Design Methodology and Tools in Factory of the Future

This paper presents a design method and tool developed to support the skill forming activities in the DigiFoF network (https://www.digifof.eu/). The focus is on training of manufacturing system design skills both as HEI education and vocational training, but preliminary design of new manufacturing systems is also supported (e.g in the development of small business process scenarios). We proposed a model-based methodology for solving of the manufacturing system design problems The methodology and the supporting tool are centred around a less abstract Domain-Specific Modelling Language (DSML). The language is easy to learn due to its few components. A modelling and simulation environment named Digital Production Planner Tool (DPPT) was generated from the metamodel of the DSML. The degree of abstraction used by this tool corresponds well to the intended use in training and preliminary design. Our method incorporates by design the possibility to impose constraints at the modelling language level to limit the modelling space to feasible/possible solutions. The resulting tool enforces these constraints in the use and supports the development of feasible designs even by inexperienced designers. The access to the conceptual model allows the translation of the model to other modelling language like Petri net. This extends the support for the design methodology. The whitepaper presents a use case for the developed method and tool: the design of a chocolate manufacturing line.

Sustainable Reconfigurable Manufacturing System Design Using Adapted Multi-Objective Evolutionary Based Approaches

Nowadays, manufacturing systems should be cost-effective and environmentally harmless to cope with various challenges in today's competitive markets. In this paper, we aim to solve an environmental oriented multi-objective reconfigurable manufacturing system design (ie., sustainable reconfigurable machines and tools selection) in the case of a single unit process plan generation. A non-linear multi-objective integer program (NL-MOIP) is presented first, where four objectives are minimised respectively, the total production cost, the total production time, the amount of the greenhouse gases emitted by machines and the hazardous liquid wastes. Second, to solve the problem, we propose four adapted versions of evolutionary approaches, namely two versions of the well known non-dominated sorting genetic algorithm (NSGA-II and NSGA-III), weighted genetic algorithms (WGA) and random weighted genetic algorithms (RWGA). To illustrate the efficiency of the four approaches, several instances of the problem are experimented and the obtained results are analysed using three metrics respectively hypervolume, spacing metric and cardinality of the mixed Pareto fronts. Moreover, the influences of the probabilities of genetic operators on the convergence of the adapted NSGA-III are analysed and TOPSIS method is used to help the decision maker ranking and selecting the best process plans.