Aluminum profile turning and extruding workshop energy consumption optimized scheduling method based on ant colony algorithm

Abstract

The invention discloses an aluminum profile turning and extruding workshop energy consumption optimized scheduling method based on the ant colony algorithm. According to the method, the aluminum profile extruding workshop production scheduling process is abstracted into a non-equal parallel machine scheduling problem with the time difference electricity price and the energy consumption of the scheduling process taken into consideration, the ant colony optimization algorithm based on the iterative calculation is provided on the basis of the analysis on this type of scheduling process, the scheduling of machines and workpieces is achieved through ants in the machine-workpiece-machine iterative mode, and the solve errors caused when the machines and the workpieces are independently scheduled in a traditional ant colony algorithm are reduced.

Description

technical field [0001] The invention relates to a workshop energy optimization scheduling technology, in particular to an ant colony algorithm-based energy consumption optimization scheduling method for an aluminum profile vehicle extrusion workshop. Background technique [0002] In actual production and manufacturing, machines with different efficiencies (non-equivalent parallel machines) often run at the same time, which brings great difficulties to the formulation of production plans. Therefore, under the condition of ensuring the normal survival of enterprises, reducing energy consumption and production costs in the production process of non-equivalent parallel machines is one of the core issues concerned by the manufacturing industry. Especially in the aluminum profile extrusion workshop, the production of aluminum profile extrusion requires a large amount of natural gas and electricity, which is a high energy consumption manufacturing process. There are peak and low pe...

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Problems solved by technology

However if figure 2 As shown, the sub-objectives of delay cost and machine energy consumption cost are not independent, but interrelated, and the goal of minimizing delay cost is often at the expense of machine energy consumption (see figure 2 Option 1, Option 2), simply performing pheromone search on the two sub-targets alone will lead to a decrease in algorithm performance

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