Solution algorithms for single-machine scheduling with learning effects and exponential past-sequence-dependent delivery times

Abstract

This paper addresses the single-machine scheduling problem with learning effects and exponential past-sequence-dependent delivery times. The objective is to determine an optimal job schedule such that total weighted completion time and maximum tardiness are minimized. To solve the general case of the problem, we propose the heuristic, simulated annealing and branch-and-bound algorithms. The computational experiment are also conducted to show that the algorithms perform effectively.