Study On Multi-objective Scheduling Of Flexible Job Shop Considering Equipment Deterioration And Energy Consumption

Due to energy consumption and related environmental impacts,today’s manufacturing companies are facing huge environmental challenges.Intelligent scheduling technology can effectively reduce energy consumption.From the perspective of production management,production scheduling has an important impact on the energy saving of manufacturing systems.The resource flexibility and complex constraints in flexible manufacturing systems make production scheduling a complex nonlinear programming problem.In actual production,the processing time of the work piece is not deterministic but increases with the deterioration effect.Also,with the increasingly serious environmental pollution problem,manufacturing companies can’t just focus on economic indicators,and the research on green manufacturing is also getting more and more attention.Therefore,we studied the flexible job shop scheduling problem considering both the deterioration effect and energy consumption.First,establish a deterioration effect model to determine the actual processing time,and use an energy consumption model to calculate the mechanical energy consumption in different states.This paper takes minimizing the maximum completion time and energy consumption as the optimization goals and establishes a flexible job shop multi-objective scheduling model that considers equipment deterioration and energy consumption at the same time.Then,an improved gray wolf algorithm is proposed to solve the established flexible job shop,scheduling model.Given the need to solve the problem of workpiece sequencing and machine allocation at the same time,the relevant steps of the improved gray wolf algorithm solution model are designed in detail.Double-layer encoding is used for machines and processes,using activity scheduling to decode,design location update mechanism,roulettebased selection strategy,cross mutation,and other related operations,and finally improve the algorithm.Finally,according to the actual production data of manufacturing company P company,the application analysis of the scheduling model in this paper is carried out.Through comprehensive experiments,the proposed model is solved and the performance of the algorithm is evaluated.Experimental results show that the model and algorithm in this paper can effectively solve this problem,and the improved gray wolf algorithm in this paper is superior to the traditional gray wolf algorithm and NSGA-II algorithm in terms of the optimal solution,convergence,and index performance.This provides a basis for decision-makers to consider the energy-saving scheduling of flexible manufacturing systems.