Deciding the Level of Automation for Internal Transportation Activities in production systems

  • Posted on: 30 August 2019
  • By: p.david
  • Updated on: 3 September 2019
Type recrutement: 
Sujet de thèse
27 (Informatique)
61 (Génie informatique, automatique et traitement du signal)
Entité et lieu: 
Laboratoire G-SCOP - Grenoble

The level of automation of production activities has been debated for a long time (Parasuraman et al., 2000; Salmi et al., 2015). The basic question formulated by Parasuraman and Sheridan as “Which system functions should be automated and to what extent?” is certainly not easy to answer since many aspects shall be studied to take a fully enlighten decision. Indeed, besides the simply technical aspects of the question (ex: kind of task, weight of parts), complementary points must be analysed like economic viability, operators acceptance, system lifecycle etc. In a previous work done on our laboratory, Anas Salmi (Salmi, 2016) has shown that for assembly operations, more than 70 criteria can have a significant impact on the decision of automation.


The purpose of this thesis is to investigate the Level of Automation for internal transportation activities. It is clear that novel robotized solutions will bring high benefits for plants operations, but their introduction in the shop floor must be carefully prepared and analysed from a human-robot cooperation perspective. The work should aim at proposing a multi-criteria approach to assess the relevance of AGVs, AIVs (Automated Intelligent Vehicle) and Material handling systems in the most effective way for real industrial case studies. To reach this goal, tasks allocation, human-robot cooperation strategies, criteria investigation and evaluation methods need to be tackled with regard to the new technologies of industry 4.0. The candidate is expected to establish a state-of-the-art study about the topic with the following main missions:


  • Criteria elicitation & problems classification: identifying the criteria and important drivers influencing the design of an ILS, in order to provide a complete understanding of the ILS design problem and of its LoA selection.


  • Performance analysis of ILS: this mission consists in providing evaluation techniques (such as discrete event simulation) to assess the performance of future ILS (including flexibility, productivity, adaptability to hazards…). Namely, evaluating the benefits of introducing industry 4.0 technologies is of prime importance.


  • Design and decision process development: this final step consists in defining a design process for ILS in production plant of the future and in validating it on real industrial case studies.