The unrelated parallel machine scheduling (PMS) problem is essential for the manufacturing industry. Scheduling will save company resources, especially time management. By solving problems quickly and precisely, can get more profit. On that note, this paper focused on PMS problems, which did not consider inherent uncertainty in processing set up by minimizing makespan tardiness. This aimed to m...

In this paper, we provide a new class of randomized approximation algorithms for scheduling problems by directly interpreting solutions to so-called time-indexed LPs as probabilities. The most general model we consider is scheduling unrelated parallel machines with release dates (or even network scheduling) so as to minimize the average weighted completion time. The crucial idea for these multi...

We study the problem of unrelated parallel-machine scheduling with deteriorating maintenance activities. Each machine has at most one maintenance activity, which can be performed at any time throughout the planning horizon. The length of the maintenance activity increases linearly with its starting time. The objective is to minimize the total completion time or the total machine load. We show t...

This paper presents a scheduling problem with unrelated parallel machines and sequencedependent setup times that minimizes the total weighted tardiness. A new branch-and-bound (B and B) algorithm is designed incorporating the lower and upper bounding schemes and several dominance properties. The lower and upper bounds are derived through an assignment problem and the composite dispatching rule ...

This paper presents a novel, multi-objective model of a parallel machines scheduling problem that minimizes the number of tardy jobs and total completion time of all jobs. In this model, machines are considered as unrelated parallel units with different speeds. In addition, there is some precedence, relating the jobs with non-identical due dates and their ready times. Sequence-dependent setup t...

In this paper, we provide a new class of randomized approximation algorithms for parallel machine scheduling problems. The most general model we consider is scheduling unrelated machines with release dates (or even network scheduling) so as to minimize the average weighted completion time. We introduce an LP relaxation in time-indexed variables for this problem. The crucial idea to derive appro...

In this paper, we consider scheduling problems with general truncated job-dependent learning effect on unrelated parallel-machine. The objective functions are to minimize total machine load, total completion (waiting) time, total absolute differences in completion (waiting) times respectively. If the number of machines is fixed, these problems can be solved in m O n 2 ( ) + time respectively, w...