Nature-inspired metaheuristic algorithms for optimization

In vehicle routing problems (VRP), the optimal allocation of transportation by considering factors such as route hardness, driver experience and vehicle worn-out has a significant effect on costs reduction and approaching real-world conditions. In this paper, a novel fuzzy mixed integer non-linear mathematical model to address the two-echelon allocation-routing problem under uncertainty is proposed by applying route and fleet conditions. The cost of allocating drivers to diverse vehicles is computed at the first echelon of the problem, considering factors such as vehicle type, vehicle wear-out, and driver experience. Additionally, different routes are defused with varying levels of hardness. The goal of the second echelon of the model is to improve reliability by defining the reliability of routes within each section. To solve the model, the Torabi and Hessini (TH), the Selimi and Ozkarahan (SO) methods, and a newly proposed approach (PIA) were utilized to transform the multi-objective model into a single-objective one. Numerical tests and performance indicators were used to validate the effectiveness of both the multi-objective mathematical model and the proposed solution method. The validation computation results indicate that the proposed solution approach outperforms both the TH and SO approaches.

Section: Literature Reviewmentioning

confidence: 99%

“…Constraints (20) and ( 21) are related to permission to travel at the first echelon. Constraint (22) is the condition for exiting a second-echelon temporary warehouse after entering it. Constraint (23) ensures that the load sent to each temporary warehouse is exactly equal to the total demand requested from it.…”

Section: Constraintsmentioning

confidence: 99%

Designing a Fuzzy Mathematical Model for a Two-Echelon Allocation-Routing Problem by Applying Route Conditions: A New Interactive Fuzzy Approach

Yadegari,

Molana,

Kashan

et al. 2024

“…Kumar, Vohra, et al (2021)expounded use of various optimization techniques for petroleum engineering. Furthermore, in the past non-linear concept-based reliability approaches have been developed by Garg & Rani, (2013); Komal et al (2010); Wu et al (2011); ; Sharma et al (2012); Pant et al, (2017); ; Uniyal et al (2022); Gopal & Panchal, (2023).…”

Section: Literature Backgroundmentioning

confidence: 99%

Reliability based framework for failure analysis in milk process industry

Gopal

Panchal

2023

The suggested hybridized framework offers a paradigm for performance optimization-reliability-based analysis of milk processing unit’s (MPU) failure behavior in the dairy industry. The proposed hybridized framework led to the development of fuzzy Jaya Based Lambda-Tau (JBLT) technique-based mathematical model for computing various performance parameters of the under-consideration unit. The availability of the system drops by 0.044% as the level of uncertainty or spread level increases from ± 15% to ± 25% and drops to 0.088% as the level of uncertainty increases from ± 25% to ± 60%. To corroborate the system’s availability downward trend, the results of JBLT approach were compared with Particle Swarm Optimization-Based Lambda-Tau (PSOBLT) and conventional Fuzzy Lambda-Tau (FLT) techniques. The analysis findings were given to the maintenance manager so they could create the best maintenance schedule for the considered plant.

“…A. provided a comprehensive overview of metaheuristic optimization techniques and their applications in engineering. A. reviewed optimization techniques for petroleum engineering, while Uniyal et al (2022) provided an overview of nature-inspired metaheuristic algorithms for optimization. A. used nature-inspired optimization algorithms to optimize the availability-cost of a butter oil processing system, while Rawat et al (2022) provided a state-of-the-art survey on the applications of the Analytical Hierarchy Process (AHP).…”

Section: Related Workmentioning

confidence: 99%

Artificial rat optimization with decision-making: A bio-inspired metaheuristic algorithm for solving the traveling salesman problem

Mzili¹,

Mzili²,

Riffi³

2023

In this paper, we present the Rat Swarm Optimization with Decision Making (HDRSO), a hybrid metaheuristic algorithm inspired by the hunting behavior of rats, for solving the Traveling Salesman Problem (TSP). The TSP is a well-known NP-hard combinatorial optimization problem with important applications in transportation, logistics, and manufacturing systems. To improve the search process and avoid getting stuck in local minima, we added a natural mechanism to HDRSO through the incorporation of crossover and selection operators. In addition, we applied 2-opt and 3-opt heuristics to the best solution found by HDRSO. The performance of HDRSO was evaluated on a set of symmetric instances from the TSPLIB library and the results demonstrated that HDRSO is a competitive and robust method for solving the TSP, achieving better results than the best-known solutions in some cases.