Sustainability has become a challenge for manufacturers. They need to maintain their process performance while also considering environmental (e.g. carbon footprint of products) and social obligations. Since the environmental impact of manufacturing activities is strongly dependent on the energy demand, improving energy efficiency has received substantial attention from industries all around the world. To take up the need of manufacturers in their decision making process tackling both economic and environmental challenges, a holistic view of the entire system is necessary. The holistic view incorporates the dynamic behavior of all the production processes (including supporting services) involved in making different products. Due to versatility of material flows within production systems (e.g. batch production, one piece flow or job shop), a generic model that can model large spectrum of production systems is highly desired from application point of view. Furthermore a system with an optimal performance is yet another necessity from manufacturer’s point of view. To address all above-mentioned issues, an integrated simulation-based optimization framework was proposed in which a simulation model represented the multi-product production system in a hierarchical structure and simulated multiple process chains. Six basic modules were embedded including two modules for Multi Product Routing and Production Planning and Control that act as heart of the developed framework. The complex product routing and various production systems were carefully conceptualized within these two modules. Two other basic modules were developed for the main energy consuming equipment (one for machine tools and process chains, and four sub-modules for technical building services including steam generation unit, compressed air system ,dust collectors and HVAC systems). Careful observations of a wide range of diverse equipment were carried out for the modules, and the basic components of a generic state-based energy consumption model were identified for each.The optimization part of the framework, an “off-the-shelf” optimization package was utilized which treated the simulation model as a black box model to evaluate the system under different settings. A weighted sum method was used to combine different objectives in case of multi objectives. The proposed methodology was applied on four different manufacturing environments; a mass production system where a small number of products with large quantity were produced , batch production with medium number of products and medium quantity, a one piece flow production environment and lastly a complex job-shop environment with large number of products in small quantities.
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