Metaheuristic Algorithms for Multi-mode Multi-project Scheduling with the Objective of Positive Cash flow Balance

Yukang He, Jingwen Zhang, Zhengwen He

Research output: Contribution to journalArticle

Abstract

This paper investigates the problem of how to achieve a positive cash flow balance by multimode multi-project scheduling. First, based on formulating cash flows for the projects, we construct an optimization model in the multi-mode multi-project context that can minimize the maximum cash flow gap and, thus, balance cash flow positively by arranging optimal execution modes and start times for activities. Then, we prove the NP-hardness of the studied problem and design two metaheuristic algorithms, namely tabu search (TS) and simulated annealing (SA), which search the desirable solutions in nested and mixed ways, respectively. Finally, taking the multi-start iterative improvement (MSII) as comparison algorithm, the performance of the two algorithms developed is evaluated through a computational experiment performed on a data set generated randomly using ProGen. From the research results, the following conclusions are drawn: The TS and SA are more suitable for solving the smaller and larger problems, respectively, while the nested searching structure may enhance the algorithm’s efficiency. With the increase of the advance payment proportion, number of milestone activities, client’s payment proportion, or project deadline, the contractor’s maximal cash flow gap decreases.
Original languageEnglish
JournalIEEE Access
Early online date30 Sep 2019
DOIs
Publication statusE-pub ahead of print - 30 Sep 2019

Fingerprint

Scheduling
Tabu search
Simulated annealing
Conjugated (USP) Estrogens
Contractors
Hardness
Experiments

Bibliographical note

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.

Cite this

@article{85b2600246924b50afe505503e85cfca,
title = "Metaheuristic Algorithms for Multi-mode Multi-project Scheduling with the Objective of Positive Cash flow Balance",
abstract = "This paper investigates the problem of how to achieve a positive cash flow balance by multimode multi-project scheduling. First, based on formulating cash flows for the projects, we construct an optimization model in the multi-mode multi-project context that can minimize the maximum cash flow gap and, thus, balance cash flow positively by arranging optimal execution modes and start times for activities. Then, we prove the NP-hardness of the studied problem and design two metaheuristic algorithms, namely tabu search (TS) and simulated annealing (SA), which search the desirable solutions in nested and mixed ways, respectively. Finally, taking the multi-start iterative improvement (MSII) as comparison algorithm, the performance of the two algorithms developed is evaluated through a computational experiment performed on a data set generated randomly using ProGen. From the research results, the following conclusions are drawn: The TS and SA are more suitable for solving the smaller and larger problems, respectively, while the nested searching structure may enhance the algorithm’s efficiency. With the increase of the advance payment proportion, number of milestone activities, client’s payment proportion, or project deadline, the contractor’s maximal cash flow gap decreases.",
author = "Yukang He and Jingwen Zhang and Zhengwen He",
note = "This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.",
year = "2019",
month = "9",
day = "30",
doi = "10.1109/ACCESS.2019.2944746",
language = "English",

}

Metaheuristic Algorithms for Multi-mode Multi-project Scheduling with the Objective of Positive Cash flow Balance. / He, Yukang; Zhang, Jingwen; He, Zhengwen.

In: IEEE Access, 30.09.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Metaheuristic Algorithms for Multi-mode Multi-project Scheduling with the Objective of Positive Cash flow Balance

AU - He, Yukang

AU - Zhang, Jingwen

AU - He, Zhengwen

N1 - This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.

PY - 2019/9/30

Y1 - 2019/9/30

N2 - This paper investigates the problem of how to achieve a positive cash flow balance by multimode multi-project scheduling. First, based on formulating cash flows for the projects, we construct an optimization model in the multi-mode multi-project context that can minimize the maximum cash flow gap and, thus, balance cash flow positively by arranging optimal execution modes and start times for activities. Then, we prove the NP-hardness of the studied problem and design two metaheuristic algorithms, namely tabu search (TS) and simulated annealing (SA), which search the desirable solutions in nested and mixed ways, respectively. Finally, taking the multi-start iterative improvement (MSII) as comparison algorithm, the performance of the two algorithms developed is evaluated through a computational experiment performed on a data set generated randomly using ProGen. From the research results, the following conclusions are drawn: The TS and SA are more suitable for solving the smaller and larger problems, respectively, while the nested searching structure may enhance the algorithm’s efficiency. With the increase of the advance payment proportion, number of milestone activities, client’s payment proportion, or project deadline, the contractor’s maximal cash flow gap decreases.

AB - This paper investigates the problem of how to achieve a positive cash flow balance by multimode multi-project scheduling. First, based on formulating cash flows for the projects, we construct an optimization model in the multi-mode multi-project context that can minimize the maximum cash flow gap and, thus, balance cash flow positively by arranging optimal execution modes and start times for activities. Then, we prove the NP-hardness of the studied problem and design two metaheuristic algorithms, namely tabu search (TS) and simulated annealing (SA), which search the desirable solutions in nested and mixed ways, respectively. Finally, taking the multi-start iterative improvement (MSII) as comparison algorithm, the performance of the two algorithms developed is evaluated through a computational experiment performed on a data set generated randomly using ProGen. From the research results, the following conclusions are drawn: The TS and SA are more suitable for solving the smaller and larger problems, respectively, while the nested searching structure may enhance the algorithm’s efficiency. With the increase of the advance payment proportion, number of milestone activities, client’s payment proportion, or project deadline, the contractor’s maximal cash flow gap decreases.

UR - https://ieeexplore.ieee.org/document/8853254/

U2 - 10.1109/ACCESS.2019.2944746

DO - 10.1109/ACCESS.2019.2944746

M3 - Article

ER -