Severe environmental problems have made green scheduling an emerging research hotspot. In this paper, a permutation flow shop energy-efficient scheduling problem that considers multiple criteria is investigated. The aim is to find the optimal job processing sequence and conveyor speed that minimise both the makespan and total energy consumption. In addition to two types of common criteria, namely, machine-based criterion (i.e. sequence-dependent setup time) and energy-based criteria (including both the transportation time control strategy and machine shutdown strategy), a human-based criterion (i.e. a position-based learning effect) is introduced. A bi-objective programming model is developed, and a multi-objective iterated greedy (MOIG) is designed to reach the Pareto front of the model. Considering that there are two types of decisions in the model (i.e. job sequence and conveyor speed), two algorithm alternatives are designed based on the job sequence and conveyor speed, respectively. Meanwhile, an acceptance criterion with advantages in terms of the convergence speed and solution diversity is proposed. Existing algorithms, including NSGA-II and MOEA/D, are introduced to evaluate the performance of the MOIG. The results emphasise the efficiency of the MOIG. Overall, the model and MOIG effectively improve the green efficiency of enterprises and can reasonably control operating costs.

With the enormous scale of remote sensing imagery generation, on-orbit computing has become a crucial paradigm to enable near real-time processing. Due to the limited onboard resources and on-orbit power supply, satellite edge computing (SEC) is developed for satellite-ground collaboration, aiding on-orbit computation. However, the intermittent satellite-to-ground transmission link poses an efficiency challenge when collaborating SEC resources. Therefore, this paper proposes a Satellite Edge Computing resource scheduling technique for On-orbit Remote Sensing imagery processing (SECORS). First, we design a remote sensing mission-specific SEC architecture, which involves an offline-online satellite working mode. Subsequently, a computational resource scheduling model (SEC-RSM) is established, including the directed acyclic graph (DAG) model and mathematical problem formulation. Next, to obtain effective scheduling solutions, we develop an end-to-end algorithm leveraging the multi-agent proximal policy optimization and heuristic rule of the earliest finish time (SEC-MPH). Finally, we build a simulation SEC platform to carry out experiments and implement several methods as the comparison including multi-objective evolutionary algorithms, deep reinforcement learning approaches, and the scheme without optimization (baseline). Simulation results show that SECORS achieves 68.87% and 66.60% reductions in time and energy for on-orbit computation. Moreover, our method improves the energy efficiency ratio by three times and achieves high processing capacity with 548 pixels per unit of power (W) and time (ms).

Nowadays, the proliferation of small satellites brings the skyrocketing rise in space data, especially the shift to on-orbit computing needs. On one hand, with the increasing volume of data generation, like high-resolution remote sensing images, on-orbit computing produces near real-time onboard solutions and quick responses. However, constrained by the limited size and energy supply of satellites, achieving energy-efficient on-orbit computing remains a crucial challenge. In this article, an on-orbit remote sensing image processing complex task scheduling model facing heterogeneous multiprocessor system (HMPS) is proposed. First, aiming at accelerating image processing, we establish a novel parallel task execution model using directed acyclic graph (DAG) to universally describe typical missions, i.e., cloud detection, geometric correction, and image classification. Subsequently, a mathematical task scheduling formulation is defined to calculate the makespan, and total energy consumption (TEC) required when executing DAG on HMPS. Second, a new Pareto-based iterated greedy optimizer (PIGO) is devised to complete the energy- and time-efficient task execution and resource allocation on HMPS through confined inserting mutation, destruction-reconstruction, and local search. Finally, we build an emulated on-orbit HMPS to conduct experiments. The results show that, in comparison with the scheme without model scheduling, the most savings of around 51% and 54% in makespan and TEC, respectively, are achieved by the proposed model. Moreover, the HMPS configured with our methodology can obtain 2.2× improvement in energy efficiency and process up to 2.56×10 ⁵ pixels per unit of power (W) and time (s).

With the explosive growth of Internet of Things (IoT) devices and the enormous data they generate, edge computing with heterogeneous architecture has been a research hotspot. Limited by a lack of power supply in the edge infrastructure, dynamic voltage and frequency scaling (DVFS)-based optimization becomes an effective way to balance energy cost and delay. However, achieving optimal performance efficiency in power-constrained edge computing environments remains a significant challenge, particularly in processing the tremendous requests from IoT endpoints. Therefore, we propose an energy-efficient task scheduling and resource allocation technique for edge-heterogeneous computing. Specifically, we first utilize the directed acyclic graph (DAG) to describe IoT requests, and formulate task scheduling and resource allocation as a multiobjective mathematical programming model. Second, a local search enhanced nondominated sorting genetic algorithm-II (LS-NSGA) is designed as the solution method, where LS seeks to improve the problem solving capacity of NSGA-II. Additionally, we develop a strategy that automatically adjusts the DVFS setting for each processor in existing scheduling solutions, exploiting the potential energy reduction. Experimental results indicate that LS-NSGA outperforms existing state-of-the-art algorithms. In the best-case scenario, our method achieves 52% lower execution time and 73% lower energy cost than alternative approaches. Moreover, the ablation study shows that the proposed adaptive DVFS strategy can achieve additional energy savings of approximately 4%–7% for various task-scale problems.

Whale optimization algorithm (WOA), known as a novel nature-inspired swarm optimization algorithm, demonstrates superiority in handling global continuous optimization problems. However, its performance deteriorates when applied to large-scale complex problems due to rapidly increasing execution time required for huge computational tasks. Based on interactions within the population, WOA is naturally amenable to parallelism, prompting an effective approach to mitigate the drawbacks of sequential WOA. In this paper, field programmable gate array (FPGA) is used as an accelerator, of which the high-level synthesis utilizes open computing language (OpenCL) as a general programming paradigm for heterogeneous System-on-Chip. With above platform, a novel parallel framework of WOA named PWOA is presented. The proposed framework comprises two feasible parallel models called partial parallel and all-FPGA parallel, respectively. Experiments are conducted by performing WOA on CPU and PWOA on OpenCL-based FPGA heterogeneous platform, to solve ten well-known benchmark functions. Meanwhile, other two classic algorithms including particle swarm optimization (PSO) and competitive swarm optimizer (CSO) are adopted for comparison. Numerical results show that the proposed approach achieves a promising computational performance coupled with efficient optimization on relatively large-scale complex problems.