Research On Generation Method And Application Of Equipment Allocation Scheme For Mission And Task

The generation of equipment combination scheme plays a vital role in the construction of the combat system.With the development of science and technology and the assembly and service of various high-tech equipment,there are more and more types of equipment to choose from when the equipment combination plan is generated.In addition,the battlefield situation is changing rapidly,requiring decision makers to quickly and efficiently develop equipment combinations.Therefore,it is of great theoretical and practical significance to select the appropriate equipment from the equipment resources to construct the most reasonable equipment combination scheme and realize the efficient use of resources under the premise of completing the mission.However,at present,there is no complete model and algorithm for the research of the mission-oriented equipment combination scheme generation method,and more is the study of the sub-problems.Starting from a given mission,this paper studies the method model from mission requirements to equipment combination scheme generation.Provide a reasonable equipment plan for the combat unit,so that the combat unit can respond quickly to the battlefield situation.The main work of this paper is as follows:(1)Establish a research framework for the generation of equipment portfolio solutions for mission tasks.This paper constructs a research framework for the task-oriented equipment combination plan generation of "mission task->capability requirements->system design->structural optimization".First,use the operational view to complete the decomposition of mission tasks,and based on the operational scenarios to sort out the combat activities and the mapping relationship with capabilities.In this process,the demand indicators of the task are divided into operational activities.Then,according to the mapping relationship between operational activities and capabilities and the aggregation rules of capability indicators,the transformation of mission task requirements to capability indicators is completed.Achieve the generation of mission requirements to capability requirements indicators.Finally,the capacity demand indicator is used as the known input,and the equipment combination plan generation model is constructed with the idea of capability gap.And consider the dynamic optimization of equipment combination scheme when equipment resources are adjusted.(2)The differential evolution algorithm based on adaptive penalty function is used to solve the model.Taking the obtained capability demand index as the known input,the equipment combination plan generation model for capacity demand is constructed with the minimum capability gap and the minimum equipment cost involved in combat.In the solution process,the model is solved by a differential evolution algorithm based on adaptive penalty function.According to the characteristics of the mission and task,the penalty function is used to convert the multi-objective to the single-objective optimization with the minimum equipment cost under the premise of meeting the capability requirements of the combat system.It not only meets the characteristics of the mission and simplifies the model solving process.(3)Design equipment combination scheme dynamic optimization algorithm to support the dynamic optimization of equipment scheme when equipment resources change.In the actual combat process,changes in equipment resources occur frequently.In the traditional mission-oriented equipment combination scheme generation process,each adjustment of equipment resources needs to re-initialize the calculation of the assembly relationship of all equipment,and it is difficult to support the timeliness requirements of actual operations.In this study,the dynamic optimization process is divided into two situations according to the time when the resource changes.First,the equipment resources change during the task execution process,which leads to the original program cannot continue to be executed.This study uses equipment resource information to increase the relevant constraints to achieve dynamic optimization based on the original program.The second is the change of equipment resources before the execution of the task.At this time,the study saves the optimal population of each optimization iteration process as the initial population of the dynamic optimization algorithm after the equipment resource is changed,so that it can be realized under the premise of ensuring the correctness of the algorithm.The algorithm converges quickly.Effectively improve the computational efficiency of equipment solution optimization.The dynamic optimization algorithm of the equipment combination scheme achieves a rapid response when equipment resources change,and provides support for decision makers in the operational process.