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In this paper we deal with the definition of a decision model for a producer operating in a multi-auction electricity market. The decisions to be taken concern the commitment of the generation plants and the quantity of energy required to offer to each auction and to cover the bilateral contracts. We propose a Multistage Stochastic Programming model in which the randomness of the clearing prices is represented by means of a scenario tree. The risk is modelled using a Conditional Value at Risk term in the objective function. Experimental results are reported to show the validity of our model and to discuss the influence of the risk parameters on the optimal value.

Handicapping systems play important roles in many sports. This paper focuses on disabled sports competitions, which primarily aim to encourage participation. Nevertheless, prizes are awarded for relative performances and should be allocated fairly. We review handicapping procedures specifically for disabled Alpine skiing and identify imperfections with this system, particularly how historical and new results are combined to determine scaling factors. Significant problems also arise due to different class sizes and variations in courses and conditions. We investigate the difficulties in establishing a suitable handicapping system and propose modifications to the existing procedures that improve their fairness. We develop simple mathematical models and statistical analyses that might be suitable, based on results data relating to several years of competition, and we evaluate the performance of our proposed method in comparison with that of the existing approach.

In inventory management, hierarchical forecasting (HF) is a hot issue: families of items are formed for which total demand is forecasted; total forecast then is broken up to produce forecasts for the individual items. Since HF is a complicated procedure, analytical results are hard to obtain; consequently, most literature is based on simulations and case studies. This paper succeeds in following a more theoretical approach by simplifying the problem: we consider estimation instead of forecasting. So, from a random sample we estimate both the total demand and the fraction of this total that individual items take; multiplying these two quantities gives a new estimate of the individual demand. Then, our research question is: Can aggregation of items, followed by fractioning, lead to more accurate estimates of individual demand? We consider two simple situations that can be analysed fully theoretically. Thirdly, a more practical situation is investigated by means of simulation.

In this paper, we consider the demand-forecasting problem of a make-to-stock system operating in a business-to-business environment where some customers provide information on their future orders, which are subject to changes in time, hence constituting imperfect advance demand information (ADI). The demand is highly volatile and non-stationary not only because it is subject to seasonality and changing trends but also because some individual client demands have significant influence on the total demand. In such an environment, traditional forecasting methods may result in highly inaccurate forecasts, since they are mostly developed for the total demand based only on the demand history, not making use of demand information and ignoring the effects of individual order patterns of the customers. We propose a forecasting methodology that makes use of individual ordering pattern histories of the product–customer combinations and the current build up of orders. Moreover, we propose making use of limited judgemental updates on the statistical forecasts prior to the use of ADI.

In this paper a reliability model based on artificial neural networks and the generalized renewal process is developed. The model is used for failure prediction, and is able to dynamically adapt to changes in the operating and environmental conditions of assets. The model is implemented for a thermal solar power plant, focusing on critical elements of these plants: heat transfer fluid pumps. We affirm that this type of model can be easily automated within the plant’s remote monitoring system. Using this model we can dynamically assign reference values for warnings and alarms and provide predictions of asset degradation. These in turn can be used to evaluate the associated economic risk to the system under existing operating conditions and to inform preventive maintenance activities

In this paper we propose using computational tests for appraising the inaccuracies of index numbers (for example, price and wage indices). This is done by programming a hypothetical situation where the true average index value is known and the variation of prices and quantities and the relation between them can be chosen. The indices are then calculated using the formulae of Laspeyres, Paasche, Fisher, the Unit index and Normalized Unit index formulae. By comparing the average values of the above formulae with the true average index value, we are able to obtain a quantitative indication of the errors in these formulae under various situations. The Normalized Unit and Fisher index formulae were found to be consistently accurate for all our tests whereas the formulae of Laspeyres and Paasche were found to have very poor accuracy in the majority of our tests. The Unit index was also consistently accurate in all our tests but its use is appropriate in the single item case thus making this formula suitable for measuring wages. In our informal discussion of stability we indicated that the Normalized unit index and the Unit index have good stability characteristics whereas the indices Laspeyres, Paasche and Fisher have poor stability characteristics. In view of the above the Normalized Unit index is the best of the formulae we have discussed for measuring prices (many item case), and the Unit index is the best of the formulae we have discussed for measuring wages (single item case).

The service sector is the largest sector of the economy in most industrialized nations, and is fast becoming the largest sector in developing nations as well. Driven by today's new business environment, including advanced telecommunications, accelerated business globalization, increased automation and highly on-demand and competitive innovations, the complexity of the operations management of service systems is continuously increasing. Managers of service systems are wrestling to deliver both traditional conflicting objectives of low operating costs and high service quality. Consequently, the problems of operations management in service systems have recently attracted a lot of attention. The most popular applications relate to Services for Supply Chains, Health Care Systems and Call Centres. Given the size and the complexity associated with their operations, service systems have emerged as a fertile ground for academic research. We were delighted therefore to be offered the opportunity to edit this special issue of the IMA Journal of Management Mathematics on operations management in service systems. We hope that through this special issue we have achieved our objective which is to advance the current state of knowledge in this area, adding in parallel significant value to relevant real-world practices. This special issue demonstrates that all the key areas addressed in Manufacturing Operations (such as demand forecasting, acquiring capacity, deploying resources and managing delivery) are now also reflected in the Services Operations agenda. However, issues such as behavioural factors, the emerging need for increased standardization and automation of the operations, handling technological advances (such as the digitalisation of systems) and cultural issues (such as those resulting from the globalization of systems, e.g. big call centres) that introduce many interesting research opportunities have not yet sufficiently explored and more remains to be done in this area to gain new interesting managerial insights. The call for this Special Issue has attracted many submissions. All manuscripts underwent a rigorous review process which resulted in the six papers presented in this special issue. This process required the cooperation of both authors and reviewers, and we thank all of them for their efforts and diligence; they all made our role as Special Issue Editors easy and enjoyable.