This paper considers the scheduling problem of parallel batch processing machines with non-identical job size and processing time. In this paper, a new mathematical model with ready time and batch size constraints is presented to formulate the problem mathematically, in which simultaneous reduction of the makespan and earliness-tardiness is the objective function. In recent years, the nature-in...

The static and analytic scheduling approach is very difficult to follow and is not always applicable in real-time. Most of the scheduling algorithms are designed to be established in offline environment. However, we are challenged with three characteristics in real cases: First, problem data of jobs are not known in advance. Second, most of the shop’s parameters tend to be stochastic. Third, th...

We consider the following online scheduling problem in which the input consists of n jobs to be scheduled on identical machines of bounded capacity g (the maximum number of jobs that can be processed simultaneously on a single machine). Each job is associated with a release time and a completion time between which it is supposed to be processed. When a job is released, the online algorithm has ...

We address the Identical Parallel Machine Scheduling Problem, one of the most important basic problems in scheduling theory, and some generalizations of it arising from real world situations. We survey the current state of the art for the most performing meta-heuristic algorithms for this class of problems, with special emphasis on recent results obtained through Scatter Search. We present insi...

We consider the setting of online computation with advice and study the bin packing problem and a number of scheduling problems. We show that it is possible, for any of these problems, to arbitrarily approach a competitive ratio of 1 with only a constant number of bits of advice per request. For the bin packing problem, we give an online algorithm with advice that is (1 + ε)competitive and uses...

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 consider the problem of scheduling a maximum profit selection of jobs on m identical machines. Jobs arrive online one by one and each job is specified by its start and end time. The goal is to determine a non-preemptive schedule which maximizes the profit of the scheduled jobs, where the profit of a job is equal to its length. Upon arrival of a new job, an online algorithm must decide whethe...

In this paper we study algorithms for online routing and machine scheduling problems. The problems are “online” because the problem instances are revealed incrementally. We first study algorithms for the online Traveling Repairman Problem (TRP), where a single server is to visit a set of locations in a network with the objective of minimizing the sum of weighted completion times. We then analyz...