Francisco Ballestín

Justification and RCPSP: A technique that pays

Abstract The objective of this paper is to show that justification is a simple technique that can be easily incorporated in diverse algorithms for the resource-constrained project scheduling problem––improving the quality of the schedules generated without generally requiring more computing time. The results of incorporating this technique in 22 different algorithms are shown. Fifteen of the new algorithms that use double justification outperform seven of the best heuristic algorithms that do not use justification. The tests have been performed on the standard test set j120 for the RCPSP generated using ProGen.

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

Abstract 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.

Resource-constrained project scheduling: A critical activity reordering heuristic

Abstract In this paper, we present a new metaheuristic algorithm for the resource-constrained project-scheduling problem. The procedure is a non-standard implementation of fundamental concepts of tabu search without explicitly using memory structures embedded in a population-based framework. The procedure makes use of a fan search strategy to intensify the search, whereas a strategic oscillation mechanism loosely related to the forward/backward technique provides the necessary diversification. Our implementation employs the topological order (TO) representation of schedules. To explore the TO vector space we introduce three types of moves, two of them based on the concept of relative criticality, and a third one based on multi-pass sampling ideas. The strategic utilisation of probabilities for move construction is another distinguishing feature of our approach. Extensive computational testing with more than 2000 problem instances shows the merit of the proposed solution method.

A Population-Based Approach to the Resource-Constrained Project Scheduling Problem

We present a population-based approach to the RCPSP. The procedure has two phases. The first phase handles the initial construction of a population of schedules and these are then evolved until high quality solutions are obtained. The evolution of the population is driven by the alternative application of an efficient improving procedure for locally improving the use of resources, and a mechanism for combining schedules that blends scatter search and path relinking characteristics. The objective of the second phase is to explore in depth those vicinities near the high quality schedules. Computational experiments on the standard j120 set, generated using ProGen, show that our algorithm produces higher quality solutions than state-of-the-art heuristics for the RCPSP in an average time of less than five seconds.

Heuristic procedures for reactive project scheduling

This paper describes new heuristic reactive project scheduling procedures that may be used to repair resource-constrained project baseline schedules that suffer from multiple activity duration disruptions during project execution. The objective is to minimize the deviations between the baseline schedule and the schedule that is actually realized. We discuss computational results obtained with priority-rule based schedule generation schemes, a sampling approach and a weighted-earliness tardiness heuristic on a set of randomly generated project instances.

Resource-Constrained Project Scheduling for Timely Project Completion with Stochastic Activity Durations

We investigate resource-constrained project scheduling with stochastic activity durations. Various objective functions related to timely project completion are examined, as well as the correlation between these objectives. We develop a GRASP-heuristic to produce high-quality solutions, using so-called descriptive sampling. The algorithm outperforms other existing algorithms for expected-makespan minimization. The distribution of the possible makespan realizations for a given scheduling policy is studied, and problem difficulty is explored as a function of problem parameters.

When it is worthwhile to work with the stochastic RCPSP

Abstract The Resource-Constrained Project Scheduling Project (RCPSP), together with some of its extensions, has been widely studied. A fundamental assumption in this basic problem is that the duration of activities is known before their execution. Very little effort has been made in developing heuristics for the RCPSP with stochastic durations, that is, when the duration of activities is given by a distribution of probability. In fact, the deterministic approach is often used even in the presence of non-trivial distributions. In this paper we discuss when it is worth the effort, in heuristic algorithms, to work with stochastic durations instead of deterministic ones. We also describe techniques that seem to be useful for a wide variety of heuristic algorithms for the stochastic problem. We develop two algorithms that include these procedures and that are capable of outperforming other existing heuristics in the literature. Computational experiments are provided on instances based on the standard set j120, generated using ProGen, and on the well-known Patterson set.

Theoretical and practical fundamentals for multi-objective optimisation in resource-constrained project scheduling problems

Project scheduling is an inherently multi-objective problem, since managers want to finish projects as soon as possible with the minimum cost and the maximum quality. However, there are only a few papers dealing with multiobjective resource-constrained project scheduling problems (MORCPSPs). Moreover, there is no theoretical study in the literature that establishes the fundamentals for correct algorithmic developments. In this paper we try to close the gap by proving several results for MORCPSPs. With these results as a basis, both exact and heuristic procedures capable of obtaining a set of efficient solutions for several important MORCPSPs can be created. We develop algorithms for the case where all objective functions are of the same type, called regular. In this case, specific codifications, techniques, and procedures can be used. Extensive computational results help decide which algorithms or techniques are the most promising for the problem. With the aid of these algorithms we study the Pareto fronts in this case. Finally, we apply a metaheuristic algorithm to a particular example of the general case in order to analyse the differences in the Pareto fronts. The project instances and Pareto fronts obtained can be downloaded from a website to facilitate comparisons with future research efforts.

A double genetic algorithm for the MRCPSP/max

This paper presents a heuristic solution procedure for a very general resource-constrained project scheduling problem. Here, multiple execution modes are available for the individual activities of the project. In addition, minimum as well as maximum time lags between different activities may be given. The objective is to determine a mode and a start time for each activity such that the temporal and resource constraints are met and the project duration is minimised. Project scheduling problems of this type occur e.g. in process industries. The heuristic is a two-phased genetic algorithm with different representation, fitness, crossover operator, etc., in each of them. One of the contributions of the paper is the optimisation in the first phase of a problem dual to the original, the searching for the best modes of the activities. Computational results show that the algorithm outperforms the state-of-the-art algorithms in medium and large instances.

Pre-emption in resource-constrained project scheduling

The Resource-Constrained Project Scheduling Project (RCPSP), together with some of its extensions, has been widely studied. A fundamental assumption in this basic problem is that activities in progress are non-preemptable. Very little effort has been made to uncover the potential benefits of discrete activity pre-emption, and the papers dealing with this issue have reached the conclusion that it has little effect on project length when constant resource availability levels are defined. In this paper we show how three basic elements of many heuristics for the RCPSP - codification, serial SGS and double justification - can be adapted to deal with interruption. The paper is mainly focussed on problem 1_PRCPSP, a generalization of the RCPSP where a maximum of one interruption per activity is allowed. However, it is also shown how these three elements can be further adapted to deal with more general pre-emptive problems. Computational experiments on the standard j30 and j120 sets support the conclusion that pre-emption does help to decrease project length when compared to the no-interruption case. They also prove the usefulness of the justification in the presence of pre-emption. The justification is a RCPS technique that can be easily incorporated into a wide range of algorithms for the RCPSP, increasing their solution quality - maintaining the number of schedules calculated.