In this paper we present a unified (probabilistic/possibilistic) model for resource-constrained project scheduling problem (RCPSP) with uncertain activity durations and a concept of a heuristic approach connected to the theoretical model. It is shown that the uncertainty management can be built into any heuristic algorithm developed to solve RCPSP with deterministic activity durations. The esse...

The Resource-Constrained Project Scheduling Problem (RCPSP) and some of its extensions have been widely studied. Many approaches have been considered to solve this problem: constraint programming (CP), Boolean satisfiability (SAT), mixed integer linear programming (MILP), branch and bound algorithms (BB) and others. In this paper, we present a new approach for solving this problem: satisfiabili...

The resource-constrained project scheduling problem with time windows (RCPSP/max) is an important generalization of a number of well studied scheduling problems. In this paper, we present a new heuristic algorithm that combines the benefits of squeaky wheel optimization with an effective conflict resolution mechanism, called bulldozing, to address RCPSP/max problems. On a range of benchmark pro...

Resource Constrained Project Scheduling Problems with minimum and maximum time lags (RCPSP/max) have been studied extensively in the literature. However, the more realistic RCPSP/max problems – ones where durations of activities are not known with certainty – have received scant interest and hence are the main focus of the paper. Towards addressing the significant computational complexity invol...

The resource constraint project scheduling problem (RCPSP) is an NP-hard benchmark problem in scheduling which takes into account the limitation of resources’ availabilities in real life production processes and subsumes open-shop, job-shop, and flow-shop scheduling as special cases. We here present an application of machine learning to adapt simple greedy strategies for the RCPSP. Iterative re...

Research concerning project planning under uncertainty has primarily focused on the stochastic resource-constrained project scheduling problem (stochastic RCPSP), an extension of the basic RCPSP, in which the assumption of deterministic activity durations is dropped. In this paper, we introduce a new variant of the RCPSP for which the uncertainty is modeled by means of resource availabilities t...

The Resource Constrained Scheduling Pro-ject Problem (RCPSP) models a large num-ber of real world problems. This paper ex-amines various Genetic Algorithms (GAs)and Tabu Search (TS) techniques for theRCPSP. These algorithms are combined withheuristic-based techniques that produce fea-sible schedules. The objective of this researchis to evaluate the e ectiveness o...

Project scheduling with uncertain durations becomes a research focus with the development of uncertainty theory. Most researches concerned aim at minimizing project makespan or project cost according to different decision criterions. However, few works considered resource constraint, which initially appeared in deterministic resource-constrained project scheduling problem (RCPSP) and reflects t...

In Resource Constrained Project Scheduling Problem (RCPSP) exact algorithms can only solve small instances, and several approximation algorithms have been proposed that can solve large instances with a quasi-optimal solution. So far there is no comparative analysis of several algorithms running on the same machine, with equal time limit and large instances; this is our main goal. A new performa...

Scheduling problems in manufacturing, logistics and project management have frequently been modeled using the framework of Resource Constrained Project Scheduling Problems with minimum and maximum time lags (RCPSP/max). Due to the importance of these problems, providing scalable solution schedules for RCPSP/max problems is a topic of extensive research. However, all existing methods for solving...