An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem

Abstract

This paper investigates the construction of an automatic algorithm selection tool for the multi-mode resourceconstrained project scheduling problem (MRCPSP). The research described relies on the notion of empirical hardness models. These models map problem instance features onto the performance of an algorithm. Using such models, the performance of a set of algorithms can be predicted. Based on these predictions, one can automatically select the algorithm that is expected to perform best given the available computing resources. The idea is to combine different algorithms in a super-algorithm that performs better than any of the components individually. We apply this strategy to the classic problem of project scheduling with multiple execution modes. We show that we can indeed significantly improve on the performance of state-of-the-art algorithms when evaluated on a set of unseen instances. This becomes important when lots of instances have to be solved consecutively. Many state-of-the-art algorithms perform very well on a majority of benchmark instances, while performing worse on a smaller set of instances. The performance of one algorithm can be very different on a set of instances while another algorithm sees no difference in performance at all. Knowing in advance, without using scarce computational resources, which algorithm to run on a certain problem instance, can significantly improve the total overall performance.

Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem

“…It would obviously be interesting to link together the performance of the algorithm(s) with the features of the instances; for example, see [50,37] and others. There are many potential features that could be used to characterise the instances, and these have often been used as part of the process of generating instances, for example [30,12].…”

“…Finally, we remark that recent work on these multi-mode single project instances by [37] has considered the problem of selecting algorithms. This is perhaps the closest in spirit to our goals, in that the aim is to take multiple options for algorithms and then to use intelligent or machine learning techniques in order to make the selection.…”

Static scheduling in consideration of resource categories in collaborative product development project

“…Here, equation (13) is the total quantity of every technology resource, A t is the executing task set during ½t À 1, t; equation (14) is the technology resource requirement constraint, r k i max = R k ; equation (15) shows that one task was only executed by one person; equation (16) shows that at the same time, one person can only do one task; and equation (17) is the time sequence constraint of task, B(s) is the preceding task set of task T s .…”

Multi‐mode resource‐constrained project scheduling using modified variable neighborhood search heuristic

“…(2014, 2016, 2018a), Cheng and Tran (), Damak et al. (), Messelis and De Causmaecker (), Palacio and Larrea (), Rezaeian et al. (), Schnell and Hartl (), Sebt et al.…”