6 research outputs found
Eco-design, circular economy & social responsibility
Our lifestyle, associated with consumption of large amounts of resources, implies to integrate eco-design in our innovations. In particular, recycling and circular economy have to be facilitated, when designing a product. Life Cycle Analyses (LCA) enable to identify the most impacting stages for environment. They often demonstrate the positive effects of recycling that i) limitates the depletion of natural resources, ii) enables energy savings, iii) lowers dissemination of pollutants, etc. Courses as well as collective projects aim at bringing to light environmental issues of eco-design and circular economy, as well as the resulting questions for engineers & researchers. Social responsibility is also identified as key for circular economy and how to train for social responsibility is discussed. Invited speakers from academia as well as industry provide complementary answers to academic ones
A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance
Increasing energy price and requirements to reduce emission are new chal-lenges faced by manufacturing enterprises. A considerable amount of energy is wasted by machines due to their underutilisation. Consequently, energy saving can be achieved by turning off the machines when they lay idle for a comparatively long period. Otherwise, turning the machine off and back on will consume more energy than leave it stay idle. Thus, an effective way to reduce energy consumption at the system level is by employing intelligent scheduling techniques which are capable of integrating fragmented short idle periods on the machines into large ones. Such scheduling will create opportunities for switching off underutilised resources while at the same time maintaining the production performance. This paper introduces a model for the bi-objective optimisation problem that minimises the total non-processing electricity consumption and total weighted tardiness in a job shop. The Turn off/Turn on is applied as one of the electricity saving approaches. A novel multi-objective genetic algorithm based on NSGA-II is developed. Two new steps are introduced for the purpose of expanding the solution pool and then selecting the elite solutions. The research presented in this paper is focused on the classical job shop envi-ronment, which is widely used in the manufacturing industry and provides considerable opportunities for energy saving. The algorithm is validated on job shop problem instances to show its effectiveness.
Keywords: Energy efficient production plannin