Genetic algorithms in statistical tolerancing

An important aspect in manufacturing design is the distribution of geometrical tolerances so that an assembly functions with given probability, while minimising the manufacturing cost. This requires a complex search over a multidimensional domain, much of which leads to infeasible solutions and which can have many local minima. As well, Monte-Carlo methods are often required to determine the probability that the assembly functions as designed. This paper describes a genetic algorithm for carrying out this search and successfully applies it to two specific mechanical designs, enabling comparisons of a new statistical tolerancing design method with existing methods. (C) 2003 Elsevier Ltd. All rights reserved.

Optimal eradication: when to stop looking for an invasive plant

The notion of being sure that you have completely eradicated an invasive species is fanciful because of imperfect detection and persistent seed banks. Eradication is commonly declared either on an ad hoc basis, on notions of seed bank longevity, or on setting arbitrary thresholds of 1% or 5% confidence that the species is not present. Rather than declaring eradication at some arbitrary level of confidence, we take an economic approach in which we stop looking when the expected costs outweigh the expected benefits. We develop theory that determines the number of years of absent surveys required to minimize the net expected cost. Given detection of a species is imperfect, the optimal stopping time is a trade-off between the cost of continued surveying and the cost of escape and damage if eradication is declared too soon. A simple rule of thumb compares well to the exact optimal solution using stochastic dynamic programming. Application of the approach to the eradication programme of Helenium amarum reveals that the actual stopping time was a precautionary one given the ranges for each parameter.

Optimal preventive maintenance of leased equipment with corrective minimal repairs

For leased equipment, the lessor carries out the maintenance of the equipment. Usually, the contract of lease specifies the penalty for equipment failures and for repairs not being carried out within specified time limits. This implies that optimal preventive maintenance policies must take these penalty costs into account and properly traded against the cost of preventive maintenance actions. The costs associated with failures are high as unplanned corrective maintenance actions are costly and the resulting penalties due to lease contract terms being violated. The paper develops a model to determine the optimal parameters of a preventive maintenance policy that takes into account all these costs to minimize the total expected cost to the lessor for new item lease. The parameters of the policy are (i) the number of preventive maintenance actions to be carried out over the lease period, (ii) the time instants for such actions, and (iii) the level of action. (c) 2005 Elsevier B.V. All rights reserved.

Maintenance Strategies for used equipment under lease

Abstract: Purpose – The aim of this research is to determine the optimal upgrade and preventive maintenance actions that minimize the total expected cost (maintenance costs+penalty costs). Design/methodology/approach – The problem is a four-parameter optimization with two parameters being k-dimensional. The optimal solution is obtained by using a four-stage approach where at each stage a one-parameter optimization is solved. Findings – Upgrading action is an extra option before the lease of used equipment, in addition to preventive maintenance action. Upgrading action makes equipment younger and preventive maintenance action lowers the ROCOF. Practical implications – There is a growing trend towards leasing equipment rather than owning it. The lease contract contains penalties if the equipment fails often and repairs are done within reasonable time period. This implies that the lessor needs to look at optimal preventive maintenance strategies in the case of new equipment lease, and upgrade actions plus preventive maintenance in the case of used equipment lease. The paper deals with this topic and is of great significant to business involved with leasing equipment. Originality/value – Nowadays many organizations are interested in leasing equipment and outsourcing maintenance. The model in this paper addresses the preventive maintenance problem for leased equipment. It provides an approach to dealing with this problem.

Teledermatology can produce high-quality care at low cost

Teledermatology can provide both accurate and reliable specialist care at a distance. This article reviews current data on the quality of care that teledermatology provides, as well as the societal cost benefits involved in the implementation of the technique. Teledermatology is most suited to patients unable to access specialist. services for geographical or social reasons. Patients are generally satisfied with the overall care that teledermatology provides. Real-time teledermatology is more expensive than conventional care for health services. However, significant savings can be expected from the patient's perspective due to reduced travel. Appropriate patient selection, improved technology and adequate clinical workloads may improve both the quality and cost effectiveness of this service.

Warranty cost analysis for second-hand products

For second-hand products sold with warranty, the expected warranty cost for an item to the manufacturer, depends on (i) the age and/or usage as well as the maintenance history for the item and (ii) the terms of the warranty policy. The paper develops probabilistic models to compute the expected warranty cost to the manufacturer when the items are sold with free replacement or pro rata warranties. (C) 2000 Elsevier Science Ltd. All rights reserved.

Probabilistic cost benefit analysis of generation investment in a deregulated electricity market

A new methodology is proposed for the analysis of generation capacity investment in a deregulated market environment. This methodology proposes to make the investment appraisal using a probabilistic framework. The probabilistic production simulation (PPC) algorithm is used to compute the expected energy generated, taking into account system load variations and plant forced outage rates, while the Monte Carlo approach has been applied to model the electricity price variability seen in a realistic network. The model is able to capture the price and hence the profitability uncertainties for generator companies. Seasonal variation in the electricity prices and the system demand are independently modeled. The method is validated on IEEE RTS system, augmented with realistic market and plant data, by using it to compare the financial viability of several generator investments applying either conventional or directly connected generator (powerformer) technologies. The significance of the results is assessed using several financial risk measures.