Comprehensive Planning of Laboratory Equipment Based on Genetic Algorithms

Area of Science:

• Operations Research
• Computer Science
• Engineering Management

Background:

• Laboratory equipment planning is crucial for modern enterprise management.
• Complex laboratory equipment planning is an NP-hard combinatorial optimization problem.
• Accurate polynomial-time algorithms are infeasible for this problem.

Purpose of the Study:

• To develop and analyze an improved genetic algorithm for comprehensive laboratory equipment planning.
• To compare the efficiency of the genetic algorithm against traditional heuristic and manual methods.
• To analyze planning solutions under constraints like test start and completion times.

Main Methods:

• Implemented a heuristic algorithm for simple laboratory equipment planning.
• Developed a general genetic algorithm with two selection operators in Python.
• Utilized three independent algorithms: manual scheduling, genetic algorithm with gene location crossover, and genetic algorithm with experimental part crossover.

Main Results:

• The improved genetic algorithm provides viable solutions for laboratory equipment planning.
• Analysis of planning solutions considered test completion time, algorithm calculation time, and equipment utilization.
• Efficiency coefficients were found to be related to energy equipment output characteristics.

Conclusions:

• The improved genetic algorithm offers an effective approach to solving the complex laboratory equipment planning problem.
• Comparison of algorithms highlights trade-offs in completion time, computational cost, and resource utilization.
• The study demonstrates the practical application of genetic algorithms in optimizing laboratory resource management.