Heuristic and exact algorithms for single-machine scheduling problems with general truncated learning effects
COMPUTATIONAL & APPLIED MATHEMATICS(2022)
摘要
This paper addresses single-machine scheduling problems with truncated learning effects. The objective is to determine the optimal job schedule such that the makespan, the total weighted completion time and the maximum lateness are to be minimized. All the considered problems are NP-hard; hence, for each problem, we propose the heuristic and branch-and-bound algorithms. Extensive numerical experiments validate the efficiency of the proposed solution algorithms on a set of randomly generated instances.
更多查看译文
关键词
Scheduling,Branch-and-bound,Learning effect,Tabu search,Simulated annealing
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要