Simulated Instances Research Articles

Logistics for a fleet of drones for medical item delivery: A case study for Louisville, KY

Unmanned Aerial Vehicles, commonly referred to asdrones, have been widely acknowledged as a promising technology for the delivery of medical and aid packages in humanitarian and healthcare logistics. In this study, we develop an optimization model to optimize the logistics for a fleet of drones for timely delivery of medical items (e.g., medicines, test kits, and vaccines) to hard-to-access locations (i.e., rural and suburban areas). We propose a novel timeslot formulation that schedules and sequences a set of trips to serve several demand locations. On each trip, a drone starts from an urban provider, visits one or more charging platforms (if required), serves a clinic in rural or suburban areas, and returns to the corresponding provider. The problem consists of selecting locations for charging stations, assigning clinics to providers, and scheduling and sequencing the trips such that the total completion time to serve all demand points is minimized. To improve the computational efficiency of the solution method, we use a preprocessing procedure to reduce the solution space by eliminating the dominated trips from the pool of trips. A set of numerical experiments is performed on simulated instances and on a case study in Louisville, KY. The results unveil interesting insights into the logistics of the proposed drone delivery system.

Modeling of Formation and Evolution of Cracks in Zirconium-Based Claddings of Nuclear Fuel Rods Within DIONISIO 3.0

Various numerical models are developed that seek to reproduce, in a simulation instance, the formation and evolution of cracks in the claddings of nuclear fuel elements. The algorithms are based on the cohesive zone method within the finite element framework. When applied to simulations involving fracture mechanics, cohesive elements have various advantages, such as not needing to know the stress state in advance, representing the nucleation of the crack, and being able to reproduce the contact between the crack surfaces after fracture, with numerous application examples for ductile materials, including metals. The models developed were included in the DIONISIO 3.0 nuclear fuel code and compared with analytical test cases, controlled tests of nuclear materials, and a large set of experimental exercises with rods subjected to steep power ramps where breakages are caused due to contact with the pellets. Similarly, these new models were used in controlled experiments where the conditions of an accident type such as a loss-of-coolant accident are reproduced, analyzing the variation of the thermohydraulic, thermomechanical, and structural parameters of a rod.

Novel Criteria of Stability for Delayed Memristive Quaternionic Neural Networks: Directly Quaternionic Method

In this paper, we fixate on the stability of varying-time delayed memristive quaternionic neural networks (MQNNs). With the help of the closure of the convex hull of a set the theory of differential inclusion, MQNN are transformed into variable coefficient continuous quaternionic neural networks (QNNs). The existence and uniqueness of the equilibrium solution (ES) for MQNN are concluded by exploiting the fixed-point theorem. Then a derivative formula of the quaternionic function’s norm is received. By utilizing the formula, the M-matrix theory, and the inequality techniques, some algebraic standards are gained to affirm the global exponential stability (GES) of the ES for the MQNN. Notably, compared to the existing work on QNN, our direct quaternionic method operates QNN as a whole and markedly reduces computing complexity and the gained results are more apt to be verified. The two numerical simulation instances are provided to evidence the merits of the theoretical results.

Lifelong Multi-Agent Path Finding in Large-Scale Warehouses

Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their goal locations without collisions. In this paper, we study the lifelong variant of MAPF, where agents are constantly engaged with new goal locations, such as in large-scale automated warehouses. We propose a new framework Rolling-Horizon Collision Resolution (RHCR) for solving lifelong MAPF by decomposing the problem into a sequence of Windowed MAPF instances, where a Windowed MAPF solver resolves collisions among the paths of the agents only within a bounded time horizon and ignores collisions beyond it. RHCR is particularly well suited to generating pliable plans that adapt to continually arriving new goal locations. We empirically evaluate RHCR with a variety of MAPF solvers and show that it can produce high-quality solutions for up to 1,000 agents (= 38.9% of the empty cells on the map) for simulated warehouse instances, significantly outperforming existing work.

Scheduling project with stochastic durations and time-varying resource requests: A metaheuristic approach

This paper studies a resource constrained project scheduling problem (RCPSP) under dynamic environments. Along with stochastic activity-durations, resource requests and resource availabilities are also prone to vary with time (i.e., that is, dynamic). To account for stochastic activity durations and the resource dynamic, a chance constrained based mathematical model is proposed. To solve the developed model, an efficient genetic algorithm (GA) based memetic algorithm (MA) is proposed. The proposed algorithm has shown excellent performance for solving 2880 30-activity and 3600 120-activity instances for RCPSP with predefined but time-varying resource requests and availabilities (i.e., RCPSP/t setting). The proposed algorithm has also demonstrated an excellent performance for the extended RCPSP/t setting with stochastic activity durations (i.e., RCPSP/td). Due to the unavailability of any real benchmark instances for this extended RCPSP, this work proposes a pragmatic approach to generate simulated benchmark instances. Numerous time-dependent benchmark instances from the project scheduling library (PSPLIB) are solved to validate the proposed MA. Rigorous experimental analysis with a standard set of 2448 newly generated problem instances for 30, 60, 90, and 120 activities indicates that the proposed meta-heuristic can solve large projects with a reasonable computational burden.

Investigation of Multivariate Profile Monitoring on Complex Thin-walled Components Batch Machined using a Sliding Time Window Cluster Method

ABSTRACT Aiming at solving the problems of low precision of quality monitoring and low sensitivity of fluctuation detection in the batch machining process of complex thin-walled parts, the multivariate profile monitoring approach was proposed. The extended error stream method was used to establish profile representation of the multi-operation machining processing fluctuation, which could describe the fluctuation of multi-part production cycle and the decline of production line represented by error sources. On this basis, the monitoring statistics of the batch complex thin-wall part machining process were built based on one-step ahead forecast error and Tsquare (OSFE-T square). Then, the profile monitoring method was developed based on the OSFE-T square monitoring model of batch parts on the different operation using the sliding time window cluster-based method. Through the Monte-Carlo simulation, the fraction correctly classified, sensitivity, specificity, false positive and false negative of the quality monitoring method were analyzed respectively, which proved that the proposed method had better performance. Finally, amachining simulation instance was presented including ten operations demonstrated the effectiveness of the machining error control method. Compared with other alternative methods, the proposed method in this paper has more advantages in the sensitivity of fluctuation detection.

The Modeling and Simulation of Chaff Release at High Speed

Commonly, chaff cartridge release the chaff based on detonating the dynamite or blasting form projectile. In order to improve the scatter effect of chaff, a novel technique of chaff release is proposed, which is be carried by a given carrier revolving around an axis at high speed. And the model of chaff motion in the phase of dispersing rapidly is established on the basis of the analysing the influence of resistance, gravity and viscous force. Simulation instance show that the method based on revolving carrier is efficacious, and some useful revelation can be elicited from the simulation. The research in this paper has great significance for the research and engineering implementation of gun launched chaff jamming projectile.

Modeling of Downlink Interference in Massive MIMO 5G Macro-Cell.

Multi-beam antenna systems are the basic technology used in developing fifth-generation (5G) mobile communication systems. In practical implementations of 5G networks, different approaches are used to enable a massive multiple-input-multiple-output (mMIMO) technique, including a grid of beams, zero-forcing, or eigen-based beamforming. All of these methods aim to ensure sufficient angular separation between multiple beams that serve different users. Therefore, ensuring the accurate performance evaluation of a realistic 5G network is essential. It is particularly crucial from the perspective of mMIMO implementation feasibility in given radio channel conditions at the stage of network planning and optimization before commercial deployment begins. This paper presents a novel approach to assessing the impact of a multi-beam antenna system on an intra-cell interference level in a downlink, which is important for the accurate modeling and efficient usage of mMIMO in 5G cells. The presented analysis is based on geometric channel models that allow the trajectories of propagation paths to be mapped and, as a result, the angular power distribution of received signals. A multi-elliptical propagation model (MPM) is used and compared with simulation results obtained for a statistical channel model developed by the 3rd Generation Partnership Project (3GPP). Transmission characteristics of propagation environments such as power delay profile and antenna beam patterns define the geometric structure of the MPM. These characteristics were adopted based on the 3GPP standard. The obtained results show the possibility of using the presented novel MPM-based approach to model the required minimum separation angle between co-channel beams under line-of-sight (LOS) and non-LOS conditions, which allows mMIMO performance in 5G cells to be assessed. This statement is justified because for 80% of simulated samples of intra-cell signal-to-interference ratio (SIR), the difference between results obtained by the MPM and commonly used 3GPP channel model was within 2 dB or less for LOS conditions. Additionally, the MPM only needs a single instance of simulation, whereas the 3GPP channel model requires a time-consuming and computational power-consuming Monte Carlo simulation method. Simulation results of intra-cell SIR obtained this way by the MPM approach can be the basis for spectral efficiency maximization in mMIMO cells in 5G systems.

A new geometric model of serrated chip formation in high-speed machining

• The serrated chip geometry can be predicted effectively with average value. • The serrated chip formation can be modeled effectively in time domain without measurement of chip geometry. • The critical shear strain rate for the disappear of crack in machining of cast aluminum alloy are presented. The geometry of serrated chip is of paramount importance to the analysis of physical and thermal phenomena in chip formation process, and it can affect tool fatigue life, surface finish and geometric accuracy of the machining part. This paper presents a novel geometry model of serrated chip segment based on chip formation and cutting force in metal cutting by considering the difference between deformation of shear band and primary shear zone. Where, focusing on the high-speed machining, it key points about the geometry of chip segment, shear band angle, secondary shear angle, third shear angle (curvature angle of second shear band), the strain and its rate, and their evolutions in chip formation process are developed, respectively. Finally, the experimental and simulated instances can verify the proposed method. The results can show that this proposed model having a high accuracy can effectively reflect the evolution of chip segment. The shear angle is silent increased with increase of feed rate, while the difference between shear angles is decreasing with increase of feed rate. While the shear strain is increasing with the increasing feed rate and decreasing with increase of cutting depth, the cutting velocity can enlarge the difference. The critical shear strain rate and cutting velocity for the disappear of crack in chip for feed rate of 0.117mm, 0.14mm and 0.187mm are 1.566e 5 1/s and 32.71m/s, 1.486e 5 1/s and 35.98m/s, 1.279e 5 1/s and 39.25m/s, respectively, which decreased with increased of feed rate.

An Ant Colony Inspired Cache Allocation Mechanism for Heterogeneous Information Centric Network

The cache allocation of in-network caching is fundamental to the quality of service in heterogeneous information centric network (ICN). The aim of the cache allocation is to efficiently allocate appropriate cache capacity to each router for storing content, avoiding the long-distance transmission cost from clients to servers. Existing works focus on the analysis of network topology. However, this way is computation-expensive for such analysis, at the same time it cannot reach a completely optimal network, due to its used multiple conflicting performance metrics. In this paper, we propose an ant colony inspired cache allocation mechanism (ACCM) for heterogeneous ICN, which is able to deal with the cache budget constraint. Specifically, we first construct an evaluation model of network performance, in terms of hit ratio, energy consumption, time latency, and throughput. Then, based on the evaluation model, we devise an ant colony inspired cache allocation mechanism, where the ICN topology system is mapped into an ant colony system and the cache allocation process is simulated by the ant colony foraging behavior. Our theoretical analysis shows that the proposed mechanism can converge to the best solution. Finally, we conduct simulations on AttMpls and TW Telecom topologies with real datasets in YouTube. Simulation results show the effectiveness and efficiency of the proposed mechanism on the simulation instances in terms of hit ratio, energy consumption, time latency, and throughput.

A Mixture Model Parameters Estimation Algorithm for Inter-Contact Times in Internet of Vehicles

Communication opportunities among vehicles are important for data transmission over the Internet of Vehicles (IoV). Mixture models are appropriate to describe complex spatial-temporal data. By calculating the expectation of hidden variables in vehicle communication, Expectation Maximization (EM) algorithm solves the maximum likelihood estimation of parameters, and then obtains the mixture model of vehicle communication opportunities. However, the EM algorithm requires multiple iterations and each iteration needs to process all the data. Thus its computational complexity is high. A parameter estimation algorithm with low computational complexity based on Bin Count (BC) and Differential Evolution (DE) (PEBCDE) is proposed. It overcomes the disadvantages of the EM algorithm in solving mixture models for big data. In order to reduce the computational complexity of the mixture models in the IoV, massive data are divided into relatively few time intervals and then counted. According to these few counted values, the parameters of the mixture model are obtained by using DE algorithm. Through modeling and analysis of simulation data and instance data, the PEBCDE algorithm is verified and discussed from two aspects, i.e., accuracy and efficiency. The numerical solution of the probability distribution parameters is obtained, which further provides a more detailed statistical model for the distribution of the opportunity interval of the IoV.

Very Large-Scale Neighborhood Search for Steel Hot Rolling Scheduling Problem With Slab Stack Shuffling Considerations

We study a steel hot rolling scheduling (HRS) problem considering slab stack shuffling (SSS), which is a kind of key issues in the steel strip production. The HRS problem is to select suitable slabs for a predefined sequence of hot rolling slots, from their respective candidate slab sets so that the number of shuffling operations of slabs is minimized. Different from the most researches, we consider a situation where there are intersections among candidate slab sets and shuffled slabs will not return original stacks. Basing on a special index method of slabs, we present an integer linear programming (ILP) to compute approximation solutions of the problem. In order to improve the solutions obtained by the ILP model, we propose a very large-scale neighborhood (VLSN) search algorithm. In the VLSN search process, the solutions of HRS problems are improved by an iteration procedure that partial solutions are interactively destroyed and repaired. In each iteration, partitioned hot rolling slots correspond to a sub-problem. In the repair operations, we propose an efficient branch-and-bound algorithm for solving sub-problem. The computational results on a number of different scale simulated instances show that the VLSN search algorithm is efficient for solving this kind of HRS problems.

Analysis of Learning Techniques for Performance Prediction in Mobile Adhoc Networks

Current advancements in cellular technologies and computing have provided the basis for the unparalleled exponential development of mobile networking and software availability and quality combined with multiple systems or network software. Using wireless technologies and mobile ad-hoc networks, such systems and technology interact and collect information. To achieve the Quality of Service (QoS) criteria, the growing concern in wireless network performance and the availability of mobile users would support a significant rise in wireless applications. Predicting the mobility of wireless users and systems performs an important role in the effective strategic decision making of wireless network bandwidth service providers. Furthermore, related to the defect-proneness, self-organization, and mobility aspect of such networks, new architecture problems occur. This paper proposes to predict and simulate the mobility of specific nodes on a mobile ad-hoc network, gradient boosting devices defined for the system will help. The proposed model not just to outperform previous mobility prediction models using simulated and real-world mobility instances, but provides better predictive accuracy by an enormous margin. The accuracy obtained helps the suggested mobility indicator in Mobile Adhoc Networks to increase the average level of performance.

An online cost allocation model for horizontal supply chains

Emerging technologies in cargo shipping delivery have provided a way to facilitate horizontal cooperation in the transportation of goods to reduce the shipping cost of the cooperating firms, but an open question in this cooperation is how to allocate the costs fairly among the participants. In this paper, we focus on routing in real time a fleet of capacitated vehicles to satisfy requests submitted by a set of customers with some of the requests unknown while assigning the service cost fairly among the requested customers. We propose a Hybrid Proportional Online Cost-Sharing (HPOCS) mechanism to tackle the cost-sharing problem and analyze its performance using simulation instances. Although HPOCS does satisfy the desirable properties, namely online fairness, budget balance, immediate response, individual rationality and ex-post incentive compatibility, it has a few drawbacks in certain scenarios. Therefore, we make two extensions to HPOCS: 1) we introduce the idea of discounts to encourage customers to request in advance to facilitate efficient vehicle routing; 2) we incorporate periodical re-optimization within the dynamic vehicle routing framework. Experimental analysis are made in both extensions to see the tradeoff between the performance and the loss of certain desirable properties.

Multi-generator GAN learning disconnected manifolds with mutual information

Original data usually lies on a set of disconnected manifolds rather than a smooth connected manifold. This causes the problem of mode collapse in the training of vanilla Generative Adversarial Network (GAN). There are many existing GAN variants that attempt to address this problem, but they result in limitations. The existing variants either produce simulated instances with low quality or generate identical simulated instances. In this study, we propose a new approach to training GAN utilizing multiple generators, a classifier and a discriminator to address mode collapse. The classifier outputs the statistical probabilities of generated data belonging to a specific category. These probabilities implicitly reflect which manifolds are captured by generators, and the correlation between generators is quantified by mutual information. Our idea views the mutual information values as a constraint to guide generators in learning different manifolds. Specifically, we traverse the generators, calculating the mutual information between each generator and the others. The calculated values are integrated into the generator loss to form a new generator loss and to update the corresponding generator’s parameters, using back-propagation. We minimize the mutual information to reduce the correlation between generators while also minimizing the generator loss. This ensures generators capture different manifolds while updating their parameters. A new minimax formula is established to train our approach in a similar spirit to vanilla GAN. We term our approach Mutual Information Multi-generator GAN (MIM-GAN). We conduct extensive experiments utilizing the MNIST, CIFAR10 and CelebA datasets to demonstrate the significant performance improvement of MIM-GAN in both achieving the highest Inception Scores and producing diverse generated data at different resolutions.

Computational frame of ligament in situ strain in a full knee model

The biomechanical function of connective tissues in a knee joint is to stabilize the kinematics-kinetics of the joint by augmenting its stiffness and limiting excessive coupled motion. The connective tissues are characterized by an in vivo reference configuration (in situ strain) that would significantly contribute to the mechanical response of the knee joint. In this work, a novel iterative method for computing the in situ strain at reference configuration was presented. The framework used an in situ strain gradient approach (deformed reference configuration) and a detailed finite element (FE) model of the knee joint. The effect of the predicted initial configuration on the mechanical response of the joint was then investigated under joint axial compression, passive flexion, and coupled rotations (adduction and internal), and during the stance phase of gait. The inclusion of the reference configuration has a minimal effect on the knee joint mechanics under axial compression, passive flexion, and at two instances (0% and 50%) of the stance phase of gait. However, the presence of the ligaments in situ strains significantly increased the joint stiffness under passive adduction and internal rotations, as well as during the other simulated instances (25%, 75% and 100%) of the stance phase of gait. Also, these parameters substantially altered the local loading state of the ligaments and resulted in better agreement with the literature during joint flexion. Therefore, the proposed computational framework of ligament in situ strain will help to overcome the challenges in considering this crucial biological aspect during knee joint modeling. Besides, the current construct is advantageous for a better understanding of the mechanical behavior of knee ligaments under physiological and pathological states and provide relevant information in the design of reconstructive treatments and artificial grafts.

Identifying Drug Sensitivity Subnetworks with NETPHIX

SummaryPhenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. We develop a computational method, named NETPHIX (NETwork-to-PHenotype association with eXclusivity) to identify subnetworks of genes whose genetic alterations are associated with drug response or other continuous cancer phenotypes. Leveraging interaction information among genes and properties of cancer mutations such as mutual exclusivity, we formulate the problem as an integer linear program and solve it optimally to obtain a subnetwork of associated genes. Applied to a large-scale drug screening dataset, NETPHIX uncovered gene modules significantly associated with drug responses. Utilizing interaction information, NETPHIX modules are functionally coherent and can thus provide important insights into drug action. In addition, we show that modules identified by NETPHIX together with their association patterns can be leveraged to suggest drug combinations.

Request acceptance in same-day delivery

We study request acceptance dynamics in same-day delivery systems by formulating the Dynamic Dispatch Waves Problem with Immediate Acceptance, which models integrated management and distribution for dynamically arriving customer requests. We consider an e-commerce platform that attempts to serve most customers with same-day delivery service. When a shopper attempts to place an order, a decision is made immediately to offer same-day delivery service (accept the request) or to deny service. Accepted requests are not available for immediate dispatch; they must be processed (picked and packed) before they are loaded for delivery. Vehicle routes are updated dynamically and serve each accepted delivery request no later than the end of the service day. In this work, we limit the study to the case of a single vehicle serving requests, potentially using multiple trips from the distribution center. The objective is to make request acceptance and distribution decisions that minimize the expected sum of vehicle travel costs and penalties for service denials. We develop a framework for dynamic decision policies over continuous time for such systems, where a feasible vehicle dispatch plan is redesigned and used to guide decisions over time. We design methods for determining an initial optimal a priori plan and for updating the plan using a heuristic roll-out procedure. Our methods are tested on a family of simulated instances against two common-sense benchmarks and an infeasible relaxed policy that allows the dispatcher to delay the acceptance or rejection of a request until the end of the service day. We demonstrate in our computational study that the cost-per-request of the best benchmark policy is on average 9.7% higher than our proposed dynamic policy and furthermore that the dynamic policy leads to only a 4.4% cost increase over the infeasible relaxed policy lower bound.

Assessing the Feasibility of Augmenting Fall Detection Systems by Relying on UWB-Based Position Tracking and a Home Robot.

Falls in the home environment are a primary cause of injury in older adults. According to the U.S. Centers for Disease Control and Prevention, every year, one in four adults 65 years of age and older reports experiencing a fall. A variety of different technologies have been proposed to detect fall events. However, the need to detect all fall instances (i.e., to avoid false negatives) has led to the development of systems marked by high sensitivity and hence a significant number of false alarms. The occurrence of false alarms causes frequent and unnecessary calls to emergency response centers, which are critical resources that should be utilized only when necessary. Besides, false alarms decrease the level of confidence of end-users in the fall detection system with a negative impact on their compliance with using the system (e.g., wearing the sensor enabling the detection of fall events). Herein, we present a novel approach aimed to augment traditional fall detection systems that rely on wearable sensors and fall detection algorithms. The proposed approach utilizes a UWB-based tracking system and a home robot. When the fall detection system generates an alarm, the alarm is relayed to a base station that utilizes a UWB-based tracking system to identify where the older adult and the robot are so as to enable navigating the environment using the robot and reaching the older adult to check if he/she experienced a fall. This approach prevents unnecessary calls to emergency response centers while enabling a tele-presence using the robot when appropriate. In this paper, we report the results of a novel fall detection algorithm, the characteristics of the alarm notification system, and the accuracy of the UWB-based tracking system that we implemented. The fall detection algorithm displayed a sensitivity of 99.0% and a specificity of 97.8%. The alarm notification system relayed all simulated alarm notification instances with a maximum delay of 106 ms. The UWB-based tracking system was found to be suitable to locate radio tags both in line-of-sight and in no-line-of-sight conditions. This result was obtained by using a machine learning-based algorithm that we developed to detect and compensate for the multipath effect in no-line-of-sight conditions. When using this algorithm, the error affecting the estimated position of the radio tags was smaller than 0.2 m, which is satisfactory for the application at hand.

Sampling from the complement of a polyhedron: An MCMC algorithm for data augmentation

We present an MCMC algorithm for sampling from the complement of a polyhedron. Our approach is based on the Shake-and-bake algorithm for sampling from the boundary of a set and provably covers the complement. We use this algorithm for data augmentation in a machine learning task of classifying a hidden feasible set in a data-driven optimization pipeline. Numerical results on simulated and MIPLIB instances demonstrate that our algorithm, along with a supervised learning technique, outperforms conventional unsupervised baselines.