GRASP Approach Research Articles

Extended aerosol and surface characterization from S5P/TROPOMI with GRASP algorithm. Part II: Global validation and Intercomparison

This paper is the second part of companion papers describing the development of GRASP approach for aerosol and surface retrieval from Sentinel-5P/TROPOMI. Here we focus on the S5P/TROPOMI GRASP aerosol and surface products global validation and systematic intercomparison with other products from independent instruments and algorithms. Specifically, we have validated the S5P/TROPOMI GRASP, Suomi-NPP/VIIRS DB and MODIS/TERRA DT + DB aerosol products with the ground-based AERONET referenced measurements using the same methodology and intercompare the validation results. In addition, the global pixel-to-pixel intercomparisons of the aerosol products (AOD, fine/coarse mode AOD and SSA) are performed over different surfaces, i.e., ocean and land surface with different NDVIs. Besides, we compared the S5P/TROPOMI GRASP, MODIS MCD43 surface BRDF/albedo as well as OMI, GOME-2 and SCIAMACHY Lambertian-Equivalent Reflectivity (LER) albedo climatology developed by Royal Netherlands Meteorological Institute (KNMI) with the surface reference dataset generated based on the synergetic retrieval of AERONET and S5P/TROPOMI measurements. Finally, the intercomparisons of the surface BRDF and albedo datasets were performed globally at the UV, VIS, NIR and SWIR parts of the spectrum. Overall, generally good agreement was observed between independent aerosol and surface datasets with a high percentage of pixels satisfying the Optimal and Target requirements. We would emphasize two advantages for TROPOMI/GRASP aerosol and surface products: (i) it provides spectral AOD together with detailed aerosol properties, such as fine/coarse mode AOD, spectral AAOD and SSA at UV, VIS, NIR and SWIR wavelengths, which are important for constraining aerosol environmental and climate effects; (ii) the TROPOMI/GRASP aerosol and surface products are globally retrieved simultaneously in a fully consistent manner.

A GRASP method for the Bi-Objective Multiple Row Equal Facility Layout Problem

The Bi-Objective Multiple Row Equal Facility Layout Problem considers both quantitative and qualitative objectives that are very useful in many scenarios like the factory design. In this work, a new multi-objective GRASP approach is proposed which applies an ensemble of four different constructive methods followed by the combination of two local search procedures, improving the results from the state of the art. Due to the superiority of this proposal, a new dataset of larger problem instances is generated, providing detailed metrics of the obtained solutions.

A GRASP algorithm for the bus crew scheduling problem

This paper proposes a GRASP approach for solving the Bus Crew Scheduling Problem (BCSP) to find high-quality solutions within short computing times. The BCSP described the process related to the assignment of drivers and conductors to a bus company's regular daily operation of a mass transit system, seeking to minimize the cost of operation and, at the same time, the improvement of the working environment by considering the satisfaction of the drivers with the assigned shifts. The BCSP has drivers in charge of covering the demand for shifts, with an assignment that contains several constraints, such as minimum and maximum work blocks, minimum rest days, and shift sequences that must not be assigned. The former GRASP algorithm is proposed with a constructive procedure, a solution repair procedure, and two local search operators. Classical instances from the literature have been adapted for the shift assignment problem by adding a satisfaction variable. Besides, the proposed approach has been tested for a real company operating articulated and feeder vehicles. The results show that the satisfaction function adds value to the assignments, substantially improving the work environment and generating favorable results in terms of time and quality of the solution.

Efficient GRASP solution approach for the Prisoner Transportation Problem

Every day, resources are allocated to the transportation of prisoners to and from services such as hospitals and court proceedings. The optimal allocation of the resources – such as protected vehicles, drivers, and guards – is a complex task that requires significant efforts of human planners. For this reason, a recent paper described the Prisoner Transportation Problem (PTP), that aims to optimize the transportation of convicts between origin–destination pairs within pre-defined time windows. In addition to the similarities with the Vehicle Routing Problem with Time Windows, the PTP presents many additional challenges induced by the use of multi-comparted vehicles and incompatibility constraints, related to the safety of both prisoners and personnel.To efficiently solve the PTP, this paper proposes a GRASP approach, that was tested on a dataset of real-size instances, and the results show significant improvements with respect to the only competing approach proposed in the literature, both in terms of solution quality and computational times required to find them.

Research on Robot Monocular Vision-Based 6DOF Object Positioning and Grasping Approach Combined With Image Generation Technology

Research on Robot Monocular Vision-Based 6DOF Object Positioning and Grasping Approach Combined With Image Generation Technology

GRASP and VNS approaches for a vehicle routing problem with step cost functions

GRASP and VNS approaches for a vehicle routing problem with step cost functions

Performance-Cost Trade-Off in Auto-Scaling Mechanisms for Cloud Computing.

Cloud computing has been widely adopted over the years by practitioners and companies with a variety of requirements. With a strong economic appeal, cloud computing makes possible the idea of computing as a utility, in which computing resources can be consumed and paid for with the same convenience as electricity. One of the main characteristics of cloud as a service is elasticity supported by auto-scaling capabilities. The auto-scaling cloud mechanism allows adjusting resources to meet multiple demands dynamically. The elasticity service is best represented in critical web trading and transaction systems that must satisfy a certain service level agreement (SLA), such as maximum response time limits for different types of inbound requests. Nevertheless, existing cloud infrastructures maintained by different cloud enterprises often offer different cloud service costs for equivalent SLAs upon several factors. The factors might be contract types, VM types, auto-scaling configuration parameters, and incoming workload demand. Identifying a combination of parameters that results in SLA compliance directly in the system is often sophisticated, while the manual analysis is prone to errors due to the huge number of possibilities. This paper proposes the modeling of auto-scaling mechanisms in a typical cloud infrastructure using a stochastic Petri net (SPN) and the employment of a well-established adaptive search metaheuristic (GRASP) to discover critical trade-offs between performance and cost in cloud services.The proposed SPN models enable cloud designers to estimate the metrics of cloud services in accordance with each required SLA such as the best configuration, cost, system response time, and throughput.The auto-scaling SPN model was extensively validated with 95% confidence against a real test-bed scenario with 18.000 samples. A case-study of cloud services was used to investigate the viability of this method and to evaluate the adoptability of the proposed auto-scaling model in practice. On the other hand, the proposed optimization algorithm enables the identification of economic system configuration and parameterization to satisfy required SLA and budget constraints. The adoption of the metaheuristic GRASP approach and the modeling of auto-scaling mechanisms in this work can help search for the optimized-quality solution and operational management for cloud services in practice.

A Grasp Approach for the Energy Minimizing Electric Vehicle Routing Problem with Drones

A Grasp Approach for the Energy Minimizing Electric Vehicle Routing Problem with Drones

A Real-Time Robotic Grasping Approach With Oriented Anchor Box

Grasping is an essential skill for robots to interact with humans and the environment. In this paper, we build a vision-based, robust, and real-time robotic grasping approach with fully convolutional neural network. The main component of our approach is a grasp detection network with oriented anchor boxes as detection priors. Because the orientation of detected grasps is significant, which determines the rotation angle configuration of the gripper, we propose the orientation anchor box mechanism to regress grasp angle based on predefined assumption instead of classification or regression without any priors. With oriented anchor boxes, the grasps can be predicted more accurately and efficiently. Besides, to accelerate the network training and further improve the performance of angle regression, angle matching is proposed during training instead of Jaccard index matching. Fivefold cross-validation results demonstrate that our proposed algorithm achieves an accuracy of 98.8% and 97.8% in image-wise split and object-wise split, respectively, and the speed of our detection algorithm is 67 frames per second (FPS) with GTX 1080Ti, outperforming all the current state-of-the-art grasp detection algorithms on Cornell Dataset both in speed and accuracy. Robotic experiments demonstrate the robustness and generalization ability in unseen objects and real-world environment, with the average success rate of 90.0% and 84.2% of familiar things and unseen things, respectively, on Baxter robot platform.

Multiobjective optimization for complex flexible job-shop scheduling problems

In this paper, we are concerned with the resolution of a multiobjective complex job-shop scheduling problem stemming from semiconductor manufacturing. To produce feasible and industrially meaningful schedules, this paper extends the recently proposed batch-oblivious approach by considering unavailability periods and minimum time lags and by simultaneously optimizing multiple criteria that are relevant in the industrial context. A novel criterion on the satisfaction of production targets decided at a higher level is also proposed. Because the solution approach must be embedded in a real-time application, decision makers must express their preferences before the optimization phase. In addition, a preference model is introduced where trade-off is only allowed between some criteria. Two a priori multiobjective extensions of Simulated Annealing are proposed, which differ in how the simultaneous use of a lexicographic order and weights is handled when evaluating the fitness. A known a posteriori approach of the literature is used as a benchmark. All the metaheuristics are embedded in a Greedy Randomized Adaptive Search Procedure. The different versions of the archived GRASP approach are compared using large industrial instances. The numerical results show that the proposed approach provides good solutions regarding the preferences. Finally, the comparison of the optimized schedules with the actual factory schedules shows the significant improvements that our approach can bring.

6DOF Object Positioning and Grasping Approach for Industrial Robots Based on Boundary Point Cloud Features

For the three-dimensional (3D) pose estimation of metal blank casts estimate in industrial production process, a novel 6-degree-of-freedom (6DOF) positioning and grasping approach for industrial robots based on boundary features and combining structured light 3D vision and the point cloud matching method is proposed. The proposed approach first uses the Gray code plus grating phase-shift algorithm to reconstruct the 3D surface information of the object. Subsequently, an improved point pair feature (PPF) matching location method based on point cloud boundary extraction is proposed to realize the accurate 6DOF pose estimation of the object. In this method, the point cloud boundary feature is used to optimize the PPF algorithm. Finally, by combining with the point cloud preprocessing process and the improved PPF matching location method, the 6DOF pose positioning of metal blank casting by industrial robots is realized. The proposed approach can accurately complete pose measurement and positioning of objects placed randomly in an open environment. The experimental results demonstrate that the proposed PPF matching location method significantly improves both matching speed and accuracy compared to the traditional PPF algorithm.

G-CREM: A GRASP approach to solve the container relocation problem for multibays

The Container Relocation Problem for multiple bays consists of finding the minimum number of moves to load a set of stacked containers on a ship according to a given loading sequence, and in minimizing the crane’s working time for an entire yard of multiple bays. This is a crucial problem for every commercial port in the world given the maximum time requirements and the costs associated with the containers’ retrieval. In this paper, we propose a Greedy Randomized Adaptive Search Procedure to solve this problem. We use a myopic function specially designed to produce feasible candidate solutions with a structure that allows a local search procedure to optimize relocations. In order to validate our approach, we use a large set of well-known Container Relocation Problems for multiples bays, as well as a statistical analysis of our results. Our experiments show new bounds for various instances.

Teleoperation Assistance with Object Recognition and Visual Feedback for Grasping Approach

Teleoperation Assistance with Object Recognition and Visual Feedback for Grasping Approach

Greedy Algorithm for Minimizing the Cost of Routing Power on a Digital Microgrid

In this paper, we propose the greedy smallest-cost-rate path first (GRASP) algorithm to route power from sources to loads in a digital microgrid (DMG). Routing of power from distributed energy resources (DERs) to loads of a DMG comprises matching loads to DERs and the selection of the smallest-cost-rate path from a load to its supplying DERs. In such a microgrid, one DER may supply power to one or many loads, and one or many DERs may supply the power requested by a load. Because the optimal method is NP-hard, GRASP addresses this high complexity by using heuristics to match sources and loads and to select the smallest-cost-rate paths in the DMG. We compare the cost achieved by GRASP and an optimal method based on integer linear programming on different IEEE test feeders and other test networks. The comparison shows the trade-offs between lowering complexity and achieving optimal-cost paths. The results show that the cost incurred by GRASP approaches that of the optimal solution by small margins. In the adopted networks, GRASP trades its lower complexity for up to 18% higher costs than those achieved by the optimal solution.

Hybridizing simulated annealing with variable neighborhood search for bipartite graph crossing minimization

Given a bipartite graph, the problem we address, called the bipartite graph crossing minimization problem (BGCMP), is to find an embedding the parts of the graph along two parallel lines so that the number of edge crossings is minimized. It is assumed that each edge is drawn as a straight line segment and edges sharing an end vertex do not cross. We propose an approach for the BGCMP, which combines a simulated annealing (SA) method with a variable neighborhood search (VNS) scheme. These two algorithms are executed iteratively. At each iteration, the solution produced by SA is submitted as input to the VNS component of the approach. Our VNS algorithm uses a local search technique which is based on a fast insertion neighborhood exploration procedure. We show that the time complexity of this procedure is O(n2), where n is the order of the graph. Another fast procedure is proposed for computing the gain in the objective function value obtained by swapping positions of two vertices. We experimentally compare our algorithm (called SA-VNS) against the tabu search algorithm as well as GRASP approach from the literature. Computational results are reported on four sets of bipartite graphs. The results demonstrate the superiority of SA-VNS over the state-of-the-art methods. The source code implementing SA-VNS is made publicly available as a benchmark for future comparisons.

A GRASP approach for solving the Blocks Relocation Problem with Stowage Plan

In this paper, we present a method for finding high quality solutions for the Blocks Relocation Problem with Stowage Plan (BRLP). The interest for this problem comes from the fact that previous research has shown that a significant amount of savings can be achieved, if the process of specifying the loading sequence takes into account both the state of the bay and the stowage plan. In this work, we propose a two step greedy algorithm for the BRLP. In the first one, a heuristic is used to select the next container to be loaded into the vessel. In the second step, a new heuristic is used to relocate obstructing containers. The solutions acquired in this way are improved using a correction procedure. The idea of the correction procedure is to use a heuristic approach to recognize undesirable properties of a solution and remove them. Finally, the method is extended towards the GRASP metaheuristic. Our computational experiments show that the proposed approach manages to significantly outperform existing methods for the BRLP.

A Self-Supervised Learning Manipulator Grasping Approach Based on Instance Segmentation

Automatic grasping is playing an important role in robotics, and traditional grasping approaches cannot deal with both occlusions and texture-less objects well. To improve the stability and accuracy of grasping in a novel and occlusion environment, a grasping approach based on instance segmentation and self-supervised learning pose estimation network is proposed to grasp objects with the manipulator in this paper. The approach can be divided into three phases: instance segmentation, pose estimation, and pose transformation. Instance segmentation predicts classification masks for each pixel. Masks which stand for the contour of objects provide a heuristic knowledge for the self-supervised learning pose estimation network. Pose estimation network has two fully connected layers and regards estimation as a self-supervised classification problem. Then, the pose can be transformed from value in pixel coordinates to actual value relative to the base coordinate of the manipulator. As a result, the manipulator can be operated to grasp by the given actual value of pose. With the help of the approach proposed in this paper, we improve the grasping accuracy by 35%, compared to the former grasping approach based on the pose estimation network on the grasping dataset of CMU. Besides, grasping with this approach on hardware also shows a high success rate. Therefore, the proposed approach is a more robust and more accurate way of grasping.

Robust Optimization for RCPSP Under Uncertainty

The aim of the present article is to optimize the robustness objective for the Resource-Constrained Project scheduling Problem (RCPSP) dealing with activity duration uncertainty. The studied robustness consists in minimizing the worst-case performance, referred to as the min-max robustness objective, among a set of initial scenarios. We propose an enhanced GRASP approach as a solution to the given scenario-based robust model. This approach is based on different priority rules in the construction phase and a forwardbackward heuristic in the improvement phase. We investigate two different benchmark data sets, the Patterson set and the PSPLIB J30 set. Experiments show that the proposed enhanced GRASP outperforms the basic procedure, and a based-evolutionary algorithm, in robustness optimization.

Respiratory Motion-Resolved Compressed Sensing Reconstruction of Free-Breathing Radial Acquisition for Dynamic Liver Magnetic Resonance Imaging.

This study aimed to demonstrate feasibility of free-breathing radial acquisition with respiratory motion-resolved compressed sensing reconstruction [extra-dimensional golden-angle radial sparse parallel imaging (XD-GRASP)] for multiphase dynamic gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced liver imaging, and to compare image quality to compressed sensing reconstruction with respiratory motion-averaging (GRASP) and prior conventional breath-held Cartesian-sampled data sets [BH volume interpolated breath-hold examination (VIBE)] in same patients. In this Health Insurance Portability and Accountability Act-compliant prospective study, 16 subjects underwent free-breathing continuous radial acquisition during Gd-EOB-DTPA injection and had prior BH-VIBE available. Acquired data were reconstructed using motion-averaging GRASP approach in which consecutive 84 spokes were grouped in each contrast-enhanced phase for a temporal resolution of approximately 14 seconds. Additionally, respiratory motion-resolved reconstruction was performed from the same k-space data by sorting each contrast-enhanced phase into multiple respiratory motion states using compressed sensing algorithm named XD-GRASP, which exploits sparsity along both the contrast-enhancement and respiratory-state dimensions.Contrast-enhanced dynamic multiphase XD-GRASP, GRASP, and BH-VIBE images were anonymized, pooled together in a random order, and presented to 2 board-certified radiologists for independent evaluation of image quality, with higher score indicating more optimal examination. The XD-GRASP reconstructions had significantly (all P < 0.05) higher overall image quality scores compared to GRASP for early arterial (reader 1: 4.3 ± 0.6 vs 3.31 ± 0.6; reader 2: 3.81 ± 0.8 vs 3.38 ± 0.9) and late arterial (reader 1: 4.5 ± 0.6 vs 3.63 ± 0.6; reader 2: 3.56 ± 0.5 vs 2.88 ± 0.7) phases of enhancement for both readers. The XD-GRASP also had higher overall image quality score in portal venous phase, which was significant for reader 1 (4.44 ± 0.5 vs 3.75 ± 0.8; P = 0.002). In addition, the XD-GRASP had higher overall image quality score compared to BH-VIBE for early (reader 1: 4.3 ± 0.6 vs 3.88 ± 0.6; reader 2: 3.81 ± 0.8 vs 3.50 ± 1.0) and late (reader 1: 4.5 ± 0.6 vs 3.44 ± 0.6; reader 2: 3.56 ± 0.5 vs 2.94 ± 0.9) arterial phases. Free-breathing motion-resolved XD-GRASP reconstructions provide diagnostic high-quality multiphase images in patients undergoing Gd-EOB-DTPA-enhanced liver examination.

A matheuristic algorithm for the three-dimensional loading capacitated vehicle routing problem (3L-CVRP)

This paper presents a hybrid algorithm for solving the Capacitated Vehicle Routing Problem with practical three-dimensional loading constraint. This problem is known as 3L-CVRP (Three-dimensional Loading Capacitated Vehicle Routing Problem). The proposed methodology consists of two phases. The first phase uses an optimization procedure based on cuts to obtain solutions for the well-known Capacitated Vehicle Routing Problem (CVRP). The second phase validates the results of the first phase of a GRASP algorithm (Greedy Randomized Adaptive Search Procedure). In particular, the GRASP approach evaluates the packing constraints for each performed route of the CVRP. The proposed hybrid algorithm uses a relaxation of the classical model of two sub-indices for the vehicle routing problem. Specifically different types of cuts are added: subtour elimination, capacity-cut constraints, and packing-cut constrains. The proposed algorithm is compared with the most efficient approaches for the 3L-CVRP on the set of benchmark instances considered in the literature. The computational results indicate that the proposed approach is able to obtain good solutions, improving some of the best-known solutions from the literature.