Sergio Cavalieri

An experimental benchmarking of two multi-agent architectures for production scheduling and control

The paper provides a first experimental contribution to the validation and comparison of multi-agent heterarchical architectures in order to point out their limits, their main peculiarities and real applicability. For this purpose, the behaviours of two particular control solutions, characterised by a different degree of decision-making delegation assigned to physical and information units in the production system, have been simulated. The experimental campaign is divided into three trials, in order to measure the performances of the two architectures not only under stable and predictable conditions, but also in case of unexpected events, such as the release of urgent manufacturing orders and the occurrence of failures in production resources.

Hybrid genetic algorithmsfor a multiple-objective scheduling problem

This paper describes the characteristics of two hybrid genetic algorithms (GAs) for generating allocation and sequencing of production lots in a flow-shop environment based on a non-linear, multi-criteria objective function. Both GAs are used as search techniques: in the first model the task of the GA is to allocate and sequence the jobs; in the second model, the GA is combined with a dispatching rule (Earliest Due Date, EDD) thus limiting its task only on the allocation of the jobs. Both GAs are characterized by a dynamic population size with dynamic birth rate, as well as by multiple-operator reproduction criteria and by adaptive crossover and mutation rates. A discrete-event simulation model has been used in order to evaluate the performances of the tentative schedules. The proposed algorithms have been subsequently compared with a classical branch and bound method.

A Decision Making Framework for Managing Maintenance Spare Parts in Case of Lumpy Demand: Action Research in the Avionic Sector

Traditionally, maintenance has been considered as a core in-house activity. Only a few years ago outsourcing maintenance has become a common practice (Taracki et al. 2006). According to Harland et al. (2003), “outsourcing involves the use of specialists to provide competences, technologies and resources”. Three fundamental factors nowadays bring firms to use outsourcing in maintenance: (i) cost reduction, since providers are more experienced, thus they can provide better service efficiency; (ii) more skills, since using external resources is a way to rapidly get new skills via qualified people and specific instruments; (iii) a highest service level, since this is normally bound to penalties established during the contracting phase measured by clear Key Performance Indicators (KPIs) to which the provider is duly responsible.

Assessing the performances of a market-like model through an experimental comparison with dispatching rules

In the last few years, changes in the manufacturing environment have increasingly driven enterprises towards the research of new ways to manage and reorganise their production systems. In particular, as regards operations management and control, research is progressively shifting from hierarchical to heterarchical architectures which are founded on distributed decision-making entities interacting through particular cooperative mechanisms.

A biomimetic approach to distributed maintenance management based on a multi-agent system.

The evolution of modern production plants and the rising performance requirements lead to the need for more advanced maintenance systems. In such a context, autonomous Intelligent Maintenance Systems (IMSs) are capable of estimating health conditions, and can be used to forecast maintenance needs and optimize maintenance schedules, therefore reducing the overall costs. In this chapter, an overview of the capabilities of IMSs is shown, and in particular a biomimetic maintenance system implemented using multi-agents is provided, in order to better understand the strength and limits of this approach.

Evaluation and assessment of two simulation software for Service Engineering

The service industry is recognized as a central element of modern economies. Services represent an important topic both for practitioners and researchers: although they may contribute substantially to a company’s bottom line, they still lack a methodological support and a systematic study for the design phase. To make service provision profitable in the long term, it is of utmost relevance to balance the excellence in the value channel to the customer with a high efficiency and productivity of the service processes. In this respect, simulation can support companies during their service engineering process by providing a support in the identification of the best scenario, as well as a qualitative and quantitative assessment of the company’s decisions. Nonetheless, no specific service-oriented simulation software is available on the market. Therefore, this chapter aims at comparing two simulation software solutions originally oriented to the manufacturing area. Through a real-case example, we discuss a comparative analysis between two widespread commercial simulation software, evaluating which one is the most suitable to be used in the service contexts.