Climate change mitigation, the goal of reducing CO₂ emissions, more stringent regulations and the increment in energy costs have pushed researchers to study energy efficiency and renewable energy sources. Manufacturing systems are large energy consumers and are thus responsible for huge greenhouse gas emissions; for these reasons, many studies have focused on this topic recently. This review aims to summarize the most important papers on energy efficiency and renewable energy sources in manufacturing systems published in the last fifteen years. The works are grouped together, considering the system typology, i.e., manufacturing system subclasses (single machine, flow shop, job shop, etc.) or the assembly line, the developed energy-saving policies and the implementation of the renewable energy sources in the studied contexts. A description of the main approaches used in the analyzed papers was discussed. The conclusion reports the main findings of the review and suggests future directions for the researchers in the integration of renewable energy in the manufacturing systems consumption models.

This paper proposes a Framework for Capacity Sharing in Cloud Manufacturing (FCSCM) able to support the capacity sharing issue among independent firms. The success of geographical distributed plants depends strongly on the use of opportune tools to integrate their resources and demand forecast in order to gather a specific production objective. The framework proposed is based on two different tools: a cooperative game algorithm, based on the Gale–Shapley model, and a fuzzy engine. The capacity allocation policy takes into account the utility functions of the involved firms. It is shown how the capacity allocation policy proposed induces all firms to report truthfully their information about their requirements. A discrete event simulation environment has been developed to test the proposed FCSCM. The numerical results show the drastic reduction of unsatisfied capacity obtained by the model of cooperation implemented in this work

In today’s competitive environment, customer demand has become increasingly diversified and customised. In order to stay competitive, the manufacturing companies need to adopt models that are able to rapidly adapt to market changes. The reconfigurable manufacturing system emerges from the scientific literature as a relevant manufacturing paradigm to meet changes and uncertainties in a dynamic manufacturing environment. This research proposes a cellular manufacturing system (CMS) with reconfigurable machines to handle the turbulent market conditions. The approach proposed concerns three mathematical models to support the design, reconfiguration and scheduling of the manufacturing system. The three linked models are developed in LINGO^® package. The main optimisation model concerns the design of the CMS, considering product demand not deterministic but described by several discrete probability scenarios. Then, an optimisation sub-model has been developed to manage the reconfiguration at fixed periods. An allocation model is used to allocate the jobs to the manufacturing cells. The proposed approach is compared to a model without reconfiguration activities. A further configuration of the models is the possibility of using remainder cells. The simulation results show that the proposed approach is a promising methodology to support the dynamic CMSs with reconfigurable machines.

Improving environmental sustainability and reducing energy cost are becoming central topics of decision-making in manufacturing. Increasingly manufactures are looking for new solutions that allow them to become more energy efficient and sustainable. Regarding energy cost issue, it is well known that the electricity costs in manufacturing systems strongly depend on the reached peak power. On the other hand, the power required by a manufacturing system affects the adoption of renewable energy sources. The use of renewables, indeed, encompasses fluctuations in energy supply and requires adapting production processes to a power constraint. The article proposes a decision model for the scheduling in a job-shop manufacturing system that simultaneously deals with the power constraint and the variable speed of machine tools. Defining the maximum value of the usable power, decisions can be taken on production scheduling, by adapting the processing speed of several machines that work simultaneously, to the power availability. The model allows decision makers to plan production, taking into account the impossibility of exceeding a certain power and assuming the minimization of the makespan as an objective function. Managers can utilize the scheduling model in all the different contexts, where there is a power limit. The test cases show how the proposed model allows to obtain an efficient scheduling under a constant and variable power constraint. The findings highlight that the model can be employed to reduce the peak power and encourage the use of renewables in manufacturing systems, thus obtaining an efficient scheduling and a more sustainable production.