New simple and efficient heuristics for the uncapacitated single allocation hub location problem

Hub-and-spoke networks are widely studied in the area of location theory. They arise in several contexts, including passenger airlines, postal and parcel delivery, and computer and telecommunication networks. Hub location problems usually involve three simultaneous decisions to be made: the optimal number of hub nodes, their locations and the allocation of the non-hub nodes to the hubs. In the uncapacitated single allocation hub location problem (USAHLP) hub nodes have no capacity constraints and non-hub nodes must be assigned to only one hub. In this paper, we propose three variants of a simple and efficient multi-start tabu search heuristic as well as a two-stage integrated tabu search heuristic to solve this problem. With multi-start heuristics, several different initial solutions are constructed and then improved by tabu search, while in the two-stage integrated heuristic tabu search is applied to improve both the locational and allocational part of the problem. Computational experiments using typical benchmark problems (Civil Aeronautics Board (CAB) and Australian Post (AP) data sets) as well as new and modified instances show that our approaches consistently return the optimal or best-known results in very short CPU times, thus allowing the possibility of efficiently solving larger instances of the USAHLP than those found in the literature. We also report the integer optimal solutions for all 80 CAB data set instances and the 12 AP instances up to 100 nodes, as well as for the corresponding new generated AP instances with reduced fixed costs. Published by Elsevier Ltd.

Robust algorithms for solving stochastic partial differential equations

A robust semi-implicit central partial difference algorithm for the numerical solution of coupled stochastic parabolic partial differential equations (PDEs) is described. This can be used for calculating correlation functions of systems of interacting stochastic fields. Such field equations can arise in the description of Hamiltonian and open systems in the physics of nonlinear processes, and may include multiplicative noise sources. The algorithm can be used for studying the properties of nonlinear quantum or classical field theories. The general approach is outlined and applied to a specific example, namely the quantum statistical fluctuations of ultra-short optical pulses in chi((2)) parametric waveguides. This example uses a non-diagonal coherent state representation, and correctly predicts the sub-shot noise level spectral fluctuations observed in homodyne detection measurements. It is expected that the methods used wilt be applicable for higher-order correlation functions and other physical problems as well. A stochastic differencing technique for reducing sampling errors is also introduced. This involves solving nonlinear stochastic parabolic PDEs in combination with a reference process, which uses the Wigner representation in the example presented here. A computer implementation on MIMD parallel architectures is discussed. (C) 1997 Academic Press.

Rehabilitation of executive dysfunction: A controlled trial of an attention and problem solving treatment group

In this study, the effectiveness of a group-based attention and problem solving (APS) treatment approach to executive impairments in patients with frontal lobe lesions was investigated. Thirty participants with lesions in the frontal lobes, 16 with left frontal (LF) and 14 with right frontal (RF) lesions, were allocated into three groups, each with 10 participants. The APS treatment was initially compared to two other control conditions, an information/education (IE) approach and treatment-as-usual or traditional rehabilitation (TR), with each of the control groups subsequently receiving the APS intervention in a crossover design. This design allowed for an evaluation of the treatment through assessment before and after treatment and on follow up, six months later. There was an improvement on some executive and functional measures after the implementation of the APS programme in the three groups. Size, and to a lesser extent laterality, of lesion affected baseline performance on measures of executive function, but there was no apparent relationship between size, laterality or site of lesion and level of benefit from the treatment intervention. The results were discussed in terms of models of executive functioning and the effectiveness of domain specific interventions in the rehabilitation of executive dysfunction.

Solving MPCC problem with the hyperbolic penalty function

The main goal of this work is to solve mathematical program with complementarity constraints (MPCC) using nonlinear programming techniques (NLP). An hyperbolic penalty function is used to solve MPCC problems by including the complementarity constraints in the penalty term. This penalty function [1] is twice continuously differentiable and combines features of both exterior and interior penalty methods. A set of AMPL problems from MacMPEC [2] are tested and a comparative study is performed.

Solving a signalized traffic intersection problem with an hyperbolic penalty function

Mathematical Program with Complementarity Constraints (MPCC) finds many applications in fields such as engineering design, economic equilibrium and mathematical programming theory itself. A queueing system model resulting from a single signalized intersection regulated by pre-timed control in traffic network is considered. The model is formulated as an MPCC problem. A MATLAB implementation based on an hyperbolic penalty function is used to solve this practical problem, computing the total average waiting time of the vehicles in all queues and the green split allocation. The problem was codified in AMPL.

Multi-agent system for distributed manufacturing scheduling with genetic algorithms and tabu search

Computerized scheduling methods and computerized scheduling systems according to exemplary embodiments. A computerized scheduling method may be stored in a memory and executed on one or more processors. The method may include defining a main multi-machine scheduling problem as a plurality of single machine scheduling problems; independently solving the plurality of single machine scheduling problems thereby calculating a plurality of near optimal single machine scheduling problem solutions; integrating the plurality of near optimal single machine scheduling problem solutions into a main multi-machine scheduling problem solution; and outputting the main multi-machine scheduling problem solution.

A public transport bus assignment problem: parallel metaheuristics assessment

Combinatorial Optimization Problems occur in a wide variety of contexts and generally are NP-hard problems. At a corporate level solving this problems is of great importance since they contribute to the optimization of operational costs. In this thesis we propose to solve the Public Transport Bus Assignment problem considering an heterogeneous fleet and line exchanges, a variant of the Multi-Depot Vehicle Scheduling Problem in which additional constraints are enforced to model a real life scenario. The number of constraints involved and the large number of variables makes impracticable solving to optimality using complete search techniques. Therefore, we explore metaheuristics, that sacrifice optimality to produce solutions in feasible time. More concretely, we focus on the development of algorithms based on a sophisticated metaheuristic, Ant-Colony Optimization (ACO), which is based on a stochastic learning mechanism. For complex problems with a considerable number of constraints, sophisticated metaheuristics may fail to produce quality solutions in a reasonable amount of time. Thus, we developed parallel shared-memory (SM) synchronous ACO algorithms, however, synchronism originates the straggler problem. Therefore, we proposed three SM asynchronous algorithms that break the original algorithm semantics and differ on the degree of concurrency allowed while manipulating the learned information. Our results show that our sequential ACO algorithms produced better solutions than a Restarts metaheuristic, the ACO algorithms were able to learn and better solutions were achieved by increasing the amount of cooperation (number of search agents). Regarding parallel algorithms, our asynchronous ACO algorithms outperformed synchronous ones in terms of speedup and solution quality, achieving speedups of 17.6x. The cooperation scheme imposed by asynchronism also achieved a better learning rate than the original one.

The non-emergency patient transport modelled as a team orienteering problem

This work presents an improved model to solve the non-emergency patients transport (NEPT) service issues given the new rules recently established in Portugal. The model follows the same principle of the Team Orienteering Problem by selecting the patients to be included in the routes attending the maximum reduction in costs when compared with individual transportation. This model establishes the best sets of patients to be transported together. The model was implemented in AMPL and a compact formulation was solved using NEOS Server. A heuristic procedure based on iteratively solving Orienteering Problems is presented, and this heuristic provides good results in terms of accuracy and computation time. Euclidean instances as well as asymmetric real data gathered from Google maps were used, and the model has a promising performance mainly with asymmetric cost matrices.

Comprensión en resolución de problemas en Física: niveles de representación e inferencias basadas en el conocimiento. Comprehension in Physics Problem-Solving: levels of representation and knowledge-based inferences.

Se propone desarrollar e integrar estudios sobre Modelado y Resolución de Problemas en Física que asumen como factores explicativos: características de la situación planteada, conocimiento de la persona que resuelve y proceso puesto en juego durante la resolución. Interesa comprender cómo los estudiantes acceden al conocimiento previo, qué procedimientos usan para recuperar algunos conocimientos y desechar otros, cuáles son los criterios que dan coherencia a sus decisiones, cómo se relacionan estas decisiones con algunas características de la tarea, entre otras. Todo ello con miras a estudiar relaciones causales entre las dificultades encontradas y el retraso o abandono en las carreras.Se propone organizar el trabajo en tres ejes, los dos primeros de construcción teórica y un tercero de implementación y transferencia. Se pretende.1.-Estudiar los procesos de construcción de las representaciones mentales en resolución de problemas de física, tanto en expertos como en estudiantes de diferentes niveles académicos.2.-Analizar y clasificar las inferencias que se producen durante las tareas de comprensión en resolución de problemas de física. Asociar dichas inferencias con procesos de transición entre representaciones mentales de diferente naturaleza.3.-Desarrollar materiales y diseños instruccionales en la enseñanza de la Física, fundamentado en un conocimiento de los requerimientos psicológicos de los estudiantes en diversas tareas de aprendizaje.En términos generales se plantea un enfoque interpretativo a la luz de marcos de la psicología cognitiva y de los desarrollos propios del grupo. Se trabajará con muestras intencionales de alumnos y profesores de física. Se utilizarán protocolos verbales y registros escritos producidos durante la ejecución de las tareas con el fin de identificar indicadores de comprensión, inferencias, y diferentes niveles de representación. Se prevé analizar material escrito de circulación corriente sea comercial o preparado por los docentes de las carreras involucradas.Las características del objeto de estudio y el distinto nivel de desarrollo en que se encuentran los diferentes ojetivos específicos llevan a que el abordaje contemple -según consideracion de Juni y Urbano (2006)- tanto la lógica cualitativa como la cuantitativa.

An Empirical Examination of the R&D Boundaries of the Firm - A Problem-Solving Perspective

We consider, both theoretically and empirically, how different organization modes are aligned to govern the efficient solving of technological problems. The data set is a sample from the Chinese consumer electronics industry. Following mainly the problem solving perspective (PSP) within the knowledge based view (KBV), we develop and test several PSP and KBV hypotheses, in conjunction with competing transaction cost economics (TCE) alternatives, in an examination of the determinants of the R&D organization mode. The results show that a firm’s existing knowledge base is the single most important explanatory variable. Problem complexity and decomposability are also found to be important, consistent with the theoretical predictions of the PSP, but it is suggested that these two dimensions need to be treated as separate variables. TCE hypotheses also receive some support, but the estimation results seem more supportive of the PSP and the KBV than the TCE.

The association between need for touch and desire for unique products and consumer (inter)dependent problem-solving

Some people cannot buy products without first touching them, believing that doing so will create more assurance and information and reduce uncertainty. The international consumer marketing literature suggests an instrument to measure consumers' necessity for pohysical contact, called Need for Touch (NFT). This paper analyzes whether the Need for Touch structure is empirically consistent. Based on a literature review, we suggest six hypotheses in order to assess the nomological, convergent, and discriminant validity of the phenomenon. Departing from these, data supported four assumptions in the predicted direction. Need for Touch was associated with Need for Input and with Need for Cognition. Need for Touch was not associated with traditional marketing channels. The results also showed the dual characterization of Need for Touch as a bi-dimensional construct. The moderator effect indicated that when the consumer has a higher (vs. lower) Need for Touch autotelic score, the experiential motivation for shopping played a more (vs. less) important role in impulsive motivation. Our Study 3 supports the NFT structure and shows new associations with the need for unique products and dependent decisions.

Solving Large Location-Allocation problems by Clustering and Simulated Annealing

Globalization involves several facility location problems that need to be handled at large scale. Location Allocation (LA) is a combinatorial problem in which the distance among points in the data space matter. Precisely, taking advantage of the distance property of the domain we exploit the capability of clustering techniques to partition the data space in order to convert an initial large LA problem into several simpler LA problems. Particularly, our motivation problem involves a huge geographical area that can be partitioned under overall conditions. We present different types of clustering techniques and then we perform a cluster analysis over our dataset in order to partition it. After that, we solve the LA problem applying simulated annealing algorithm to the clustered and non-clustered data in order to work out how profitable is the clustering and which of the presented methods is the most suitable

Metaheuristics for the bus-driver scheduling problem

We present new metaheuristics for solving real crew scheduling problemsin a public transportation bus company. Since the crews of thesecompanies are drivers, we will designate the problem by the bus-driverscheduling problem. Crew scheduling problems are well known and severalmathematical programming based techniques have been proposed to solvethem, in particular using the set-covering formulation. However, inpractice, there exists the need for improvement in terms of computationalefficiency and capacity of solving large-scale instances. Moreover, thereal bus-driver scheduling problems that we consider can present variantaspects of the set covering, as for example a different objectivefunction, implying that alternative solutions methods have to bedeveloped. We propose metaheuristics based on the following approaches:GRASP (greedy randomized adaptive search procedure), tabu search andgenetic algorithms. These metaheuristics also present some innovationfeatures based on and genetic algorithms. These metaheuristics alsopresent some innovation features based on the structure of the crewscheduling problem, that guide the search efficiently and able them tofind good solutions. Some of these new features can also be applied inthe development of heuristics to other combinatorial optimizationproblems. A summary of computational results with real-data problems ispresented.