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Introduction/purpose: Tracked vehicles play a vital role across various domains, from military operations to construction and agriculture. This study focuses on improving the efficiency of tracked vehicles by optimizing both gear ratios and gear-shifting strategies while preserving other performance aspects. Methods: The optimization process involves a genetic algorithm for determining optimal gear ratios, considering performance constraints. Furthermore, the paper introduces a gear-shifting optimization algorithm aimed at enhancing fuel economy to the maximum, while allowing for a valid comparison between two sets of gear ratios. Results: Optimizing gear ratios leads to substantial reductions in fuel consumption, as the engine operates within more efficient regions. Additionally, the optimized gear-shifting strategy further enhances efficiency, resulting in a fuel consumption reduction exceeding 12%, when combined with the optimized gear ratios. Conclusions: This paper offers a direct and robust approach for optimizing powertrain gear ratios and gear-shifting strategies in tracked vehicles. The results demonstrate significant improvements in fuel efficiency without compromising other critical vehicle performance parameters. [ABSTRACT FROM AUTHOR]

Robots are now widely used in product disassembly lines, which significantly improves end-of-life (EOL) product disassembly efficiency. Most of the current research on disassembly line balancing problems focuses on decomposing one product. More than one product can be disassembled concurrently, which can further improve the efficiency. Moreover, uncertainty such as the depreciation of EOL products, may result in disassembly failure. In this research, a stochastic multi-product robotic disassembly line balancing model is established using an AND/OR graph. It takes the precedence relationship, cycle constraint, and disassembly failure into consideration to maximize the profit and minimize the energy consumption for disassembling multiple products. A Pareto-improved multi-objective brainstorming optimization algorithm combined with stochastic simulation is proposed to solve the problem. Furthermore, by conducting experiments on some real cases and comparing with four state-of-the-art multi-objective optimization algorithms, i.e., the multi-objective discrete gray wolf optimizer, artificial bee colony algorithm, nondominated sorting genetic algorithm II, and multi-objective evolutionary algorithm based on decomposition, this paper validates its excellent performance in solving the concerned problem. [ABSTRACT FROM AUTHOR]

Cloud computing, a technology providing flexible and scalable computing resources, faces a critical challenge in task scheduling, directly impacting system performance and customer satisfaction. The task scheduling problem's NP-completeness makes finding solutions difficult. To address this, researchers propose a hybrid algorithm combining the Grey Wolf Optimization Algorithm (GWO) and the Genetic Algorithm (GA). The hybrid GWO-GA algorithm targets multi-objective task scheduling in cloud computing, aiming to minimize makespan, energy consumption, and cost. Enhancements to the proposed algorithm involve leveraging the genetic algorithm's crossover and mutation operator. Furthermore, the GA-based GWO algorithm's faster convergence in large scheduling problems presents an advantage. Evaluation using the Cloudsim toolkit demonstrates the proposed algorithm's efficiency compared to existing methods. We have used both synthetic and real world data set. The results are verified using the Analysis of Variance (ANOVA) statistical tool. Experimental results showcase its effectiveness in minimizing makespan, energy consumption, and computational cost. Particularly, the proposed algorithm outperforms traditional GWO, GA, and PSO algorithms in terms of makespan, cost, and energy consumption, achieving reductions of 19%, 21%, and 15%, respectively, when compared to each approach. Additionally, it yields energy savings of 17%, 19%, and 23% compared to GWO, GA, and PSO, respectively, while reducing total scheduling costs by 13%, 17%, and 22%. These findings demonstrate the efficacy of the proposed algorithm in addressing the task scheduling problem in cloud computing environments. • A hybrid task scheduling strategy based on GA and GWO is proposed. • Introducing mutation and crossover to preserve wolf diversity and boost local search in the algorithm. • A novel fitness function is proposed considering multiple objectives simultaneously. • We have synthetic and real world data set for the simulation. • Results with CloudSim show the enhanced performance of proposed algorithm. [ABSTRACT FROM AUTHOR]

• An energy model is set up based on energy consumption analyses of machining processes; • NC codes are structurally evaluated and then popularised into the energy model; • A hybrid GA-ACA algorithm is designed to minimise air-cutting toolpaths to optimise the energy model. Machining processes comprise numerous energy consumption activities. Given the significance of the circular economy and manufacturing sustainability to modern societies, it is paramount to design effective methodologies to accomplish energy-efficient machining processes. With this aim, this research presents a new approach of energy consumption optimisation for machining processes based on numerical control (NC) programs. In the approach, the following innovative characteristics are exhibited: (i) An energy model is systematically established based on a detailed analysis of energy consumption activities in machining processes; (ii) NC programs for specific machining processes are assessed in detail and popularised into the energy model for instantiation; (iii) An optimisation algorithm hybridising the genetic algorithm and the ant colony algorithm is designed to minimise air-cutting toolpaths to optimise the energy model. Two case studies were conducted to validate the presented approach. The case studies revealed that the accuracy of the energy model was 95.3% of the actual energy consumption. The studies also showed that, based on the optimised energy model, the total length of air-cutting toolpaths was reduced by 43.8%, and the total machining time was diminished by 25.8%. It can be concluded that the developed approach can achieve substantial energy savings, and therefore it is highly promising to support machining industries to meet their sustainable targets. [ABSTRACT FROM AUTHOR]

The objective of this paper is to focus on the technical issues of single-stage thermo-electric coolers (TECs) and two-stage TECs and then apply new methods in optimizing the dimensions of TECs. In detail, some metaheuristics--simulated annealing (SA) and differential evolution (DE)--are applied to search the optimal design parameters of both types of TEC, which yielded cooling rates and coefficients of performance (COPs) individually and simultaneously. The optimization findings obtained by using SA and DE are validated by applying them in some defined test cases taking into consideration non-linear inequality and non-linear equality constraint conditions. The performance of SA and DE are verified after comparing the findings with the ones obtained applying the genetic algorithm (GA) and hybridization technique (HSAGA and HSADE). Mathematical modelling and parameter setting of TEC is combined with SA and DE to find better optimal findings. The work revealed that SA and DE can be applied successfully to solve single-objective and multi-objective TEC optimization problems. In terms of stability, reliability, robustness and computational efficiency, they provide better performance than GA. Multi-objective optimizations considering both objective functions are useful for the designer to find the suitable design parameters of TECs which balance the important roles of cooling rate and COP. [ABSTRACT FROM AUTHOR]