Determining k-possible critical paths using Tawanda's non-iterative optimal tree algorithm for shortest route problems

Tawanda, Trust

doi:10.1504/ijor.2018.092737

Cited by 5 publications

(4 citation statements)

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“…For a K-node network, the algorithm computes the shortest path in K−1 iterations. Tawanda [16] developed a non-iterative algorithm for determining the shortest path, and the algorithm transforms a network to a tree through arc and node replications. Maposa et al [17] proposed a non-iterative shortest path algorithm; the algorithm makes use of a n × n tableau to compute the shortest path.…”

Section: Literature Reviewmentioning

confidence: 99%

A Labelling Method for the Travelling Salesman Problem

et al. 2023

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The travelling salesman problem (TSP) is a problem whereby a finite number of nodes are supposed to be visited exactly once, one after the other, in such a way that the total weight of connecting arcs used to visit these nodes is minimized. We propose a labelling method to solve the TSP problem. The algorithm terminates after K−1 iterations, where K is the total number of nodes in the network. The algorithm’s design allows it to determine alternative tours if there are any in the TSP network. The computational complexity of the algorithm reduces as iterations increase, thereby making it a powerful and efficient algorithm. Numerical illustrations are used to prove the efficiency and validity of the proposed algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

A Labelling Method for the Travelling Salesman Problem

et al. 2023

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“…Future studies will also consider introducing fuzzy theory to the Extended TANYAKUMU labelling method for shortest paths problem. Lastly, we will consider extending the shortest path algorithm to compute critical paths in project networks (Tawanda, 2018;Munapo et al, 2008).…”

Section: Conclusion and Further Researchmentioning

confidence: 99%

“…Twelve criteria were used to compare the algorithm to other algorithms such as the Dijkstra algorithm and the dynamic algorithm, among others. Tawanda (2018) computed k possible critical paths in the project network using Tawanda's non-iterative optimal tree algorithm for the shortest path problem. The algorithm was compared with the Critical Path Method (CPM) and the modified Dijkstra's algorithm.…”

Section: Introductionmentioning

confidence: 99%

Extended TANYAKUMU Labelling Method to Compute Shortest Paths in Directed Networks

Tawanda,

Munapo,

Kumar

et al. 2023

Int. j. math. eng. manag. sci.

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Shortest path problem (SPP) has various applications in areas such as telecommunications, transportation and emergency services, and postal services among others. As a result, several algorithms have been developed to solve the SPP and related problems. The current paper extends the TANYAKUMU labelling method for solving the Travelling salesman problem (TSP) to solve SPP in directed transportation networks. Numerical illustrations are used to prove the validity of the proposed method. The main contributions of this paper are as follows: (i) modification of TSP algorithm to solve single source SPP, (ii) the developed method numerically evaluated on four increasingly complex problems of sizes 11×11, 21×21, 23×23 and 26×26 and lastly (iii) the solutions obtained from solving these four problems are compared with those obtained by Minimum incoming weight label (MIWL) algorithm. The proposed algorithm computed the same shortest paths as the MIWL algorithm on all four problems.

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“…Network compilation means identifying the project paths with their durations. There are several methods to determine the critical path [see, for instance, the study by Tawanda (2018)]. In this paper, every path is described using a set of activities arranged in chronological order.…”

Section: Monte Carlo Simulation (Mcs)mentioning

confidence: 99%

Mitigation Controller: Adaptive Simulation Approach for Planning Control Measures in Large Construction Projects

Kammouh

Nogal

Binnekamp

et al. 2021

J. Constr. Eng. Manage.

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Probabilistic Monte Carlo simulations are often used to determine a project's completion time given a required probability level. During project execution, schedule changes negatively affect the probability of meeting the project's completion time. A manual trial and error approach is then conducted to find a set of mitigation measures to again arrive at the required probability level. These are then implemented as scheduled activities. The mitigation controller (MitC) proposed in this paper automates the search for finding the most costeffective set of mitigation measures using multiobjective linear optimization so that the probability of timely completion remains at the required level. It considers different types of uncertainties and risk events in the probabilistic simulation. Moreover, it removes the fundamental modeling error that exists in the traditional probabilistic approach by incorporating human control and adaptive behavior in the simulation. Its usefulness is demonstrated using an illustrative example derived from a recent Dutch construction project in which delay is not permitted. It is shown that the MitC is capable of identifying the most effective mitigation strategies allowing for substantial cost savings.

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Determining k-possible critical paths using Tawanda's non-iterative optimal tree algorithm for shortest route problems

Cited by 5 publications

References 0 publications

A Labelling Method for the Travelling Salesman Problem

A Labelling Method for the Travelling Salesman Problem

Extended TANYAKUMU Labelling Method to Compute Shortest Paths in Directed Networks

Mitigation Controller: Adaptive Simulation Approach for Planning Control Measures in Large Construction Projects

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