Physical Sciences and Mathematics Commons^™

A Genetic Algorithm For Multiobjective Hard Scheduling Optimization, Elías Niño, Carlos Ardila, Alfredo J. Perez, Yexid Donoso

Computer Science Faculty Publications

This paper proposes a genetic algorithm for multiobjective scheduling optimization based in the object oriented design with constrains on delivery times, process precedence and resource availability. Initially, the programming algorithm (PA) was designed and implemented, taking into account all constraints mentioned. This algorithm’s main objective is, given a sequence of production orders, products and processes, calculate its total programming cost and time.
Once the programming algorithm was defined, the genetic algorithm (GA) was developed for minimizing two objectives: delivery times and total programming cost. The stages defined for this algorithm were: selection, crossover and mutation. During the first stage, the …

Decentralized Resource Allocation And Scheduling Via Walrasian Auctions With Negotiable Agents, Huaxing Chen, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

This paper is concerned with solving decentralized resource allocation and scheduling problems via auctions with negotiable agents by allowing agents to switch their bid generation strategies within the auction process, such that a better system wide performance is achieved on average as compared to the conventional walrasian auction running with agents of fixed bid generation strategy. We propose a negotiation mechanism embedded in auctioneer to solicit bidders’ change of strategies in the process of auction. Finally we benchmark our approach against conventional auctions subject to the real-time large-scale dynamic resource coordination problem to demonstrate the effectiveness of our approach.

A Dynamic Energy-Aware Model For Scheduling Computationally Intensive Bioinformatics Applications, Sachin Pawaskar, Hesham Ali

Computer Science Faculty Proceedings & Presentations

High Performance Computing (HPC) resources are housed in large datacenters, which consume huge amounts of energy and are quickly demanding attention from businesses as they result in high operating costs. On the other hand HPC environments have been very useful to researchers in many emerging areas in life sciences such as Bioinformatics and Medical Informatics. In this paper, we provide a dynamic model for energy aware scheduling (EAS) in a HPC environment; we use a widely used bioinformatics tool named BLAT (BLAST-like alignment tool) running in a HPC environment as our case study. Our proposed EAS model incorporates 2-Phases: an …

A Hybrid Genetic Algorithm For The Student-Aware University Course Timetabling Problem, Ernesto Ferrer Queiros Nunez

Mathematics, Statistics, and Computer Science Honors Projects

Traditionally, academic institutions schedule courses using constraints that ensure that instructors and courses do not overlap in available rooms and time periods; students' planning needs are rarely taken into account. This problem becomes particularly acute for students in liberal arts institutions, because they have multiple graduation requirements in addition to their chosen academic program. My research builds on the University Course Timetabling Problem (UCTP) to include students' scheduling needs. This approach to the UCTP problem uses a combination of a genetic algorithm and case-based reasoning.

To improve the performance of the genetic algorithm, I use a group-based genetic algorithm to …

Support-Based Distributed Optimisation: An Approach To Radiotherapy Patient Scheduling, Graham Billiau, Chee-Fon Chang, Andrew Alexis Miller, Aditya K. Ghose

Faculty of Informatics - Papers (Archive)

In the health system inefficiency leads to poor use of scarce expensive resources. Lengthy patient treatment waiting time can result from inefficiency in scheduling. The use of state-of-the art multi-agent and distributed computing technologies can provide a solution to address this problem. However, distributed optimisation in such a multi-agent setting poses an important challenge that requires protocols to enable agents to optimise shared objectives without necessarily revealing all of their private constraints. In this study we show that if the problem is expressed as a Dynamic Distributed Constraint Optimisation Problem a powerful algorithm such as SBDO can be deployed to …