Structured construction and simulation of nondeterministic stochastic activity networks

In this work we deal with nondeterministic stochastic activity networks (NDSANs). Their stochastic character results from activity durations, which are given by nonnegative continuous random variables. The nondeterministic behavior of an NDSAN is a consequence of its variable topology, based on two additional features. First, by associating choice probabilities with the immediate successors of an activity, some branches of execution are not always taken. Second, by allowing iterated executions of a group of activities according to predetermined probabilities, the number of times an activity is to be executed is not determined a priori. These properties lead to a wide variety of activity networks, capable of modelling many real situations in process engineering and project management. We describe a simple, recursively structured construction of NDSANs, which both provides a coherent syntactic mechanism to incorporate the two abovementioned nondeterminism features and allows the analytic formulation of completion time. This construction also directly gives rise to a recursive simulation algorithm for NDSANs, whose repeated execution produces an estimate of the probability distribution of the completion time of the network. We also report on real-world case studies, using the Komolgorov–Smirnov statistic for validation.     

Adaptive policies for multi-mode project scheduling under uncertainty

In this paper we propose an adaptive model for multi-mode project scheduling under uncertainty. We assume that there is a due date for concluding the project and a tardiness penalty for failing to meet this due date, and that several distinct modes may be used to undertake each activity. We define scheduling policies based on a set of thresholds. The starting time of the activity is compared with those thresholds in order to define the execution mode.We propose a procedure, based on the electromagnetism heuristic, for choosing a scheduling policy. In computational tests, we conclude that the adaptive scheduling policy found by using the model and the heuristic solution procedure is consistently better than the optimal non-adaptive policy. When the different modes have very different characteristics and there is a reasonable difference between the average duration of the project and the due date, the cost advantage of the adaptive policy becomes very significant.     

An integrated decision support approach for project investors in risky and uncertain environments

In project investment decisions, it is often assumed that estimated values of project parameters are certain and they would not deviate by the time. However, project parameters normally change during a life cycle of the project. Therefore, an existence of a deviation or gap between forecasted values and actual values is inevitable. Because of the uncertainty of the future, forecasting the true and exact values of project parameters is almost impossible. In this study, an integrated decision support approach based on simulation and fuzzy set theory is proposed for project investors in risky and uncertain environments. This approach determines the risk levels of the projects and helps investors to make investment decisions. In the scope of the study, a flowchart is presented to guide to decision maker in different situations of information uncertainty that belongs to project parameter values. Via this flowchart, the values of project parameters can be chosen depending on how they are determined (deterministic, stochastic or fuzzy) by project analyst. Besides, calculating and analyzing the project risk in all possible situations would be easier. Illustrative examples are given to demonstrate the application of this approach.     

The relevance of the “alphorn of uncertainty” to the financial management of projects under uncertainty

In this paper, both the duration and the cost of an activity are modeled as random variables, and accordingly, the cumulative cost at each time point also becomes a random variable along a project’s progress. We first present the new concept of an “alphorn of uncertainty” (AoU) to describe the domain of cumulative cost variation throughout the life of a project and subsequently apply it to assess the project’s financial status over time. The shape of the alphorn was obtained by mixing Monte Carlo sampling with Gantt chart analysis, which enabled us to determine a project’s financial status related to specific payment modes. To validate the AoU, we designed and conducted an extensive numerical experiment using a randomly generated data set of activity networks. The results indicate that the AoU may be a promising method for the financial management of projects under uncertainty. Furthermore, financial status under uncertain conditions is not sensitive to an activity’s choice of duration distributions or to the form of cost functions. However, payment rules can greatly affect financial status over the duration of a project.     

A management oriented approach to reduce a project duration and its risk (variability)

The effects of different activities on risk and the expected completion time of a project are not the same; various activities have various effects on the successful completion of a project. Based on this fact, one of the most important issues in project management is to determine important activities and the amount of effort that should be assigned to control them, thereby completing the project successfully. In this paper, one index and one method are proposed to satisfy this necessity. Our main hypothesis is, expending effort on activities and controlling them actively results in activities risk reduction. Based on this hypothesis, the proposed index and method are compared to existing indices in the literature. Comparison results show that the proposed index and method strongly surpass other indices.     

Competence-driven project portfolio selection,scheduling and staff assignment

This paper presents a new model for project portfolio selection, paying specific attention to competence development. The model seeks to maximize a weighted average of economic gains from projects and strategic gains from the increment of desirable competencies. As a sub-problem, scheduling and staff assignment for a candidate set of selected projects must also be optimized. We provide a nonlinear mixed-integer program formulation for the overall problem, and then propose heuristic solution techniques composed of (1) a greedy heuristic for the scheduling and staff assignment part, and (2) two (alternative) metaheuristics for the project selection part. The paper outlines experimental results on a real-world application provided by the E-Commerce Competence Center Austria and, for a slightly simplified instance, presents comparisons with the exact solution computed by CPLEX.     

A hybrid genetic algorithm for the resource-constrained project scheduling problem

In this paper we propose a Hybrid Genetic Algorithm (HGA) for the Resource-Constrained Project Scheduling Problem (RCPSP). HGA introduces several changes in the GA paradigm: a crossover operator specific for the RCPSP; a local improvement operator that is applied to all generated schedules; a new way to select the parents to be combined; and a two-phase strategy by which the second phase re-starts the evolution from a neighbour’s population of the best schedule found in the first phase. The computational results show that HGA is a fast and high quality algorithm that outperforms all state-of-the-art algorithms for the RCPSP known by the authors of this paper for the instance sets j60 and j120. And that it is competitive with other state-of-the-art heuristics for the instance set j30.     

Project scheduling: a review of recent books

This paper is a review of three related books:Erik L. Demeulemeester and Willy S. Herroelen, Project Scheduling, A Research Handbook, Kluwer, 2002, ISBN 1-4020-7051-9, 708 pp., $185, €202.Ulrich Dorndorf, Project Scheduling with Time Windows—From Theory to Applications, Springer, 2002, ISBN 3-7908-1516-0, 175 pp., €37.95.Klaus Neumann, Christoph Schwindt and Jürgen Zimmermann, Project Scheduling with Time Windows and Scarce Resources—Temporal and Resource-Constrained Project Scheduling with Regular and Nonregular Objective Functions, Springer, 2003, ISBN 3-540-40125-3, 398 pp., $ 84.95, €49.95.     

Project selection,scheduling and resource allocation with time dependent returns

In this paper we formulate and analyze the joint problem of project selection and task scheduling. We study the situation where a manager has many alternative projects to pursue such as developing new product platforms or technologies, incremental product upgrades, or continuing education of human resources. Project return is assumed to be a known function of project completion time. Resources are limited and renewable. The objective is to maximize present worth of profit. A general mathematical formulation that can address several versions of the problem is presented. An implicit enumeration procedure is then developed and tested to provide good solutions based on project ordering and a prioritization rule for resource allocation. The algorithm uses an imbedded module for solving the resource-constrained project scheduling problem at each stage. The importance of integrating the impact of resource constraints into the selection of projects is demonstrated.     

A rule for slack allocation proportional to the duration in a PERT network

In this paper, we define a new rule for the resolution of the slack allocation problem in a PERT network. This problem exists of allocating existing extra time in some paths among the activities belonging to those paths. The allocation rule that we propose assigns extra time to the activities proportionally to their durations in such a way that no path duration exceeds the completion time of the whole project. This time allocation enables us to make a schedule for the PERT project under study. We give two characterizations of the rule and we compare it with others that have been previously defined in the literature.     

Multi-mode resource-constrained project scheduling using RCPSP and SAT solvers

This paper reports on a new solution approach for the well-known multi-mode resource-constrained project scheduling problem (MRCPSP). This problem type aims at the selection of a single activity mode from a set of available modes in order to construct a precedence and a (renewable and non-renewable) resource feasible project schedule with a minimal makespan. The problem type is known to be NP-hard and has been solved using various exact as well as (meta-)heuristic procedures.The new algorithm splits the problem type into a mode assignment and a single mode project scheduling step. The mode assignment step is solved by a satisfiability (SAT) problem solver and returns a feasible mode selection to the project scheduling step. The project scheduling step is solved using an efficient meta-heuristic procedure from literature to solve the resource-constrained project scheduling problem (RCPSP). However, unlike many traditional meta-heuristic methods in literature to solve the MRCPSP, the new approach executes these two steps in one run, relying on a single priority list. Straightforward adaptations to the pure SAT solver by using pseudo boolean non-renewable resource constraints has led to a high quality solution approach in a reasonable computational time. Computational results show that the procedure can report similar or sometimes even better solutions than found by other procedures in literature, although it often requires a higher CPU time.     

An evaluation of the adequacy of project network generators with systematically sampled networks

This paper evaluates and compares different network generators to generate project scheduling problem instances based on indicators measuring the topological network structure. We review six topological network indicators in order to describe the detailed structure of a project network. These indicators were originally developed by [L.V. Tavares, J.A. Ferreira and J.S. Coelho, The risk of delay of a project in terms of the morphology of its network, European Journal of Operational Research 119 (1999), 510–537] and have been modified, or sometimes completely replaced, by alternative indicators to describe the network topology. The contribution of this paper is twofold. Firstly, we generate a large amount of different networks with four project network generators. Our general conclusions are that none of the network generators are able to capture the complete feasible domain of all networks. Additionally, each network generator covers its own network-specific domain and, consequently, contributes to the generation of data sets. Secondly, we perform computational results on the well-known resource-constrained project scheduling problem to prove that our indicators are reliable and have significant, predictive power to serve as complexity indicators.     

PROGRESS: Optimally solving the generalized resource-constrained project scheduling problem

This paper deals with the generalized resource-constrained project scheduling problem (GRCPSP) which extends the well-known resource-constrained project scheduling problem (RCPSP) by considering job specific release and due dates, non-negative minimum start-to-start time lags as well as time-varying resource availabilities. The structure of the project is represented by an acyclic network diagram. Though the extensions are of high practical importance, only a few exact solution procedures have been presented in the literature so far. Therefore, a new exact procedure PROGRESS is developed which includes new dominance rules as well as enhancements of existing ones. For evaluating the efficiency experimentally, new GRCPSP instances with 30 and 60 jobs are considered which extend the standard benchmark sets for the RCPSP generated by ProGen. PROGRESS shows superior performance when applied to the GRCPSP and is also very competitive in comparison to approaches proposed for the RCPSP.     

Scheduling projects with stochastic activity duration to maximize expected net present value

Although uncertainty is rife in many project management contexts, little is known about adaptively optimizing project schedules. We formulate the problem of adaptively optimizing the expected present value of a project’s cash flow, and we show that it is practical to perform the optimization. The formulation includes randomness in activity durations, costs, and revenues, so the optimization leads to a recursion with a large state space even if the durations are exponentially distributed. We present an algorithm that partially exercises the “curse of dimensionality” as computational results demonstrate. Most of the paper is restricted to exponentially distributed task durations, but we sketch the adaptation of the algorithm to approximate any probability distribution of task duration.     

A minmax regret approach to the critical path method with task interval times

The execution of a given project, with a number of interrelated tasks due to precedence constraints, represents a challenge when one must to control the available resources and the compromised due dates. In this paper, we analyse this problem under uncertain individual task completing times, specifically, we will assume that a given range, for the admissible values of each individual completing time, is available. Taking into account that the precedence relations between tasks must be preserved, each realization of the admissible execution times for the set of tasks will define a new scenario determining the ending time for the project and the subset of critical tasks.     

Nash equilibria in competitive project scheduling

We consider the problem of scheduling activities of a project by a firm that competes with another firm that has to perform the same project. The profit that a firm gets from each activity depends on whether the firm finishes the activity before or after its competitor. It is required to find a Nash equilibrium solution or show that no such solutions exist. We present a structural characterization of Nash equilibrium solutions, and a low order polynomial algorithm for the problem.     

On path correlation and PERT bias

Most studies of project time estimation assume that (a) activity times are mutually independent random variables; many also assume that (b) path completion times are mutually independent. In this paper, we subject the impact of both these assumptions to close scrutiny. Using tools from multivariate analysis, we make a theoretical study of the direction of the error in the classical PERT method of estimating mean project completion time when correlation is ignored. We also investigate the effect of activity dependence on the normality of path length via simulation.     

A heuristic procedure for solving the dynamic probabilistic project expediting problem

Because of activity duration uncertainties, large-scale projects can often be modeled most realistically as probabilistic activity networks. The complex interactions among activities with uncertain durations virtually assures a low probability that these projects will be completed before predetermined due dates. As a result, it is often necessary to expedite individual activities in these projects to improve due date performance. This research introduces a dynamically applied matrix simulation approach for selecting expediting options in order to control the probability of successful project completion before predefined due dates. Experiments are conducted to demonstrate the ability of this new approach to generate quality alternatives and efficiently evaluate large-scale projects.     

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.     

A genetic algorithm for the resource constrained multi-project scheduling problem

This paper presents a genetic algorithm for the resource constrained multi-project scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a heuristic that builds parameterized active schedules based on priorities, delay times, and release dates defined by the genetic algorithm. The approach is tested on a set of randomly generated problems. The computational results validate the effectiveness of the proposed algorithm.