Search results

The purpose of this paper is to develop a model which schedules activities and allocates resources in a resource constrained project management problem. This paper also considers learning rate and uncertainties in the activity durations.

An activity schedule with requirements of different resource units is used to calculate the objectives: makespan and resource efficiency. A comparisons between non-dominated sorting genetic algorithm – II (NSGA-II) and non-dominated sorting genetic algorithm – III (NSGA-III) is done to calculate near optimal solutions. Buffers are introduced in the activity schedule to take uncertainty into account and learning rate is used to incorporate the learning effect.

The results show that NSGA-III gives better near optimal solutions than NSGA-II for multi-objective problem with different complexities of activity schedule.

The paper does not considers activity sequencing with multiple activity relations (for instance partial overlapping among different activities) and dynamic events occurring in between or during activities.

The paper helps project managers in manufacturing industry to schedule the activities and allocate resources for a near-real world environment.

This paper takes into account both the learning rate and the uncertainties in the activity duration for a resource constrained project management problem. The uncertainty in both the individual durations of activities and the whole project duration time is taken into consideration. Genetic algorithms were used to solve the problem at hand.

To define strategic directions for the Russia’s social, economic, scientific and technological development in 2011-2013, a large-scale foresight study including the deep analysis of prospects of biotechnology development there was undertaken (Russia 2030: Science and Technology Foresight). This paper aims to present results of this research.

The study was based on a combination of technology-push and market-pull approaches that aimed not only to identify most promising science and technology (S&T) areas but also to understand how they can be realized in practice. Representatives from federal authorities, science and business were involved in the project to create future visions of technological directions; analyze grand challenges, weak signals and wild cards; and set research and development (R&D) priorities.

According to results of the study, Russia has a potential for biotech sector development, although the level of R&D in the majority of areas is lagging behind that in the USA and leading EU countries. However, there are several advanced applied research areas where efforts can be focused. Among them are high-performance genomics and post-genomics research platforms, systems and structural biology, microbial metabolic engineering, plant biotechnology and microbial strains and consortia for development of symbiotic plant–microbial communities.

Concentration of available resources of government and business on biotechnological sector development can help to find answers for challenges that Russia faces today or will face tomorrow. It will help to pick up on the current level of research activities, improve the quality of personnel training, make this area the engine of the economy and carry out the so-called new industrialization of the country, building a new, high-tech device industry.