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A cloud computing based many objective type-2 fuzzy logic system for mobile field workforce area optimization

Starkey, A and Hagras, H and Shakya, S and Owusu, G and Mohamed, A and Alghazzawi, D (2016) 'A cloud computing based many objective type-2 fuzzy logic system for mobile field workforce area optimization.' Memetic Computing, 8 (4). 269 - 286. ISSN 1865-9284

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Abstract

Large scale optimization problems in the real world are often very complex and require multiple objectives to be satisfied. This applies to industries that employ a large mobile field workforce. Sub-optimal allocation of tasks to engineers in this workforce can lead to poor customer service, higher travel costs and higher CO2 emissions. One solution is to create optimal working areas, which are geographical regions containing many task locations, where the engineers can work. Finding the optimal design of these working areas as well as assigning the correct engineers to them is known as workforce optimization and is a very complex problem, especially when scaled up over large areas. As a result of the vast search space, given by this problem, meta heuristics like genetic algorithms and multi-objective genetic algorithms, are used to find solutions to the problem in reasonable time. However, the hardware these algorithms run on can play a big part in their day-to-day use. This is because the environment in which the engineers are working within is changing on a daily bases. This means that there are severe time-restrictions on the optimization process if the working areas were to be optimized every day. One way to tackle this is to move the optimization system to the cloud where the computing resources available are often far greater than personal desktops or laptops. This paper presents our proposed cloud based many objective type-2 fuzzy logic system for mobile field workforce area optimization. The proposed system showed that utilizing cloud computing with multi-threading capabilities significantly reduce the optimization time allowing greater population sizes, which led to improved working area designs to satisfy the faced objectives.

Item Type: Article
Uncontrolled Keywords: Type-2 fuzzy logic; Genetic algorithms; Cloud computing; Multi-objective Optimization
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 22 Nov 2016 13:09
Last Modified: 29 Jun 2018 12:15
URI: http://repository.essex.ac.uk/id/eprint/18179

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