Search-based decision ordering to facilitate product line engineering of Cyber-Physical System

Abstract

Industrial Cyber Physical Systems (CPSs) are naturally complex. Manual configuration of CPS product lines is error-prone and inefficient, which warrants the need for automated support of product configuration activities such as decision inference and decision ordering. A fully automated solution is often impossible for CPSs since some decisions must be made manually by configuration engineers and thus requiring an interactive and step-by-step configuration solution. Having an interactive solution with tool support in mind, we propose a search-based solution (named as Zen-DO) to support optimal ordering of configuration steps. The optimization objective has three parts: 1) minimizing overall manual configuration steps, 2) configuring most constraining decisions first, and 3) satisfying ordering dependencies among variabilities. We formulated our optimization objective as a fitness function and investigated it along with four search algorithms: Alternating Variable Method (AVM), (1+1) Evolutionary Algorithm (EA), Genetic Algorithm, and Random Search (a comparison baseline). Their performance is evaluated in terms of finding an optimal solution for two real-world case studies of varying complexity and results show that AVM and (1+1) EA significantly outperformed the others.