We study the earliness-tardiness scheduling problem on a single machine with due date assignment and controllable processing times. We analyze the problem with three different due date assignment methods and two different processing time functions. For each combination of these, we provide a polynomialtime algorithm to find the optimal job sequence, due date values and resource allocation minim...

This paper considers the problem of scheduling a given number of jobs on a single machine to minimize total earliness and tardiness when family setup times exist. The paper proposes optimal branch-and-bound algorithms for both the group technology assumption and if the group technology assumption is removed. A heuristic algorithm is proposed to solve larger problems with the group technology as...

In this paper, we propose n-jobs to be processed on Single Machine Scheduling Problem (SMSP) involving fuzzy processing time and fuzzy due dates. The different due dates for each job be considered which meet the demand of customer with more satisfaction level. The main objective of this paper is the total penalty cost to be minimum in the schedule of the jobs on the single machine. This cost is...

In this paper we compare simulated annealing and tabu search approaches to a single machine common due date assignment and scheduling problem with jobs available at different times. The objective is to minimize the total weighted sum of earliness, tardiness and due date costs.

Preventive maintenance is the essential part of many maintenance plans. From the production point of view, the flexibility of the maintenance intervals enhances the manufacturing efficiency. On the contrary, the maintenance departments tend to know the timing of the long term maintenance plans as certain as possible. In a single-machine production environment, this paper proposes a simulation–o...

We consider a single machine earliness/tardiness scheduling problem with general weights, ready times and due dates. Using completion time information obtained from the optimal solution to a preemptive relaxation, we generate feasible solutions to the original non-preemptive problem. We report extensive computational results demonstrating the speed and effectiveness of this approach.