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Item type:Publication, Resource levelling in projects considering different activity execution modes and splitting(Emerald, 2021-01-01)This study aims to develop a mathematical programming model for preemptive multi-mode resource-constrained project scheduling problems in construction with the objective of levelling resources considering renewable and non-renewable resources. Design/methodology/approach: The proposed model was solved by the exact method and the genetic algorithm integrated with the solution modification procedure coded with MATLAB software. The Taguchi method was applied for setting the parameters of the genetic algorithm. Different numerical examples were used to show the validation of the proposed model and the capability of the genetic algorithm in solving large-sized problems. In addition, the sensitivity analysis of two parameters, including resource factor and order strength, was conducted to investigate their impact on computational time. Findings: The results showed that preemptive activities obtained better results than non-preemptive activities. In addition, the validity of the genetic algorithm was evaluated by comparing its solutions to the ones of the exact methods. Although the exact method could not find the optimal solution for large-scale problems, the genetic algorithm obtained close to optimal solutions within a short computational time. Moreover, the findings demonstrated that the genetic algorithm was capable of achieving optimal solutions for small-sized problems. The proposed model assists construction project practitioners with developing a realistic project schedule to better estimate the project completion time and minimize fluctuations in resource usage during the entire project horizon. Originality/value: There has been no study considering the interruption of multi-mode activities with fluctuations in resource usage over an entire project horizon. In this regard, fluctuations in resource consumption are an important issue that needs the attention of project planners. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The sustainable two-echelon location-routing problem considering combined open and closed routes under uncertainty(Research Square, 2022-03-14)Location-routing is an extremely important problem in supply chain management. In the location-routing problem, decisions are made about the location of facilities such as distribution centers as well as the set of vehicle routes. Today, organizations seek to reduce the transportation cost by outsourcing which leads to a specific type of transportation problems called open routing. On the other hand, the growing concerns of environmental impacts have led to paying more attention to environmental issues and reducing the environmental impacts of logistics activities. To this end, in this paper, both open and closed routes are simultaneously addressed by developing a multi-objective mixed integer linear programming model that included three economic, environmental, and social responsibility aspects. The three objective functions of the proposed model encompass the minimization of total costs and greenhouse gas emissions, and the maximization of employment rate and economic development. Also, in this study, a different type of routing is considered in each echelon. A small-sized problem instance is solved using the Augmented Epsilon Constraint (AEC) method with the CPLEX Optimizer Solver for the validation of the proposed model. Due to the NP-Hardness of the problem, two efficient metaheuristic algorithms of Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Stochastic Fractal Search (MOSFS) are exploited to solve the medium and large size problems. The performance of the algorithms is compared in terms of time, MID, diversity, spacing, SNS, and RAS indexes. The results show that the MOSFS algorithm outperforms the NSGA-II based on several indexes. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An efficient intelligent intrusion detection system using fuzzy logic based on the particle swarm optimization algorithm: A case study(IOS Press, 2024-10-25)This paper presents an intelligent intrusion detection system using fuzzy logic based on particle swarm optimization algorithm. The main goal of this research is to survey the convergence capability of the particle swarm optimization algorithm using fuzzy logic in intelligent intrusion detection of a designable system. In order to simulate intelligent attacks on a system, KDD99 data are used. Based on the findings, the Particle Swarm Optimization (PSO) algorithm is highly capable of detecting an intelligent attack on a system. In this study, we considered 1800 times attack, in which the PSO algorithm was capable of repelling attacks in 7.24 seconds and converged. The best convergence occurred at stage 775, and then all attacks were eliminated from the system. Results showed that the stability and convergence of the system improved after each attack. Also, the number of attacks increased to 2500 times to investigate unpredictable intrusions and converge accrued at the attack 771st. Finally, the results obtained by the PSO algorithms were compared to the results obtained by the Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm. The findings indicate that the PSO algorithm is highly capable of detecting intelligent intrusions into a system. It is also suggested to employ this algorithm in cloud computing systems because of its high capability of repelling smart attacks. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhanced leakage detection and estimation via a hybrid genetic algorithm and high-order sliding modes observer approach(Institute of Electrical and Electronics Engineers Inc., 2024-01-01)This paper introduces a hybrid approach designed for both detecting and estimating the magnitude of leaks in oil pipelines. The method integrates a High Order Sliding Mode Observer(HOSMO) with a Super Twisting Algorithm to serve as an observer for state estimation of the system. A parameterized model based on momentum and mass balance equations with discretization is used, where the parameters are the location and magnitude of the leakage. To find these parameters, it incorporates the Genetic Algorithm to solve an optimization problem that relies on a function cost related to the error norm between measurements and states estimation from HOSMO in order to measure the difference between the model with an assumed leakage and the real leakage. The solution of the minimization problem represents the leak position and magnitude. The feasibility and effectiveness of this method are evaluated using a simulation model representing a 306 km sector of the North-Peruvian Oil Pipeline. The results demonstrate its robustness against noise, showcasing a precision of ±250 m in pinpointing the location of leaks. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Optimizing Inventory in Convenience Stores to Maximize ROI Using Random Forest and Genetic Algorithms(Multidisciplinary Digital Publishing Institute (MDPI), 2026-03-01)Background: Convenience stores face volatile demand and a direct trade-off between stock-outs and overstocking, both of which affect service levels and profitability. This study aims to optimize inventory management through a reproducible forecasting-and-optimization workflow, assessing its impact on return on investment (ROI) and operational metrics, such as fill rate and stockouts. Methods: The workflow integrates daily, store-level transactions with external covariates, constructs temporal and lag features, and trains a Random Forest (RF) model using chronological splitting and time-series validation. Daily forecasts are then aggregated to the monthly level and used as inputs to an inventory simulation and an ROI-based economic model. Building on this simulation, a Genetic Algorithm (GA) optimizes the parameters of a monthly replenishment policy, incorporating minimum-coverage constraints. Results: In testing, the forecasting model achieved a mean absolute percentage error (MAPE) below 13%, and the RF+GA scheme outperformed the 28-day moving average baseline (MA28) in ROI across all five stores, with an average improvement of 4.52 percentage points; statistical significance was confirmed using the Wilcoxon test. Conclusions: Overall, the RF+GA approach serves as a decision-support tool that generates monthly order quantities consistent with demand and operational constraints, delivering verifiable improvements in both economic and service metrics.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An efficient intelligent intrusion detection system using fuzzy logic based on the Particle Swarm Optimization algorithm: A case study(IOS Press, 2025-04-01)This paper presents an intelligent intrusion detection system using fuzzy logic based on particle swarm optimization algorithm. The main goal of this research is to survey the convergence capability of the particle swarm optimization algorithm using fuzzy logic in intelligent intrusion detection of a designable system. In order to simulate intelligent attacks on a system, KDD99 data are used. Based on the findings, the Particle Swarm Optimization (PSO) algorithm is highly capable of detecting an intelligent attack on a system. In this study, we considered 1800 times attack, in which the PSO algorithm was capable of repelling attacks in 7.24 s and converged. The best convergence occurred at stage 775, and then all attacks were eliminated from the system. Results showed that the stability and convergence of the system improved after each attack. Also, the number of attacks increased to 2500 times to investigate unpredictable intrusions and converge accrued at the attack 771st. Finally, the results obtained by the PSO algorithms were compared to the results obtained by the Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm. The findings indicate that the PSO algorithm is highly capable of detecting intelligent intrusions into a system. It is also suggested to employ this algorithm in cloud computing systems because of its high capability of repelling smart attacks.1