non-dominated sorting genetic algorithm-ii

In today's competitive market, manufacturers and service providers are continuously seeking ways to reduce costs and save time to gain a competitive edge. One of the most significant challenges they face is the vehicle routing problem (VRP), which is crucial due to its direct impact on the delivery time of services or products. Efficient vehicle routing not only enhances delivery performance but also optimizes the overall network, resulting in reduced operational costs. This study focuses on evaluating the VRP specifically for trucks while incorporating sustainability indicators into the analysis. The key sustainability indicators considered include social, economic, and environmental aspects. By integrating these indicators, the study aims to address multiple objectives simultaneously: reducing delivery time, minimizing costs, and mitigating the environmental impact of vehicle operations.The primary objective of this research is to minimize overall costs, fuel consumption, and route complexity associated with truck deliveries. Given the growing concern over environmental issues, there is a strong emphasis on improving methods to reduce greenhouse gas (GHG) emissions and streamline logistics processes. The research addresses these concerns by proposing a model that not only aims to enhance operational efficiency but also contributes to environmental protection and social responsibility.To achieve these objectives, the study employs advanced optimization techniques, specifically the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO). These methods are utilized to solve the VRP while balancing the trade-offs between various objectives, such as cost reduction, fuel efficiency, and route optimization.The results of the study indicate that the proposed model successfully improves aspects of environmental protection and social responsibility while simultaneously addressing economic concerns. The integration of sustainability indicators into the vehicle routing problem provides a comprehensive approach to optimizing logistics operations, highlighting the importance of considering environmental and social factors alongside economic performance.Overall, this research contributes to the field by offering a refined model for tackling the VRP, with a focus on sustainability. The findings underscore the potential for optimization algorithms to drive improvements in both operational efficiency and environmental stewardship, ultimately supporting more sustainable and socially responsible practices in the transportation and logistics industry.

Exploratory drilling is one of the most important and costly stages of mineral exploration procedures, so the continuation of mining activities depends on the gathered data during this stage. Due to the importance of cost and time-saving in the performance of mineral exploration projects, the effective parameters for reducing the cost and time of drilling activities should be investigated and optimized. Road construction and the sequence of the drilling boreholes by drilling rigs are among these parameters. The main objectives of this research were to optimize the overall road construction cost and the difference in length drilled by each drilling rig. The problem has been modeled as a Multi-Objective Multiple Traveling Salesman Problem (MOmTSP) and solved by the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Finally; the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method has been used to find the optimal solution among the solutions obtained by the NSGA-II.

In this research, a tri-objective mathematical model is proposed for the Transportation-Location-Routing problem. The model considers a three-echelon supply chain and aims to minimize total costs, maximize the minimum reliability of the traveled routes and establish a well-balanced set of routes. In order to solve the proposed model, four metaheuristic algorithms, including Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Water Cycle Algorithm (MOWCA), Multi-objective Particle Swarm Optimization (MOPSO) and Non-Dominated Sorting Genetic Algorithm- II (NSGA-II) are developed. The performance of the algorithms is evaluated by solving various test problems in small, medium, and large scale. Four performance measures, including Diversity, Hypervolume, Number of Non-dominated Solutions, and CPU-Time, are considered to evaluate the effectiveness of the algorithms. In the end, the superior algorithm is determined by Technique for Order of Preference by Similarity to Ideal Solution method.

Ever-increasing energy demand has led to geographic expansion of transmission lines and their complexity. In addition, higher reliability is expected in the transmission systemsdue to their vital role in power systems. It is very difficult to realize this goal by conventional monitoring and control methods. Thus, phasor measurement units (PMUs) are used to measure system parameters. Although installation of PMUsincreases the observability and system reliability, high installation costs of these devices requireplacing them appropriately in proper positions. In this research, multi-objective placement of PMUs with the aims of improving investment and risk costs in power systems is performed along with observability constraint. Then, PMU placement problem is solved as an optimization problem using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Finally, the performance of the proposed method is tested on standard IEEE 24-bus test system and Roy Billiton IEEE 31-bus test system.

Wide-area monitoring, protection, and control (WAMPAC) is a key factor in the implementation of smart transmission grids. WAMPAC has a crucial role in detection and prevention of widespread events with an ultimate goal of improving the electricity service reliability. Several mathematical techniques were proposed for the optimal phasor measurement units (PMU) placement (OPP) problem which represents the first step toward the development of WAMPAC. These techniques consider either the solution to the network observability or realization of specific PMU applications. This paper proposes a multi-objective framework for OPP which emphasizes the reliability of power systems. The objective functions minimize the investment cost and maximize the system reliability. Since the objectives are in conflict, the non-dominated sorting genetic algorithm II approach is adopted as an intelligent state sampling tool to find non-dominated solutions (Pareto front). The final placement scheme among Pareto points is chosen by a Fuzzy decision making approach.

In this paper, a comprehensive model is proposed to design a network for multi-period, multi-echelon, and multi-product inventory controlled the supply chain. Various marketing strategies and guerrilla marketing approaches are considered in the design process under the static competition condition. The goal of the proposed model is to efficiently respond to the customers’ demands in the presence of the pre-existing competitors and the price inelasticity of demands. The proposed optimization model considers multiple objectives that incorporate both market share and total profit of the considered supply chain network, simultaneously. To tackle the proposed multi-objective mixed-integer nonlinear programming model, an efficient hybrid meta-heuristic algorithm is developed that incorporates a Taguchi-based non-dominated sorting genetic algorithm-II and a particle swarm optimization. A variable neighborhood decomposition search is applied to enhance a local search process of the proposed hybrid solution algorithm. Computational results illustrate that the proposed model and solution algorithm are notably efficient in dealing with the competitive pressure by adopting the proper marketing strategies.