Performance Computation Methods for Composition of Tasks with Multiple Patterns in Cloud Manufacturing

Abstract

Task composition in cloud manufacturing involves the selection of appropriate services from the cloud manufacturing platform and combining them to process the task with the purpose of achieving its expected performance. Calculation methods for achieving the performance expected by customers when the task has two or more composition patterns (e.g. sequential and switching pattern) are necessary because most tasks have multiple composition patterns in cloud manufacturing. Previous studies, however, have focused only on a single composition pattern. In this paper, we regard a task as a directed acyclic graph, and propose graph-based algorithms to obtain cost, execution time, quality and reliability of a task having multiple composition patterns. In addition, we model the task composition problem by introducing cost and execution time as performance attributes, and quality and reliability as basic attributes in the Kano model. Finally, an experiment to compare the performances of three metaheuristic algorithms (namely, variable neighbourhood search, genetic, and simulated annealing) is conducted to solve the problem. The experimental result shows that the variable neighbourhood search algorithm yields better and more stable solutions than the genetic algorithm and simulated annealing algorithms.