1
|
Luo C, Guo S. HSMVS: heuristic search for minimum vertex separator on massive graphs. PeerJ Comput Sci 2024; 10:e2013. [PMID: 38855221 PMCID: PMC11157518 DOI: 10.7717/peerj-cs.2013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 04/01/2024] [Indexed: 06/11/2024]
Abstract
In graph theory, the problem of finding minimum vertex separator (MVS) is a classic NP-hard problem, and it plays a key role in a number of important applications in practice. The real-world massive graphs are of very large size, which calls for effective approximate methods, especially heuristic search algorithms. In this article, we present a simple yet effective heuristic search algorithm dubbed HSMVS for solving MVS on real-world massive graphs. Our HSMVS algorithm is developed on the basis of an efficient construction procedure and a simple yet effective vertex-selection heuristic. Experimental results on a large number of real-world massive graphs present that HSMVS is able to find much smaller vertex separators than three effective heuristic search algorithms, indicating the effectiveness of HSMVS. Further empirical analyses confirm the effectiveness of the underlying components in our proposed algorithm.
Collapse
Affiliation(s)
- Chuan Luo
- School of Software, Beihang University, Beijing, China
| | - Shanyu Guo
- School of Software, Beihang University, Beijing, China
| |
Collapse
|
2
|
Montiel-Arrieta LJ, Barragan-Vite I, Seck-Tuoh-Mora JC, Hernandez-Romero N, González-Hernández M, Medina-Marin J. Minimizing the total waste in the one-dimensional cutting stock problem with the African buffalo optimization algorithm. PeerJ Comput Sci 2023; 9:e1728. [PMID: 38192486 PMCID: PMC10773734 DOI: 10.7717/peerj-cs.1728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 11/08/2023] [Indexed: 01/10/2024]
Abstract
The one-dimensional cutting-stock problem (1D-CSP) consists of obtaining a set of items of different lengths from stocks of one or different lengths, where the minimization of waste is one of the main objectives to be achieved. This problem arises in several industries like wood, glass, and paper, among others similar. Different approaches have been designed to deal with this problem ranging from exact algorithms to hybrid methods of heuristics or metaheuristics. The African Buffalo Optimization (ABO) algorithm is used in this work to address the 1D-CSP. This algorithm has been recently introduced to solve combinatorial problems such as travel salesman and bin packing problems. A procedure was designed to improve the search by taking advantage of the location of the buffaloes just before it is needed to restart the herd, with the aim of not to losing the advance reached in the search. Different instances from the literature were used to test the algorithm. The results show that the developed method is competitive in waste minimization against other heuristics, metaheuristics, and hybrid approaches.
Collapse
|
3
|
Sarhan AY, B. Melhim LK, Jemmali M, El Ayeb F, Alharbi H, Banjar A. Novel variable neighborhood search heuristics for truck management in distribution warehouses problem. PeerJ Comput Sci 2023; 9:e1582. [PMID: 37869458 PMCID: PMC10588704 DOI: 10.7717/peerj-cs.1582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 08/17/2023] [Indexed: 10/24/2023]
Abstract
Logistics and sourcing management are core in any supply chain operation and are among the critical challenges facing any economy. The specialists classify transport operations and warehouse management as two of the biggest and costliest challenges in logistics and supply chain operations. Therefore, an effective warehouse management system is a legend to the success of timely delivery of products and the reduction of operational costs. The proposed scheme aims to discuss truck unloading operations problems. It focuses on cases where the number of warehouses is limited, and the number of trucks and the truck unloading time need to be manageable or unknown. The contribution of this article is to present a solution that: (i) enhances the efficiency of the supply chain process by reducing the overall time for the truck unloading problem; (ii) presents an intelligent metaheuristic warehouse management solution that uses dispatching rules, randomization, permutation, and iteration methods; (iii) proposes four heuristics to deal with the proposed problem; and (iv) measures the performance of the proposed solution using two uniform distribution classes with 480 trucks' unloading times instances. Our result shows that the best algorithm is O I S ~ , as it has a percentage of 78.7% of the used cases, an average gap of 0.001, and an average running time of 0.0053 s.
Collapse
Affiliation(s)
- Akram Y. Sarhan
- Department of Information Technology, College of Computing and Information Technology at Khulis, University of Jeddah, Jeddah, Saudi Arabia
| | - Loai Kayed B. Melhim
- Department of Health Information Management and Technology, College of Applied Medical Sciences, University of Hafr Al Batin, Hafr Al Batin, Saudi Arabia
| | - Mahdi Jemmali
- MARS Laboratory, University of Sousse, Sousse, Tunisia
- College of Computing and Informatics, University of Sharjah, Sharjah, United Arab Emirates
- Department of Computer Science and Information, College of Science at Zulfi, Majmaah University, Al-Majmaah, Saudi Arabia
- Department of Computer Science, Higher Institute of Computer Science and Mathematics, Monastir Uuniversity, Monastir, Tunisia
| | - Faycel El Ayeb
- Unit of Scientific Research, Applied College, Qassim University, Saudi Arabia
- GRIFT Research Group, CRISTAL Laboratory, National School of Computer Sciences, La Manouba University, Manouba, Tunisia
| | - Hadeel Alharbi
- Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Hail, Saudi Arabia
| | - Ameen Banjar
- Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| |
Collapse
|
4
|
A Self-Adjusting Search Domain Method-Based Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4212556. [PMID: 36262613 PMCID: PMC9576347 DOI: 10.1155/2022/4212556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 09/20/2022] [Accepted: 09/21/2022] [Indexed: 11/22/2022]
Abstract
As a nondeterministic polynomial (NP) problem, the flexible job shop scheduling problem (FJSP) is a difficult problem to be solved in terms of finding an acceptable solution. In last decades, genetic algorithm (GA) displays very promising performance in the field. In this article, a hybrid algorithm combining global and local search with reinitialization (GLRe)-based GA is proposed to minimize makespan for FJSP. The solution of FJSP is conveniently represented by a double-layer chromosome representation method, which is convenient for subsequent genetic operations, that is, sorting of operations and selection of machines. Two strategies of choosing the job with the most remaining operations (CRO) and 6-dimensional variable determined search position (6D-VSP) are proposed as two components for GA, which are applied to generate a population with superior quality and reduce the global search space during the initialization stage. At the same time, in order to prevent the loss of diversity during evolution, a reinitialization strategy is introduced in the later stage of evolution to adaptively adjust the search domain of the problem. Finally, two sets of benchmark data are tested. The experimental results demonstrate the accuracy and effectiveness of the GLRe proposed in this article for solving FJSP.
Collapse
|