Yau-Ren Shiau

Single-machine group scheduling problems with deterioration consideration

In many realistic situations, a job processed later consumes more time than the same job when it is processed earlier; this phenomenon is known as deteriorating jobs. However, job deterioration is relatively unexplored in the context of group technology. In this paper, we consider deterioration in the framework of minimizing the makespan and the total completion time in single-machine group scheduling problems. We show that these problems remain polynomially solvable when deterioration is considered.

A SINGLE-MACHINE DETERIORATING JOB SCHEDULING PROBLEM WITH A NON-REGULAR CRITERION

In scheduling environments with deterioration, a job processed later consumes more time than that same job when processed earlier. The deteriorating job scheduling problems have been widely studied in the last two decades. However, no result of the completion time variance has been reported. In this study, we consider the variance of job completion time minimization problem on a single machine. It is assumed that the job processing time is a simple linear function of its starting time. We show that an optimal schedule is V-shaped with respect to the job deteriorating rates. A heuristic algorithm utilized the V-shaped property is then proposed, and a computational experiment shows that the proposed heuristic algorithm is quite accurate.

A two-agent single-machine scheduling problem with late work criteria

This paper addresses a two-agent scheduling problem where the objective is to minimize the total late work of the first agent, with the restriction that the maximum lateness of the second agent cannot exceed a given value. Two pseudo-polynomial dynamic programming algorithms are presented to find the optimal solutions for small-scale problem instances. For medium- to large-scale problem instances, a branch-and-bound algorithm incorporating the implementation of a lower bounding procedure, some dominance rules and a Tabu Search-based solution initialization, is developed to yield the optimal solution. Computational experiments are designed to examine the efficiency of the proposed algorithms and the impacts of all the relative parameters.