FieldPlan: tactical field force planning in BT

In a highly competitive market, BT1 faces tough challenges as a service provider for telecommunication solutions. A proactive approach to the management of its resources is absolutely mandatory for its success. In this paper, an AI-based planning system for the management of parts of BT’s field force is presented. FieldPlan provides resource managers with full visibility of supply and demand, offers extensive what-if analysis capabilities and thus supports an effective decision making process.IFIP International Conference on Artificial Intelligence in Theory and Practice - Industrial Applications of AIRed de Universidades con Carreras en Informática (RedUNCI

FieldPlan: tactical field force planning in BT

In a highly competitive market, BT1 faces tough challenges as a service provider for telecommunication solutions. A proactive approach to the management of its resources is absolutely mandatory for its success. In this paper, an AI-based planning system for the management of parts of BT’s field force is presented. FieldPlan provides resource managers with full visibility of supply and demand, offers extensive what-if analysis capabilities and thus supports an effective decision making process.IFIP International Conference on Artificial Intelligence in Theory and Practice - Industrial Applications of AIRed de Universidades con Carreras en Informática (RedUNCI

The global status of insect resistance to neonicotinoid insecticides

This document is the Accepted Manuscript version of the following article: Chris Bass, Ian Denholm, Martin S. Williamson, and Ralf Nauen, ‘The global status of insect resistance to neonicotinoid insecticides’, Pesticide Biochemistry and Physiology, Vol. 121, pp. 78-87, June 2015. The Version of Record is available online at doi: https://doi.org/10.1016/j.pestbp.2015.04.004. Published by Elsevier Copyright © 2015 Elsevier Inc.The first neonicotinoid insecticide, imidacloprid, was launched in 1991. Today this class of insecticides comprises at least seven major compounds with a market share of more than 25% of total global insecticide sales. Neonicotinoid insecticides are highly selective agonists of insect nicotinic acetylcholine receptors and provide farmers with invaluable, highly effective tools against some of the world's most destructive crop pests. These include sucking pests such as aphids, whiteflies, and planthoppers, and also some coleopteran, dipteran and lepidopteran species. Although many insect species are still successfully controlled by neonicotinoids, their popularity has imposed a mounting selection pressure for resistance, and in several species resistance has now reached levels that compromise the efficacy of these insecticides. Research to understand the molecular basis of neonicotinoid resistance has revealed both target-site and metabolic mechanisms conferring resistance. For target-site resistance, field-evolved mutations have only been characterized in two aphid species. Metabolic resistance appears much more common, with the enhanced expression of one or more cytochrome P450s frequently reported in resistant strains. Despite the current scale of resistance, neonicotinoids remain a major component of many pest control programmes, and resistance management strategies, based on mode of action rotation, are of crucial importance in preventing resistance becoming more widespread. In this review we summarize the current status of neonicotinoid resistance, the biochemical and molecular mechanisms involved, and the implications for resistance management.Peer reviewedFinal Accepted Versio

Fast Local Search and Guided Local Search and Their Application to British Telecom's Workforce Scheduling Problem

This paper reports a Fast Local Search (FLS) algorithm which helps to improve the efficiency of hill climbing and a Guided Local Search (GLS) Algorithm which is developed to help local search to escape local optima and distribute search effort. To illustrate how these algorithms work, this paper describes their application to British Telecom's workforce scheduling problem, which is a hard real life problem. The effectiveness of FLS and GLS are demonstrated by the fact that they both out-perform all the methods applied to this problem so far, which include simulated annealing, genetic algorithms and constraint logic programming. I. Introduction Due to their combinatorial explosion nature, many real life constraint optimization problems are hard to solve using complete methods such as branch & bound [17, 14, 21, 23]. One way to contain the combinatorial explosion problem is to sacrifice completeness. Some of the best known methods which use this strategy are local search methods, the ba..

Function Optimization using Guided Local Search

In this report, we examine the potential use of Guided Local Search (GLS) for function optimization. In order to apply GLS, the function to be minimized is augmented with a set of penalty terms that enable local search to escape from local minima. The function F6 is used to demonstrate the proposed technique. 1. Introduction In this report, we present preliminary findings on the potential use of Guided Local Search (GLS) for function optimization. GLS is a meta-heuristic for guiding local search [3] to escape local minima and visit promising solutions. GLS has been used to tackle difficult combinatorial optimization problems [7,5] and derives itself from the GENET network for constraint satisfaction problems [6]. Function optimization can be seen as a combinatorial problem by encoding real variables as binary strings [2]. In the simple case of binary encoding, binary string values are converted to integers which then are scaled by the appropriate coefficient to give real values in the..

Guided Local Search

Guided Local Search (GLS) is an intelligent search scheme for combinatorial optimization problems. A main feature of the approach is the iterative use of local search. Information is gathered from various sources and exploited to guide local search to promising parts of the search space. The application of the method to the Travelling Salesman Problem and the Quadratic Assignment Problem is examined. Results reported show that the algorithm outperforms or compares very favorably with well-known and established optimization techniques such as simulated annealing and tabu search. Given the novelty of the approach and the very encouraging results, the method could have an important contribution to the development of intelligent search techniques for combinatorial optimization