SummaryElectric vehicles (EVs) are drastically growing, and their emergence is more popular. Moreover, with the constraint driving range, the batteries of these vehicles are rechargeable throughout the routes in various situations. Here, a new problem is formulated with the inclusion of both battery swapping and partial recharging decisions. The energy level and the routing directions are more important, and the investigators intend to handle these issues effectually. However, the major constraint is routing and time window. Here, a heuristic model (LTS‐RSS) is designed to find the best matching solution. A novel Random Sub‐Space (RSS) and local Tabu search (LTS) are modeled to handle these issues. The probabilistic model of RSS is anticipated by integrating the consequences of time windows and distances. The experimentation is done with an online database and used for performance validation. The outcomes show that the newly modeled (LTS‐RSS) approach enhances the significance of the model. The outcomes of all the instances with diverse strategies enhance the model's robustness and stability for resolving these issues. The empirical analysis is done with MATLAB 2020b simulator, and metrics like optimization of routing solution, the best vehicle, best distance, and numbers of vehicles are evaluated, and the outcomes are compared with various other approaches.

Heterogeneous multi-cloud environments make use of a collection of varied performance rich cloud resources, linked with huge-speed, performs varied applications which are of computational nature. Applications require distinct computational features for processing. Heterogeneous multi-cloud domain well suits to satisfy the computational need of very big diverse nature of collection of tasks. Mapping problem provides an optimal solution in scheduling tasks to distributed heterogeneous clouds is termed NP-complete, which leads to the ultimate establishment of heuristic problem solving technique. Identifying the heuristic which is appropriate and best still exists as a complicated problem. In this paper, to address scheduling collection of 'n' tasks in two groups among a set of 'm' clouds, we propose three heuristics PTL (Pair-Task Threshold Limit), PTMax-Min, and PTMin-Max. Firstly to determine the tasks scheduling order, proposed heuristics based on the tasks attributes calculate tasks threshold value. Tasks sorted in descending value of threshold. Group G1 comprises tasks ordered in descending value of threshold. Group G2 comprises remaining tasks ordered in ascending value of threshold. Secondly, tasks form Group 1 are scheduled rst based on minimum completion time, and then tasks in Group 2 are scheduled. The proposed heuristicsare compared with existing heuristics, namely MCT, MET, Min-Min using benchmark dataset. Heuristics PTL, PTMax-Min, and PTMin-Max bring out reduced makespan compared to MCT, MET, and Min-min.

Heterogeneous multi-cloud environments make use of a collection of varied performance rich cloud resources, linked with huge-speed, performs varied applications which are of computational nature. Applications require distinct computational features for processing. Heterogeneous multi-cloud domain well suits to satisfy the computational need of very big diverse nature of collection of tasks. Mapping problem provides an optimal solution in scheduling tasks to distributed heterogeneous clouds is termed NP-complete, which leads to the ultimate establishment of heuristic problem solving technique. Identifying the heuristic which is appropriate and best still exists as a complicated problem. In this paper, to address scheduling collection of ‘n’ tasks in two groups among a set of 'm' clouds, we propose three heuristics PTL (Pair-Task Threshold Limit), PTMax-Min, and PTMin-Max. Firstly to determine the tasks scheduling order, proposed heuristics based on the tasks attributes calculate tasks threshold value. Tasks sorted in descending value of threshold. Group G1 comprises tasks ordered in descending value of threshold. Group G2 comprises remaining tasks ordered in ascending value of threshold. Secondly, tasks form Group 1 are scheduled first based on minimum completion time, and then tasks in Group 2 are scheduled. The proposed heuristicsare compared with existing heuristics, namely MCT, MET, Min-Min using benchmark dataset. Heuristics PTL, PTMax-Min, and PTMin-Max bring out reduced makespan compared to MCT, MET, and Min-min.