Comparison of constraint logic programming and distributed problem solving: a case study for interactive, efficient and practicable job-shop scheduling

The job-shop scheduling issue is more and more described not only in terms of efficiency (e.g. Makespan), but also in terms of interactivity and practicability. The aim of this paper is to evaluate the ability of two approaches inherited from artificial intelligence domain to contribute to the solving of this issue: Constraint Logic Programming (CLP) on the one hand, and, on the other hand, Distributed Problem Solving (DPS) inherited from Distributed Artificial Intelligence (DAI). This analysis is achieved through the use of two specific computerised tools: Constraint Handling In Prolog (CHIP) as a CLP software and the Distributed Production Scheduling System (DPSS) as a distributed problem solving system. These tools are then evaluated in terms of interactivity, efficiency and practicability. Interactivity is discussed according to qualitative points of view such as the ability to provide efficient decision support, a set of alternative solutions and the possibility to parameterise the algorithms. Efficiency is described in terms of optimality or sub-optimality by the analysis of the Makespan criterion vs. fixed computation time. Practicability is associated to the industrial viability of the methods: ability to cope with real industrial case study or ability to face real industrial contexts. Evaluation is then performed through a multiple criteria analysis. This analysis is achieved given an increasing number of operations to perform. The results highlight the high complementary level of these two approaches, allowing us to provide a framework for a joint integration, which shall be optimised when taking into account the assets of each approach according to the three evaluation criteria.