Two novel combined approaches based on TLBO and PSO for a partial interdiction/fortification problem using capacitated facilities and budget constraint

Khanduzi, Raheleh; Maleki, Hamid Reza; Akbari, Reza

doi:10.1007/s00500-018-3005-4

Cited by 11 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Khanduzi et al [6] proposed a partial interdiction/fortification model for capacitated facilities that considered budget constraints. In this model, the defender was responsible for allocating resources to protect the system and minimize overall system losses.…”

Section: Possible Backup Assignment Potential Warehouse (Fortified)mentioning

confidence: 99%

Optimizing Warehouse Location and Fortification: A Mixed-Integer Nonlinear Modeling Approach

Oshan

2024

Preprint

View full text Add to dashboard Cite

This study presents a mathematical model for optimizing warehouse-location decisions in supply-chain network design. Previous studies have modeled the facility-location problem and provided fortification plans and solutions. However, these studies did not consider multi-capacity warehouses. The proposed model identifies optimal warehouse locations, sizes, and branch-warehouse assignments, and it determines fortification measures to mitigate the risk of warehouse failure. The model is formulated as a mixed-integer nonlinear problem and is subsequently linearized using standard linearization techniques combined with a proposed method based on the average-probability approximation. A case study of a Canadian company is used to illustrate the model's efficacy. Sensitivity analysis results demonstrate that when the numbers of fortified and built warehouses are equal, failure probability has no effect on the objective function of the model. Thus, this study presents a comprehensive model that can be used to assist businesses in decision-making regarding their warehouse operations to lower their risk of failure and optimize their use of warehouse infrastructure.

show abstract

Section: Possible Backup Assignment Potential Warehouse (Fortified)mentioning

confidence: 99%

Optimizing Warehouse Location and Fortification: A Mixed-Integer Nonlinear Modeling Approach

Oshan

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In recent years, the research trend moves toward considering fortification in problem and tri level (defender-attacker-defender) models were developed to explore effect of fortifying networks against intentional attacks (Hesam Sadati, Aksen, and Aras 2020; Khanduzi, Reza Maleki, and Akbari 2018;Matuschke et al 2017;Sadeghi, Seifi, and Azizi 2017;Smith and Song 2020;Zheng 2017). A number of solution structures are used to solve tri-level fortification problems include solution approaches based on strong duality, enumerative procedures, and decomposition approaches (Lozano, Smith, and Kurz 2017).…”

Section: *Corresponding Authormentioning

confidence: 99%

A tri-level fortification model for edge protection against intentional attacks in the capacitated vehicle routing problem

Nobari

Shirazi

MirHassani

2022

Preprint

View full text Add to dashboard Cite

In recent decades, the issue of interdiction in human-made infrastructures has become increasingly important. The world, after 9/11, recognized the importance of addressing major infrastructures vulnerabilities and protecting them against intentional disruptions. Networks such as transportation networks or electrical grids are one of the most important network infrastructures in which any disruption can cause irreparable costs and customer service declination. Therefore, the phenomenon of interdiction and fortification of network components against possible attacks such as terrorist acts or military operations has been of great importance in the field of research. In this research we develop a tri-level model to obtain optimal edge protect plan for existing network against deliberate attacks on the capacitated vehicle routing problem (CVRP). The vehicle routing problem is one of the most important issues in the distribution and transportation field, where a fleet of vehicles tries to meet demand points in the least possible cost. Protecting edges from deliberate disturbances in this issue has not been addressed in the literature so far. The problem is strictly NP-Hard and we employ the heuristic approaches to solve it. The main idea behind our approach is to iteratively sample the tri-level solution space so that instead of solving the CVRP. This approach called backward sampling framework. We also introduce a Nested Genetic Algorithm (NGA) to accelerate algorithm implementation which numerical experiments prove the effectiveness of this approach in solving the problem.

show abstract

“…Several MH optimizers have been developed in the literature as robust tools that are successfully utilized to solve FS problems. The most common used MH optimizers used in this direction are particle swarm optimization (PSO) [21], genetic algorithms (GA) [22], firefly algorithm [23], manta ray foraging optimizer [24], grey wolf optimizer [25], differential evolution (DE) [26], Harris hawk optimizer (HHO) [27], Henry gas optimizer [28,29], political optimizer [30], flower pollination optimizer [31], Aquila optimizer [32], crow search optimizer [33], cat swarm optimizer [34], and ecosystem-based optimizer [35]. According to the no free lunch theorem (NFLT), there is not a single algorithm that can be successfully used for solving all problems.…”

Section: Introductionmentioning

confidence: 99%

A Quantum-Based Chameleon Swarm for Feature Selection

et al. 2022

View full text Add to dashboard Cite

The Internet of Things is widely used, which results in the collection of enormous amounts of data with numerous redundant, irrelevant, and noisy features. In addition, many of these features need to be managed. Consequently, developing an effective feature selection (FS) strategy becomes a difficult goal. Many FS techniques, based on bioinspired metaheuristic methods, have been developed to tackle this problem. However, these methods still suffer from limitations; so, in this paper, we developed an alternative FS technique, based on integrating operators of the chameleon swarm algorithm (Cham) with the quantum-based optimization (QBO) technique. With the use of eighteen datasets from various real-world applications, we proposed that QCham is investigated and compared to well-known FS methods. The comparisons demonstrate the benefits of including a QBO operator in the Cham because the proposed QCham can efficiently and accurately detect the most crucial features. Whereas the QCham achieves nearly 92.6%, with CPU time(s) nearly 1.7 overall the tested datasets. This indicates the advantages of QCham among comparative algorithms and high efficiency of integrating the QBO with the operators of Cham algorithm that used to enhance the process of balancing between exploration and exploitation.

show abstract

Two novel combined approaches based on TLBO and PSO for a partial interdiction/fortification problem using capacitated facilities and budget constraint

Cited by 11 publications

References 21 publications

Optimizing Warehouse Location and Fortification: A Mixed-Integer Nonlinear Modeling Approach

Optimizing Warehouse Location and Fortification: A Mixed-Integer Nonlinear Modeling Approach

A tri-level fortification model for edge protection against intentional attacks in the capacitated vehicle routing problem

A Quantum-Based Chameleon Swarm for Feature Selection

Contact Info

Product

Resources

About