Vincent Van Peteghem

A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem

In this paper we present a genetic algorithm for the multi-mode resource-constrained project scheduling problem (MRCPSP), in which multiple execution modes are available for each of the activities of the project. We also introduce the preemptive extension of the problem which allows activity splitting (P-MRCPSP). To solve the problem, we apply a bi-population genetic algorithm, which makes use of two separate populations and extend the serial schedule generation scheme by introducing a mode improvement procedure. We evaluate the impact of preemption on the quality of the schedule and present detailed comparative computational results for the MRCPSP, which reveal that our procedure is amongst the most competitive algorithms.

An experimental investigation of metaheuristics for the multi-mode resource-constrained project scheduling problem on new dataset instances

In this paper, an overview is presented of the existing metaheuristic solution procedures to solve the multi-mode resource-constrained-project scheduling problem, in which multiple execution modes are available for each of the activities of the project. A fair comparison is made between the different metaheuristic algorithms on the existing benchmark datasets and on a newly generated dataset. Computational results are provided and recommendations for future research are formulated.

GOVERNMENTAL ACCOUNTING REFORM: EVOLUTION OF THE IMPLEMENTATION IN FLEMISH MUNICIPALITIES

Some studies measured the success of adopting governmental accounting reforms revealing conceptual and practical problems. However, these empirical studies only consider the starting point assuming that implementation difficulties are just transition problems that will disappear automatically in time. This study concentrates on how implementing a governmental reform evolves after a number of years. Looking at 1995, 1997 and 1999, it reveals that the level of compliance in Flemish municipalities increased only slightly in 1997 and remained unchanged in 1999. It evidences that there is no self-regulating effect of implementing governmental reforms, even after a period of almost 5 years of experience.

Using Resource Scarceness Characteristics to Solve the Multi-Mode Resource-Constrained Project Scheduling Problem

In the past decades, resource parameters have been introduced in project scheduling literature to measure the scarceness of resources of a project instance. In this paper, we incorporate these resource scarceness parameters in the search process to solve the multi-mode resource constrained project scheduling problem, in which multiple execution modes are available for each activity in the project. Therefore, we propose a scatter search algorithm, which is executed with different improvement methods, each tailored to the specific characteristics of different renewable and nonrenewable resource scarceness values. Computational results prove the effectiveness of the improvement methods and reveal that the procedure is among the best performing competitive algorithms in the open literature.

An Artificial Immune System for the Multi-Mode Resource-Constrained Project Scheduling Problem

In this paper, an Artificial Immune System (AIS) for the multi-mode resource-constrained project scheduling problem (MRCPSP), in which multiple execution modes are available for each of the activities of the project, is presented. The AIS algorithm makes use of mechanisms which are inspired on the vertebrate immune system performed on an initial population set. This population set is generated with a controlled search method, based on experimental results which revealed a link between predefined profit values of a mode assignment and its makespan. The impact of the algorithmic parameters and the initial population generation method is observed and detailed comparative computational results for the MRCPSP are presented.

Influence of learning in resource-constrained project scheduling

Learning effects for the discrete time/resource trade-off scheduling problem are analyzed.The impact of learning is investigated by a computational experiment.The accuracy and margin of error in project schedules is investigated. Learning effects assume that the efficiency of a resource increases with the duration of a task. Although these effects are commonly used in machine scheduling environments, they are rarely used in a project scheduling setting. In this paper, the effect of learning in a project scheduling environment is studied and applied to the discrete time/resource trade-off scheduling problem (DTRTP), where each activity has a fixed work content for which a set of execution modes (duration/resource requirement pairs) can be defined. Computational results emphasize the significant impact of learning effects on the project schedule, measure the margin of error made by ignoring learning and show that timely incorporation of learning effects can lead to significant makespan improvements.

Heuristic Methods for the Resource Availability Cost Problem

In this chapter, an Invasive Weed Optimization (IWO) algorithm for the resource availability cost problem is presented, in which the total cost of the (unlimited) renewable resources required to complete the project by a prespecified project deadline should be minimized. The IWO algorithm is a new search strategy, which makes use of mechanisms inspired by the natural behavior of weeds in colonizing and finding a suitable place for growth and reproduction. All algorithmic components are explained in detail and computational results for the RACP are presented. The procedure is also executed to solve the RACP with tardiness (RACPT), in which lateness of the project is permitted with a predefined penalty.