Evolutionary optimization of due date based objectives in unrestricted identical parallel machine scheduling problems

Parallel machine scheduling, involves the allocation of jobs to the system resources (a bank of machines in parallel). A basic model consisting of m machines and n jobs is the foundation of more complex models. Here, jobs are allocated according to resource availability following some allocation rule. In the specialised literature, minimisation of the makespan has been extensively approached and benchmarks can be easily found. This is not the case for other important objectives such as the maximum tardiness and the number of tardy jobs. These problems are NP-hard for 2 ≤ m ≤ n, and conventional heuristics and evolutionary algorithms (EAs) have been developed to provide acceptable schedules as solutions. To solve the unrestricted identical parallel machine scheduling problems, this paper proposes MCMP-SRI and MCMP-SRSI, which are two multirecombination schemes that combine studs, random and seed immigrants. Evidence of the improved behaviour of the EAs when inserting problem-specific knowledge is provided. Experiments and results are discussed.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Evolutionary optimization of due date based objectives in unrestricted identical parallel machine scheduling problems

Parallel machine scheduling, involves the allocation of jobs to the system resources (a bank of machines in parallel). A basic model consisting of m machines and n jobs is the foundation of more complex models. Here, jobs are allocated according to resource availability following some allocation rule. In the specialised literature, minimisation of the makespan has been extensively approached and benchmarks can be easily found. This is not the case for other important objectives such as the maximum tardiness and the number of tardy jobs. These problems are NP-hard for 2 ≤ m ≤ n, and conventional heuristics and evolutionary algorithms (EAs) have been developed to provide acceptable schedules as solutions. To solve the unrestricted identical parallel machine scheduling problems, this paper proposes MCMP-SRI and MCMP-SRSI, which are two multirecombination schemes that combine studs, random and seed immigrants. Evidence of the improved behaviour of the EAs when inserting problem-specific knowledge is provided. Experiments and results are discussed.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Solving unrestricted parallel machine scheduling problems via evolutionary algorithms

Parallel machine scheduling, also known as parallel task scheduling, involves the assignment of multiple tasks onto the system architecture’s processing components (a bank of machines in parallel). A basic model involving m machines and n independent jobs is the foundation of more complex models. Here, the jobs are allocated according to resource availability following some allocation rule. The completion time of the last job to leave the system, known as the makespan (Cmax), is one of the most important objective functions to be minimized, because it usually implies high utilization of resources, but other important objectives must be also considered. These problems are known in the literature [9, 11] as unrestricted parallel machine scheduling problems. Many of these problems are NP-hard for 2≤ m ≤ n, and conventional heuristics and evolutionary algorithms (EAs) have been developed to provide acceptable schedules as solutions. This presentation shows the problem of allocating a number of non-identical independent tasks in a production system. The model assumes that the system consists of a number of identical machines and only one task may execute on a machine at a time. All schedules and tasks are non-preemptive. A set of well-known conventional heuristics will be contrasted with evolutionary approaches using multiple recombination and indirect representations.Eje: Informática de GestiónRed de Universidades con Carreras en Informática (RedUNCI

13th International Postgraduate Research Conference 2017 : conference proceedings

Welcome to the 13th International Postgraduate Research Conference (IPGRC 2017) hosted by the School of the Built Environment at University of Salford, UK. This year’s IPGRC is organised as part of the International Research Week 2017- ‘Shaping Tomorrow’s Built Environment: Construction and Design for the Modern World’ and also the year we celebrate the 50th anniversary of Salford as a University, which makes this year’s conference very special. This conference creates a unique opportunity for researchers from Salford and other parts of the world to share their research interests, and outputs and to network and interact within a professional and friendly environment, with high profile academics and leaders within the built environment. This year’s conference brings together participants from a number of countries including the UK, USA, Australia, New Zealand, Canada, Sri Lanka, Hong Kong, Iran, Italy, Ireland, Norway, India, Brazil, South Korea, Nigeria, Turkey, UAE, South Africa, Iraq, Ghana, Estonia, Saudi Arabia and many more. The conference received over 100 papers and posters covering the following themes: • Business, Economics and Finance • Property and Project Management • ICT, Technology and Engineering • People, Skills and Education • Design and Urban Development • Sustainability and Environmental Systems Conference will provide a forum for novel discussions into the development and application of new and emerging practices to challenge current design and construction practice in the areas of people, process and technology issues. On behalf of School of the Built Environment, the conference co-chairs and organisers, we wish you an enjoyable and fruitful experience. We hope that you will obtain useful feedback to your research work, gain insight from work of others and forge connections for future

Performance measurement in construction research & development: The use of case study research approach

The process of finding solutions to the research problem does not follow a clear sequential approach, but often takes unexpected turns due to the uncertainties of the research process and its outcomes. However, appropriate research design would be able to identify any problems and pitfalls that the researcher may come across during the process. In this regard, consideration of the research philosophy pertaining to the study helps a researcher in choosing the appropriate approach for a study. Not only the philosophical stance, but also the research problem under investigation and its underling circumstances influence the selection of a research approach. Accordingly, this paper discusses the factors that drive the selection of a case study as the research approach with particular reference to the use of single case study to undertake an in depth inquiry regarding the impact of performance measurement towards construction research and development. Further, this paper discusses the incorporation of multi-phase, multi perspective and multi-method approaches within the single case study to build valid theory