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One of the main problems facing hospital managers is in coping with the variability in demand for the services that the hospital provides. This is particularly the case in the Critical Care Unit (CCU), where inability to provide adequate facilities on demand can lead to serious consequences. Admissions to CCUs may be categorized under two headings: unplanned (emergency) and planned (elective). The length of stay (LoS) in the CCU is heavily dependent on the admission category: unplanned admissions have a much longer LoS on average than elective patients. In this paper, we propose a mathematical model that shows how improvements in bed management may be achieved by distinguishing between these two categories of patients. The vast majority of previous literature in this field is concerned only with steadystate conditions, whereas in reality, activities in virtually all hospital environments are very much time dependent. This paper goes some way to addressing this problem. [ABSTRACT FROM PUBLISHER]

The purpose of this paper is to evaluate the performance of a sample of thirteen commercial banks listed on the Athens Exchange by applying a two-step procedure. In the first step, data envelopment analysis (DEA) is used to model performance in two dimensions: profitability efficiency and efficiency in market value generation. In the second step, a tobit model is bootstrapped in order to identify the drivers of performance. The contribution of this paper to the literature is twofold: to improve on the existing methods employing a new variant of DEA profitability approach by using burden as an (undesirable) output variable in modelling profitability efficiency and to better explain (from a computational statistics perspective) DEA efficiency levels inboth dimensions examined by employing a bootstrapped tobit model. Performance inefficiency is uncovered in both dimensions, but the real problem of inefficiency of the sampled banks is the lower level of performance in market value generation, rather than profitability. Results do not point out positive links between profitability efficiency and performance in the stock market. Relatively large banks exhibit better performance on profitability; most of them reached their optimum size scale, whereas they tend to have the worst performance with respect to the stock market. Some smaller banks seem to have some prospects in market value generation, while performance in the stock market can be explained by cost to income ratio. [ABSTRACT FROM PUBLISHER]

Selecting the best markets from a large number of different markets with varying levels of capability and potential is a complicated and time-consuming task requiring multiple criteria decision-making solution approaches. One of the uses of data envelopment analysis (DEA) is market selection. This paper proposes a new model for selecting the best markets. The proposed model is based on DEA and considers dual-role factors, weight restrictions and imprecise data, simultaneously. This paper is the first study that applies advanced DEA models for selecting markets. The illustrative application addresses applicability of the proposed model. [ABSTRACT FROM PUBLISHER]

This paper presents a review of time series filtering and its applications in mathematical finance. A summary of results of recent empirical studies with market data are presented for yield curve modelling and stochastic volatility modelling. The paper also outlines different approaches to filtering of non-linear time series. [ABSTRACT FROM PUBLISHER]

Statistical models of football (soccer) match outcomes have potential applications to areas such as the development of team rankings and football betting markets. Much of the published work in this context has typically focused on the use of generalized linear models, which are non-dynamic in the sense that the parameters in the model, which often represent the underlying abilities of each team, are assumed to remain constant over time. Dynamic generalized linear models (DGLMs) on the other hand allow the abilities of each team to vary over time. This paper illustrates the application of a DGLM in the context of football match outcome prediction and describes improvements on similar work previously presented by the author, in relation to the estimation of a parameter in the model, referred to as the evolution variance, which is crucial in terms of optimizing the predictive performance of these types of models. Match results data from the Scottish Premier League from 2003/2004 to 2005/2006 are used to show that the DGLM approach provides improved predictive probabilities of future match outcomes compared to the non-dynamic form of the model. DGLMs are also Bayesian in terms of their structure and so a Bayesian approach to parameter estimation is required. This paper therefore illustrates a practical implementation of the DGLM model that can easily be deployed using the freely available software WinBUGS. [ABSTRACT FROM AUTHOR]

A build-to-order (BTO) product is produced according to customers’ requirements and specifications. Returning such a product is generally not allowed. However, if a return policy is offered, the producer may gain significant competitive advantages. The producer can gain the advantage of the return policy if the product design is highly modular. Modularity in design implies that the product is made up of a number of modules and expresses the degree to which the product can be disassembled into components, which keep their full initial value. When a BTO product with modular design is returned, it is easily disassembled into a number of components, which are used to build new BTO products. A firm applying a return policy to BTO products refunds to the customer a value for the returned product. This amount is critically related to the level of modularity. Mukhopadhyay & Setoputro (2005, Optimal return policy and modular design for build-to-order products. J. Oper. Manage., 23, 496–506) developed a profit maximization model to jointly determine optimal return policy and modularity level for BTO products in a two parties (producer–buyer) supply chain system, using selling price as a given exogenous parameter. In this paper, the above model is extended by considering the selling price of the BTO product as an additional decision variable. From the resulting three-dimensional constrained maximization problem, closed-form optimal solutions for the decision variables are obtained under certain conditions imposed on the model's parameters. A comparative static and numerical analysis of optimal selling price, refund and modularity level is also conducted. [ABSTRACT FROM AUTHOR]

Instead of forecasting demand for individual items separately, hierarchical forecasting is often used: total demand is forecasted for a collection of items; this total forecast then is broken down to produce the desired individual demand forecasts. To allow analytical analyses, we considered in a previous paper the simpler problem of hierarchical estimation. So from a random sample of demand periods, we estimated both the total demand for a number of items and the fraction of this total that an individual item takes; multiplying these two quantities gives the hierarchical estimate for each individual demand. From the joint distribution of the individual demands, we here present a fast and general method for finding the bias and variance of the corresponding hierarchical estimator. The method is compared with our previous results and two new applications are added. [ABSTRACT FROM PUBLISHER]

This paper employs for the first time data envelopment analysis (DEA) and stochastic frontier analysis (SFA) as two performance measurement competing approaches to assess efficiency in the Greek mining industry. These two frontier estimation methodologies overcome the limitations of the partial productivity measures by explicitly considering two inputs and one output in the measurement of efficiency for the period 1970–1996. The paper is also innovative in utilizing a bootstrapping approach in DEA to aggregated industry (time series) data as an alternative to the more common DEA point estimates. In particular, the bootstrapping approach used relies on the homogeneity assumption that the distribution of the efficiency scores is independently distributed over the sample; the results from DEA and SFA are more comparable under this assumption as it corresponds to the independence assumption regarding the distribution of the inefficiency term in SFA. The two different approaches to performance evaluation, as used here, do not provide confirmation of each other's findings since they are based on different principles and treat the data in different ways. Although the joint use made here of DEA and SFA provides results that are consistent with points of view that have regarded these two approaches as mutually exclusive alternatives, this paper demonstrates that from a policy perspective DEA and SFA can be utilized in tandem on a common data set to assess the efficiency and investigate the return to scale patterns at the sectoral level. [ABSTRACT FROM PUBLISHER]

This paper deals with a continuous review, two-echelon serial (two locations) inventory system with perishable items. Transportation times and the lifetime of items are fixed. When the age of an item has reached its lifetime, the item is useless and consequently discarded from the system. The downstream location faces Poisson demand, and demand that cannot be met immediately is backordered. In this paper, we develop an efficient approximate technique for evaluation of (S – 1, S) policies. In a simulation study, we evaluate the quality of our approximation. The results reveal that our approximate technique works well in most cases. [ABSTRACT FROM PUBLISHER]

This paper proposes a regression-based simulation algorithm for multi-period mean-variance portfolio optimization problems with constraints under a high-dimensional setting. For a high-dimensional portfolio, the least squares Monte Carlo algorithm for portfolio optimization can perform less satisfactorily with finite sample paths due to the estimation error from the ordinary least squares (OLS) in the regression steps. Our algorithm, which resolves this problem e, that demonstrates significant improvements in numerical performance for the case of finite sample path and high dimensionality. Specifically, we replace the OLS by the least absolute shrinkage and selection operator (lasso). Our major contribution is the proof of the asymptotic convergence of the novel lasso-based simulation in a recursive regression setting. Numerical experiments suggest that our algorithm achieves good stability in both low- and higher-dimensional cases. [ABSTRACT FROM AUTHOR]

In this paper, the passing ability of football players is determined by building three generalized additive mixed models that each explains a different aspect of a pass' success: difficulty, risk and potential. The models are built on data from the 2014–2016 seasons of the Norwegian top division Eliteserien, and their predictive power is tested on the 2017 season. The results provide insight into the factors affecting the success of a pass in Eliteserien. These include the location of the pass, the relationship to previous passes and to situations such as throw-ins, corners, free kicks or tackles, as well as conditions specific to the Eliteserien, such as the time of season and the ground surface type. Finally, the key pass makers in the league are identified. [ABSTRACT FROM AUTHOR]

The effect of advertising media and sales effort on future demand of merchandize is considered. In an oligopolistic marketing system, it is quite natural to boost sales by using advertising and sales effort to earn more money from a business sector. Determination of demand and costs due to advertising and sales effort is quite difficult. The approach in this paper is to concentrate on investment for the purpose of advertising and increasing sales effort in order to maximize profit. The paper extends an inventory model over finite and infinite time horizons where demand is influenced by the level of stock, advertising media and sales effort. The associated profit maximization problem is solved by the 'Euler-Lagrange method'. The model is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

Radiotherapy planning is vital for ensuring the maximum level of effectiveness of treatment. In the planning task, there are at least two connected decision problems that can be modelled and solved using operational research techniques: determining the best position of the radiotherapy machine (beam angle problem) and the optimal dose delivered through each beam (dose distribution problem). This paper presents a mathematical optimization model for solving the combined beam angle and dose distribution problems in the presence of multiple objectives. A matheuristic based on Tabu Search (called TSrad) is developed to solve realistic large-scale instances. The performance of the proposed method is assessed on two prostate cancer instances, namely a single computed tomography (CT) slice and a set of CT slices (three-dimensional problem). For the single-slice problem, the results of TSrad are compared to the optimal solutions obtained by an exact method. Our experiments show that TSrad is able to achieve optimality for some instances. For the multi-slice problem, our experiments show that TSrad produces viable solutions that can be attained in a reasonable computational time. [ABSTRACT FROM AUTHOR]

The evolution of wireless systems has led recently to the deployment of cooperative network infrastructures where networks based on different technologies cooperate together to offer innovative services which the networks individually could not offer. Cooperative network infrastructures are hybrid systems. Examples of hybrid systems are already in use in areas as diverse power systems and locomotion systems. It is very important to apply efficient system management techniques to hybrid systems that produce advantageous business case scenarios for each participating system and efficient use of the available resources. In this paper, scenarios are investigated where cooperation between an integrated project (IP)-based broadcast network and an IP-based mobile telecommunications network has the potential to allow services to be provided to mobile users which would not be economical to offer over either an IP-based broadcast network or an IP-based mobile telecommunications network alone. The paper presents a novel technique for determining which network to use to deliver such services at a given point in time. The application of this technique in appropriate scenarios has the potential to generate additional income for both the IP-based broadcast network operator and the IP-based mobile telecommunications network operator in an infrastructure where the operators cooperate to offer innovative services. The paper explains the construction of utility functions for cooperative IP-based broadcast and mobile telecommunications networks. These utility functions are then used to provide results that enable the efficiency of the management of the network to be assessed in terms of the utility volume generated by the innovative services provided. [ABSTRACT FROM AUTHOR]

Besides multiple voting rights, board representation and shareholders' monitoring, control tunnelling over firms can be reached through pyramids, cross-ownership and other complex features. This phenomenon is frequent in Europe and Asia. However, the theoretical literature has not yet converged towards a well-defined and robust measurement of integrated control that takes into account the threshold for control as applied in practice. Based on graph theory, this paper aims at filling this gap and proposes a new algorithm for evaluating the control tunnelling exerted by the firms' ultimate shareholders. Then, the paper discusses the various forms of control existing next to voting shares, like multiple voting rights, board representation and active monitoring, before suggesting ways to include them into the modelling of control. [ABSTRACT FROM AUTHOR]

This paper presents an approach that quantifies the performance of the maintenance system in terms of the quality of service, offered by maintenance to the production system. Maintenance is discussed as involving both technical and economic issues. This influences the definition of maintenance quality and maintenance performance used in this work. This is because maintenance can be considered of high quality if the latter is measured (or evaluated) by only technical terms such as quality rate. However, this does not necessarily mean that maintenance is so when its performance is measured in economic terms. This issue is important and is highlighted in the paper. The motivation for writing the paper is the need for a scientific means of conflict resolution between the maintenance and production department. There are frequently complaints from the production department that the quality of the machines soon after maintenance is sometimes poor, thus leading to a shortened period of production. The methodology adopted hinges on the framework in which scrap generation, rework and reject are partly due to the malfunction of the machine. Thus, if a machine has a deteriorated state soon after maintenance, it is likely to produce rejects, scrap or reworked materials. It is argued that the measurement of performance using this approach is helpful and could be used for planning purposes. The approach utilized in this work is new, and represents a contribution to the maintenance performance literature. [ABSTRACT FROM AUTHOR]

Commonly used repair rate models for repairable systems in the reliability literature are renewal processes, generalised renewal processes or non-homogeneous Poisson processes. In addition to these models, geometric processes (GP) are studied occasionally. The GP, however, can only model systems with monotonously changing (increasing, decreasing or constant) failure intensities. This paper deals with the reliability modelling of failure processes for repairable systems where the failure intensity shows a bathtub-type non-monotonic behaviour. A new stochastic process, i.e. an extended Poisson process, is introduced in this paper. Reliability indices are presented, and the parameters of the new process are estimated. Experimental results on a data set demonstrate the validity of the new process. [ABSTRACT FROM AUTHOR]

This paper presents a surveillance method based on the game theory which is used by the ISO to find whether a power supplier in an electricity market has market power. The paper uses the supply function equilibrium model to analyse the generation suppliers' bidding behaviour and models the ISO's market power monitoring problem as a bi-level multi-objective problem. The outer sub-problem is a multi-objective problem which maximizes suppliers' payoffs, while the inner one is the ISO's market clearing problem based on the locational marginal pricing mechanism. A discrete method is adopted to find ‘good enough’ solutions, in a continuous bidding strategy space, which are the intersection of all suppliers' optimal response spaces according to Nash equilibrium. The paper utilizes the IEEE 118-bus system to illustrate the application of the proposed method with three suppliers as price setters in the energy market and the other generators as price takers. The numerical results show that the transmission congestion may enhance the suppliers' ability to exercise market power. Likewise, suppliers' gaming behaviour could relieve the transmission congestion. It is shown that applying price caps is an efficient way of mitigating market power. [ABSTRACT FROM AUTHOR]

In this paper, the problem is addressed of how to organize a new product development process that is fast on the one hand and that provides good-quality results on the other hand. Several planning techniques, like PERT and CPM, are available to analyse the completion time or product release time of such complex processes. Although these techniques are all well known and are widely used in project management, they do not address the iterative mode of operation that is characteristic for such uncertain processes. Neither do they offer a tool or guidelines to 'design' a composition of iterative processes because they are analytical. In this paper, a quantitative concept is presented for modelling the release time of a single uncertain iterative activity, as a random variable to deal with the probabilistic aspect in a simple way. From this simple model, the complexity is extended systematically to model fundamentally different configurations, that are on the one hand simple enough to be studied analytically and on the other hand exhibit their fundamentally different release characteristics, as experienced in real life. From the analysis, guidelines are formulated for organizing or (re)configuring a complex process configuration. It is demonstrated that organizing uncertain processes for a fast product release requires a balance between the exploitation of the principles of concurrent engineering and the risk of overrunning time targets. An important factor in this balance is the decision structure for the release of intermediate results. The 'empowerment' structure, a structure where decisions are not clustered as milestones but are made without any delay, offers great opportunity in terms of small mean release times and small variances thereof. [ABSTRACT FROM AUTHOR]

This paper discusses the issues underlying the International Accounting Standards Board (IASB)'s review of accounting for pension obligations. Pension obligations are currently dealt with by International Accounting Standard 19. This contains three important features, which are currently under review: (1) A smoothing mechanism (the 'corridor') for recognizing actuarial gains and losses. (2) An expected rate of return on plan assets, which is different from the actual (achieved) return. (3) An asset ceiling, which limits the extent to which surplus plan assets are recognized in the accounts of the employer. The IASB's current thinking on these issues is explained, and comparisons are made with the UK's controversial Financial Reporting Standard 17. The paper concludes with a brief discussion of two important aspects of measuring defined benefit obligations that are not being considered in the present review. [ABSTRACT FROM AUTHOR]

In this paper, we present a Quasi-Monte Carlo approach for pricing European-style Asian options, i.e. for options whose pay-off depends on the average price of the underlying asset where the average is extended over a fixed period up to the maturity date. Following a recent development in mathematical finance, we assume that the log returns of the asset are not normally but hyperbolically distributed. This hypothesis is approved by several authors with different statistic tests on real financial data. The aim of this paper is to advance the hyperbolic model to the pricing of Asian options, since there only exist pricing formulae for plain vanilla options and some types of exotic options (e.g. power call options, barrier options) so far. We show how one can obtain prices of general Asian options in such incomplete markets in an efficient way. [ABSTRACT FROM AUTHOR]

This paper studies a telecommunications company's optimal network investment strategies. The objective of the company is to maximize the value of the network. According to our numerical examples we are not able to consider a single point-to-point connection separately from the network, because the network demands and capacities affect each other. Therefore, the whole network structure has to be considered in the point-to-point investments. [ABSTRACT FROM AUTHOR]

This paper is concerned with the development and application of stationary models for scheduling single and multiple preventive maintenance (PM) situations focusing on issues of model implementation. The first part of the paper deals with the practical implementation of the basic single PM scheduling model based on the renewal process. The main practical difficulty is lifetime‐distribution selection for small data sets which is typical in PM situations. Thus a sensitivity analysis of optimal PM interval to selected life distributions following PM and failures (corrective maintenance) is carried out. It has been found that the selected pair of distributions using AIC criteria as well as the Weibull–Weibull fitted pair have the smallest availability loss in estimating the optimal PM interval. The second part of this paper is concerned with modelling multi‐PM situations—something which has received very little attention in the literature despite its frequent implementation in real life. A multi‐PM model based on the renewal process is discussed. The model assumes a multi‐PM interval which is an integer multiple of the single PM intervals and different renewal functions following each type of PM. The procedure of model implementation is discussed through numerical example. [ABSTRACT FROM PUBLISHER]

The paper reviews recent developments and future needs in modelling credit risk in the retail portfolio and their recent regulatory implications, against the implications of the new framework proposed by the Basel Committee on Banking Supervision. It introduces four related papers arising from a conference held at the Bank of England in November 2000, before outlining the history of the requirements and the determination of the minimum capital required by banks to cover credit risk on their assets. The rationale for the mathematics behind the proposed new international ‘internal‐ratings based approach,’ relying on banks' own models, is then briefly discussed, with relevant bibliography for further reading. [ABSTRACT FROM PUBLISHER]

Focuses on the application of operational research in sport. Growing interest in the application of mathematics to sport; Published works on operational research in sport; Details of a special issue of "IMA Journal of Management Mathematics" dedicated to operational research in sport.

This paper extends the Malmquist productivity index by considering an important factor called the 'balance factor'. Although factors such as efficiency, scale and technology have been considered before, here the effect of imposed strategies on decision-making units (DMUs) is considered. For this purpose, we develop a new model to calculate the balance of DMUs and take into account the effect of this factor in the Malmquist index in addition to three previous factors. The objective of defining the balance factor is to compute to what extent DMUs are aligned with strategies defined by policy-makers. An empirical case study illustrates the need for such a new index and compares the traditional and extended Malmquist index. [ABSTRACT FROM AUTHOR]

This paper investigates a time consistent multiperiod mean-variance (MV) portfolio selection problem under the Markov regime-switching framework. We use a vector auto-regression model to forecast the values of market factors and then predict the returns of risky assets by using a regime-dependent linear multifactor model. By introducing the notion of separable expected conditional mapping, we construct a time consistent multiperiod MV model with regime switching. Under the self-financing constraint, we derive an analytical optimal investment policy satisfying time consistency and the corresponding MV efficient frontier by using dynamic programming. Empirical results are provided to illustrate the reasonability and practicality of the proposed new model and the derived explicit investment strategy. Especially, we find that the investor invests more in risky assets under a bull market than that under a consolidation market or a bear market; the MV efficient frontier under a bull market is much superior to those determined under a consolidation market and a bear market. [ABSTRACT FROM AUTHOR]

In this paper, we present a model for supply chain network design (SCND) with anticipation of variable delivered price competition in markets under deterministic and price-dependent demands and with the presence of rivals. The objective is to design a new SC under a capacity constraint in order to maximize the future profit of the SC in competing markets. The structure of the network of the new SC is assumed to be set 'once and for all' as a strategic decision; then the price adjustment is possible throughout the course of the competition as a decision at the operation level. In this paper, a new model for oligopolistic competitive SCND is proposed for each of the following two strategies: (1) 'single product strategy' and (2) 'substitutable product strategy'. Finally, we illustrate the model through several numerical examples. [ABSTRACT FROM AUTHOR]

This paper presents a new heuristic for fast approximation of VaR (Value-at-Risk) and CVaR (conditional Value-at-Risk) for financial portfolios, where the net worth of a portfolio is a non-linear function of possibly non-Gaussian risk factors. The proposed method is based on mapping non-normal marginal distributions into normal distributions via a probability conserving transformation and then using a quadratic, i.e. Delta-Gamma, approximation for the portfolio value. The method is very general and can deal with a wide range of marginal distributions of risk factors, including non-parametric distributions. Its computational load is comparable with the Delta-Gamma-Normal method based on Fourier inversion. However, unlike the Delta-Gamma-Normal method, the proposed heuristic preserves the tail behaviour of the individual risk factors, which may be seen as a significant advantage. We demonstrate the utility of the new method with comprehensive numerical experiments on simulated as well as real financial data. [ABSTRACT FROM AUTHOR]

This paper deals with the interaction between polluting firms, tax inspectors and politicians in a corrupted context.We construct a theoretical game model with incomplete information to discuss the effects of such interaction on environmental policy. In this respect, we believe that the State may pursue environmental protection by employing two alternative strategies: on the one hand, the State can, through greater incentive for the tax inspector, increase the monitoring level that reduces the evasion and thus increase tax revenues (incentive channel); on the other hand, the State can, through greater environmental expenses, increase the compliance of the polluting firm which means lower evasion and, thus greater tax revenues (compliance channel). Clearly, more environmental expenses mean, ceteris paribus, less public resources for the tax inspector's incentive, and vice versa. In this context, we demonstrate that, for a country with a high (low) level of incentives, the incentive (compliance) channel is more efficient than the compliance (incentive) channel. [ABSTRACT FROM AUTHOR]

The clustering problem has many applications in machine learning, operations research and statistics. We propose three algorithms to create starting solutions for improvement algorithms for the minimum sum of squares clustering problem. We test the algorithms on 72 instances that were investigated in the literature. We found five new best known solutions and matched the best known solution for 66 of the remaining 67 instances. Thus, we are able to demonstrate that good starting solutions combined with a simple local search get results comparable with, and sometimes even better than, more sophisticated algorithms used in the literature. [ABSTRACT FROM AUTHOR]

The transportation problem is one of the most popular problems in linear programming. Over the course of time a multitude of exact solution methods and heuristics have been proposed. Due to substantial progress of exact solvers since the mid of the past century, the interest in heuristics for the transportation problem over the past few decades has been reduced to their potential as starting methods for exact algorithms. However, in the context of ever increasing problem dimensions, a thorough cost-benefit analysis of exact methods versus heuristics is asked for. For this reason, this paper contributes an in-depth study of heuristics with respect to their performance in terms of computation time and objective value. Furthermore, we consider—to the best of our knowledge for the first time—simple efficient dual heuristics to obtain performance certificates based on weak duality without the need to solve the problem exactly. We test these heuristics in conjunction with state-of-the-art solvers on a variety of test problems. Thus, we especially close the gap to rather outdated comparative studies from the past century. For specific random test problems we extend previous approaches to provide a consistent and flexible problem generator for transportation problems with known solutions. Based on our numerical results, it can be concluded that primal and dual heuristics are able to rapidly generate good approximations for specific randomly generated problem instances but—as expected—are not able to yield satisfactory performance in realistic instances. [ABSTRACT FROM AUTHOR]

While the utility of intelligence as force multiplier during warfare is widely accepted there have been few attempts to quantify its benefits. In this paper Lanchester combat models are developed to understand how superiority in intelligence can compensate for an inferior force ratio and how the time for one side to defeat the other is affected by the use of intelligence. It is found that intelligence does act as a force multiplier; however, its utility to compensate for inferior force ratio is less than commonly appreciated, proportional to the square root of the relative advantage in intelligence. Similarly, the time to defeat is proportional to the inverse of the square root of the relative advantage in intelligence, so that greatly increasing one side's superiority in intelligence only produces a modest decrease in the time to defeat. The Lanchester combat models are extended to a hyperbolic system of partial differential equation (PDE) to investigate how intelligence influences manoeuvre warfare. These suggest that high tempo attacking operations are less sensitive to the effects of intelligence than slower operations. [ABSTRACT FROM AUTHOR]

This paper considers issues of sustainability in the context of spare parts logistics. In particular, the single product replenishment problem is formulated and an optimal order quantity is derived by minimizing a cost function that includes both economic and environmental costs relating to repair and replacement, including the environmental cost of the production of new parts and the disposal of used parts. The optimal means of transport, the optimal repair policy, and the corresponding optimal value of the sustainable economic order quantity (SOQ) are calculated. A numerical example derived from industrial field data is presented to test the model. Results are discussed and compared with classical EOQ model solutions. SOQ values larger than EOQ, slow means of transportation and high repair rates minimize the cost function, while fast means of transportation are identified as the optimal choice only when transport distances are short and spare parts demand is highly variable. [ABSTRACT FROM AUTHOR]

This paper presents a semi-parametric method for bootstrapping non-stochastic estimates of Malmquist indices and the second-stage regressions with a coherent data-generating process (DGP) for panel data. It emphasizes the importance of using censoring techniques instead of truncation in the DGP. To keep the panel structure, the fixed effects censored least square (CLS) method is preferred in the algorithm. Two smoothing processes in the DGP using fixed effects CLSs and cross-sectional censored least absolute deviations are proposed and compared. This semi-parametric bootstrap process can be used to delete artificial correlation amongst the estimated efficiencies in panel data. Although the exposition gives only output-oriented indices, the method can easily be extended to the input-oriented model. Finally, an empirical case study is carried out on Irish dairy farms. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) uses data on multiple inputs and outputs of a set of decision making units to measure their efficiency. However, the classic DEA model does not take non-discretionary factors into consideration. These factors are not controllable, play an important role in production processes, and ignoring them leads to poor estimation of efficiency. This paper proposes a two-stage procedure to handle non-discretionary inputs. To this end, first, a new imaginary environment is created from the existing environments and next, in this new dummy environment, the multiplier form of the DEA model is used. We perform a simulated comparison to show the advantages of the proposed approach. Finally, a real case study of bank branches with two non-discretionary variables is used to demonstrate the applicability of our approach. [ABSTRACT FROM AUTHOR]

Random sums play an important role in insurance claim theory and risk models. This paper aims to explore the numerical characteristics of dependent random sums using copulas. We obtain theoretical formulae for the expectation and variance of random sums in the case of a general copula. The expectation is an extension of the Wald equality and can be used to estimate prospective value, while variance provides a theoretical guarantee for measuring the risk premium. As corollaries, the corresponding results for the compound Poisson process and random sums using Farlie-Gumbel-Morgenstern copulas have been obtained. We also conduct stochastic simulation to verify the accuracy of our theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, a recovery inventory system in which a product is remanufactured by the original equipment manufacturer is considered. The remanufacturing process brings used products up to quality standards that are as rigorous as those of new products. The planning horizon is finite and the demand rate is time varying. For satisfying the demand, there are two options: either new items are produced or the used items are repaired back to an 'as new' condition, before being sold again. The returned products are a fraction of the demand and they are stored in the recoverable stocking point (recoverable inventory). At the beginning of the planning horizon, the demand is satisfied by new manufacturing lots of unequal size. When the production stops, the demand is satisfied by remanufacturing lots of unequal size (used products are recovered at a fixed rate) and the intervals between multiple production and repair runs are of unequal length and variable. The aim of this study is the determination of these time intervals, which leads to the formulation of a special optimization problem. The solution of this problem enables the determination of the newly produced and remanufactured quantities that minimize the total cost over the planning horizon. Also a simple procedure is proposed which relies on the decoupling of the problem into two sub-problems and determines the optimal time point of switching from a manufacturing activity to a remanufacturing one. [ABSTRACT FROM AUTHOR]

This paper focuses on the design of a distribution network problem in a three-tiered supply chain under uncertainty. The objective is to determine the optimal number, locations and capacities of plants and warehouses to minimize the overall network costs over a variety of economic growth scenarios. For this purpose, a mixed integer linear programming model is extended in a robust optimization framework and then three heuristic approaches based on genetic and memetic algorithms and a mathematical programming approach are used to solve this problem. The effectiveness of the proposed heuristics and the trade-off between model robustness and solution robustness is investigated and directions for further researches are presented. [ABSTRACT FROM AUTHOR]

Competition between rival brands within the same category gives rise to special competition/substitution effects of great interest to involved firms. In homogeneous product categories, the model structure should allow distinguishing between within-brand and cross-brand word-of-mouth effects. In this paper, we propose a model that, besides separating these two imitative sources, also allows for a change in the parameter values of the first entrant as soon as the second one enters the market. We prove that our model has a closed-form solution, allowing parameters to be estimated with sales data. Moreover, we compare our model with other unbalanced models, both from a theoretical point of view and from an empirical one, by assessing their performance on pharmaceutical drug data. [ABSTRACT FROM AUTHOR]

A supply chain relationship with a supplier who is stronger than a manufacturer is considered. The manufacturer purchases custom components from the dominant supplier, and then incurs a processing cost before they can sell the product to end customers where the demand for the product is a random variable with a given continuous distribution. For the supplier, there is asymmetric information about the manufacturer's cost structure which is described as a continuous random variable. This paper constructs a principal-agent model in a supplier-led supply chain to maximize the supplier's profits which include the wholesale profits and the transfer payment from the manufacturer. The proposed model is shown to be a dynamic optimization problem. The optimal wholesale price and transfer payment are obtained by solving the optimization problem based on Pontryagin's maximum principle. The optimal contract under symmetric information is also obtained. Some managerial implications are provided for the supplier contract design in a supplier-led environment. Finally, a numerical example is given to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM PUBLISHER]

A reinsurance contract involves two parties: an insurer and a reinsurer. Most existing optimal reinsurance treaties consider only the interest of one party. In this paper, we consider the interests of both the insurer and reinsurer and derive the optimal reinsurance contract in the form of the combination of quota-share and stop-loss reinsurance under the optimization criteria that maximize the joint survival probability. We first study the joint survival probability of insurers and reinsurers. We then derive conditions for the existence of the optimal reinsurance retentions and determine the optimal retentions that maximize the joint survival probability under expected value reinsurance premium principle. [ABSTRACT FROM AUTHOR]

Many optimization problems involve parameters which are not known in advance, but can only be forecast or estimated. Such problems fit perfectly into the framework of robust optimization that, given optimization problems with uncertain parameters, looks for solutions that will achieve good objective function values for the realization of these parameters in given uncertainty sets. The aim of this paper is to investigate and compare the alternative forms of robustness in the context of portfolio asset allocation. Starting with a relaxed form of robustness, which allows one to specify not only the values of the uncertainty parameters, but also their degree of feasibility, in the first part of the paper we propose a family of relaxed robust models, called norm–portfolio models, which use general norms to relax the classical notion of robustness. Then, in the second part we test some norm–portfolio models, as well as various robust strategies from the literature, with real market data on different data sets. To the best of our knowledge, this is the first attempt on comparing robust strategies of different kinds in the framework of portfolio asset allocation. [ABSTRACT FROM AUTHOR]

This study suggests a novel application of Inverse Data Envelopment Analysis (InvDEA) in strategic decision making about mergers and acquisitions in banking. The conventional DEA assesses the efficiency of banks based on the information gathered about the quantities of inputs used to realize the observed level of outputs produced. The decision maker of a banking unit willing to merge/acquire another banking unit needs to decide about the inputs and/or outputs level if an efficiency target for the new banking unit is set. In this paper, a new InvDEA-based approach is developed to suggest the required level of the inputs and outputs for the merged bank to reach a predetermined efficiency target. This study illustrates the novelty of the proposed approach through the case of a bank considering merging with or acquiring one of its competitors to synergize and realize higher level of efficiency. A real data set of 42 banking units in Gulf Corporation Council countries is used to show the practicality of the proposed approach. [ABSTRACT FROM AUTHOR]

Many real-world optimization problems are combinatorial optimization problems subject to dynamic environments. In such dynamic combinatorial optimization problems (DCOPs), the objective, decision variables and/or constraints may change over time, and so solving DCOPs is a challenging task. Metaheuristics are a good choice of tools to tackle DCOPs because many metaheuristics are inspired by natural or biological evolution processes, which are always subject to changing environments. In recent years, DCOPs have attracted a growing interest from the metaheuristics community. This paper is a tutorial on metaheuristics for DCOPs. We cover the definition of DCOPs, typical benchmark problems and their characteristics, methodologies and performance measures, real-world case study and key challenges in the area. Some future research directions are also pointed out in this paper. [ABSTRACT FROM AUTHOR]

Editorial. Focuses on the publication of research papers presented at the International Federation of Operational Research Societies 2002 conference. Workshops on the use of technologies; Development of software as a platform for a joint community; Publication of methodology.

In this paper, we deal with group multiattribute utility analysis incorporating the preferences of multiple interested individuals. Since it is difficult to repeatedly ask these individuals questions for determining parameters of a multiattribute utility function, we gather preference information of them by asking questions that are not difficult to answer, and develop a method for selecting an alternative consistent with the preference information. Assuming that the multiattribute utility function has the multiplicative form and the corresponding single-attribute utility functions are already identified, we evaluate the trade-off between attributes by utilizing neural networks whose inputs and outputs are the preference information elicited from the interested individuals and the scaling constants for the multiattribute utility function, respectively. By performing computational experiments, we verify that the proposed method can generate scaling constants properly. Furthermore, using the preference relations from two groups with different degrees of the preferential heterogeneity, we examine the effectiveness of the proposed method, and then we show that the results of the numerical applications are reasonable and proper. [ABSTRACT FROM AUTHOR]

The geometric process has been widely applied in reliability engineering and other areas since its introduction. One of its assumptions is that the times between occurrences of events are independent. This assumption is rather restrictive and can limit its application in the real world. This paper extends the geometric process to a new process, which we call the semi-geometric process, by relaxing this assumption. Some probabilistic properties of the process are derived and parameter estimation is described. A numerical example, based on a real-world data set, is used to illustrate the model and validate the estimation methodology. [ABSTRACT FROM AUTHOR]