Catálogo de publicaciones - libros

The paper proposes applying the population-learning algorithm to solving both the single-mode and the multi-mode resource-constrained pro-ject scheduling problems (denoted as RCPSP and MRCPSP, respectively) with makespan minimization as an objective function. The paper contains problem formulation and a description of the proposed implementation of the population learning algorithm (PLA). To validate the approach a computational experiment has been carried out. It has involved 1440 instances of the RCPSP and 3842 instances of the MRCPSP obtained from the available benchmark data sets. Results of the experiment show that the proposed PLA implementation is an effective tool for solving the resource-constrained project scheduling problems. In case of the RCPSP instances the algorithm in a single run limited to 50000 solutions generated has produced results close to the results of the best known algorithms as compared with average deviation from critical path. In case of the MRCPSP instances the proposed algorithm in a single run has produced solutions with mean relative error value below 1.6% as compared with optimal or best known solutions for benchmark problems.

This study proposes, develops and tests a hybrid neural approach (HNA) for the resource constrained project scheduling problem. The approach is a hybrid of the adaptive-learning approach (ALA) for serial schedule generation and the augmented neural network (AugNN) approach for parallel schedule generation. Both these approaches are based on the principles of neural networks and are very different from Hopfield networks. In the ALA approach, weighted processing times are used instead of the original processing times and a learning approach is used to adjust weights. In the AugNN approach, traditional neural networks are augmented in a manner that allows embedding of domain and problem-specific knowledge. The network architecture is problem specific and a set of complex neural functions are used to (i) capture the constraints of the problem and (ii) apply a priority rule-based heuristic. We further show how forward-backward improvement can be integrated within the HNA framework to improve results. We empirically test our approach on benchmark problems of size J30, J60 and J120 from PSPLIB. Our results are extremely competitive with existing techniques such as genetic algorithms, simulated annealing, tabu search and sampling.

This paper deals with the lead optimization phase of pharmaceutical research where a number of leads (molecules as a basis for potential drugs) are processed by different departments in order to optimize their biochemical characteristics. We depict each lead as a project and model the problem as a static multi-project selection and scheduling problem under resource constraints with the objective to maximize the weighted work performed. For solving the problem we propose two heuristics. We assess their performance in a computational study and we point out one dominant method. Furthermore we show the impact of problem parameters such as the extend to which projects can be crashed.

Grids link together computers, data, sensors, large scale scientific instruments, visualization systems, networks and people. They can provide very large pools of computer resources, enable distributed collaborations and deliver increased efficiency and on-demand computing capabilities. The complexity of Grids on one hand and the requirements towards performance and capability on the other hand call for efficient resource management and scheduling mechanisms. Such mechanisms must take into account not only the hardware and software resources, user jobs and applications, but also policies of the resource owners. Policies usually describe cost models for the resource usage, security mechanisms, quality of service of resource provisioning etc. The problem of scheduling jobs in real Grid environments is very difficult. Due to lack of time characteristics of jobs, and difficulties in characterizing the overall system, traditional OR techniques usually fail or achieve very weak results. Usually, best effort scheduling is the best option. There are, however, some ways to deal with the problems described above.

The main goal of this paper it to present some practical issues of scheduling Grid jobs. Methods and techniques described in the paper are used in a Grid scheduling system, called GRMS (Grid Resource Management System) developed at Poznan Supercomputing and Networking Center. GRMS is widely used in many Grid infrastructures worldwide.

Recent results on resource-constrained project scheduling with time windows are reviewed. General temporal constraints (resulting from minimum and maximum time lags between project activities), several different types of scarce resources, and a large variety of time-based, financial, and resource-based objective functions are considered. Emphasis is placed on an order-based structural analysis of the feasible region of project scheduling problems and a classification and discussion of objective functions important to practice, which can be exploited for constructing efficient solution procedures. After those structural issues, methods for solving time-constrained project scheduling problems are proposed. Next, the resolution of conflicts for renewable, allocatable, synchronizing, changeover, and cumulative resources and thus the solving of corresponding resource-constrained project scheduling problems are studied. Finally, new applications of resource-constrained project scheduling are presented: factory pick-up of new cars and batch scheduling in process industries.

Some of the most challenging issues that arise in the domain of distributed manufacturing technology and management include manufacturability analysis, validation and evaluation of process plans, partnership in virtual enterprises, process design, and optimization of production plans and schedules. These issues are easily unified within a framework of a project-driven manufacturing concept which is focusing on small and medium size enterprises (SMEs) where products are manufactured based on make-to-order or build-to-order principle.

Regardless of character and scope of business activities a modern enterprise, has to build a project-driven development strategy in order to respond to challenges imposed by growing complexity and globalization. Managers need to be able to utilize a modern decision support tools as to undertake optimal business decisions in further strategic perspective of enterprise operation. In this context this contribution covers various issues of project management engineering while focusing in the field of Project-Driven Manufacturing, particularly in domains regarding the development of novel constraint programming based mathematical models and related decision-making methods as well as their implementation into the task oriented decision support systems aimed at project-driven SMEs management.