TY - EJOU
AU - Win-Chin Lin,
AU - Chin-Chia Wu,
AU - Kejian Yu,
AU - Yong-Han Zhuang,
AU - Shang-Chia Liu,
TI - On the Use of Genetic Algorithm for Solving Re-entrant Flowshop Scheduling with Sum-of-processing-times-based Learning Effect to Minimize Total Tardiness
T2 - Intelligent Automation \& Soft Computing
PY - 2018
VL - 24
IS - 4
SN - 2326-005X
AB - Most research studies on scheduling problems assume that a job visits certain machines only one time.
However, this assumption is invalid in some real-life situations. For example, a job may be processed
by the same machine more than once in semiconductor wafer manufacturing or in a printed circuit
board manufacturing machine. Such a setting is known as the “re-entrant flowshop”. On the other
hand, the importance of learning effect present in many practical situations such as machine shop, in
different branches of industry and for a variety of corporate activities, in shortening life cycles, and in
an increasing diversity of products in the manufacturing environment. Inspired by these observations,
this paper addresses a re-entrant m-machine flowshop scheduling problems with time-dependent
learning effect to minimize the total tardiness. The complexity of the proposed problem is very difficult.
Therefore, in this paper we first present four heuristic algorithms, which are modified from existing
algorithms to solve the problem. Then, we use the solutions as four initials to a genetic algorithm.
Finally, we report experimental performances of all the proposed methods for the small and big
numbers of jobs, respectively
KW - Total tardiness; re-entrant flowshop; Genetic algorithm; Learning effect
DO - 10.1080/10798587.2017.1302711