A Two-Individual-Based Evolutionary Algorithm for Flexible Assembly Job Shop Scheduling Problem with Uncertain Interval Processing Times
Source abstract
Abstract
The assembly job shop is a prevalent production organization mode in manufacturing enterprises. During the processing and assembly of products, operation processing times are influenced by numerous factors, leading to significant uncertainty. This paper investigates the flexible assembly job shop scheduling problem (FAJSP) with uncertain processing times, where processing times are represented as variable interval numbers. We develop a robust optimization model for the FAJSP, utilizing confidence level estimation to determine the ranges of processing times and reformulating the model based on the chance-constrained method. A two-individual-based master–apprentice evolutionary (MAE) algorithm is proposed. Two effective encoding schemes are designed to prevent the generation of infeasible solutions under assembly sequence constraints. Additionally, a decoding method based on interval scheduling theory is devised to accurately represent interval processing times. Case studies are conducted to validate the effectiveness of the proposed robust optimization model and demonstrate the superiority of the MAE algorithm.
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Research context
OpenAlex topics: Scheduling and Optimization Algorithms · Advanced Manufacturing and Logistics Optimization · Elevator Systems and Control
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China
Institute of Artificial Intelligence, Donghua University, Shanghai 201620, China
Funding
Shanghai Science and Technology Innovation Action Plan
References (30)
Show source-provided references
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