Hybrid genetic algorithm for a type-II robust mixed-model assembly line balancing problem with interval task times
Source abstract
Abstract
The type-II mixed-model assembly line balancing problem with uncertain task times is a critical problem. This paper addresses this issue of practical significance to production efficiency. Herein, a robust optimization model for this problem is formulated to hedge against uncertainty. Moreover, the counterpart of the robust optimization model is developed by duality. A hybrid genetic algorithm (HGA) is proposed to solve this problem. In this algorithm, a heuristic method is utilized to seed the initial population. In addition, an adaptive local search procedure and a discrete Levy flight are hybridized with the genetic algorithm (GA) to enhance the performance of the algorithm. The effectiveness of the HGA is tested on a set of benchmark instances. Furthermore, the effect of uncertainty parameters on production efficiency is also investigated.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Assembly Line Balancing Optimization · Scheduling and Optimization Algorithms · Advanced Manufacturing and Logistics Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (32)
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