NP is as easy as detecting unique solutions
Theoretical Computer Science
Scheduling of project networks by job assignment
Management Science
Resource-constrained project scheduling: a survey of recent developments
Computers and Operations Research
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
The measure of Pareto optima applications to multi-objective metaheuristics
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Solving system-level synthesis problem by a multi-objective estimation of distribution algorithm
Expert Systems with Applications: An International Journal
Computers and Operations Research
Hi-index | 0.01 |
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.