A combined CLP-MILP approach for scheduling commodities in a pipeline

This paper addresses the problem of developing an optimization model to aid the operational scheduling in a real-world pipeline scenario. The pipeline connects refinery and harbor, conveying different types of commodities (gasoline, diesel, kerosene, etc.). An optimization model was developed to determine pipeline scheduling with improved efficiency. This model combines constraint logic programming (CLP) and mixed integer linear programming (MILP) in a CLP-MILP approach. The proposed model uses decomposition strategies, continuous time representation, intervals that indicate time constraints (time windows), and a series of operational issues, such as the seasonal and hourly cost of electric energy (on-peak demand hours). Real cases were solved in a matter of seconds. The computational results have demonstrated that the model is able to define new operational points to the pipeline, providing significant cost savings. Indeed the CLP-MILP model is an efficient tool to aid operational decision-making within this real-world pipeline scenario.     

Branching strategies in a branch-and-price approach for a multiple objective nurse scheduling problem

The efficient management of nursing personnel is of critical importance in a hospital’s environment comprising a vast share of the hospital’s operational costs. The nurse scheduling process affects highly the nurses’ working conditions, which are strongly related to the provided quality of care. In this paper, we consider the rostering over a mid-term period that involves the construction of duty timetables for a set of heterogeneous nurses. In scheduling nursing personnel, the head nurse is typically confronted with various (conflicting) goals complying with different priority levels which represent the hospital’s policies and the nurses’ preferences. In constructing a nurse roster, nurses need to be assigned to shifts in order to maximize the quality of the constructed timetable satisfying the case-specific time related constraints imposed on the individual nurse schedules. Personnel rostering in healthcare institutions is a highly constrained and difficult problem to solve and is known to be NP-hard. In this paper, we present an exact branch-and-price algorithm for solving the nurse scheduling problem incorporating multiple objectives and discuss different branching and pruning strategies. Detailed computational results are presented comparing the proposed branching strategies and indicating the beneficial effect of various principles encouraging computational efficiency.     

Software transactional memories: an approach for multicore programming

The recent advance of multicore architectures and the deployment of multiprocessors as the mainstream computing platforms have given rise to a new concurrent programming impetus. Software transactional memories (STM) are one of the most promising approaches to take up this challenge. The aim of a STM system is to discharge the application programmer from the management of synchronization when he/she has to write multiprocess programs. His/her task is to decompose his/her program into a set of sequential tasks that access shared objects, and to decompose each task in atomic units of computation. The management of the required synchronization is ensured by the associated STM system. This paper presents two existing STM systems, and a new one based on time-window mechanism. The paper, which focuses mainly on STM principles, has an introductory and survey flavor.     

On stabilization for a class of nonlinear stochastic time-delay systems： a matrix inequality approach

This paper treats the feedback stabilization of nonlinear stochastic time-delay systems with state and control-dependent noise. Some locally （globally） robustly stabilizable conditions are given in terms of matrix inequalities that are independent of the delay size. When it is applied to linear stochastic time-delay systems, sufficient conditions for the state-feedback stabilization are presented via linear matrix inequalities. Several previous results are extended to more general systems with both state and control-dependent noise, and easy computation algorithms are also given.     

Electric potential in the earth’s ionosphere: a numerical model

Modeling the global distribution of the electric potential in the Earth’s ionosphere is based on the solution of a 2D continuity equation in the ionospheric-magnetospheric current circuit. The potential distribution is described by the boundary value problem for an elliptic system of partial differential equations on the spherical shell approximating the ionosphere, which is divided into three subregions with nonlocal boundary conditions. Implementation of the boundary conditions, which reflect the continuity of the common current circuit and potential equalization at the boundaries of the polar caps, is leading to the mutual dependence of the potential distribution within the northern and southern caps and their influence on the potential distribution in the midlatitude region. The problem is solved by an iterative method with a regularizing operator which is inverted using the separation of the variables and the fast Fourier transform with respect to the azimuthal variable and the sweep method with respect to the latitudinal one.     

On numerical approximation of a variational–hemivariational inequality modeling contact problems for locking materials

This paper is devoted to numerical analysis of a new class of elliptic variational–hemivariational inequalities in the study of a family of contact problems for elastic ideally locking materials. The contact is described by the Signorini unilateral contact condition and the friction is modeled by a nonmonotone multivalued subdifferential relation allowing slip dependence. The problem involves a nonlinear elasticity operator, the subdifferential of the indicator function of a convex set for the locking constraints and a nonconvex locally Lipschitz friction potential. Solution existence and uniqueness result on the inequality can be found in Migórski and Ogorzaly (2017) . In this paper, we introduce and analyze a finite element method to solve the variational–hemivariational inequality. We derive a Céa type inequality that serves as a starting point of error estimation. Numerical results are reported, showing the performance of the numerical method.     

A model-based approach for multiple QoS in scheduling: from models to implementation

Meeting multiple Quality of Service (QoS) requirements is an important factor in the success of complex software systems. This paper presents an automated, model-based scheduler synthesis approach for scheduling application software tasks to meet multiple QoS requirements. As a first step, it shows how designers can meet deadlock-freedom and timeliness requirements, in a manner that (i) does not over-provision resources, (ii) does not require architectural changes to the system, and that (iii) leaves enough degrees of freedom to pursue further properties. A major benefit of our synthesis methodology is that it increases traceability, by linking each scheduling constraint with a specific pair of QoS property and underlying platform execution model, so as to facilitate the validation of the scheduling constraints and the understanding of the overall system behaviour, required to meet further QoS properties.     

Class 2+1 Hybrid BDF-Like methods for the numerical solutions of ordinary differential equations

In this article, the details of new hybrid methods have been presented to solve systems of ordinary differential equations (ODEs). These methods are based on backward differentiation formulae (BDF) where one additional stage point (or off-step point) and two step points have been used in the first derivative of the solution to improve the absolute stability regions compared with some existing methods such as BDF, extended BDF (EBDF) and modified EBDF (MEBDF). Stability domains of our new methods have been obtained showing that these methods, we say Class 2+1 Hybrid BDF-Like methods, are A-stable for order p, p=3,4, and A(α)-stable for order p, p=5, 6, 7, 8. Numerical results are also given for five test problems.     

YAARC: yet another approach to further reducing the rate of conflict misses

Traditional set associative caches are seriously prone to conflict misses. We propose an adapted new skewed associative architecture as an attempt to alleviate this problem. It has already been shown that skewed associative caches can reduce the rate of conflict misses by using different hash functions to index different banks. Building on this observation, we propose yet another approach to further reduce the rate of conflict misses, nicknamed YAARC (Yet Another Approach to Reducing Conflicts) that uses different hash functions to index into a single bank. Mathematical modeling and simulation results are exploited to evaluate the impact of YAARC on the rate of conflict misses. Mathematical analysis show the superiority of YAARC caches over set and skewed associative caches from the conflict miss point of view. Simulations, using some benchmarks from SPEC CPU2000 benchmark suit that former researchers have reported them as the best candidates for cache performance evaluation, also show nearly 43% conflict miss rate improvement for the skewed associative cache over the set associative cache, and nearly 31% improvement for the YAARC cache over the skewed associative cache. This implies that YAARC caches considerably outperform set and skewed associative caches from the conflict miss point of view. Since production of YAARC caches require a dispensable amount of hardware overhead, they can be considered as a cost effective approach to minimize the rate of conflict misses.

The WKB Local Discontinuous Galerkin Method for the Simulation of Schrödinger Equation in a Resonant Tunneling Diode

In this paper, we develop a multiscale local discontinuous Galerkin (LDG) method to simulate the one-dimensional stationary Schrödinger-Poisson problem. The stationary Schrödinger equation is discretized by the WKB local discontinuous Galerkin (WKB-LDG) method, and the Poisson potential equation is discretized by the minimal dissipation LDG (MD-LDG) method. The WKB-LDG method we propose provides a significant reduction of both the computational cost and memory in solving the Schrödinger equation. Compared with traditional continuous finite element Galerkin methodology, the WKB-LDG method has the advantages of the DG methods including their flexibility in h-p adaptivity and allowance of complete discontinuity at element interfaces. Although not addressed in this paper, a major advantage of the WKB-LDG method is its feasibility for two-dimensional devices.     

Boundary variational ‘principles’ for inequality structural analysis problems and numerical applications

The aim of the present paper is the derivation of boundary variational ‘principles’ for inequality problems i.e. for problems having as variational formulations variational or hemivariational inequalities. Using first the principles of minimum potential and complementary energy we derive first saddle point formulations for the problems using appropriate Lagrangians. Then we eliminate by an appropriate elimination technique the internal degrees of freedom and we obtain two minimum ‘principles’ having as unknowns the normal displacements and reactions of the boundary region respectively. Analogously we treat the case of hemivariational inequalities. The theory is applied to the inclusion and inhomogeneity problem and is illustrated by numerical examples solved both by the F.E.M. and the B.E.M.     

A message passing strategy for array redistributions in a torus network

The array redistribution problem occurs in many important applications in parallel computing. In this paper, we consider this problem in a torus network. Tori are preferred to other multidimensional networks (like hypercubes) due to their better scalability (IEE Trans. Parallel Distrib. Syst. 50(10), 1201–1218, [2001]). We present a message combining approach that splits any array redistribution problem in a series of broadcasts where all sources send messages of the same size, thus a balanced traffic load is achieved. Unlike existing array redistribution algorithms, the scheme introduced in this work eliminates the need for data reorganization in the memory of the source and target processors. Moreover, the processing of the scheduled broadcasts is pipelined, thus the total cost of redistribution is reduced.

Knows what it knows: a framework for self-aware learning

We introduce a learning framework that combines elements of the well-known PAC and mistake-bound models. The KWIK (knows what it knows) framework was designed particularly for its utility in learning settings where active exploration can impact the training examples the learner is exposed to, as is true in reinforcement-learning and active-learning problems. We catalog several KWIK-learnable classes as well as open problems, and demonstrate their applications in experience-efficient reinforcement learning.     

Constructing exponential estimates in compartmental systems with distributed delays: an approach based on the hale–lunel inequality

This paper presents a method for constructing exponential estimates in a compartmental system with distributed delays on the basis of the Hale–Lunel inequality and its application. The practical importance of this study is illustrated by a pharmacokinetic model from anesthesiology.     

Don’t care words with an application to the automata-based approach for real addition

Automata have proved to be a useful tool in infinite-state model checking, since they can represent infinite sets of integers and reals. However, analogous to the use of binary decision diagrams (bdds) to represent finite sets, the sizes of the automata are an obstacle in the automata-based set representation. In this article, we generalize the notion of “don’t cares” for bdds to word languages as a means to reduce the automata sizes. We show that the minimal weak deterministic Büchi automaton (wdba) with respect to a given don’t care set, under certain restrictions, is uniquely determined and can be efficiently constructed. We apply don’t cares to improve the efficiency of a decision procedure for the first-order logic over the mixed linear arithmetic over the integers and the reals based on wdbas.     

A new model for the integrated vehicle-crew-rostering problem and a computational study on rosters

Operational planning within public transit companies has been extensively tackled but still remains a challenging area for operations research models and techniques. This phase of the planning process comprises vehicle-scheduling, crew-scheduling and rostering problems. In this paper, a new integer mathematical formulation to describe the integrated vehicle-crew-rostering problem is presented. The method proposed to obtain feasible solutions for this binary non-linear multi-objective optimization problem is a sequential algorithm considered within a preemptive goal programming framework that gives a higher priority to the integrated vehicle-crew-scheduling goal and a lower priority to the driver rostering goals. A heuristic approach is developed where the decision maker can choose from different vehicle-crew schedules and rosters, while respecting as much as possible management’s interests and drivers’ preferences. An application to real data of a Portuguese bus company shows the influence of vehicle-crew-scheduling optimization on rostering solutions.     

Schooling Is the Problem: A Plaidoyer for Its Deinstitutionalization

Abstract:

Education appears to be in a perpetual crisis. In this article, I suggest that one of the key contributing factors in educational crisis is the institution of schooling, which re/produces the failures to learn and, thereby, contributes to the re/production of inequities that it (schooling) is supposed to overcome. Ideologies and practices intended to bring about equity through schooling re/produce societal inequity. I show how activity theory allows us to understand that the origin of inequities lies in societal relations, which are relations of ruling. The implications are that a real change of schooling does not come from this or that curricular change but in a reformation of schooling as societal activity generally and science and mathematics education specifically. In ending, I suggest using the deinstitutionalization of psychiatric care as a model for rethinking schooling. Thus, as Bazzul (this issue), I make the beginning of a plaidoyer for redrawing the lines of society by rethinking the very institution of schooling rather than its curriculum contents and practices.     

Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications

Interacting and annealing are two powerful strategies that are applied in different areas of stochastic modelling and data analysis. Interacting particle systems approximate a distribution of interest by a finite number of particles where the particles interact between the time steps. In computer vision, they are commonly known as particle filters. Simulated annealing, on the other hand, is a global optimization method derived from statistical mechanics. A recent heuristic approach to fuse these two techniques for motion capturing has become known as annealed particle filter. In order to analyze these techniques, we rigorously derive in this paper two algorithms with annealing properties based on the mathematical theory of interacting particle systems. Convergence results and sufficient parameter restrictions enable us to point out limitations of the annealed particle filter. Moreover, we evaluate the impact of the parameters on the performance in various experiments, including the tracking of articulated bodies from noisy measurements. Our results provide a general guidance on suitable parameter choices for different applications.