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To manage the risk of insurance companies, a reinsurance transaction is among the myriad risk management mechanisms the top ranked choice. In this paper, we study the design of optimal reinsurance contracts within a risk measure minimization framework and subject to the Vajda condition. The Vajda condition requires the reinsurer to take an increasing proportion of the loss when it increases and therefore imposes constraints on the indemnity function. The distortion-risk-measure-based objective function is very generic, and allows for, for example, an objective to minimize the risk-adjusted value of the insurer’s liability, and for heterogeneous beliefs regarding the loss distribution by the insurer and reinsurer. Under a mild condition, we propose a backward-forward optimization method that is based on a marginal indemnity function formulation. To show the applicability and simplicity of our strategy, we provide three concrete examples with the Value-at-Risk: one with the risk-adjusted value of the insurer’s liability, one with an objective function that follows from imposing Pareto optimality, and one with heterogeneous beliefs. We conclude this paper with an empirical application with Danish fire insurance losses and the Value-at-Risk and the Tail Value-at-Risk.

Chronic obstructive pulmonary disease (COPD) is characterized by a largely irreversible obstruction of the airways, and is one of the leading causes of chronic morbidity and mortality worldwide. This paper illustrates the use of Data Envelopment Analysis (DEA) to assess the potential for cost savings at COPD inpatient episode level. The analysis uses the length of stay of each episode as a surrogate for expenditure on that episode while allowing for the medical condition of the patient and the quality of care received. We find substantial possible reductions in length of stay which would translate to cost savings. The paper also explores differences both between hospitals and between care teams within hospitals so that cost efficient protocols of treatment can be identified and disseminated.

The rapid development of information technologies enables recycling companies to purchase and collect used products from consumers through both traditional and Internet-based online channels. Because an online channel transmits price information instantly to consumers, choosing the best time to announce the recycling price (i.e., acquisition price) of used products to consumers has become a critical problem for recycling companies. This paper seeks to solve this problem by developing a game-theoretic model describing a dual-channel reverse supply chain consisting of a recycling company and a third-party collector in which the recycling company purchases products not only through a third-party collector, but also directly from consumers online. We derive two major results by solving the model. The first is that first-mover advantage arises, which indicates that each firm constituting a dual-channel reverse supply chain should announce its own recycling price before the other. This first result is notable because it is exactly opposite to conventional wisdom that the second-mover advantage of pricing usually emerges when price competition occurs among firms in a horizontal relationship, which is well known in noncooperative game theory. The second result is that the recycling company can maximize its own profit and consumers' surplus by announcing its recycling price in the online channel before or upon, but not after, determining the transfer price paid to the collector for products collected in the offline channel. Both results can be used as practical decision-making guidelines in dual-recycling channel reverse supply chain management.

Although Operational Research (OR) has successfully provided many methodologies to address complex decision problems, in particular based on the rationality principle, there has been too little discussion regarding their limited consideration in IT evaluation practice and associated decision making satisfaction levels in an organisational context. The aim of this paper is to address these issues through providing a current account of diffusion and infusion of OR methodologies in IT decision making practice, and by analysing factors affecting decision making satisfaction from a Technological, Organisational, and Environmental (TOE) framework in the context of IT induced business transformations. We developed a structural equation model and conducted an empirical survey, which supported four out of five developed research hypotheses. Our results show that while Decision Support Systems (DSS), holistic IT evaluation methods, and management support seem to positively affect individual satisfaction, legislative regulation has an adverse effect. Results also revealed a persistent methodology diffusion and infusion gap. The paper discusses implications in each of these aspects and presents opportunities for future work. (authors' abstract)

Several activity-based transportation models are now becoming operational and are entering the stage of application for the modelling of travel demand. Some of these models use decision rules to support its decision-making instead of principles of utility maximization. Decision rules can be derived from different modelling approaches. In a previous study, it was shown that Bayesian networks outperform decision trees and that they are better suited to capture the complexity of the underlying decision-making. However, one of the disadvantages is that Bayesian networks are somewhat limited in terms of interpretation and efficiency when rules are derived from the network, while rules derived from decision trees in general have a simple and direct interpretation. Therefore, in this study, the idea of combining decision trees and Bayesian networks was explored in order to maintain the potential advantages of both techniques. The paper reports the findings of a methodological study that was conducted in the context of Albatross, which is a sequential rule based model of activity scheduling behaviour. To this end, the paper can be situated within the context of a series of previous publications by the authors to improve decision-making in Albatross. The results of this study suggest that integrated Bayesian networks and decision trees can be used for modelling the different choice facets of Albatross with better predictive power than CHAID decision trees. Another conclusion is that there are initial indications that the new way of integrating decision trees and Bayesian networks has produced a decision tree that is structurally more stable.

A key step in data-driven decision making is the choice of a suitable mathematical model. Complex models that give an accurate description of reality may depend on many parameters that are difficult to estimate; in addition, the optimization problem corresponding to such models may be computationally intractable and only approximately solvable. Simple models with only a few unknown parameters may be misspecified, but also easier to estimate and optimize. With such different models and some initial data at hand, a decision maker would want to know which model produces the best decisions. In this paper we propose a decision-based model-selection method that addresses this question.

This paper discusses analytical advances in evolutionary methods with a view towards their possible applications in the space-economy. For this purpose, we present a brief overview and illustration of models actually available in the spatial sciences which attempt to map the complex patterns of spatial/social networks. Particular attention is given to new emerging tools, such as neural networks (NNs) and evolutionary algorithms (EAs), belonging to the field of neurocomputing. The main part of the paper focuses specifically on EAs, in order to better comprehend the potentiality and applicability of this new methodological tool, also in comparison with “conventional” approaches (like logit and spatial interaction models). A historical review of EAs, together with an exposition of their theoretical structure, is offered. Moreover, an empirical application related to the modal split analysis in the European freight transport network shows the potential of EAs in this particular context of analysis. The final section concludes and points to new research directions. It is mainly based on the idea that, since EAs are optimising techniques generating mechanisms of natural selection and genetics, it is methodologically interesting to investigate their compatibility with the rational behavioural paradigm underlying conventional logit and spatial interaction models (and thus also the potential compatibility between natural selection and economic choice).

In many applications of data envelopment analysis, there are situations in which a central body manages a large set of similar units delivering some services. In such multi-unit organizations, the central management desires a mechanism by which the local management of each unit is incentivized to perform towards the improvement of the performance of the organization as a whole. In a recent paper, Afsharian et al. (2017) have proposed a system of incentives under these circumstances. In their approach, – which relies on the original concept of super-efficiency – units with outstanding performance are identified and incentivized by some reward compatible with the level of their impact on the overall performance of the organization. We discuss why the conventional super-efficiency approach may not be optimal in such situations. We revisit the definition of the collective impact and propose a new method, which can identify – in a controlled manner – a subset of k outstanding DMUs among n existing units in the system. The proposed approach is illustrated using data from a German retail bank.

In this paper, we present a new model to analyze the risk and the expected level of firm performance. This model is based on the multi-factor approach to risk, in which unexpected performance is explained through sensitivities to unexpected changes of risk factors. Instead of using the multi-factor approach for the analysis of security portfolios, it is used to analyze performance measures of firms. In this paper the multi-factor approach is not only used to analyze risk, but also to analyze the expected level of performance. Furthermore, it is analyzed how instruments, as for instance projects, can be used to change the risk and the expected level of performance. An illustrative application in the field of finance is presented, although the model can also be applied in other areas.

Applying Agent-Based Modeling and Simulation (ABMS) methodology, this paper analyzes the impact of alternative production-sales policies on the diffusion of a new product and the generated NPV of profit. The key features of the ABMS model, that captures the marketplace as a complex adaptive system, are: (i) supply chain capacity is constrained; (ii) consumers' new product adoption decisions are influenced by marketing activities as well as positive and negative word of mouth (WOM) between consumers; (iii) interactions among consumers taking place in the context of their social network are captured at the individual level; and (iv) the new product adoption process is adaptive. Conducting over 1 million simulation experiments, we determined the "best" productionsales policies under various parameter combinations based on the NPV of profit generated over the diffusion process. The key findings are as follows: (1) on average, the build-up policy with delayed marketing is the preferred policy in the case of only positive WOM as well as the case of positive and negative WOM. This policy provides the highest expected NPV of profit on average and it also performs very smoothly with respect to changes in build-up periods. (2) It is critical to consider the significant impact of negative word-of-mouth on the outcomes of alternative production-sales policies. Neglecting the effect of negative word-of-mouth can lead to poor policy recommendations, incorrect conclusions concerning the impact of operational parameters on the policy choice, and suboptimal choice of build-up periods. (authors' abstract)

In this paper, we study inventory pooling coalitions within a decentralized distribution system consisting of a manufacturer, a warehouse (or an integration center), and n retailers. At the time their orders are placed, the retailers know their demand distribution but do not know the exact value of the demand. After certain production and transportation lead time elapses, the orders arrive at the warehouse. During this time, the retailers can update their demand forecasts. We first focus on cooperation among the retailers – the retailers coordinate their initial orders and can reallocate their orders in the warehouse after they receive more information about their demand and update their demand forecasts. We study two types of cooperation: forecast sharing and joint forecasting. By using an example, we illustrate how forecast sharing collaboration might worsen performance, and asymmetric forecasting capabilities of the retailers might harm the cooperation. However, this does not happen if the retailers possess symmetric forecasting capabilities or they cooperate by joint forecasting, and the associated cooperative games have non-empty cores. Finally, we analyze the impact that cooperation and non-cooperation of the retailers has on the manufacturer’s profit. We focus on coordination of the entire supply chain through a three-parameter buyback contract. We show that our three-parameter contract can coordinate the system if the retailers have symmetric margins. Moreover, under such a contract the manufacturer benefits from retailers’ cooperation since he can get a share of improved performance.

This paper models airline competition as a two-stage game in frequency and prices, allowing for asymmetric frequency equilibria. The approach follows the spatial multiproduct oligopoly literature. The dynamic structure gives airlines an incentive to choose asymmetric frequency equilibria such that price competition is reduced. This feature is most pronounced in the case of inelastic demand, for which a maximum differentiation result is derived. We apply the model in a simulation study of airline deregulation of the Amsterdam-Maastricht market in The Netherlands, calculating welfare effects for various types of p © 2006 Elsevier B.V. All rights reserved.

In this paper we present and illustrate using real-life data a framework for managing an investment portfolio in which the investment opportunities are described in terms of a set of attributes and part of this set is intended to capture the effects on society. Here we link with the emerging literature on SRI: socially responsible investment. Given the multi-attribute descriptions of the individual investment opportunities we show how these can be combined into portfolios with the same attributes at the portfolio level. Also we show how a manager can systematically be supported in the choice between different portfolio profiles. As part of the framework we use multi-criteria decision tools.

This paper studies the mailing frequency problem that addresses the issue of how often to send a mailing to an individual customer in order to establish a profitable long-term relation rather than targeting profitable groups of customers at every new mailing instance. The mailing frequency is optimized using long-term objectives but restricts the decisions to the number of mailings to send to the individual over consecutive finite planning periods. A stochastic dynamic programming model is formulated for this problem that can easily be applied to various direct marketing frameworks such as catalog sales or charity organizations. The model is calibrated for a large Dutch non-profit organization and shows that substantial improvements can be achieved by approaching the mailing strategy with the mailing frequency problem, both in the number of mailings to send and in the profits resulting from the responses.

The paper is devoted to a practical case taking place in the Rotterdam harbour. A group of stevedoring companies founded a pool of dockers to be able to share human resources. In this way they cover their strongly fluctuating workload. A system to control the pool is developed. On the one hand this system divides the liabilities of the pool among the stevedores and on the other hand it rules the priorities of assignment of human resources. The history leading to the development of this system is discussed, the system itself is sketched and finally some experiences with the system are given. In an appendix some mathematical aspects of the assignment rules are treated.