The efficient frontier for spot and forward purchases: an application to electricity

A local electricity distribution company (LDC) can reduce its exposure to the inherent risks of spot-price volatility and uncertain future demand via forward contracts. Management's problem is to determine the optimal forward-contract purchase. We propose a practical three-stage approach for dealing with the problem. The first stage determines an optimal purchase by solving a cost-constrained risk-minimization problem. The second stage derives the efficient frontier of tradeoffs between expected cost and cost risk from the first-stage solution, at various bounds on the expected cost. The optimal solution is found by melding the frontier with management's risk preferences. In the third stage, the model's parameters are estimated from data typically available to an LDC and used to determine its forward-contract purchase.     

Common due date scheduling with autonomous and induced learning

Up to now the few existing models, that consider learning effects in scheduling, concentrate on learning-by-doing (autonomous learning). But recent contributions to the literature on learning in manufacturing organizations emphasize the important impact of proactive investments in technological knowledge on the learning rate (induced learning). In the present paper, we focus on a scheduling problem where the processing times decrease according to a learning rate, which can be influenced by an initial cost-inducing investment. Thus we have integrated into our model both aspects of learning––autonomous and induced––thereby highlighting the management's responsibility to invest in technological knowledge enhancement. We have been able to derive some structural properties of the problem and present a polynomially bound solution procedure which optimally solves the problem by using these properties. The optimal solution to the scheduling problem contains––of course–– information on the optimal level of proactive investments in learning.     

IP-MAGS: an incomplete preference-based multiple attribute group support system

In this research, we develop a group decision support system to deal with multiple attribute group decision-making problems, which involve getting incomplete judgements of individual preference and aggregating the judgements by means of the additive preference model. Dominance-based decision-making rules are built in and applied to obtain a group's preferred alternative. The proposed system, above all, allows for more various forms of incomplete judgements than prior systems that were designed to handle group decision problems. A user-friendly graphical interface enables users to easily encode their incomplete judgements. Further, the system helps individuals revise their preference judgements by referring them to their own decision results in comparison with the group's aggregated decision result. The system is a web-based application system, which enables bidirectional communications between individuals and the system. Any individual who is involved in a group decision-making problem is able to participate in the decision-making process from a remote site. Furthermore, we present a real-life case study on the selection of a branch office server that has been carried out using the proposed system.     

A Bayesian priorization procedure for AHP-group decision making

This paper proposes a new priorization procedure for Analytic Hierarchy Process Group Decision Making (AHP-GDM). Unlike the methods normally employed in AHP-GDM, this process does not require intermediate filters for the actors’ initial judgements. The procedure is based on a Bayesian analysis of the problem and in general, it provides more efficient estimates than the techniques conventionally applied in the literature for AHP-GDM: aggregation of individual judgements (AIJ) and aggregation of individual priorities (AIP). The proposed procedure naturally extends to the analysis of incomplete and/or imprecise pairwise comparison matrices, enhancing realism, practicality and scope. The methodology has been illustrated by the analysis of a case study.     

Preference disaggregation for measuring and analysing customer satisfaction: The MUSA method

The MUlticriteria Satisfaction Analysis (MUSA) method for measuring and analysing customer satisfaction is presented in this paper. The MUSA method is a preference disaggregation model following the principles of ordinal regression analysis (inference procedure). The integrated methodology evaluates the satisfaction level of a set of individuals (customers, employees, etc.) based on their values and expressed preferences. Using satisfaction survey's data, the MUSA method aggregates the different preferences in unique satisfaction functions. This aggregation–disaggregation process is achieved with the minimum possible errors. The main advantage of the MUSA method is that it fully considers the qualitative form of customers' judgements and preferences. The development of a set of quantitative indices and perceptual maps makes possible the provision of an effective support for the satisfaction evaluation problem. This paper also presents the reliability analysis of the provided results, along with a simple numerical example that demonstrates the implementation process of the MUSA method. Finally, several extensions and future research in the context of the presented method are discussed.     

Reductive-holistic cycle: A model for the study of the didactic procedure

The concept ofreality level may be useful as a catalyst among several systems in the area of knowledge. This concept is leading us to ask, if we can make a reduction from a reality level to another, that is to the problem ofreductionism. Relative to it is the problem ofholism. At the end these concepts are connected to the category theory and adjoint functors. Within the framework of this aspect we set up a model for the study of the didactic procedure. This model is a feedback system between two reality levels or categories, these of the teacher and of the student. So, the article seeks to enhance and improve the teaching of mathematics by its attempt to understand both student's and teacher's knowledge in the same terms.     

QUOPLAN: A System for Optimizing Sales Quota-Bonus Plans

This paper describes an IBM-PC menu-driven and user-friendly procedure which can help sales management in setting up optimal quota-bonus plans. For a given sales force, the resulting quotabonus plan maximizes the firm's current profits subject to (1) keeping every individual sales-force member at least at his/her current level of satisfaction (or eventually, increasing this level), and (2) being consistent and harmonious across sales representatives. The QUOPLAN system is composed of two submodels. The first submodel is used by salespersons for eliciting their own utility functions, and is essentially based upon the principles of conjoint analysis. The second submodel is used by management for reconciling all the individuals' judgements and the company's objectives into a consistent quota-bonus plan.     

A partial cooperation model for non-unique linear two-level decision problems

This paper examines a linear static Stackelberg game where the follower's optimal reaction is not unique. Traditionally, the problem has been approached using either an optimistic or a pessimistic framework, respectively, representing the two extreme situations of full cooperation and zero cooperation from the follower. However, partial cooperation from the follower is a viable option. For partial cooperation, the leader's optimal strategy may be neither optimistic nor pessimistic. Introducing a cooperation index to describe the degree of follower cooperation, we first formulate a partial cooperation model for the leader. The two-level problem is then reformulated into a single-level model. It is shown that the optimistic and pessimistic situations are special cases of the general model, and that the leader's optimal choice may be an intermediate solution.     

An Application of Travelling-Salesman Routines to Solve Pattern-Allocation Problems in the Glass Industry

This paper focusses on an often encountered constraint in real-life cutting-stock problems. The constraints require that pieces corresponding to the same order are not spread too much over the production run. This elimination of order spread is called pattern allocation or cutting sequencing. In this paper, a two-stage procedure to solve the two-dimensional pattern-allocation problem is suggested. The first stage consists of solving the cutting-stock problem without the sequencing constraint. In the second stage a sequencing problem is used for the ordering of the cutting patterns in an optimal or near-optimal way. The sequencing problem is formulated as a travelling-salesman model, and the model is solved by Lin's 3-optimal method. Computational experience is reported from a case study in the glass industry.     

A new approach for weight derivation using data envelopment analysis in the analytic hierarchy process

Recently, some researches have been carried out in the context of using data envelopment analysis (DEA) models to generate local weights of alternatives from pairwise comparison matrices used in the analytic hierarchy process (AHP). One of these models is the DEAHP. The main drawback of the DEAHP is that it generates counter-intuitive priority vectors for inconsistent pairwise comparison matrices. To overcome the drawbacks of the DEAHP, this paper proposes a new procedure entitled Revised DEAHP, and it will be shown that this procedure generates logical weights that are consistent with the decision maker's judgements and is sensitive to changes in data of the pairwise comparison matrices. Through a numerical example, it will be shown that the Revised DEAHP not only produces correct weights for inconsistent matrices but also does not suffer from rank reversal when an irrelevant alternative is added or removed.     

Weather Risk in Offshore Projects

Risk analysis tools have been used to help manage various projects. This paper describes a case study in which an extension to the stochastic project network model was developed for a risk analysis of an oil platform installation, quantifying the possible impact of the weather on the project's schedule. Examination of the weather data suggested the use of a Markov weather model combined with a separate residence time distribution for key states. The weather model was incorporated into the stochastic project network allowing the interactions of the various project uncertainties to be examined. While the weather introduced a significant additional risk to the project, analysis of management's options indicated that much of the risk might be avoided. In particular, the analysis quantified the benefits of scheduling the project start to take advantage of the seasonal variations and hiring heavy duty equipment to operate in more arduous conditions.     

An Efficient Procedure for Differential Weighting of Committee Members' Subjective Estimates

For many problems, the judgements of individuals are critical data. This paper suggests how an aggregate judgement may be computed using differential weights for each judge. Basic data include proficiency rankings made by each judge for all judges. These are then used both to calculate a mean proficiency rank, and a weight based on divergence of each individual judge's rankings of all judges from the mean proficiency ranks.     

Improving envelopment in Data Envelopment Analysis under variable returns to scale

In a Data Envelopment Analysis model, some of the weights used to compute the efficiency of a unit can have zero or negligible value despite of the importance of the corresponding input or output. This paper offers an approach to preventing inputs and outputs from being ignored in the DEA assessment under the multiple input and output VRS environment, building on an approach introduced in Allen and Thanassoulis (2004) for single input multiple output CRS cases. The proposed method is based on the idea of introducing unobserved DMUs created by adjusting input and output levels of certain observed relatively efficient DMUs, in a manner which reflects a combination of technical information and the decision maker’s value judgements. In contrast to many alternative techniques used to constrain weights and/or improve envelopment in DEA, this approach allows one to impose local information on production trade-offs, which are in line with the general VRS technology. The suggested procedure is illustrated using real data.     

A Benders decomposition method for discretely-constrained mathematical programs with equilibrium constraints

We present a new methodology to solve discretely-constrained mathematical programs with equilibrium constraints (DC-MPECs). Typically these problems include an upper planning-level optimization with some discrete decision variables (eg, build/don’t build) as well as a lower operations-level problem often described by an optimization or nonlinear complementarity problem. This lower-level problem may also include some discrete variables. MPECs are very challenging problems to solve and the inclusion of integrality constraints makes this class of problems even more computationally difficult. We develop a new variant of the Benders algorithm combined with a heuristic procedure that decomposes the domain of the upper-level discrete variables to solve the resulting DC-MPECs. We provide convergence theory as well as a number of numerical examples, some derived from energy applications, to validate the new method. It should be noted that the convergence theory applies if the heuristic procedure correctly identifies a decomposition of the domain so that the lower-level problem's optimal value function is convex. This is challenging but our numerical results are positive.     

Foreign Currency Inventory Management in a Branch Bank

A branch bank may decide to stock one or more foreign currencies to meet demands from its customers and may also accumulate foreign currency as a result of customer transactions. This paper presents the branch bank's foreign currency problem as an inventory management problem and derives good (and in many cases, optimal) decision rules for this type of cash management problem.While the model presented is similar to models which have been proposed for cash management, the foreign currency problem has a number of features which lead to distinct differences. The model was developed for the Canadian situation but is generalizable to any branch having a significant volume of foreign currency transactions.     

A Hybrid High-Order Markov Chain Model for Computer Intrusion Detection

A hybrid model based mostly on a high-order Markov chain and occasionally on a statistical-independence model is proposed for profiling command sequences of a computer user in order to identify a “signature behavior” for that user. Based on the model, an estimation procedure for such a signature behavior driven by maximum likelihood (ML) considerations is devised. The formal ML estimates are numerically intractable, but the ML-optimization problem can be substituted by a linear inverse problem with positivity constraint (LININPOS), for which the EM algorithm can be used as an equation solver to produce an approximate ML-estimate. The intrusion detection system works by comparing a user's command sequence to the user's and others' estimated signature behaviors in real time through statistical hypothesis testing. A form of likelihood-ratio test is used to detect if a given sequence of commands is from the proclaimed user, with the alternative hypothesis being a masquerader user. Applying the model to real-life data collected from AT&T Labs–Research indicates that the new methodology holds some promise for intrusion detection.     

Using 3D Animation Techniques to Help with the Experimental Design and Analysis Phase of a Visual Interactive Simulation Project

This paper describes an interactive 3D animation approach that may help with the investigation, understanding and interpretation of results for a Visual Interactive Simulation (VIS) project. The method uses a graphics algorithm to draw on a computer screen ‘3D contour maps’ which show the response of a simulation model to changes in its input parameters. A previous paper showed that it is possible for a neural network to learn the response of a simulation. This paper shows that the speed of response of a neural network can be exploited so that 3D animation sequences of the simulation's results can be produced. It is possible to rotate the ‘3D contour map’, zoom, pan, or generally view the projection from different perspectives. Two example problems are described. The paper suggests that this approach can further improve the quality of VIS by being able to give comprehensive graphic 3D sensitivity analyses to the original problem under investigation.     

Deadlock avoidance for systolic communication

Under the systolic communication model, each cell (or processor) in a parallel processing system can operate directly on data residing at the cell's input queues and move computed results directly to the cell's output queues. Incoming and outgoing data need not be stored in the cell's local memory, if not required by the computation. By avoiding these local memory accesses, systolic communication can achieve high efficiency when executing many systolic algorithms. Though efficient, systolic communication may lead to deadlocks at run time if data arriving at a cell's input queues are improperly ordered. This paper describes the nature of this deadlock problem, gives an abstract formulation of the problem, and provides a deadlock avoidance strategy.     

Reducing inconsistency measured by the geometric consistency index in the analytic hierarchy process

This paper presents a theoretical framework and a procedure for revising the judgements and improving the inconsistency of an Analytic Hierarchy Process (AHP) pairwise comparison matrix when the Row Geometric Mean (RGM) is used as the prioritisation procedure and the Geometric Consistency Index (GCI) is the inconsistency measure. Inconsistency is improved by slightly modifying the judgements that further reduce the GCI. Both the judgements and the derived priority vector will be close to the initial values. A simulation study is utilised to analyse the performance of the algorithm. The proposed framework allows the specification of the procedure to particular interests. A numerical example illustrates the proposed procedure.