Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting

Demand functions for goods are generally cyclical in nature with characteristics such as trend or stochasticity. Most existing demand forecasting techniques in literature are designed to manage and forecast this type of demand functions. However, if the demand function is lumpy in nature, then the general demand forecasting techniques may fail given the unusual characteristics of the function. Proper identification of the underlying demand function and using the most appropriate forecasting technique becomes critical. In this paper, we will attempt to explore the key characteristics of the different types of demand function and relate them to known statistical distributions. By fitting statistical distributions to actual past demand data, we are then able to identify the correct demand functions, so that the the most appropriate forecasting technique can be applied to obtain improved forecasting results. We applied the methodology to a real case study to show the reduction in forecasting errors obtained

Re-purposing Google maps visualisation for teaching logistics systems

Routing is the process of selecting appropriate paths and ordering waypoints in a network. It plays an important part in logistics and supply chain management as choosing the optimal route can minimise distribution costs. Routing optimisation, however, is a difficult problem to solve and computer software is often used to determine the best route. In an Australian postgraduate course on logistics systems, Logware, a dated software packaged with a textbook was used for teaching routing. A need was felt to replace the software with a more modern tool with an ad-vanced graphical user interface and a high level of visualisation capabilities for a more engaging student experience and better learning outcomes

Unambiguous Acquisition and Tracking Technique for General BOC Signals

This article presents a new unambiguous acquisition and tracking technique for general Binary Offset Carrier (BOC) ranging signals, which will be used in modern GPS, European Galileo system and Chinese BeiDou system. The test criterion employed in this technique is based on a synthesized correlation function which completely removes positive side peaks while keeping the sharp main peak. Simulation results indicate that the proposed technique completely removes the ambiguity threat in the acquisition process while maintaining relatively higher acquisition performance for low order BOC signals. The potential false lock points in the tracking phase for any order BOC signals are avoided by using the proposed method. Impacts of thermal noise and multipath on the proposed technique are investigated; the simulation results show that the new method allows the removal of false lock points with slightly degraded tracking performance. In addition, this method is convenient to implement via logic circuits

Generating compact classifier systems using a simple artificial immune system

Current artificial immune system (AIS) classifiers have two major problems: 1) their populations of B-cells can grow to huge proportions, and 2) optimizing one B-cell (part of the classifier) at a time does not necessarily guarantee that the B-cell pool (the whole classifier) will be optimized. In this paper, the design of a new AIS algorithm and classifier system called simple AIS is described. It is different from traditional AIS classifiers in that it takes only one B-cell, instead of a B-cell pool, to represent the classifier. This approach ensures global optimization of the whole system, and in addition, no population control mechanism is needed. The classifier was tested on seven benchmark data sets using different classification techniques and was found to be very competitive when compared to other classifiers

A social network analysis of the co-authorship network of the Australasian Conference of Information Systems from 1990 to 2006

Using bibliographic data extracted from an Endnote database, social network analysis techniques were used to generate and analyse a network of co-authors with the aim of developing an understanding of the research community that produces the research knowledge published by the Australasian Conference on Information Systems (ACIS). The ACIS community was found to be a healthy small-world community that kept evolving in order to provide an environment that supports collaboration and sharing of ideas between researchers. It was also found that, unlike a similar analysis of the European Conference (ECIS), the Australasian scene was not dominated by a couple of key researchers as quite a significant number of popular researchers were identified

Designing a hierarchical fuzzy logic controller using the differential evolution approach

In conventional fuzzy logic controllers, the computational complexity increases with the dimensions of the system variables; the number of rules increases exponentially as the number of system variables increases. Hierarchical fuzzy logic controllers ( HFLC) have been introduced to reduce the number of rules to a linear function of system variables. However, the use of hierarchical fuzzy logic controllers raises new issues in the automatic design of controllers, namely the coordination of outputs of sub- controllers at lower levels of the hierarchy. In this paper, a method is described for the automatic design of an HFLC using an evolutionary algorithm called differential evolution ( DE). The aim in this paper is to develop a sufficiently versatile method that can be applied to the design of any HFLC architecture. The feasibility of the method is demonstrated by developing a two- stage HFLC for controlling a cart - pole with four state variables. The merits of the method are automatic generation of the HFLC and simplicity as the number of parameters used for encoding the problem are greatly reduced as compared to conventional methods

On simplifying the automatic design of a fuzzy logic controller

With the availability of a wide range of evolutionary algorithms such as genetic algorithms, evolutionary programming, evolution strategies and differential evolution, every conceivable aspect of the design of a fuzzy logic controller has been optimized and automated. Although there is no doubt that these automated techniques can produce an optimal fuzzy logic controller, the structure of such a controller is often obscure and in many cases these optimizations are simply not needed. We believe that the automatic design of a fuzzy logic controller can be simplified by using a generic rule base such as the Mac Vicar-Whelan rule base and using an evolutionary algorithm to optimize only the membership functions of the fuzzy sets. Furthermore, by restricting the overlapping of fuzzy sets, using triangular membership functions and singletons, and reducing the number of parameters to represent the membership functions, the design can be further simplified. This paper describes this method of simplifying the design and some experiments performed to ascertain its validity