Comparison of Data Mining and Mathematical Models for Estimating Fuel Consumption of Passenger Vehicles

A number of analytical models have been described in the literature to estimate the fuel consumption of vehicles, most of which require a wide range of vehicle and trip related parameters as input data, which might limit the practical applicability of these models if such data were not readily available. To overcome this drawback, this study describes the development of three data mining models to estimate fuel consumption of a vehicle, including linear regression, artificial neural network and support vector machines. The paper presents comparison results with five instantaneous fuel consumption models from the literature using real data collected from three passenger vehicles on three routes. The results indicate that while the prediction accuracy of the instantaneous fuel consumption models varies across the data sets, those obtained by the regression models are significantly better and more robust against changes in input data

Exact/heuristic hybrids using rVNS and hyperheuristics for workforce scheduling

In this paper we study a complex real-world workforce scheduling problem. We propose a method of splitting the problem into smaller parts and solving each part using exhaustive search. These smaller parts comprise a combination of choosing a method to select a task to be scheduled and a method to allocate resources, including time, to the selected task. We use reduced Variable Neighbourhood Search (rVNS) and hyperheuristic approaches to decide which sub problems to tackle. The resulting methods are compared to local search and Genetic Algorithm approaches. Parallelisation is used to perform nearly one CPU-year of experiments. The results show that the new methods can produce results fitter than the Genetic Algorithm in less time and that they are far superior to any of their component techniques. The method used to split up the problem is generalisable and could be applied to a wide range of optimisation problems

Strategic supplier performance evaluation::a case-based action research of a UK manufacturing organisation

The main aim of this research is to demonstrate strategic supplier performance evaluation of a UK-based manufacturing organisation using an integrated analytical framework. Developing long term relationship with strategic suppliers is common in today׳s industry. However, monitoring suppliers׳ performance all through the contractual period is important in order to ensure overall supply chain performance. Therefore, client organisations need to measure suppliers׳ performance dynamically and inform them on improvement measures. Although there are many studies introducing innovative supplier performance evaluation frameworks and empirical researches on identifying criteria for supplier evaluation, little has been reported on detailed application of strategic supplier performance evaluation and its implication on overall performance of organisation. Additionally, majority of the prior studies emphasise on lagging factors (quality, delivery schedule and value/cost) for supplier selection and evaluation. This research proposes both leading (organisational practices, risk management, environmental and social practices) and lagging factors for supplier evaluation and demonstrates a systematic method for identifying those factors with the involvement of relevant stakeholders and process mapping. The contribution of this article is a real-life case-based action research utilising an integrated analytical model that combines quality function deployment and the analytic hierarchy process method for suppliers׳ performance evaluation. The effectiveness of the method has been demonstrated through number of validations (e.g. focus group, business results, and statistical analysis). Additionally, the study reveals that enhanced supplier performance results positive impact on operational and business performance of client organisation

A simple state-based prognostic model for railway turnout systems

The importance of railway transportation has been increasing in the world. Considering the current and future estimates of high cargo and passenger transportation volume in railways, prevention or reduction of delays due to any failure is becoming ever more crucial. Railway turnout systems are one of the most critical pieces of equipment in railway infrastructure. When incipient failures occur, they mostly progress slowly from the fault free to the failure state. Although studies focusing on the identification of possible failures in railway turnout systems exist in the literature, neither the detection nor forecasting of failure progression has been reported. This paper presents a simple state-based prognostic method that aims to detect and forecast failure progression in electro-mechanical systems. The method is compared with Hidden Markov Model based methods on real data collected from a railway turnout system. Obtaining statistically sufficient failure progression samples is difficult considering that the natural progression of failures in electro-mechanical systems may take years. In addition, validating the classification model is difficult when the degradation is not observable. Data collection and model validation strategies for failure progression are also presented

A variable neighbourhood search algorithm for job shop scheduling problems

Variable Neighbourhood Search (VNS) is one of the most recent metaheuristics used for solving combinatorial optimization problems in which a systematic change of neighbourhood within a local search is carried out. In this paper, a variable neighbourhood search algorithm is proposed for Job Shop Scheduling (JSS) problem with makespan criterion. The results gained by VNS algorithm are presented and compared with the best known results in literature. It is concluded that the VNS implementation is better than many recently published works with respect to the quality of the solution. © Springer-Verlag Berlin Heidelberg 2006

Parallel variable neighbourhood search algorithms for job shop scheduling problems

Variable neighbourhood search (VNS) is one of the most recent metaheuristics used for solving combinatorial optimization problems in which a systematic change of neighbourhood with a local search is carried out. However, as happens with other metaheuristics, it takes a long time to reach some useful solutions while solving some sort of hard combinatorial problems such as job shop scheduling (JSS). Parallelization is one of the most considerable policies to overcome this matter. In this paper, firstly, a number of VNS algorithms are examined for JSS problems and then four different parallelization policies are taken into account to determine efficient parallelization for VNS algorithms. The experimentation reveals the performance of various VNS algorithms and the efficiency of policies to follow in parallelization. In the end, the unilateral-ring topology, a noncentral parallelization method, is found as the most efficient policy

Variable neighbourhood search for job shop scheduling problems

Variable Neighbourhood Search (VNS) is one of the most recent metaheuristics used for problem solving in which a systematic change of neighbourhood within a local search is carried out. In this paper, an investigation on implementing VNS for job shop scheduling problems is carried out tackling benchmark suites collected from OR library. The idea is to build the best local search and shake operations based on neighbourhood structure available. The results are presented and compared with the recent approaches in the literature. It is concluded that the VNS algorithm can generally find better results. © 2006 ACADEMY PUBLISHER