Generating business process recommendations with a population-based meta-heuristic

In order to provide both guidance and flexibility to users during process execution, recommendation systems have been proposed. Existing recommendation systems mainly focus on offering recommendation according to the process optimization goals (time, cost…). In this paper we offer a new approach that primarily focuses on maximizing the flexibility during execution. This means that by following the recommendations, the user retains maximal flexibility to divert from them later on. This makes it possible to handle (possibly unknown) emerging constraints during execution. The main contribution of this paper is an algorithm that uses a declarative process model to generate a set of imperative process models that can be used to generate recommendations

Introducing Learning Effects in Resource-constrained Project Scheduling

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, we study and model learning effects in a project scheduling environment and apply the model to the discrete time/resource trade-o_ 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.

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 use these resource scarceness parameters to differentiate in the search process needed 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 most competitive algorithms in the open literature.project scheduling, scatter search, multi-mode RCPSP, resource scarceness matrix

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.

An Artificial Immune System Algorithm for the Resource Availability Cost Problem

In this paper, an Artificial Immune System (AIS) algorithm for the resource availability cost problem (RACP) is presented, in which the total cost of the (unlimited) renewable resources required to complete the project by a pre-specified project deadline should be minimized. The AIS algorithm makes use of mechanisms inspired by the vertebrate immune system and includes different algorithmic components, such as a new fitness function, a probability function for the composition of the capacity lists, and a K-means density function in order to avoid premature convergence. All components are explained in detail and computational results for the RACP are presented.

GOVERNMENTAL ACCOUNTING REFORM: EVOLUTION OF THE IMPLEMENTATION IN FLEMISH MUNICIPALITIES

Some studies measured the success of adopting governmental accounting reforms (New Public Management) revealing many 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 Flemish municipalities it reveals that the level of compliance has merely increased in 1995-1997 and it has remained unchanged in 1997-99. It evidences that there is no self-regulating effect of implementing governmental reforms, even after a period of almost 5 years of experience.

An Invasive Weed Optimization Algorithm for the Resource Availability Cost Problem

In this paper, an Invasive Weed Optimization (IWO) algorithm for the Resource Availability Cost Problem (RACP) is presented, in which the total cost of the (unlim- ited) renewable resources required to complete the project by a pre-specified 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. In this paper, the algorithm is used for the first time to solve a project scheduling problem. All algorithmic compo- nents are explained in detail and computational results for the RACP are presented.