MAPSS, a Multi-Aspect Partner and Service Selection Method

TL;DR

This paper introduces MAPSS, a genetic algorithm-based method for selecting optimal partners and services in Service-Oriented Virtual Organization Breeding Environments, enhancing the composition process for complex business workflows.

Contribution

The paper presents a novel multi-aspect partner and service selection method, MAPSS, tailored for SOVOBEs, utilizing genetic algorithms to improve selection accuracy and process efficiency.

Findings

MAPSS effectively selects suitable partners and services based on competencies and relations.

The method improves the efficiency of partner and service selection in SOVOBEs.

MAPSS demonstrates promising results in complex service composition scenarios.

Abstract

In Service-Oriented Virtual Organization Breeding Environments (SOVOBEs), services performed by people, organizations and information systems are composed in potentially complex business processes performed by a set of partners. In a SOVOBE, the success of a virtual organization depends largely on the partner and service selection process, which determines the composition of services performed by the VO partners. In this paper requirements for a partner and service selection method for SOVOBEs are defined and a novel Multi-Aspect Partner and Service Selection method, MAPSS, is presented. The MAPSS method allows a VO planner to select appropriate services and partners based on their competences and their relations with other services/partners. The MAPSS method relies on a genetic algorithm to select the most appropriate set of partners and services.