Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

The importance of efficient logistics management for chemical industry considered to be one of the important issues inprocess industries. Motivated from a chemical factory operating in Turkey, cost efficient daily logistics planning usingmultiple third party logistics providers (3PL) with different contracting schemes are analyzed. In order to reduceplanning time and maximize the physical vehicle capacity utilization, problem of the company is formulated as a mixedinteger mathematical model, and a two-phase solution approach is proposed. Using the real life daily shipmentrequirement data the proposed model and the solution methodology is tested. Furthermore, impact of variouscompany practices and types of different transportation pricing schemes are analyzed to better design 3PL contracts.The initial results are very promising where optimum solution for large scaled problem can be obtained in seconds andthe daily shipment planning can be updated dynamically whenever it is necessary. The analysis indicates that costsavings through holistic planning is robust to contracting schemes and specific clauses are not always needed toguarantee certain service quality. We believe that the efficiency achieved through the integration of such techniquescan become highly attractive for further applications in the industry.

This paper describes the simulation of movement control of a one-degree-of-freedom articulated robot arm SCARAactuated by a pair of McKibben pneumatic artificial muscles. The pneumatic artificial muscle is the actuator andemulates the behavior of biological muscles; due to its nonlinear behavior, there is also a need to develop controlsystems for robot arms using this type of actuator. Research begins with the transfer function that represents, inmathematical language, the movement of the robot arm’s joints; this allows using a PID controller on the transferfunction and generating data to train the Multilayer Perceptron Artificial Neural Network (RNAPM). So far, the PIDcontrol system has been able to control the movement of robot arms but, based on experimental tests, the RNAPMhas proved to outperform the PID control’s response time by up to 2.95 seconds, minimizing the angular error by 1.3°and avoiding the oscillation problem due to its continuous, constant behavior.

This research intends to find out any development of a robust multi-objective for lead time optimal control problem in amulti-stage assembly system model. Assembly system modeling is possible by the help of the open queue network. Aworking station includes one or infinite servers and just manufacturing or assembly operations are performed therein.Each part has a separate entry process and independent of each other. It is completely based upon Poissonprocess.Serving Lead Time of Stations are also independent of each other and therefore exponential distribution ofeach parameter is controllable. All stations have bounded uncertain unrecyclable wastes which are completelyindependent in compliance with Erlang distribution. Uncertainty in the problem parameters has been suggested asrobust multi-objective optimal control model in which we have three incompatible target functions including cyclicoperation cost minimization, average lead time minimization and lead time variance. Finally, target progress methodhas been applied in order to achieve serving optimal speeds and solve discrete time of the main problemapproximately. The proposed model could present a suitable solution even for the same problem as mentioned inother related papers along with some considerable results in parameter uncertainty conditions.

This paper proposes a structural equation model to assess the effects of some risk factors in the supply chainperformance. The model includes demand, suppliers and processes as risk factors of a case study in Ciudad Juarez,Chihuahua, Mexico. The model, assessed following a structural equation modeling methodology (using AMOS 16.0),indicated that the demand (considered as an independent factor) has a direct positive relationship with suppliers,politics, and manufacturing factors. As a consequence the suppliers have an effect on the flexibility factor. Theflexibility has a direct positive relationship with the customer service factor. The results also indicate that theinfrastructure factor does not have any relationship with the others assessed factors. The study has importantimplications for researches and practitioners in the manufacturing sector. It allows evaluating risk activities that havenegative effects on the performance of supply chain in manufacturing exports companies in Mexico.

The production of soft drinks involves two main stages: syrup preparation and bottling. To obtain the lots sequence inthe bottling stage, three approaches are studied. They are based on the sub-tour elimination constraints used inmathematical models for the Asymmetric Traveling Salesman Problem. Two of the mathematical models are from theliterature and use classical constraints. The third model includes multi-commodity flow constraints to eliminatedisconnected subsequences. The computational behavior of the three models is studied using instances generatedwith data from the literature. The numerical results show that there are considerable differences among the threemodels and indicates that the multi-commodity formulation provides good results but it requires far morecomputational effort when the instances are solved by a commercial software.

This paper presents a method for data model partitioning of power distribution network. Modern DistributionManagement Systems which utilize multiprocessor systems for efficient processing of large data model areconsidered. The data model partitioning is carried out for parallelization of analytical power calculations. The proposedalgorithms (Particle Swarm Optimization (PSO) and distributed PSO algorithms) are applied on data model describinglarge power distribution network. The experimental results of PSO and distributed PSO algorithms are presented.Distributed PSO algorithm achieves significantly better results than the basic PSO algorithm.

This article deals with the relationship between a flood risk assessment and the humanitarian logistics process designrelated to emergency events caused by flooding. The magnitude and timing of the flooding is estimated using aforecasting model that requires a hydrologic component to convert rainfall into runoff as well as a hydraulic componentto route the flow through the stream network predicting time and severity of the flood wave. Once these componentsare known, and with the intention of mitigating the impact of the flood wave on the population, we provide the relevantaspects to define humanitarian aid and evacuation plans including processes and metrics of it. Finally, an examplethat integrates both methodologies is included.

Knowledge extraction by just listening to sounds is a distinctive property. Speech signal is more effective means ofcommunication than text because blind and visually impaired persons can also respond to sounds. This paper aims todevelop a cost effective, and user friendly optical character recognition (OCR) based speech synthesis system. TheOCR based speech synthesis system has been developed using Laboratory virtual instruments engineeringworkbench (LabVIEW) 7.1.

This work deals with the light pipe problem. In this problem it is necessary to both put circles inside circles and circlesinside a rectangle. A mathematical model that considers the thickness of the pipes is introduced. This generates anew optimization problem that is harder to solve than other packing problems. The mathematical description of theproblem is introduced and some ideas of how to solve the problem are explained.

Bayesian Belief Network (BBN) is an appealing classification model for learning causal and noncausal dependencies among a set of query variables. It is a challenging task to learning BBN structure from observational data because of pool of large number of candidate network structures. In this study, we have addressed the issue of goodness of data fitting versus model complexity. While doing so, we have proposed discriminant function which is non-parametric, free of implicit assumptions but delivering better classification accuracy in structure learning. The contribution in this study is twofold, first contribution (discriminant function) is in BBN structure learning and second contribution is for Decision Stump classifier. While designing the novel discriminant function, we analyzed the underlying relationship between the characteristics of data and accuracy of decision stump classifier. We introduced a meta characteristic measure AMfDS (herein known as Affinity Metric for Decision Stump) which is quite useful in prediction of classification accuracy of Decision Stump. AMfDS requires a single scan of the dataset.