Models and algorithms for solving combined vehicle and crew scheduling problems with rest constraints : an application to road feeder service planning in air cargo transportation

We present a mathematical model and two algorithms for solving a complex combined vehicle and crew scheduling problem. The problem arises in the area of road feeder service (RFS) for air cargo transportation where cargo airlines engage specifically equipped RFS-carriers to serve so-called lines, i.e. regular weekly patterns of trips starting and ending at the central hub, respectively. The complexity of the problem stems from the time windows, the rest regulations for drivers and the highly heterogenous requirements with respect to the fleet. The model can be applied to different planning scenarios at the RFS-carrier as well as the airline. The model and method has been incorporated into a decision support system called block.buster where sequences of single trips are combined to feasible blocks starting and ending at the hub and then combined to feasible vehicle round trips.     

Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

Owing to massive technological developments in Internet of Things (IoT) and cloud environment, cloud computing (CC) offers a highly flexible heterogeneous resource pool over the network, and clients could exploit various resources on demand. Since IoT-enabled models are restricted to resources and require crisp response, minimum latency, and maximum bandwidth, which are outside the capabilities. CC was handled as a resource-rich solution to aforementioned challenge. As high delay reduces the performance of the IoT enabled cloud platform, efficient utilization of task scheduling (TS) reduces the energy usage of the cloud infrastructure and increases the income of service provider via minimizing processing time of user job. Therefore, this article concentration on the design of an oppositional red fox optimization based task scheduling scheme (ORFO-TSS) for IoT enabled cloud environment. The presented ORFO-TSS model resolves the problem of allocating resources from the IoT based cloud platform. It achieves the makespan by performing optimum TS procedures with various aspects of incoming task. The designing of ORFO-TSS method includes the idea of oppositional based learning (OBL) as to traditional RFO approach in enhancing their efficiency. A wide-ranging experimental analysis was applied on the CloudSim platform. The experimental outcome highlighted the efficacy of the ORFO-TSS technique over existing approaches.