Heuristics for the Variable Sized Bin Packing Problem Using a Hybrid P-System and CUDA Architecture

TL;DR

This paper introduces two new heuristics for the NP-hard Variable Sized Bin Packing Problem, leveraging a hybrid P system model implemented in CUDA to efficiently find good solutions for large instances.

Contribution

The paper presents a novel hybrid P system model with CUDA implementation for solving the variable sized bin packing problem, improving solution speed and quality.

Findings

Effective heuristics for large instances (10,000 items) in seconds

Hybrid P system reduces implementation complexity

Competitive results compared to existing algorithms

Abstract

The Variable Sized Bin Packing Problem has a wide range of application areas including packing, scheduling, and manufacturing. Given a list of items and variable sized bin types, the objective is to minimize the total size of the used bins. This problem is known to be NP-hard. In this article, we present two new heuristics for solving the problem using a new variation of P systems with active membranes, which we call a hybrid P system, implemented in CUDA. Our hybrid P-system model allows using the polarity and labels of membranes to represent object properties which results in reducing the complexity of implementing the P-system. We examine the performance of the two heuristics, and compare the results with those of other known algorithms. The numerical results show that good solutions for large instances (10000 items) of this problem could be obtained in a very short time (seconds) using our CUDA simulator.