Linear Trade-off Research Articles

Partial dominance in branch-price-and-cut algorithms for vehicle routing and scheduling problems with a single-segment tradeoff

For many variants of vehicle routing and scheduling problems solved by a branch-price-and-cut (BPC) algorithm, the pricing subproblem is an elementary shortest-path problem with resource constraints (SPPRC) typically solved by a dynamic-programming labeling algorithm. Solving the SPPRC subproblems consumes most of the total BPC computation time. Critical to the performance of the labeling algorithms and thus the BPC algorithm as a whole is the use of effective dominance rules. Classical dominance rules rely on a pairwise comparison of labels and have been used in many labeling algorithms. In contrast, partial dominance describes situations where several labels together are needed to dominate another label, which can then be safely discarded. In this work, we consider SPPRCs, where a linear tradeoff describes the relationship between two resources. We derive a unified partial dominance rule to be used in ad hoc labeling algorithms for solving such SPPRCs as well as insights into its practical implementation. We introduce partial dominance for two important variants of the vehicle routing problem, namely the electric vehicle routing problem with time windows with a partial recharge policy and the split-delivery vehicle routing problem with time windows (SDVRPTW). Computational experiments show the effectiveness of the approach, in particular for the SDVRPTW, leading to an average reduction of 20% of the total BPC computation time, with savings of 30% for the more difficult instances requiring more than 600 s of computation time.

Pareto-optimal front generation for the bi-objective JIT scheduling problems with a piecewise linear trade-off between objectives

This paper proposes a novel method of Pareto front generation from a set of piecewise linear trade-off curves typically encountered in bi-objective just-in-time (JIT) scheduling problems. We have considered the simultaneous minimization of total weighted earliness and tardiness (TWET) and total flowtime (TFT) objectives in a single-machine scheduling problem (SMSP) with distinct job due dates allowing inserted idle times in the schedules. An optimal timing algorithm (OTA) is presented to generate the trade-off curve between TWET and TFT for a given sequence of jobs. The proposed method of Pareto front generation generates a Pareto-optimal front constituted of both line segments and points. Further, we employ a simple local search method to generate sequences of jobs and their respective trade-off curves, which are trimmed and merged to generate the Pareto-optimal front using the proposed method. Computational results obtained using problem instances of different sizes reveal the efficiency of the proposed OTA and the Pareto front generation method over the state-of-the-art methodologies adopted from the literature.

A Linear Trade-off Group TOPSIS method with application for Internet of Things devices ranking

Multi-criteria methods are decision-making methods used when considering multiple criteria simultaneously in decision-making processes. Among the multi-criteria methods, a widely used method that has proven its effectiveness in many applications is the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). In this paper we propose a modification of the TOPSIS method by using another way of calculating the relative closeness coefficient (RCC) and taking into account a group of decision makers. The modified version of the TOPSIS method, which we shall call the Linear Trade-off Group TOPSIS method (LTG-TOPSIS), replaces the RCC from the classical TOPSIS method with one that depends on a parameter that takes values in the unit interval. By using the parameter, the RCC is calculated as a linear combination between distances of an alternative to the ideal and anti-ideal solutions. This approach facilitates a management of the compromise between the two distances. An implementation of the LTG-TOPSIS method is analysed for a IoT devices ranking. By varying the parameter of the proposed method, a set of IoT devices rankings is obtained and the change in the ranking is studied. A comparison of the rankings obtained with LTG-TOPSIS and with the classical TOPSIS method is performed.

Financial Interpretation of Feller’s Factorization

The infinitesimal generator of a time-homogeneous univariate diffusion process is a second-order linear ordinary differential operator. Feller (1952) famously factorized this generator into successive differentiations with respect to scale and speed measure. Later, Feller (1957) also factored an extended generator that loads also on the identity operator in a particular way. We provide a novel financial interpretation of these factorization results and show that they produce an operator representation of a conditionally linear risk-return tradeoff when the conditioning variable evolves like a one-dimensional diffusion process.

Theory and Applications of the Transmuted Continuous Bernoulli Distribution

Modern applied statistics naturally give rise to the continuous Bernoulli distribution (data fitting, deep learning, computer vision, etc). On the mathematical side, it can be viewed as a one-parameter distribution corresponding to a special exponential distribution restricted to the unit interval. As a matter of fact, manageable extensions of this distribution have great potential in the same fields. In this study, we motivate a transmuted version of the continuous Bernoulli distribution with the goal of analyzing proportional data sets. The feature of the created transmuted continuous Bernoulli distribution is an additional parameter that realizes a linear tradeoff between the min and max of two continuous random variables with the continuous Bernoulli distribution. The standard study process is respected: we derive some mathematical properties of the proposed distribution and adopt the maximum likelihood estimation technique in estimating the unknown parameters involved. A Monte Carlo simulation exercise was conducted to examine and confirm the asymptotic behavior of the obtained estimates. In order to show the applicability of the proposed distribution, three proportional data sets are analyzed and the results obtained are compared with competitive distributions. Empirical findings reveal that the transmuted continuous Bernoulli distribution promises more flexibility in fitting proportional data sets than its competitors.

A probabilistic graph-based method to solve precision-diversity dilemma in recommender systems

Over the last few years, with significant growth of information on the web, users are swamped with a huge amount of information. Recommender system (RS) is an information retrieval technology that aims to provide relevant items to users by considering their preferences. Although several studies in the literature have proposed new RSs to improve the precision of recommendations, some studies have shown that increasing the diversity of recommendations is one way to increase the quality of the user’s experience and to solve the over-fitting problem. While some previous works have tried to make a trade-off between precision and diversity, they could not provide a linear and deterministic trade-off between precision and diversity. As such, these methods are either impractical or infeasible in a real RS. In this paper, we propose a new method to recommend items which are fit to users’ preferences. The method consists of following steps: i) finding users’ preferences using a limited user study, ii) proposing a probabilistic graph-based recommender system (PGBRS) to recommend items with a desirable level of precision and diversity by considering items that are likely to be preferred by the user in the future. PGBRS is a dataset-independent method which is significantly flexible to update with a low implementation complexity.

First Data of Reproductive Biology of Metlapilcoatlus indomitus (Serpentes: Viperidae) in Captivity

The Jumping Viper genus Metlapilcoatlus is composed of five species distributed throughout the Mesoamerican region, with M. indomitus being endemic to Honduras. While there have been numerous publications about its distribution, basic data concerning the species natural history (reproduction, diet, and behavior) remain poorly known. Here we expand on the knowledge of M. indomitus through an ex-situ study of two captive individuals inside a herpetarium. Over the course of three years from October 11, 2017, we monitored reproductive patterns of courtship, copulation, and reproductive potential. We register two events of courtship and copulation, and three deliveries with a total of 61 neonates (26, 14, and 21 respectively). We found significant differences in the total length of the neonates between broods (p = 3.557e-12). Our data mark a strong correlation and linear trade-off between the number of offspring per delivery and their length, with neonates of broods containing fewer offspring being of greater length, whereas those neonates from larger broods were shorter in average.

Extremum Seeking With Enhanced Convergence Speed for Optimization of Time-Varying Steady-State Behavior of Industrial Motion Stages

Recently, an extremum-seeking control (ESC) approach has been developed for optimization of generically time-varying steady-state responses of nonlinear systems. A generic filter structure was introduced, the so-called dynamic cost function, which has been instrumental in facilitating the use of ESC in the more generic, time-varying context. However, the dynamic cost function must operate sufficiently slow compared to the time-varying nature of the system responses, thereby compromising the convergence speed of the ESC scheme. In this work, a modified ESC approach is proposed that incorporates explicit knowledge about the user-defined dynamic cost function, able to enhance the convergence speed of the ESC scheme. Moreover, we provide a stability analysis for this extended approach. The main contribution of this work is the experimental demonstration of both ESC approaches for the performance optimal tuning of a variable-gain control (VGC) strategy employed on a high-accuracy industrial motion stage setup, exhibiting generically time-varying steady-state responses. VGC is able to enhance the system performance by balancing the typical linear control tradeoff between low-frequency disturbance suppression properties and sensitivity to high-frequency disturbances in a more desirable manner. We experimentally show that, for the unknown disturbance situation at hand, the variable-gain controller can be automatically tuned using both ESC approaches to achieve the optimal system performance. In addition, enhanced convergence speed with the modified ESC approach is evidenced experimentally.

The Window Fill Rate in a Multiple Location Inventory System with Periodic Review and Order Crossover

Deliveries in global supply chains are often made through lengthy shipping routes that are subject to many delays such as border crossings, inspections and so forth. Consequently, orders frequently crossover, that is, their order of arrival is not the same as the order that they were issued. In this paper we model a multiple location inventory system with Poisson demand and periodic review in which orders may crossover. The system’s performance measure is the window fill rate, i.e., the probability that a customer arriving to the system is served within her tolerable wait. We show that when spares are scarce the system’s overall performance decreases if spares are allotted equitably. Additionally, we show that there is a linear tradeoff between the tolerable wait and the number of spares needed to maintain a given performance level. The observations have practical implications to inventory mangers. First, that when resources are scarce it is optimal to cluster spares to only few locations. In contrast, when resources are abundant, then a more equitable solution is optimal. Second, that it is possible to design simple contracts that reward customers for their patience, or alternatively, that charge customers a premium for expedited service.

Investigation of the effects of silica aerogel particles on thermal and mechanical properties of epoxy composites

Aerogel particle filled epoxy was fabricated in this work by investigating the effect of resin viscosity on pore infiltration and density of resulting composites. Aerogel porous structure was successfully preserved by carefully selecting epoxy viscosity for mixing procedure. The effect of aerogel content and particle size was subsequently investigated on thermal conductivity and compressive properties of epoxy composites. The results showed that incorporating aerogel particles into the resin could lead to over 40% reduction in both density and thermal conductivity. However, there was a linear trade-off between compressive properties and thermal conductivity of silica aerogel/epoxy composites. A wide range of aerogel particle sizes was also studied and it was found that the size effect was dependent on the aerogel content. Overall, the results of the presented work allow conclusions to be drawn regarding the usefulness of delayed wet mixing technique and the impact of particle size and loading on aerogel filled resin composites.

Impact of heat modification conditions on the removal of N-nitrosodimethylamine by polyamide reverse osmosis membranes

Advanced wastewater treatment using a reverse osmosis (RO) membrane is a key separation process for ensuring the removal of chemical hazards so that treated wastewater can be used for potable purposes. This study systematically assesses the effects of heat modification conditions of three commercial RO membranes on the removal of a challenging chemical of emerging concern–N-nitrosodimethylamine (NDMA). The RO membranes were modified in pure water at the heat treatment temperature between 70 and 100 °C; 90–100 °C achieved the highest NDMA rejection during separation tests. A lower pH during heat treatment generally resulted in higher NDMA rejection, however a solution pH lower than four can reduce NDMA rejection depending on the presence of a protective layer on the RO membrane surface. Overall, a linear tradeoff between NDMA rejection and water permeance was commonly observed among the heat-treated RO membranes. The stability of a heat-modified membrane in water permeance and NDMA rejection was demonstrated over a week-long test using treated wastewater. The enhanced removal of NDMA by heat-treated RO membranes was speculated to occur due to the shrinking of the passage of solutes–free-volume holes. This study demonstrates the importance of heat modification conditions on RO membranes to achieve a high NDMA rejection.

Role of Multi-Scale Microstructure in the Degradation of Al Wire for Power Transmission

As common conductive materials, Al wires are used on overhead transmission lines under long-term heating conditions. In this study, the strength degradation behavior and the strength-electrical conductivity relation of the commercially pure Al wires (CPAWs) and the Al-Fe wires (AFWs) annealed at various temperatures were investigated based on the microstructure evolution. The strength degradation rate of the AFW is always higher than that of the CPAW. A linear trade-off relation between strength and electrical conductivity for the annealed Al wires are clarified. The results reveal that the mechanisms behind the trade-off relation between the strength and the electrical conductivity for the annealed CPAWs and the annealed AFWs are the recovery of dislocations and the obvious increase of grain width, which leads to the decrease of strength and the increase of electrical conductivity. The coalescence of precipitate in the AFW leads to the obvious decrease of strength, which results in the higher strength degradation rate for the AFW as compared with that for the CPAW. Consequently, the principle of microstructure design for anti-degradation of Al wire is presented.

Contact-implicit trajectory optimization using variational integrators

Contact constraints arise naturally in many robot planning problems. In recent years, a variety of contact-implicit trajectory optimization algorithms have been developed that avoid the pitfalls of mode pre-specification by simultaneously optimizing state, input, and contact force trajectories. However, their reliance on first-order integrators leads to a linear tradeoff between optimization problem size and plan accuracy. To address this limitation, we propose a new family of trajectory optimization algorithms that leverage ideas from discrete variational mechanics to derive higher-order generalizations of the classic time-stepping method of Stewart and Trinkle. By using these dynamics formulations as constraints in direct trajectory optimization algorithms, it is possible to perform contact-implicit trajectory optimization with significantly higher accuracy. For concreteness, we derive a second-order method and evaluate it using several simulated rigid-body systems, including an underactuated biped and a quadruped. In addition, we use this second-order method to plan locomotion trajectories for a complex quadrupedal microrobot. The planned trajectories are evaluated on the physical platform and result in a number of performance improvements.

A linear time-cost tradeoff problem with multiple interim assessments within multiple projects in parallel

We consider a project scheduling problem in which the jobs can be compressed by using additional resources to meet the corresponding due dates. The project consists of multiple independent subprojects with completely ordered jobs. Some jobs have their own due dates for interim assessments of whole project. A penalty cost arises from the tardiness of a job, but it can be avoided through the compression of some jobs, which requires an additional cost. The objective is to minimize the total tardiness penalty and compression costs. We investigate optimality properties and develop an algorithm to find an optimal schedule in strongly polynomial time.

Ten Principles for More Conservative, Care-Full Diagnosis.

Physicians must navigate a balance between under- and overdiagnosis, both of which may harm patients. The authors discuss core principles to help find this balance and foster a thoughtful, patient-...

基于多目标线性规划的甲积峪小流域生态系统服务权衡优化

基于多目标线性规划的甲积峪小流域生态系统服务权衡优化

Speed-accuracy tradeoff of fingertip force control with visual/audio/haptic feedback

Abstract Fitts’ law has been widely used in the human-computer interaction (HCI) field, especially for Graphical User Interface (GUI) design. However, most studies on Fitts’ law were performed with motion control tasks under visual feedback while only a few endeavor to measure human force control behavior. How quickly humans can exert a constant force with a required accuracy and whether this speed-accuracy tradeoff obeys Fitts’ law still needs to be explored. In this paper, human capabilities for controlling absolute magnitudes of fingertip force with discrete visual/audio/haptic feedback cues were observed and compared. Eighteen participants applied constant forces by pressing a force sensor with their index fingers in the three feedback modes respectively. Response time of 24 pairs (4 × 6) of Magnitude-Tolerance conditions were measured. The results showed that the response time obeyed Fitts’ law within a certain range of force accuracy in all the three feedback modes, while the Linear speed-accuracy tradeoff model was almost superior for the force control process than the Meyer formulation and the Shannon formulation. The response time in the audio feedback mode was the shortest among the three feedback conditions. The results may be used as guidelines for applications that rely on accurate and quick force control under different feedback conditions such as fast tapping tasks on a touch screen.

Effect of extended‐duration thromboprophylaxis on venous thromboembolism and major bleeding among acutely ill hospitalized medical patients: a bivariate analysis

Background Among acutely ill hospitalized medical patients, extended-duration thromboprophylaxis reduces the risk of venous thromboembolism (VTE), but some pharmacologic strategies have been associated with greater risks of major bleeding, thereby offsetting the net clinical benefit (NCB). Methods To assess the risk-benefit profile of anticoagulation regimens, a previously described bivariate method that does not assume a linear risk-benefit tradeoff and can accommodate different margins for efficacy and safety was performed to simultaneously assess efficacy (symptomatic VTE) and safety (major bleeding) on the basis of data from four randomized controlled trials of extended-duration (30-46days) versus standard-duration (6-14days) thromboprophylaxis among 28227 patients (EXCLAIM, ADOPT, MAGELLAN and APEX trials). Results Extended thromboprophylaxis with full-dose betrixaban (80mg once daily) was superior in efficacy and non-inferior in safety to standard-duration enoxaparin, and showed a significantly favorable NCB, with a risk difference of -0.51% (-0.89% to -0.10%) in the bivariate outcome. Extended enoxaparin was superior in efficacy and inferior in safety (bivariate outcome: 0.03% [-0.37% to 0.43%]), whereas apixaban and rivaroxaban were non-inferior in efficacy and inferior in safety (-0.20% [-0.49% to 0.17%] and 0.23% [-0.16% to 0.69%], respectively). Reduced-dose betrixaban did not show a significant difference in either efficacy or safety (0.41% [-0.85% to 1.94%]). Conclusions In a bivariate analysis that assumes non-linear risk-benefit tradeoffs, extended prophylaxis with full-dose betrixaban was superior to standard-duration enoxaparin, whereas other regimens failed to simultaneously achieve both superiority and non-inferiority with respect to symptomatic VTE and major bleeding in the management of acutely ill hospitalized medical patients.

Boolean language operations on nondeterministic automata with a pushdown of constant height

We study the cost of Boolean operations on constant height nondeterministic pushdown automata, i.e., on the ordinary pushdown automata with a limit on the size of the pushdown. For intersection, we show an exponential simulation and prove that the exponential blow-up is necessary in the worst case. For union, instead, we provide a linear trade-off while, for complement, we show a double exponential simulation and prove a single exponential lower bound. The same gaps for Boolean operations with regular languages have been shown for traditional nondeterministic automata with unrestricted pushdown.

Determinant Coding: A Novel Framework for Exact-Repair Regenerating Codes

The exact-repair regenerating codes for distributed storage system are studied in this work. A novel coding scheme is proposed for code construction for any $(n,k,d=k)$ system. It is shown that the proposed codes are optimum, in the sense that any operating point satisfying the lower bound for the storage-bandwidth trade-off can be achieved with the proposed construction. As a consequence, the optimum linear trade-off of exact-regenerating system is fully characterized for any systems with $d=k$ . The proposed codes are linear, and can be generated by multiplication of an encoder matrix with a so-called data matrix. The exact regenerating property is provided based on fundamental properties of matrix determinants, and in particular, generalized Laplace expansion for determinant. Thus the code is called determinant code . Importantly, the field size required for this code construction is $\Theta (n)$ , the total number of nodes in the system.