Abstract
Manufacturing enterprises nowadays face the challenge of increasing energy prices and requirements to reduce their emissions. Most reported work on reducing manufacturing energy consumption today focuses on the need to improve the efficiency of resources (machines) largely ignoring the potential for energy reducing on the system-level where the operational method can be employed as the energy saving approach. The advantage is clearly that the scheduling and planning approach can also be applied across existing legacy systems and does not require large investment. Therefore, a multi-objective scheduling method is developed in this paper with reducing energy consumption as one of the objectives. This research focuses on classical job shop environment which is widely used in the manufacturing industry. A model for the bi-objectives problem that minimises total electricity consumption and total weighted tardiness is developed and the Non-dominant Sorting Genetic Algorithm is employed as the solution to obtain the Pareto front. A case study based on a modified 10 × 10 job shop is presented to show the effectiveness of the algorithm and to prove the feasibility of the model.
Highlights
Energy is one of the most vital resources for manufacturing
Reducing electricity consumption as well as keeping good performance in classical scheduling objectives in job shops is a difficult problem that can take a large amount of time to optimally solve
The model for the Multi-objective Total Non-processing Electricity Consumption (NPE) and Total Weighted Tardiness (TWT) Job Shop Scheduling problem has been developed in this paper
Summary
Energy is one of the most vital resources for manufacturing. In the last 50 years the consumption of energy by the industrial sector has more than double and industry currently consumes about half of the world’s energy (Mouzon et al, 2007). The increasing price of energy and the current trend of sustainability have exerted new pressure on manufacturing enterprises They have to reduce energy consumption both to save cost and to become more environmentally friendly. Avram and Xirouchakis (2011) have developed a methodology to estimate the energy requirements during use phase of spindle and feed axis according to automatic programming tool (ATP) file. This kind of method provides a potential faster way to estimate energy consumption of machining processes. This method considers the entire machine tool system by taking into account steady-state and transient regimes. This method considers the entire machine tool system by taking into account steady-state and transient regimes. Dahmus and Gutowski (2004) and Kordonowy (2003) developed a system level research which includes energy requirement for material removal process itself, and associated processes such as axis feed
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