An implementation of the parallel schedule-generation scheme for applying Microsoft Excel's Evolutionary Solver to the resource-constrained project scheduling problem RCPSP

Trautmann, Ν.; Gnägi, Mario

doi:10.1109/ieem.2016.7797831

Cited by 1 publication

(1 citation statement)

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“…Meanwhile, according to the work of Lova and Tormos (), if the model's objective is to minimize the mean project delay and/or the multi‐project duration, the parallel schedule generation scheme yields better results than the serial scheme. Moreover, after solving a set of 360 nontrivial problem instances with J30 activities (single‐mode RCPSPs) from the standard test set project scheduling library (PSPLIB), Trautmann and Gnägi () found a surprisingly high number of optimal instances, and considerably smaller average gap to the optimal solution than for the serial SGS. Considering these facts, this work considers a variant of the parallel schedule generation scheme proposed by Kolisch (), while we generalize that SGS to the MRCPSPs with both renewable and nonrenewable resources.…”

Section: The Modified Variable Neighborhood Search Heuristicmentioning

confidence: 99%

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

Chakrabortty

Abbasi

Ryan

2019

Int Trans Operational Res

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This paper aims at providing a fast near‐optimum solution to the multi‐mode resource‐constrained project scheduling problems (MRCPSPs), for projects with activities that have known deterministic renewable and nonrenewable resource requirements. The MRCPSP is known to be nondeterministic polynomial‐time hard and has been solved using various exact, heuristic, and meta‐heuristic procedures. In this paper, a modified variable neighborhood search heuristic algorithm is used as an advanced optimization technique that suits scheduling problems. For the experimental study, we have considered a standard set of 3929 multi‐mode benchmark instances from the project scheduling library with a range of projects comprising 10–30 activities. Moreover, for a better comparison, this research also considers a standard set of 4320 newly developed multi‐mode instances from MMLIB50, MMLIB100, and MMLIB+ datasets. With the limit of 50,000 schedules on these datasets, our proposed algorithm provides better makespan for 106, 34, and 1601 instances, respectively, which justifies the efficiency of the proposed algorithm, particularly for projects with a larger number of activities. The results reported in this paper can be used as a benchmark for other researchers to compare and improve.

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Section: The Modified Variable Neighborhood Search Heuristicmentioning

confidence: 99%