Research On Green Single Machine Scheduling Problem Based On Improved Ant Colony Algorithm

Single machine scheduling is an important problem in the field of production scheduling.The comprehensive research and application of its scheduling strategy and optimization technology can more effectively understand and solve the more complex multi-machine scheduling problem.With the current intensification of energy,cost and environmental conflicts,the large amount of energy demand and energy waste in production has brought huge restriction to the survival and development of enterprises.Enterprises urgently need to change the traditional mode of production management,rationally allocate dispatching resources through modern dispatching technology,and realize the green modern manufacturing mode of coordinated optimization of production and energy consumption indicators.As the key link to realize green manufacturing,green job shop scheduling is more complex than traditional scheduling problem,and has more academic research significance and engineering application value.At present,the research on the single machine scheduling problem mainly focuses on the expansion of scheduling constraints and the improvement of optimization algorithm.There are few researches on the green scheduling model of equipment manufacturing energy consumption.Based on this,this paper takes a kind of single machine scheduling problem as the research object,takes the ant colony optimization algorithm with better global optimization performance as the means,takes the design of efficient scheduling algorithm as the research focus.An improved ant colony scheduling algorithm for single-machine scheduling problem under total weighted delay index is constructed.On this basis,the green scheduling model and solution method for collaboratively considering delay cost and manufacturing energy consumption are discussed.(1)An improved ant colony algorithm(PDUACO)based on pheromone difference update strategy is proposed.Aiming at the shortcomings of basic ant colony algorithms in combinatorial optimization problems such as single machine scheduling,the initial pheromone setting method combining node "cost" information is proposed.The pheromone difference updating strategy based on positive and negative feedback mechanism is designed.The proposed improved algorithm was tested by 14 benchmark examples,and the effectiveness of the improved algorithm was verified.(2)An improved ant colony optimization method for single machine total weighted delay scheduling problem(SMTWTS)is proposed.Firstly,the general model of SMTWTS problem is given.Based on the design idea of PDUACO Modified ant colony algorithm,and combined with MDD(Modified Due Date)rule,the heuristic information is modified reasonably.At the same time,an improved ant colony scheduling algorithm based on pheromone difference update is proposed by combining the selection rules of diversity workpiece and adding the local optimization strategy considering only some adjacent workpieces.The improved algorithm is verified with 30 benchmark examples and compared with other algorithms.The results show that the proposed algorithm has higher efficiency and stability.(3)The ant colony green scheduling algorithm for equipment manufacturing energy consumption is studied.By considering the energy consumption of machine processing and noload under different states under the sequence correlation dependent preparation time scheduling model,the integrated optimization model of collaborative consideration of delay cost and manufacturing energy consumption is constructed.The green scheduling algorithm of ant colony based on Pareto solution set is proposed to realize the equilibrium optimization of production index and energy consumption indicators.The proposed optimization method is used to solve the test case,and the integrated application research of production task and production energy consumption is given in combination with the production practice of a welding workshop of an equipment enterprise.The relevant application results show the feasibility and effectiveness of the green scheduling model and the improved algorithm.Finally,the work of this dissertation is summarized and some further works to be developed in the future are presented.