Business process improvement using multi-objective optimisation

Multi-objective optimisation using the Bees Algorithm

This paper describes the first application of the Bees Algorithm to multi-objective optimisation problems. The Bees Algorithm is a search procedure inspired by the way honey bees forage for food. A standard mechanical design problem, the design of a welded beam structure, was used to benchmark the Bees Algorithm. The results obtained show the robust performance of the Bees Algorithm.

Phylogenetic Inference using Evolutionary Multi-objective Optimisation

Evolutionary relationships among species are usually (i) illustrated by means of a phylogenetic tree and (ii) inferred by optimising some measure of fitness, such as the total evolutionary distance between species or the likelihood of the tree (given a model of the evolutionary process and a data set). The combinatorial complexity of inferring the topology of the best tree makes phylogenetic in...

Application of Multi-Objective Optimisation to Process Measurement System Design

Multi-objective optimisation (MOO) has been used with an equation solver data reconciliation software to develop a tool for sensor system design based on modifying the sensitivity matrix of a simulated process. MOO enables searching for the best tradeoff between two conflicting objectives: the cost of the system and the precision of key performance indicators (KPI) (variables that have to be me...

Business Process Improvement, Business Process Reengineering, and Business Process Management

• Customer satisfaction: The result of optimizing and aligning business processes to fulfi ll customers’ needs, wants, and desires. • Cost reduction: The result of optimizing operations and supplier processes. • Cycle and fulfi llment time reduction: The result of optimizing the manufacturing and logistics processes. • Quality: The result of optimizing the design, development, and production pr...

Multi-objective Binary Search Optimisation