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Valentin Yakovlevich Afanasyev,Vladimir Fedorovich Ukolov,Vitaly Vasilievich Kuzmin 대한산업공학회 2021 Industrial Engineeering & Management Systems Vol.20 No.4
Energy is one of the most important pillars in any production system. Recent studies show that with increasing energy consumption in manufacturing industries, energy efficiency is one of the most important challenges in factories. On the other hand, reducing energy consumption may increase delays in the delivery of production orders. For this rea-son, in this paper, a new mathematical model is presented to optimize the workshop flow with the aim of reducing energy consumption, reducing completion time and reducing latency. In this mathematical model, the limitations of using machines and determining the speed level for each machine are considered. In order to validate the mathemati-cal model, a numerical problem is designed and the results of its optimization are examined and analyzed. Also, the relationship between energy consumption, completion time and delay time has been studied and analyzed, as well as the effect of machine speed on each of the objectives of the mathematical model.
Valentin Yakovlevich Afanasyev,Vladimir Fyodorovich Ukolov,Ekaterina Alexandrovna Tregubova 대한산업공학회 2021 Industrial Engineeering & Management Systems Vol.20 No.4
In recent years, supply chain management with the aim of making the supply chain more efficient and reducing over-all costs, is one of the key issues addressed in every industry. In today's energy supply chain, determining the level of sales for manufactured goods in specific time periods and to specific customer groups is very important. This becomes even more important when we have to do this for sensitive marine products that have different customer categories. This article presents a model for determining the optimal level of sales of thermal energy in two levels and according to customer demand in different time periods to determine the optimal level of sales using accurate methods and some meta-innovative methods. For this purpose, two meta-heuristic methods of particle swarm optimization (PSO) and the evolutionary algorithm CPSO have been used.