Parallel implementation of an exact two-phase method for the bi-objective knapsack problem

Khadidja Chaabane, Sadek Bouroubi, Younes Djellouli
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Abstract

This paper introduces a parallel implementation of an exact two-phase method for solving the bi-objective knapsack problem on a CPU-GPU system. We utilize the Branch-and-Bound procedure in both phases, along with a highly efficient reduction technique to generate all efficient solutions. However, in the first phase, we focus on identifying all supported extreme efficient solutions, followed by reducing the dimension of the problem using an object efficiency reduction algorithm. The second phase is responsible for generating all unsupported efficient solutions. We develop a combined algorithm incorporating both phases, which is implemented in the CUDA language. Our study investigates the impact of parallel computing performance on various numerical instances compared to other exact methods in the literature. Additionally, we confirm the effectiveness of our proposed parallel-solving method by testing uncorrelated instances.
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并行实施双目标 Knapsack 问题的精确两阶段法
本文介绍了一种在 CPU-GPU 系统上并行实施的两阶段精确方法,用于解决双目标 knapsack 问题。我们在两个阶段都使用了分支与边界(Branch-and-Bound)程序,以及高效的还原技术来生成所有有效解。不过,在第一阶段,我们的重点是识别所有支持的极端高效解,然后使用对象效率缩减算法降低问题的维度。第二阶段负责生成所有不支持的高效解决方案。我们开发了一种包含这两个阶段的组合算法,并通过 CUDA 语言实现。与文献中的其他精确方法相比,我们的研究调查了并行计算性能对各种数值实例的影响。此外,我们还通过测试不相关的实例证实了我们提出的并行求解方法的有效性。
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