The current industrial constraints on production systems, especially availability problems

are complicating maintenance managers’ mission and making longer and further performance

improvement process. Dealing with these problems in a wiser managerial vision respecting

sustainability dimensions would be more efficient to optimize all resources. In this paper, and

after addressing the lean/sustainability challenge in a the literature to define main research

orientations and critical points in manufacturing and then maintenance specific context, two

case studies have been conducted in two production systems in Morocco and Canada, within

the objective to set a clearer scene of the lean philosophy implementation in maintenance

and within the sustainability scope from an empirical perspective. To activate the social dimension

being often non-integrated in the lean/sustainability initiatives, the article authors

reveal an original research direction assigning maintenance logistics as the leading part of our

approach to cover all sustainability dimensions. Furthermore, its management is discussed

for the first time in a sustainable framework, where the authors propose a new model considering

the lean/sustainable perspective and inspired by the rich Human-Machine interaction

memory to solve daily maintenance problems exploiting the operators’ experience feedback.

The new industrial era, industry 4.0, leans on Cyber Physical Systems CPS. It is an emergent approach of Production System design that consists of the intimate integration between physical processes and information computation and communication systems. The CPSs redefine the decision-making process in shop floor level to reach an intelligent shop floor control. The scheduling is one of the most important shop floor control functions. In this paper, we propose a cooperative scheduling based on multi-agents modelling for Cyber Physical Production Systems. To validate this approach, we describe a use case in which we implement a scheduling module within a flexible machining cell control tool.

The Job Shop scheduling problem is widely used in industry and has been the subject of study by several researchers with the aim of optimizing work sequences. This case study provides an overview of genetic algorithms, which have great potential for solving this type of combinatorial problem. The method will be applied manually during this study to understand the procedure and process of executing programs based on genetic algorithms. This problem requires strong decision analysis throughout the process due to the numerous choices and allocations of jobs to machines at specific times, in a specific order, and over a given duration. This operation is carried out at the operational level, and research must find an intelligent method to identify the best and most optimal combination. This article presents genetic algorithms in detail to explain their usage and to understand the compilation method of an intelligent program based on genetic algorithms. By the end of the article, the genetic algorithm method will have proven its performance in the search for the optimal solution to achieve the most optimal job sequence scenario.