Energy-efficient scheduling of AGV-assisted robotic flexible flowshops under learning and processing time uncertainty.

ABSTRACT: This study addresses an energy-efficient flexible flow shop scheduling problem (EEFFSP) that integrates automated guided vehicles (AGVs), sequence-dependent setup times, and the learning effect under fuzzy uncertainty in processing times and learning coefficients. The energy consumption is related to both machines and AGVs in such a way that higher speeds result in higher energy consumption. A mixed-integer programming model is developed to simultaneously minimize makespan and total energy consumption, considering the operation of both machines and AGVs. To handle uncertainty, a fuzzy programming approach based on Jiménez's ranking method is employed. The problem's multi-objective nature is tackled using approaches: the classical AUGMECON method, NSGA, and the NSGA-II algorithms. The propo

SUBMITTER: Dehnavi S 

PROVIDER: S-EPMC12824364 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature