In this paper we introduce the concept of due date assignment in the project scheduling literature. Despite the fact that due date assignment problems belongs to the core of the machine scheduling literature, no attempts have been made to tackle this problem in a project scheduling environment. However, of obvious practical importance, an optimal assignment of due dates is of primary interest to the project manager. In a recent research paper on project scheduling with due dates, the problem has been restricted to considering projects with pre-assigned due dates. In reality, due dates are the results of negotiations, rather than simply dictated by the client of the project. In this paper we consider this negotiation process and take a contractor's point of view who faces the problem of assigning due dates to a particular project, based on the negotiation arguments of the client. We show that the problem under study can be solved by means of the combination of different ideas from the operations research community. Keywords: Project Management, Due date assignment, Weighted earliness-tardiness costs, Due dates,

The paper describes the results of an exploratory research study on the application of programme management in six companies. A classification of programmes is developed that may help in understanding the differences between programmes and the managerial impact of these differences. The research shows that the formalised and rigorous approach as described in most programme management handbooks is not widely adopted. The cases show less centralisation, less formalisation and less management of the interdependencies between the projects in the programme than one would expect on the basis of the programme management literature. This is especially the case in programmes that originate as a grouping of a set of existing projects. Yet, formalisation is mentioned as the main success factor in managing programmes. Keywords: Programme management, project management, management of change

In literature, both topological and resource-related measures are used to predict the difficulty of a project scheduling problem. Rapid progress regarding solution procedures has resulted in the development of a number of data generators in order to generate instances under a controlled design and in different standard sets with problem instances. These complexity measures need to serve as predictors for the complexity of the problem under study. In this paper, we report on results for the topological structure of a network. The contribution of this paper is threefold. First, we review six topological network indicators in order to describe the structure of a network in a detailed way. These indicators were originally developed by [20] and have been modified or sometimes completely replaced by alternative indicators in order to give a better description of the topology of a network. Secondly, we generate a large amount of different networks with four network generators. This allows us to draw conclusions on both the performance of different network generators and to give a critical remark on well-known datasets from literature. Our general conclusions are that none of the network generators are able to capture the complete feasible domain of all networks. Moreover, each network generator covers its own network-specific domain and, consequently, contributes to the generation of instance data sets. Finally, we perform computational results on the well-known resource-constrained project scheduling problem to proof that our indicators are reliable and have significant predictive power to serve as complexity indicators. Keywords: Networks, Topological structure, Graphs, Project Scheduling instances

The resource-constrained project scheduling problem (RCPSP) is one of the most challenging problems in project scheduling. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions for more challenging problem instances. In this paper, we present a new genetic algorithm (GA) that, in contrast of a conventional GA, makes use of two separate populations. This bi-population genetic algorithm (BPGA) operates on both a population of left-justified schedules and a population of right-justified schedules in order to fully exploit the features of the iterative forward/backward local search scheduling technique. Comparative computational results reveal that this procedure can be considered as today's best performing RCPSP heuristic.

Earned value project management is a well-known management system that integrates cost, schedule and technical performance. It allows the calculation of cost and schedule variances and performance indices and forecasts of project cost and schedule duration. The earned value method provides early indications of project performance to highlight the need for eventual corrective action. Earned value management was originally developed for cost management and has not widely been used for forecasting project duration. However, recent research trends show an increase of interest to use performance indicators for predicting total project duration. In this paper, we give an overview of the state-of-the-art knowledge for this new research trend to bring clarity in the often confusing terminology. The purpose of this paper is three-fold. First, we compare the classic earned value performance indicators SV & SPI with the newly developed earned schedule performance indicators SV(t) & SPI(t). Next, we present a generic schedule forecasting formula applicable in different project situations and compare the three methods from literature to forecast total project duration. Finally, we illustrate the use of each method on a simple one activity example project and on real-life project data. Keywords: Earned value, earned duration, earned schedule, project duration forecasting

In the last few decades, several effective algorithms for solving the resource-constrained project scheduling problem have been proposed. However, the challenging nature of this problem, summarised in its strongly NP-hard status, restricts the effectiveness of exact optimisation to relatively small instances. In this paper, we present a new meta-heuristic for this problem, able to provide near-optimal heuristic solutions. The procedure combines elements from scatter search, a generic population-based evolutionary search method, and a recently introduced heuristic method for the optimisation of unconstrained continuous functions based on an analogy with electromagnetism theory, hereafter referred to as the electromagnetism meta-heuristic. We present computational experiments on standard benchmark datasets, compare the results with current state-of-the-art heuristics, and show that the procedure is capable of producing consistently good results for challenging instances of the resource-constrained project scheduling problem. We also demonstrate that the algorithm outperforms state-of-the-art existing heuristics. Keywords: project scheduling, heuristics, scatter search, electromagnetism

In this paper, we present a scatter search algorithm for the well-known nurse scheduling problem (NSP). This problem aims at the construction of roster schedules for nurses taking both hard and soft constraints into account. The objective is to minimize the total preference cost of the nurses and the total penalty cost from violations of the soft constraints. The problem is known to be NP-hard. The contribution of this paper is threefold. First, we are, to the best of our knowledge, the first to present a scatter search algorithm for the NSP. Second, we investigate two different types of solution combination methods in the scatter search framework, based on four different cost elements. Last, we present detailed computational experiments on a benchmark dataset presented recently, and solve these problem instances under different assumptions. We show that our procedure performs consistently well under many different circumstances, and hence, can be considered as robust against case-specific constraints. Keywords: meta-heuristics, scatter search, nurse scheduling