A GA-based heuristic algorithm for non-permutation two-machine robotic flow-shop scheduling problem of minimizing total weighted completion time

Abstract

We discuss a scheduling problem for a twomachine robotic flow-shop with a bounded intermediate station and robots which is realistic in FMCs (flexible manufacturing cells). The problem asks to minimize the total weighted completion time. It is NP-hard. In this paper, we propose a heuristic algorithm based on GA (Genetic Algorithm) which is applicable to the problem, and which allows not only permutation, but also non-permutation schedules, because the latter has possibility to improve the former for this objective function. It is shown by numerical experiment that the proposed method is more effective than existing heuristics, and that there are some situations where the non-permutation scheduling is better than the permutation one.

Cyclic production for robotic cells served by multi-function robots with resumable processing regime

“…Zarandi et al [13] considered twomachine SFRC scheduling problem with sequencedependent setup times. Finally, considering non-cyclic environments, Li et al [14] developed a method for a two-machine SFRC with a limited intermediate hopper.…”

A Closed Loop Inverse Kinematics Solver Intended for Offline Calculation Optimized with GA

“…Applications of GA in the field of robotics has received quite a bit of interest over the years. A GA based heuristic was used for flow shop scheduling in [26]. Two PID controllers for controlling the links of a two-link planar rigid robotic manipulator was optimized using GA in [27].…”