Operations Research
New bounds for the identical parallel processor weighted flow time problem
Management Science
A PTAS for Minimizing the Total Weighted Completion Time on Identical Parallel Machines
Mathematics of Operations Research
An Ant Colony Optimization Approach to the Probabilistic Traveling Salesman Problem
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Handbook of Scheduling: Algorithms, Models, and Performance Analysis
Handbook of Scheduling: Algorithms, Models, and Performance Analysis
Nonclairvoyant scheduling to minimize the total flow time on single and parallel machines
Journal of the ACM (JACM)
Operations Research
Online Scheduling of a Single Machine to Minimize Total Weighted Completion Time
Mathematics of Operations Research
Stochastic Machine Scheduling with Precedence Constraints
SIAM Journal on Computing
On-line scheduling to minimize average completion time revisited
Operations Research Letters
Load balancing a priori strategy for the probabilistic weighted flowtime problem
Computers and Industrial Engineering
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We address the probabilistic generalization of weighted flow time on parallel machines. We present some results for situations which ask for ''long-term robust'' schedules of n jobs (tasks) on m parallel machines (processors): on any given day, only a random subset of jobs needs to be processed. The goal is to design robust a priori schedules (before we know which jobs need to be processed) which, on a long-term horizon, are optimal (or near optimal) with respect to total weighted flow time. The originality of this work is that probabilities are explicitly associated with data such that further classical properties of a task (processing time and weight) we consider a probability of presence. After motivating this investigation we analyze the computational complexity, analytical properties, and solution procedures for these problems. Special care is also devoted to assess experimentally the performance of a priori strategies.