Dependent Constraints Research Articles

Assembly line balancing and worker assignment considering workers’ expertise and perceived physical effort

In manual assembly systems, workers’ differences in terms of skills, level of expertise and perceived physical effort largely affect the assembly line balancing and system performance. Traditional long-term strategic decisions may not respond to workforce changes and needs, resulting in frequent requests for line rebalancing. In this study, we propose a methodological framework and an easy-to-use Assembly Line Worker Assignment and Rebalancing Problem with different options: workers’ assignment considering their performance variability, integration of worker dependent physical exertion constraints and possibility to use trainers to assist inexperienced workers. A bi-objective linear programming model is proposed aiming to minimise the cycle time and the number of reassigned tasks to respect the initial design while integrating new workers with different characteristics. The -constraint approach is used to build Pareto frontiers for this bi-objective problem. This approach is applied to three real cases. The obtained results show that the developed model can be successfully used in manufacturing companies to help the production managers to deal with workforce turnover and skills heterogeneity.

Unrelated parallel machines scheduling with dependent setup times in textile industry

The problem tackled in this paper is an unrelated parallel machines scheduling problem with machine and sequence dependent setup times, machine eligibility and different resource types constraints. This subject is inspired from a textile industry partner case. The different characteristics of this problem have never been combined as it is depicted in this paper. Two resolution methods are exposed. A new mathematical programming is proposed to have an exact resolution approach of this complex problem. An improved genetic algorithm (GA) with dedicated operators is also presented in order to solve the industrial case in a reasonable computational time.

Broadband sound speed and attenuation of a mud layer from acoustic propagation experiments in the New England Mud Patch

A series of Seabed Characterization Experiments (SBCEX) have been conducted in the New England Mud Patch area, where a top mud layer overlays a sand sediment. Using broadband acoustic signals from the SBCEX, the sound speed and attenuation of the mud layer as a function of frequency (from a few tens of Hz up to around 1 kHz) have been estimated by various inversion algorithms based on (1) modal dispersion curves and transmission loss [reported at the ASA Spring meeting, 2016], (2) Airy phase [Spring, 2017], (3) modal amplitude [Fall, 2017], (4) spatial coherence measurements [Spring, 2018], (5) mid-frequency modal dispersion analysis [Fall, 2019], and (6) ground waves [Fall, 2022]. This paper summarizes these inverted sound speed and attenuation, which are obtained at various frequencies independently (without using the frequency dependent constraints from any geo-acoustic models), therefore the inverted results could be used to verify the validity of geo-acoustic models. The performances of these inversion methods are evaluated. The performance assessment is helpful for one to select appropriate inversion algorithms and to choose optimal acoustic data for geo-acoustic inversion. [Work supported by ONR Ocean Acoustics.]

Trajectory Planning for an Autonomous Vehicle in Spatially Constrained Environments

Road shoulders and slopes often appear in unstructured environments. They make 2.5D vehicle trajectory planning commonly seen in our daily life, which lies on a 2D manifold embedded in a 3D space. The height difference of these terrains brings spatially dependent constraints on vehicle maneuvers, such as the limit on vehicle steering for vehicle tire protection when a vehicle approaches a road shoulder edge. These constraints have an “if-else” structure since they are activated only when the vehicle passes through the local area with a height difference, making the restriction on variables coupled with the judgment of variables. This makes the application of state-of-art optimization-based planners challenging. To solve this problem, we devise an approximation formulation for these constraints in the trajectory planning optimization problem, whose solution depends on a proper initial guess for the optimizer. We propose a two-stage trajectory planning framework, where the first stage improves the hybrid A* algorithm by adding spatially dependent constraints into node expansion to provide the initial guess. Then, the optimization problem with the formulated spatially dependent constraints is solved for further trajectory smoothness and quality. Finally, the simulation results validate the fast and high-quality planning performance of our proposed framework.

Simple Models Outperform More Complex Big-Leaf Models of Daily Transpiration in Forested Biomes.

Transpiration makes up the bulk of total evaporation in forested environments yet remains challenging to predict at landscape-to-global scales. We harnessed independent estimates of daily transpiration derived from co-located sap flow and eddy-covariance measurement systems and applied the triple collocation technique to evaluate predictions from big leaf models requiring no calibration. In total, four models in 608 unique configurations were evaluated at 21 forested sites spanning a wide diversity of biophysical attributes and environmental backgrounds. We found that simpler models that neither explicitly represented aerodynamic forcing nor canopy conductance achieved higher accuracy and signal-to-noise levels when optimally configured (rRMSE=20%; R 2=0.89). Irrespective of model type, optimal configurations were those making use of key plant functional type dependent parameters, daily LAI, and constraints based on atmospheric moisture demand over soil moisture supply. Our findings have implications for more informed water resource management based on hydrological modeling and remote sensing.

Random Games Under Elliptically Distributed Dependent Joint Chance Constraints

We study an n-player game with random payoffs and continuous strategy sets. The payoff function of each player is defined by its expected value and the strategy set of each player is defined by a joint chance constraint. The random constraint vectors defining the joint chance constraint are dependent and follow elliptically symmetric distributions. The Archimedean copula is used to capture the dependence among random constraint vectors. We propose a reformulation of the joint chance constraint of each player. Under mild assumptions on the players’ payoff functions and 1-dimensional spherical distribution functions, we show that there exists a Nash equilibrium of the game.

A critique of length and bias dependent constraints for 1T-DRAM operation through RFET

Capacitorless dynamic memory (one transistor dynamic random access memory (1T-DRAM)) operation in a reconfigurable field effect transistor (RFET) is critically governed by different lengths associated with the architecture. These lengths consisting of ungated region (L UG), control gate (L CG), polarity gate (L PG), storage region length (L S), and total length (L T) can be sensitive to the fabrication process, and hence, critical for 1T-DRAM. This work presents an insightful critique of the above mentioned lengths for realising optimal 1T-DRAM performance. It is shown that RFET with highest values of L S/L T and L CG/L T shows good short channel immunity but does not necessarily ensure enhanced 1T-DRAM metrics. Results indicate that for a fixed L T, retention time can vary over a wide range (550 ms to 8.7 s) depending on the values of L S/L T and L CG/L T, and hence, appropriate optimization is imperative. The work contributes towards better understanding and optimizing L CG/L T to ensure improved 1T-DRAM metrics in terms of enhanced retention (>64 ms), acceptable sense margin (>6 µA µm−1), current ratio (>104) with low values of read (2 ns) and write (1 ns) time to further extend multi-functional facets of nanoscale RFETs for memory applications. In addition, the effect of traps, process sensitivity, reduced number of voltage levels, and disturbance caused by shared word line (WL)/bit line (BL) are also analysed in this work. Results indicate that state ‘0’ of the cell sharing BL with the selected cell is strongly affected by BL disturbance. WL disturbance primarily impacts state ‘1’ of the cell sharing WL with selected cell (only for write 1 and read operations).

$O(\log^2{k}/\log\log{k})$-Approximation Algorithm for Directed Steiner Tree: A Tight Quasi-Polynomial Time Algorithm

In the directed Steiner tree (DST) problem, we are given an $n$-vertex directed edge-weighted graph, a root $r$, and a collection of $k$ terminal nodes. Our goal is to find a minimum-cost subgraph that contains a directed path from $r$ to every terminal. We present an $O(\log^2 k/\log\log{k})$-approximation algorithm for DST that runs in quasi-polynomial time, i.e., in time $n^{{poly}\log (k)}$. By assuming the projection game conjecture and ${NP}\not\subseteq{\bigcap}_{0<\epsilon<1}{ZPTIME}(2^{n^\epsilon})$ and adjusting the parameters in the hardness result of [Halperin and Krauthgamer, Polylogarithmic inapproximability, in Proceedings of the 35th Annual ACM Symposium on Theory of Computing, 2003, pp. 585--594], we show the matching lower bound of $\Omega(\log^2{k}/\log\log{k})$ for the class of quasi-polynomial time algorithms, meaning that our approximation ratio is asymptotically the best possible. Our algorithm is proceeded by reducing DST to an intermediate problem, namely, the group Steiner tree on trees with dependency constraint problem, which we approximate using the framework developed by [Rothvoß, Directed Steiner Tree and the Lasserre Hierarchy, preprint, arxiv:1111.5473, 2011] and [Friggstad et al., Linear programming hierarchies suffice for directed Steiner tree, in Proceedings of the 17th Annual Conference on Integer Programming and Combinatorial Optimization, 2014, pp. 285--296].

A deep reinforcement learning based hybrid algorithm for efficient resource scheduling in edge computing environment

Edge computing can greatly decrease the delay between users and cloud servers, which can significantly improve system service performance. However, it remains challenging for more efficient scheduling and allocation of users’ application demands with dependence constraints to edge cloud servers. Due to the randomness of the initial population, traditional intelligent optimization algorithms have poor convergence speed in addressing resource scheduling. Therefore, to minimize the execution time of the application, this paper proposes a hybrid algorithm to solve the resource scheduling problem with parallelism and subtask dependency. To improve the convergence speed of the algorithm, this paper makes full use of the features of deep Q networks (DQN) and genetic algorithms (GA). The initial population of GA is generated using DQN. Finally, to evaluate the effectiveness of our proposed algorithm, this paper selects three real scientific workflows for experiments. The experimental results show that the hybrid algorithm can converge quickly and improve the optimization effect in a short time.

Research on Dynamic Programming Strategy of Bayesian Network Structure Learning

Bayesian network structure learning based on dynamic programming strategy can be used to find the optimal graph structure compared with approximate search methods. The traditional dynamic programming method for Bayesian network structure learning is a depth-first-based strategy, which is inefficient. We proposed two methods to solve this problem. First, the dependency constraints were used to prune the process of calculating redundancy scores. The constraints were obtained by the conditional independence test from the observed data sets. However, it was difficult to guarantee the accuracy of the constraints, which may have led to a decrease in the accuracy of the method. Second, we proposed a breadth-first-based strategy, which enhanced efficiency greatly while also ensuring global optimality. Experimental results showed that on the standard network data sets, compared with the dynamic programming based on depth-first search (DFSDP) algorithm, dynamic programming based on constraints (CBDP) could reduce the average running time by 57.10% and that dynamic programming based on breadth-first search (BFSDP) could reduce the average running time by 50.02%. On the UCI data sets, compared with DFSDP, CBDP reduced the average running time by 40.71%, and BFSDP reduced the average running time by 81.78%.

Extracting Dynamical Understanding From Neural-Mass Models of Mouse Cortex.

New brain atlases with high spatial resolution and whole-brain coverage have rapidly advanced our knowledge of the brain's neural architecture, including the systematic variation of excitatory and inhibitory cell densities across the mammalian cortex. But understanding how the brain's microscale physiology shapes brain dynamics at the macroscale has remained a challenge. While physiologically based mathematical models of brain dynamics are well placed to bridge this explanatory gap, their complexity can form a barrier to providing clear mechanistic interpretation of the dynamics they generate. In this work, we develop a neural-mass model of the mouse cortex and show how bifurcation diagrams, which capture local dynamical responses to inputs and their variation across brain regions, can be used to understand the resulting whole-brain dynamics. We show that strong fits to resting-state functional magnetic resonance imaging (fMRI) data can be found in surprisingly simple dynamical regimes—including where all brain regions are confined to a stable fixed point—in which regions are able to respond strongly to variations in their inputs, consistent with direct structural connections providing a strong constraint on functional connectivity in the anesthetized mouse. We also use bifurcation diagrams to show how perturbations to local excitatory and inhibitory coupling strengths across the cortex, constrained by cell-density data, provide spatially dependent constraints on resulting cortical activity, and support a greater diversity of coincident dynamical regimes. Our work illustrates methods for visualizing and interpreting model performance in terms of underlying dynamical mechanisms, an approach that is crucial for building explanatory and physiologically grounded models of the dynamical principles that underpin large-scale brain activity.

Energy-aware task scheduling and offloading using deep reinforcement learning in SDN-enabled IoT network

The fifth-generation (5G) mobile network services have made tremendous growth in the Internet of Things (IoT) network. A counters number of battery-powered IoT devices are deployed to serve diverse scenarios, e.g., smart cities, autonomous farming, smart manufacturing, to name but a few. In this context, energy consumption became one of the most critical concerns in interconnecting smart IoT devices in such scenarios. Additionally, whenever these IoT devices are distributed in space and time-evolving, they are expected to deliver high volume data scalably/predictably while minimizing end-to-end latency. Furthermore, edge IoT nodes often face the biggest hurdle of performing optimal resource distribution and achieving high-performance levels while coping with the variability of task handling, energy conservation, and ultra-reliable low-latency.This paper investigates an energy-aware and low-latency oriented computing task scheduling problem in a Software-Defined Fog-IoT Network. First, we formulate the online task assignment and scheduling problem as an energy-constrained Deep Q-Learning process as a kickoff. The latter strives to minimize the network latency while ensuring energy efficiency by saving battery power under the constraints of application dependence. Then, given the task arrival process, we introduce a deep reinforcement learning (DRL) approach for dynamic task scheduling and assignment in the Software-Defined Networking (SDN)-enabled edge networks. We conducted comprehensive experiments and compared the introduced algorithm to three pioneering deep learning algorithms (i.e., deterministic, random, and A3C agents). Extensive simulation results demonstrated that our proposed solution outperforms these algorithms. Additionally, we highlight the characterizing feature of our design, energy-awareness, as it offers better energy-saving by up to 87% compared against the other approaches. We have shown that the offloading scheme could perform more task assignments with the available battery power by up to 50% less time delay. Our results back our claims that the solution we propose can readily be used to dynamically optimize task scheduling and assignment of complex jobs with task dependencies in distributed Fog IoT networks.

Steerable Music Generation which Satisfies Long-Range Dependency Constraints

Although music is full of repetitive motifs and themes, artificially intelligent temporal sequence models have yet to demonstrate the ability to model or generate musical compositions that satisfy steerable, long-range constraints needed to evoke such repetitions. Markovian approaches inherently assume a strictly limited range of memory while neural approaches—despite recent advances in evoking long-range dependencies—remain largely unsteerable. More recent models have been published that attempt to evoke repetitive motifs by imposing unary constraints at intervals or by collating copies of musical segments. Although the results of these methods satisfy long-range dependencies, they come with significant—potentially prohibitive—sacrifices in the musical coherence of the generated composition or in the breadth of satisfying compositions which the model can create. We present R<small>EGUALR</small> non-homogeneous Markov models as a solution to the long-range dependency problem which uses R<small>ELATIONAL</small> automata to enforce binary constraints to compose music with repeating motifs. The solution we present preserves musical coherence (i.e., Markovian constraints) for the duration of the generated compositions and significantly increases the range of satisfying compositions that can be generated.

Energy-Efficient Scientific Workflow Scheduling Algorithm in Cloud Environment

Scheduling extensive scientific applications that are deadline-aware (usually referred to as workflow) is a difficult task. This research provides a virtual machine (VM) placement and scheduling approach for effectively scheduling process tasks in the cloud environment while maintaining dependency and deadline constraints. The suggested model’s aim is to reduce the application’s energy consumption and total execution time while taking into account dependency and deadline limitations. To select the VM for the tasks and dynamically deploy/undeploy the VM on the hosts based on the jobs’ requirements, an energy-efficient VM placement (EVMP) algorithm is presented. Demonstrate that the proposed approach outperforms the existing PESVMC (power-efficient scheduling and VM consolidation) algorithm.

Addressing Syntax-Based Semantic Complementation: Incorporating Entity and Soft Dependency Constraints into Metonymy Resolution

State-of-the-art methods for metonymy resolution (MR) consider the sentential context by modeling the entire sentence. However, entity representation, or syntactic structure that are informative may be beneficial for identifying metonymy. Other approaches only using deep neural network fail to capture such information. To leverage both entity and syntax constraints, this paper proposes a robust model EBAGCN for metonymy resolution. First, this work extracts syntactic dependency relations under the guidance of syntactic knowledge. Then the work constructs a neural network to incorporate both entity representation and syntactic structure into better resolution representations. In this way, the proposed model alleviates the impact of noisy information from entire sentences and breaks the limit of performance on the complicated texts. Experiments on the SemEval and ReLocaR dataset show that the proposed model significantly outperforms the state-of-the-art method BERT by more than 4%. Ablation tests demonstrate that leveraging these two types of constraints benefits fine pre-trained language models in the MR task.

Multiple Benefit Thresholds Problem in Online Social Networks: An Algorithmic Approach

An important problem in the context of viral marketing in social networks is the Influence Threshold (IT) problem, which aims at finding some users (referred to as a seed set) to begin the process of disseminating their product’s information so that the benefit gained exceeds a predetermined threshold. Even though, marketing strategies exhibit different in several realistic scenarios due to market dependence or budget constraints. As a consequence, picking a seed set for a specific threshold is not enough to come up with an effective solution. To address the disadvantages of previous works with a new approach, we study the Multiple Benefit Thresholds (MBT), a generalized version of the IT problem, as a result of this phenomenon. Given a social network that is subjected to information distribution and a set of thresholds, T={T1,T2,…,Tk},Ti&gt;0, the issue aims to seek the seed sets S1,S2,…,Sk with the lowest possible cost so that the benefit achieved from the influence process is at the very least T1,T2,…,Tk, respectively. The main challenges of this problem are a #NP-hard problem and the estimation of the objective function #P-Hard under traditional information propagation models. In addition, adapting the exist algorithms many times to different thresholds can lead to large computational costs. To address the abovementioned challenges, we introduced Efficient Sampling for Selecting Multiple Seed Sets, an efficient technique with theoretical guarantees (ESSM). At the core of our algorithm, we developed a novel algorithmic framework that (1) can use the solution to a smaller threshold to find that of larger ones and (2) can leverage existing samples with the current solution to find that of larger ones. The extensive experiments on several real social networks were conducted in order to show the effectiveness and performance of our algorithm compared with current ones. The results indicated that our algorithm outperformed other state-of-the-art ones in terms of both the total cost and running time.

Periodic Solutions of a Phase-Field Model with Hysteresis

In the present paper we consider a partial differential system describing a phase-field model with temperature dependent constraint for the order parameter. The system consists of an energy balance equation with a fairly general nonlinear heat source term and a phase dynamics equation which takes into account the hysteretic character of the process. The existence of a periodic solution for this system is proved under a minimal set of assumptions on the curves defining the corresponding hysteresis region

Session-based social and dependency-aware software recommendation

With the increase of complexity of modern software, social collaborative coding and reuse of open source software packages become more and more popular, which thus greatly enhances the development efficiency and software quality. However, the explosive growth of open source software packages exposes developers to the challenge of information overload. While this can be addressed by conventional recommender systems, they usually do not consider particular constraints of social coding such as social influence among developers and dependency relations among software packages. In this paper, we aim to model the dynamic interests of developers with both social influence and dependency constraints, and propose the Session-based Social and Dependency-aware software Recommendation (SSDRec) model. This model integrates recurrent neural network (RNN) and graph attention network (GAT) into a unified framework. An RNN is employed to model the short-term dynamic interests of developers in each session and two GATs are utilized to capture social influence from friends and dependency constraints from dependent software packages, respectively. Extensive experiments are conducted on real-world datasets and the results demonstrate that our model significantly outperforms the competitive baselines.

Accurate model for the primordial black hole mass distribution from a peak in the power spectrum

We examine the shape of the primordial black hole mass distribution arising from a peak in the primordial power spectrum. In light of improvements to the modeling, we revisit the claim that the effects of critical collapse produce a distribution that is not described by the commonly assumed lognormal, showing that this conclusion remains valid, particularly for narrow peaks where the shape of the mass distribution is insensitive to the peak properties and critical collapse determines a minimum width. We propose some alternative models that may better describe the shape, both for the narrow peak case and for much broader peaks where the effect of the peak shape is significant. We highlight the skew-lognormal and a generalized model motivated by the physics of critical collapse as the best of these possible alternatives. These models can be used as an accurate and fast approximation to the numerically calculated mass distribution, allowing for efficient implementation in an MCMC analysis. We advocate the use of one of these two models instead of the lognormal with sufficiently accurate data, such as future LIGO--Virgo observations, or when considering strongly mass dependent constraints on the PBH abundance.

Configuring ADAS Platforms for Automotive Applications Using Metaheuristics.

Modern Advanced Driver-Assistance Systems (ADAS) combine critical real-time and non-critical best-effort tasks and messages onto an integrated multi-core multi-SoC hardware platform. The real-time safety-critical software tasks have complex interdependencies in the form of end-to-end latency chains featuring, e.g., sensing, processing/sensor fusion, and actuating. The underlying real-time operating systems running on top of the multi-core platform use static cyclic scheduling for the software tasks, while the communication backbone is either realized through PCIe or Time-Sensitive Networking (TSN). In this paper, we address the problem of configuring ADAS platforms for automotive applications, which means deciding the mapping of tasks to processing cores and the scheduling of tasks and messages. Time-critical messages are transmitted in a scheduled manner via the timed-gate mechanism described in IEEE 802.1Qbv according to the pre-computed Gate Control List (GCL) schedule. We study the computation of the assignment of tasks to the available platform CPUs/cores, the static schedule tables for the real-time tasks, as well as the GCLs, such that task and message deadlines, as well as end-to-end task chain latencies, are satisfied. This is an intractable combinatorial optimization problem. As the ADAS platforms and applications become increasingly complex, such problems cannot be optimally solved and require problem-specific heuristics or metaheuristics to determine good quality feasible solutions in a reasonable time. We propose two metaheuristic solutions, a Genetic Algorithm (GA) and one based on Simulated Annealing (SA), both creating static schedule tables for tasks by simulating Earliest Deadline First (EDF) dispatching with different task deadlines and offsets. Furthermore, we use a List Scheduling-based heuristic to create the GCLs in platforms featuring a TSN backbone. We evaluate the proposed solution with real-world and synthetic test cases scaled to fit the future requirements of ADAS systems. The results show that our heuristic strategy can find correct solutions that meet the complex timing and dependency constraints at a higher rate than the related work approaches, i.e., the jitter constraints are satisfied in over 6 times more cases, and the task chain constraints are satisfied in 41% more cases on average. Our method scales well with the growing trend of ADAS platforms.