transformation procedure using binary variables is illustrated to reduce MCMTP into MOTP. Most of the MOTPs are solved by goal programming (GP) approach, but the solution of MOTP may not be satisfied all times by the decision maker when the objective functions of the proposed problem contains interval-valued aspiration levels. To overcome this difficulty, here we propose the approaches of revised multi-choice goal programming (RMCGP) and conic scalarizing function into the MOTP, and then we compare among the solutions. Two numerical examples are presented to show the feasibility and usefulness of our paper. The paper ends with a conclusion and an outlook on future studies.

The discrete time “bomber problem” has been one of the longest standing open problems in operations research. In particular, the validity of one of the natural monotonicity conjectures—known as property (B)—has been an unresolved issue since 1968. In this paper we report 41 counterexamples to property (B) of this problem. We have found them by computing the exact solutions for nearly one million pairs of parameter values utilizing the GNU multiple precision arithmetic library. All our counterexamples can readily be verified using a simple Mathematica program included in this paper.

Blood transfusion services are vital components of healthcare systems all over the world. In this paper, a generalized network optimization model is developed for a complex blood supply chain in accordance with Iranian blood transfusion organization (IBTO) structure. This structure consist of four types facilities. Blood collection centers, blood collection and processing centers, mobile teams and blood transfusion center have various duties in IBTO structure. The major contribution is to develop a novel hybrid approach based on stochastic programming, e-constraint and robust optimization (HSERO) to simultaneously model two types of uncertainties by including stochastic scenarios for total blood donations and polyhedral uncertainty sets for demands. An accelerated stochastic Benders decomposition algorithm is proposed to solve the problem modeled in this paper. To speed up the convergence of the solution algorithm, valid inequalities are introduced to get better quality lower bounds. In addition, a Pareto-optimal cut generation scheme is used to strengthen the Benders optimality cuts. Numerical illustrations are given to verify the mathematical formulation and also to show the benefits of using the HSERO approach. At the end, the performance improvements achieved by the valid inequalities and the Pareto-optimal cuts are demonstrated in a real world application.

The aim of this paper is to compare the performance of a local solution technique—namely Sequential Linear Programming (SLP) employing random starting points—with state-of-the-art global solvers such as Baron and more sophisticated local solvers such as Sequential Quadratic Programming and Interior Point for the pooling problem. These problems can have many local optima, and we present a small example that illustrates how this can occur.We demonstrate that SLP—usually deemed obsolete since the arrival of fast reliable SQP solvers, Interior Point Methods and sophisticated global solvers—is still the method of choice for an important class of pooling problems when the criterion is the quality of the solution found within a given acceptable time budget. On this measure SLP significantly ourperforms all other tested algorithms.In addition we introduce a new formulation, the qq-formulation, for the case of fixed demands, that exclusively uses proportional variables. We compare the performance of SLP and the global solver Baron on the qq-formulation and other common formulations. While Baron with the qq-formulation generates weaker bounds than with the other formulations tested, for both SLP and Baron the qq-formulation finds the best solutions within a given time budget. The qq-formulation can be strengthened by pq-like cuts in which case the same bounds as for the pq-formulation are obtained. However the associated time penalty due to the additional constraints results in poorer solution quality within the time budget.

This paper proposes a multi-start randomized heuristic for solving real-life crew rostering problems in airlines. The paper describes realistic constrains, regulations, and rules that have not been considered in the literature so far. Our algorithm is designed to provide quality solutions satisfying these real-life specifications while, at the same time, it aims at balancing the workload distribution among the different crewmembers. Thus, our approach promotes corporate social responsibility by distributing the workload in a fair way and avoiding that some crewmembers get unnecessarily overstressed. Despite its importance in real-life applications, these aspects have seldom been considered in the crew scheduling literature, where most solving approaches refer to simplified models and are tested on non-realistic benchmarks. The experimental tests show that our algorithm is capable of generating feasible quality solutions to real-life crew rostering problems in just a few seconds. These times are orders of magnitude lower than the times currently employed by some airlines to obtain a single feasible solution, since the ‘optimal’ solutions provided by most commercial software usually require additional adjustments in order to meet all the real-life specifications.

The quay crane scheduling problem is one of the major problems of quayside operational planning in container terminals. The operational efficiency of quay cranes is a large determinant of the overall container terminal efficiency; thus, in an effort to maximize throughput, more and more emphasis is placed on systematically addressing and improving quay crane operations. However, the resulting formulations are highly complex and thus not solvable using commercial software. In the first part of the present paper, we develop a formulation that overcomes this challenge. This allows for solving the model using CPLEX, even for large size instances, which other notable work from the literature fails to solve. The second part of this paper addresses a crucial point which has rarely been accounted for, which is ship stability. A heuristic is developed to solve the extended problem, as it is no longer solvable in CPLEX. The remaining objective of this work is to extend this problem to the multi-ship case. However, once again the problem is insolvable for large instances using CPLEX, even without accounting for stability constraints. We develop a Lagrangian relaxation based algorithm that decomposes the problem by ship, which is solved efficiently as a single ship case. The Lagrangian multipliers are updated using the cutting plane method and the solution of the Lagrangian master problem provides an upper bound on the optimal value of the Lagrangian lower bound. Upper bounds on the optimal value of the original problem are obtained using a constructive heuristic, and through computational experiments we demonstrate the performance of the Lagrangian relaxation-based procedures.

Few topics have generated more discourse in recent years than big data analytics. Given their knowledge of analytical and mathematical methods, operations research (OR) scholars would seem well poised to take a lead role in this discussion. Unfortunately, some have suggested there is a misalignment between the work of OR scholars and the needs of practicing managers, especially those in the field of operations and supply chain management where data-driven decision-making is a key component of most job descriptions. In this paper, we attempt to address this misalignment. We examine both applied and scholarly applications of OR-based big data analytical tools and techniques within an operations and supply chain management context to highlight their future potential in this domain. This paper contributes by providing suggestions for scholars, educators, and practitioners that aid to illustrate how OR can be instrumental in solving big data analytics problems in support of operations and supply chain management.

Bootstrapping time series is one of the most acknowledged tools to study the statistical properties of an evolutive phenomenon. An important class of bootstrapping methods is based on the assumption that the sampled phenomenon evolves according to a Markov chain. This assumption does not apply when the process takes values in a continuous set, as it frequently happens with time series related to economic and financial phenomena. In this paper we apply the Markov chain theory for bootstrapping continuous-valued processes, starting from a suitable discretization of the support that provides the state space of a Markov chain of order \(k \ge 1\). Even for small k, the number of rows of the transition probability matrix is generally too large and, in many practical cases, it may incorporate much more information than it is really required to replicate the phenomenon satisfactorily. The paper aims to study the problem of compressing the transition probability matrix while preserving the “law” characterising the process that generates the observed time series, in order to obtain bootstrapped series that maintain the typical features of the observed time series. For this purpose, we formulate a partitioning problem of the set of rows of such a matrix and propose a mixed integer linear program specifically tailored for this particular problem. We also provide an empirical analysis by applying our model to the time series of Spanish and German electricity prices, and we show that, in these medium size real-life instances, bootstrapped time series reproduce the typical features of the ones under observation.

The paper studies a lacquer production scheduling problem formulated as a resource constrained project scheduling problem with general temporal constraints (i.e., positive and negative time-lags). This real-world scheduling problem requires so-called take-give resources that are needed from the beginning of an activity to the completion of another activity of the production process. Furthermore, we consider sequence dependent changeover times on take-give resources. We formulate this problem by mixed integer linear programming and we suggest a parallel heuristic to solve the problem. This heuristic exploits a time symmetry mapping which allows an easy construction of a schedule in the backward time orientation. In the second part of the paper, it is proven that the time symmetry mapping is bijective and involutive even for the problem with general temporal constraints, changeover times, and take-give resources. The motivation to use this mapping is to improve the performance of the heuristic and to simplify its implementation. Finally, the performance of the heuristic algorithm is evaluated on a set of lacquer production benchmarks requiring take-give resources and on standard benchmarks for the resource constrained project scheduling problem with general temporal constraints where we found new better solutions in 16 and 12 instances out of 90 for UBO500 and UBO1000 respectively.

Over the past 30 years managers have increasingly focused on improving inventory management both within their own firms and across the supply chain. To this end, inventory turns metrics have been adopted as a popular tool for measuring flow velocity through the inventory and the efficiency of inventory-related asset utilization. There are multiple computational methods thereof currently used by practitioners, but each has some flaws. In particular, consensus is lacking as to (1) how to measure inventory flow and (2) how to measure the inventory level. Clearly, high-quality accounting information is essential for an accurate assessment of all inventory performance metrics. Unfortunately, when comparing efficiencies across firms or diagnosing and correcting intra-firm inefficiencies, choices of specific accounting rules and distortions between fair market values and corresponding accounting book values can lead to potentially misleading results. This paper presents finite-horizon versions of Little’s Law and elucidates their connection to inventory turns and restricted sojourn times in inventory, defined as the portion of the full sojourn time that falls within a prescribed time period. In particular, it explains when the reciprocal of sample inventory turns coincides with the sample average of restricted sojourn times. As such, the paper provides a unified prescriptive model for correctly relating inventory turns to sojourn times through an inventory system, thereby facilitating more accurate intra-firm and cross-firm comparisons.

This paper addresses an assembly line balancing problem in which each station is allowed to have more than one worker, with minimization of two objectives: (i) the number of workers used in the line and (ii) the number of stations opened in the line. Each station has an own workpiece, and workers at the same station simultaneously perform different assembly tasks on its workpiece. For solving the balancing problem of these lines, a new mixed integer programming formulation is presented, and then a simulated annealing based heuristic method is proposed. The new formulation requires less number of variables and constraints. The basic property of the proposed heuristic is that it is directly applied to Gantt representations of problem's solutions. Moreover, the paper includes some important neighborhood generation properties for Gantt representations of solutions. Both methods are compared with existing methods available in the literature. Experimental study indicates that the proposed methods can yield promising results.

Technological progress is considered an important means of decreasing energy consumption. However, rebound effects caused by energy efficiency improvements directly affect the realization of energy savings and emission reduction. This paper focuses on the main theory and methodology of direct and indirect rebound effects. Using 30 sets of provincial panel data and national input–output data for China from 2007, this paper builds a co-integrating equation, a panel error correction model, and an 8-sector energy-input–output model. We subsequently estimate the direct and indirect rebound effects of urban residential electricity use. The results indicate that in the long term the direct plus indirect partial rebound effect is 0.79; in the short term it is 0.78. Thus, the majority of the expected electricity reduction in Chinese urban residential energy consumption arising from efficiency improvement may be offset. These rebound effects impair the functioning of energy efficiency policies. Therefore, the Chinese government should not improve energy efficiency alone—they must also take into consideration the relevant energy-pricing reforms when formulating energy policies.

In this paper we consider retrial queues with two way communication and finite orbit. We will develop a functional approximation of the stationary performances of this queue where the parameter of interest is the outgoing rate. Specifically, we will apply the Taylor series expansion method to propagate parametric uncertainty to performance measures. We provide an analysis of the $$\hbox {M}_{1},\, \hbox {M}_{2}/\hbox {G}_{1},\, \hbox {G}_{2}/1$$ retrial queue with two way communication and finite orbit. The sensitivity analysis is carried out by considering several numerical examples. More specifically, this paper proposes a numerical approach based on Taylor series expansion with a statistical aspect for analyzing the stationary performances of the considered queueing model, where we assume that the outgoing rate is not assessed in a perfect manner. Additionally, approximate expressions of the probability density functions, the expectation and the variance of the performance measures are obtained and compared to the corresponding Monte Carlo simulations results.

Applying Data Envelopment Analysis (DEA) to real-world public policy issues can raise many interesting complications beyond those considered in standard models of DEA. One of these complications arises if the funding levels of public service providers, and their ability to attract and retain clients and able staff, depend upon the quality of the output which they produce. This dependency introduces additional inter-relationships between inputs and outputs beyond the uni-directional Production Possibility Frontier (PPF) relationship considered by standard DEA models. The paper therefore analyses the multiplier effects which can be generated by these additional relationships, in which key resource inputs become endogenous variables subject to the external environmental variables which the public service provider faces across these different relationships. The magnitude of these multiplier effects can be captured by focusing DEA on the estimation of an Achievement Possibility Frontier, which reveals the wider set of opportunities which are available to a public service provider to improve its own output quality than that revealed by the estimation of the PPF associated with standard models of DEA. In doing so, the paper enables DEA to be still applied, but in modified form, to the estimation of the scope for improved output of any given public service provider in the presence of such resource endogeneity.

Emergency management with oil spill is a very complex decision problem. This paper targets efforts to propose and develop a new technology: an extension of grey relational analysis for facilitating group consensus model to deal with this problem. In this model, firstly, two parts of the extension of grey relational analysis are presented and proposed. One is to simultaneously compute grey relational degree to positive reference sequence (PRS) and negative reference sequence (NRS), on the basis of the basic concept of a relative closeness degree of TOPSIS. The other is to determine index weights by a developed mathematical optimization model implemented by Matlab 2012a, which also matches the basic concept of the first part of the extension. Secondly, a group consensus facilitation method based on three-dimension leg-mark selected location method is proposed to aggregate individual preferences in order to address the problem of ranking inconsistency during the evaluation of multi-criteria decision making methods. What’s more, the calculation steps and processes of n-dimension leg-mark selected location method for facilitating group consensus are given and explored. A simulation case study on oil spill emergency management demonstrates and verifies the feasibility and effectiveness of our proposed model by comparative analysis with the previous research papers.

This paper provides a simple framework for the valuation of exotic derivatives within shadow costs of incomplete information and short sales. The specific features of the OTC markets with comparison to the organized markets require an additional investment to obtain information about the financial products, to process data, to elaborate models, etc. The shadow cost includes two components. The first component is the product of pure information cost due to imperfect knowledge. The second component represents the additional cost caused by the short-selling constraint. Information costs are linked to Merton’s (J Fianance 42:483–510, 1987) model of capital market equilibrium with incomplete information, CAPMI. This model is extended by Wu et al. (Rev Quant Finance Account 7:119–136, 1996) who propose incomplete-information capital market equilibrium with heterogeneous expectations and short sale restrictions, GCAPM. This model is used in our paper to provide for the first time in the literature analytic solutions for derivatives in the presence of both shadow costs of incomplete information and short sales. Our methodology incorporates shadow costs of incomplete information and short sales in the options and their underlying securities. We provide formulas using the standard Black and Scholes method or the martingale method. Since shadow costs are important in the presence of illiquidity, the formulas are useful for the valuation of OTC derivatives.

This paper considers a single-server queueing model in which the customers are served in batches of varying size depending on predetermined thresholds as well as available inventory. There is a finite buffer for the inventory and the service of every customer requires an inventory item. An (s, S) -type inventory system is used for the models considered in this paper. Initially, the model is studied in detail using the matrix-analytic method by assuming all the underlying random variables to be exponentially distributed. Thereafter, an outline of the model in a more general set up is also presented. Due to complexity of the model when more general assumptions are made on the underlying random variables, simulation is opted after a satisfactory validation with the analytic counterpart of the exponential model. Finally, some illustrative numerical examples are also presented to accomplish our analysis.

In this paper we use the burst factor of a packet stream, which is defined in a general setting, to quantify the long-term variability, or burstiness, of such a stream. We briefly review some existing results to show that this parameter plays an important role in the performance assessment and dimensioning of buffers in network nodes, even in a non-Markovian setting. We then focus on the calculation of this parameter at the egress of a discrete-time GI / D / 1 queueing system, considering different routing scenarios, and show how it can be expressed in terms of the parameters that characterise the arrival process in such a queue. In addition, we demonstrate how these results can be applied to evaluate the buffer performance in the subsequent nodes of a network. The analytic results that are derived throughout this paper are supported by simulations.

In a security screening system, a tighter screening policy not only increases the security level, but also causes congestion for normal people, which may deter their use and decrease the approver’s payoff. Adapting to the screening policies, adversary and normal applicants choose whether to enter the screening system. Security managers could use screening policies to deter adversary applicants, but could also lose the benefits of admitting normal applicants when they are deterred, which generates a tradeoff. This paper analyzes the optimal screening policies in an imperfect two-stage screening system with potential screening errors at each stage, balancing security and congestion in the face of strategic normal and adversary applicants. We provide the optimal levels of screening strategies for the approver and the best-response application strategies for each type of applicant. This paper integrates game theory and queueing theory to study the optimal two-stage policies under discriminatory and non-discriminatory screening policies. We extend the basic model to the optimal allocation of total service rate to the assumed two types of applicants at the second stage and find that most of the total service rate are assigned to the service rate for the assumed “Bad” applicants. This paper provides some novel policy insights which may be useful for security screening practices.

Support vector machines (SVM) is one of the well known supervised classes of learning algorithms. Basic SVM models are dealing with the situation where the exact values of the data points are known. This paper studies SVM when the data points are uncertain. With some properties known for the distributions, chance-constrained SVM is used to ensure the small probability of misclassification for the uncertain data. As infinite number of distributions could have the known properties, the robust chance-constrained SVM requires efficient transformations of the chance constraints to make the problem solvable. In this paper, robust chance-constrained SVM with second-order moment information is studied and we obtain equivalent semidefinite programming and second order cone programming reformulations. The geometric interpretation is presented and numerical experiments are conducted. Three types of estimation errors for mean and covariance information are studied in this paper and the corresponding formulations and techniques to handle these types of errors are presented.

Optimization models have a wide range of applications in economics and game theory. This special issue of the Annals of Operations Research contains selected and refereed papers on the topic optimization models with economic and game theoretic applications. Most of the papers in this special issue were presented at the International Symposium on Applied Optimization and Game Theoretic Models (ISAOGTM13) held during January 9–11, 2013, at the Indian Statistical Institute, Delhi Centre. This International Symposiumwas designed to promote research and applications in applied optimization and game theory by bringing together leading experts and other specialists in the field around the world with young scholars. This symposium mainly focused on classical and modern optimization theory, algorithms (local and global aspects), stochastic optimization, structured optimization, as well as related topics in applied mathematics that contain applications of optimization models in economics and game theory. Around 120 participants including speakers participated in this symposium. This symposium was an event under the project optimization and reliability modeling funded by the Indian Statistical Institute. Having broad international appeal it provided an excellent opportunity to disseminate the latest major achievements and to explore new directions and perspectives, dealingwith topics of fundamental importance in applied optimization and other related sciences (economics, physics, engineering). A special session titled Professor Santosh N. Kabadi Memorial Session was organized on combinatorial optimization to recall the memory of our dear friend Professor Santosh N. Kabadi, University of New

One of the primary objectives of this paper is to generalize Hunter’s bound to m-regular hypergraphs. In particular, new upper and lower bounds for the probability of the union of events is provided in this paper. The lower bounds generalize Hunter’s bound as well. All new bounds are derived from the dual feasible solutions of Boole’s linear programming problem.

Preface: Advances in Theoretical and Practical Combinatorial Optimization Said Salhi1, Richard Eglese2, Eleni Hadjiconstantinou3 This issue consists of research papers dedicated, though not limited, to the International Symposium in Combinatorial Optimisation (CO2016), chaired by Said Salhi and held at the University of Kent, Canterbury, 1-3 September 2016. Of the many good papers it attracted, 35 were considered suitable for the next stage of review, resulting in 20 being accepted mostly after two rounds of reviews. We divided the issue into three parts, each covering a wider though well-defined area within combinatorial optimization (CO). These three parts, which are not completely distinct, are referred to for simplicity as Mathematically-Oriented (MO), Heuristically-Based (HB) and Practically-Focussed (PF). MO contains seven papers. These include a paper on the integration of network flows in a coloring algorithm by Koster, Scheidweiler, and Tieves; one on a polynomial time algorithm for the minimum flow problem by Khodayifar, Raayatpanah, and Pardalos, and one on a formulation in multi-level capacitated facility location by Irawan and Jones. A.Salhi, Alsoufi, and Yang provide a mixed integer program combined with an evolutionary approach for seaside operations in container ports, while Zare, Borrero, Zeng and Prokopyev have contributed a note on new linearized formulations in bilevel linear integer problems. Fuzzy logic is explored for portfolio optimization by Liagkouras and Metaxiotis, and for supplier selection by Chang. HB covers six papers. Azizi, using a genetic algorithm, explores hub and spoke networks under disruption. Knust and Xie produce a simulated annealing algorithm for nurse rostering, while Pei, Cheng, Liu, Pardalos, and Kong investigate Variable Neighbourhood Search (VNS) for single and parallel machine serial batching. A skewed VNS is adopted by Derbel, Jarbaoui, and Bhiri for the heterogeneous fleet vehicle routing problem, and an ascent-escent VNS for the detection of community (cluster) structures in a network is discussed by Džamic, Aloise, and Mladenovic. The last paper in this category is by Brimberg, Mladenovic, Todosijevic, and Urosevic, who investigate the capacitated clustering problem using a variant of VNS. PF has seven papers that treat inspiring implementations of CO for real-life applications. For example, in the area of scheduling, the use of spare parts in vessel maintenance is dealt with by Kian, Bektas, and Ouelhadj, while shift scheduling for power station staff is explored by Shuib and Kamarudin. The avoidance of obstacles in off shore wind farms is optimized by Klein and Haugland, and the costing of a product within a supply chain is performed by de Matta. In the health area, the maximization of the expected number of kidney transplants is performed by Alvelos, Klimentova, and Viana; a preventive health care network is designed by Davari; and the layout of an operating theater is produced by Chraibi, Osman, and Kharraja. Finally, we are grateful to Professor E. Boros for trusting our academic judgment in acting as guest editors of the Annals of Operations Research special issue devoted, but not restricted, to CO2016. We are also thankful to the managing editor, Katie D’Agosta, for her patience and valuable support in preparing this special issue. 1 Centre for Logistics and Heuristic Optimisation, Kent Business School, University of Kent, Canterbury, UK 2 Lancaster University Management School, Lancaster, UK 3 School of Business and Law, Frederick University Cyprus, Cyprus.

The proper allocation/distribution of limited resources is a traditional problem with various applications. The mathematical formulation of such problems usually includes constraints describing the set of feasible solutions (feasible set), from which the (nearly) optimal or equilibrium solution should be selected. Often the feasible set is more difficult to determine than to find the optimal or equilibrium solution. Alternatively, the already known feasible set often makes it easier to select the optimal or equilibrium solution. In some other cases, any feasible solutions are the same satisfactory, additional optimization is needless. Accordingly, the main or only task in many cases is to determine the feasible set itself. In the paper, a new theorem is proved for the explicit expression of properly assigned (dependent) variables by means of the other (independent) variables in a system of inequality and quadratic equality constraints. The sum of the (nonnegative) variables can be either prefixed or not. The constraints may describe the feasible set in various resource allocation tasks (possibly in optimization or game-theoretical contexts) or in other problems. Two new lemmas are proved for supporting the proof of the above mentioned theorem, nevertheless, they can also be considered independent results, which may help future mathematical derivations. Supported by a further new lemma, a practical algorithm is derived for assigning in a feasible way the independent variables, to which (possibly limited) arbitrary nonnegative values can be prescribed. Various practical examples are provided to facilitate utilizing the results.

Capacities are a common tool in decision making. Each capacity determines a core, which is a polytope formed by additive measures. The problem of eliciting a single probability from the core is interesting in a number of fields: in coalitional game theory for selecting a fair way of splitting the wealth between the players, in the transferable belief model from evidence theory or for transforming a second order into a first order model. In this paper, we study this problem when the goal is to determine the centroid of the core of a capacity, and we compare four approaches: the Shapley value, the average of the extreme points, the incenter with respect to the total variation distance and the limit of a procedure of uniform contraction. We show that these four centroids do not coincide in general, we give some sufficient conditions for their equality, and we analyse their axiomatic properties. We also discuss how to define a notion of centrality measure indicating the degree of centrality of an additive measure in the core. Finally, we also analyse these four centroids in the more general context of imprecise probabilities.

Additive Manufacturing (AM) is a disruptive production technology that challenges many well-established, conventional practices in Operations and Supply Chain Management decisions. Consequently, new context-specific decision problems have appeared in this area, while existing decision problems must be adapted or extended to this context. In this overview, these decision problems in AM are highlighted and classified, describing the different decision in an unified manner and citing the underlying OR techniques that have been applied to solve them. The aim of the paper is that, by presenting an overview of the AM-related problems in a systematic, OR- or Decision-Making-centred (rather than technology-centred) manner, the OR community can become more aware of this stream of research and thus be more active and contribute with some high-quality work. Open research challenges, as well as avenues for future research are also discussed.

Producción Científica, A preference–approval on a set of alternatives consists of a weak order on that set and, additionally, a cut-off line that separates acceptable and unacceptable alternatives. In this paper, we propose a new method for defining the distance between preference–approvals taking into account jointly the disagreements in preferences and approvals for each pair of alternatives. The proposed distance is compared to the existing distance functions to deal with clustering problems. Specifically, we prove that our metric improves the estimated clusters in terms of both stability and accuracy., Agencia Estatal de Investigación (Project PID2021-122506NB-I00), University of Palermo (Projects: FFR_D16_PLAIA and FFR_D16_SCIANDRA)

A sustainability program is about implementing essential sustainability practices at various intensities that will not harm people because of firm actions. This paper formulates an optimization model to identify an optimal intensity of implementation of selected sustainability practices referred as sustainability program for maximising manufacturing industry sustainability performance for a known set of budgetary and minimum thresholds constraints with respect to economic, social and socio-economic criteria. Besides modelling the paper proposes a random search procedure embedding the NN to determine the optimal sustainability program and explains the usefulness with a sample problem. Sensitivity analysis is carried out to understand the behaviour of the model. It is observed that the sustainability performances depend on the constraints such as budget limitation and threshold values of performance criterion.

Conventional data envelopment analysis (DEA) models make the assumption of non-negativity and real values in the input and output of the systems that are under study. This paper combines these two interrelated ideas. One is the non-radial measurement of efficiency by establishing an aggregate directional distance formulation of the DEA model (ADDM). Another is the introduction of an integer directional distance function. Usually, directional distance formulations of DEA and ADDM projections of efficient targets for inefficient decision making units (DMUs) have non-integer values. In this paper directional distance function is modified and a mixed integer directional distance formulation of DEA is proposed. This model guarantees integer targets for inefficient DMUs and is applicable to measure efficiency even when input and output variables are negative integer values.

Coordination and integration among various members of a supply chain has become one of the vital strategies for the modern manufacturers to gain competitive advantages. This paper studies an NP-hard problem of the two-stage scheduling in which jobs are processed by two parallel machines and delivered to the distribution center with the objective of minimizing the makespan. There exists an unavailable interval on one of the machines due to preventive maintenance and an unfinished job can resume processing after the machine becomes available again. Wang and Cheng (Nav Res Logist 54:11–20, 2007) first studied this problem and presented an algorithm H2 with a worst-case ratio of 5/3. This paper proposes a new algorithm which leads to a best possible solution with a worst-case ratio of 3/2.

Electricity generation currently is the main industrial source of air emissions in the United States. Both researchers and practitioners are interested in conducting studies to evaluate the ecological performance of this industry, in order to propose solutions to curb emissions of air pollutants and to improve the efficiency of converting fossil resources into electric energy. In this paper, data envelopment analysis (DEA) is used to assess ecological efficiency where air emissions are used as undesirable outputs. Although conventional DEA does not require a priori information on the input and output weights, weight restrictions can be incorporated to reflect a user’s preference over the performance metrics, or to refine the DEA results. Adding weight restrictions voids the fact that DEA scores are independent of the units of measurement. To incorporate weight constraints in ecological efficiency assessment, this paper develops a DEA model that is units-invariant when weight restrictions are imposed. Moreover, the proposed model is equivalent to the standard units-invariant DEA model when weight restrictions are not present.

This paper studies the issue of coordinating equipment maintenance operations with capital investment strategy in the presence of random equipment failures. The traditional approach, developed by Kamien and Schwartz (KS) in their celebrated paper published in 1971, is to formulate the problem as a deterministic optimal control problem with the probability of machine failure as the state variable. With this approach, the optimal policy is deterministic. As a major departure from the KS approach, we explicitly model the underlying stochastic process of machine failures. Our analysis of the stochastic dynamic programming model offers new insights into the problem. Under a long planning horizon with a limited replacement opportunity, each individual machine serves as a revenue generator and contributes a significant amount to the profit of the system. In contrast, when the replacement budget is quite generous over a relatively short planning horizon, adding one extra machine only helps as a backup for unexpected failures of the machines purchased before it. An interesting result derived from this comparison is that a deterministic policy turns out to be optimal for the former, while a state-contingent policy must be applied to the latter. In other words, the deterministic KS approach does not work in general when a chain of machine replacement is considered. We further characterize the effects of the discount rate, productivity deterioration, learning, decision delay, and technology advancement on the optimal policy.

This paper introduces a phase-type software reliability model (PH-SRM) and develops parameter estimation algorithms with grouped data. The PH-SRM is one of the most flexible models, which contains the existing non-homogeneous Poisson process (NHPP) models, and can approximate any type of NHPP-based models with high accuracy. Hence PH-SRM is promising to reduce the effort to select the best models in software reliability assessment. However, PH-SRM may involve many parameters compared to typical NHPP models. Thus the efficient parameter estimation algorithm is required. This paper enhances the parameter estimation algorithms for PH-SRM, so that they can handle grouped data. The grouped data is commonly applied to collect the data such as the number of bugs per day in practice. Thus the presented algorithms are helpful for the reliability assessment in practical software development project. Concretely, we consider the EM (expectation–maximization) algorithm for PH-SRM with both fault-detection time and grouped data. Finally, we examine performance of PH-SRM from the viewpoints of fitting ability.

Inevitable simplifications of models of real world activities lead to some inadequacies. In the DEA literature several methods were proposed to overcome such difficulties. Some authors proposed to use specific production units in the primal space of inputs and outputs as a starting point in order to improve the frontier of the DEA models. In our previous papers, we introduced the notion of terminal units. It was proved that only terminal units form necessary and sufficient set of units for improving the frontier. In this paper, the relationship between all sets of units proposed for improving the frontier is established. Our theoretical results are confirmed by extensive and instructive graphical examples and also verified by computational experiments using real-life data sets.

When a unit is running successive works with cycle times, it would be uneconomical or impractical to do maintenances during any working cycle. This paper firstly reviews two replacement models proposed in literatures as follows: (1) replacement is done over a planned time T at the completion of the working cycle, which is called over-time replacement, and (2) replacement is done over damage level Z at the completion of the forthcoming working cycle, which is called over-level replacement. From the viewpoint of replacement cost savings, we secondly focus on comparisons of over-time replacement and its standard age-based policy, over-level replacement and its standard condition-based policy. Furthermore, modified over-time and over-level replacement costs are obtained to specify critical points at which these new replacements, rather than their standard polices, would save more replacement costs. Discussions in this paper are given analytically and computed numerically. 2015, Springer Science+Business Media New York. Scopus

The usage of the Internet has grown substantially in recent times. This has resulted in high volumes of data traffic. There is a concomitant rise in bandwidth demands that could result in excessive download delays (or latency). Thus, a single-server system is no more a prudent choice for data storage. Replication of content and placing them on multiple servers is a method that is used to reduce latency. However, this solution comes at a huge cost. Moreover, replicating objects randomly does not necessarily improve system performance. It is possible to arrive at a solution to the problem of placing content so as to achieve better cost performance. Other performance measures include latency, load balancing and data availability. We refer to the problem of locating content as data location problem in information networks, or DLPIN. The choice of server locations, query routing strategy and user assignment are some of the important problems that require attention along with the location of the data/content. Resource constraints and the nature of traffic (static/dynamic) are two important parameters in the problem environment, and therefore are key distinguishing features in the models. The main contribution of this paper is a novel classification and study of DLPIN on the basis of problem features. The research in this area started with files, the smallest units of allocation. Gradually, files and programs, database segments and entire databases (or mirrors) have been studied. We design examples from these use cases to elaborate a variety of problems in a comprehensive review. Facility location models from physical logistics are extensively used to model these problems. Our paper presents a literature survey of such mathematical models for data location problems. We present a gap analysis that provides pointers to possible future research in this area. This paper also serves to document the success in the use of mathematical programming approaches for data location in information networks.