A Genetic Programming Method Based on Stochastic Resource Constrained Multi-project Scheduling

Abstract

The invention discloses a genetic planning method based on random resource-limited multi-project scheduling, including an initialization stage: collecting and initializing genetic planning parameters to obtain a priority rule expression set; obtaining an initial mixed priority rule set based on the priority rule expression set ;Generation stage: Evaluate population priority rules through NSGA-II algorithm to achieve iterative optimization and obtain non-dominant priority rule sets; Selection stage: weighted traditional priority rule set and non-dominant priority rule set First, the target priority rule scheduling combination is obtained, which is used to realize the priority rule combination scheduling of the engineering project in multiple states. The present invention breaks the defect of using a single priority rule for decision-making in traditional genetic planning, and further improves the comprehensive performance of decision-making. It is of great significance and engineering application value for the scheduling of random resource-constrained multi-projects.

Description

technical field [0001] The invention belongs to the field of project scheduling optimization, in particular to a genetic programming method based on random resource-limited multi-project scheduling. Background technique [0002] Resource Constrained Project Scheduling Problem (RCPSP) is the most core and classic NP-hard problem in project management. However, in actual engineering, there are a lot of problems such as random interference and multi-project collaborative execution (for example, 90% of project management is executed under multi-project), the traditional RCPSP model is difficult to adapt, so RCPSP is extended to random Resource Constrained Multi-Project Scheduling Problem (SRCMPSP). [0003] Because the SRCMPSP model is more suitable for engineering practice, it has been widely studied. Because it also belongs to the field of combinatorial optimization, and because of its strong optimization ability and search ability in other problems with the same neighborhoo...

AI Technical Summary

Problems solved by technology

However, the meta-heuristic technology is an iterative optimization method accompanied by a large number of random operations, which has two defects in the solution of SRCMPSP: 1) SRCMPSP is a random problem, and the meta-heuristic algorithm with a large number of random searches will further deteriorate the robustness. Thus affecting the scheduling effect; 2) The meta-heuristic algorithm has an iterative process, which takes time, and the randomness problem will increase the frequency of rescheduling, further increasing the calculation cost

However, the existing super-heuristic technology is still applied to static problems in the field of project scheduling, and the SRCMPSP model, which is more biased towards engineering applications, has not been applied to solve

At the same time, the priority rule discrimination generated by existing hyperheuristic techniques will remain fixed throughout the decision process

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