Programming In Conjunction Research Articles

Mixed‐integer linear programming optimization for the Supply Chain Game

AbstractIn this article, we describe the use of linear programming in conjunction with an online simulation game in an undergraduate supply chain management class. Supply chain management is a required course for students enrolled in a Business Administration program with a concentration in management. It delivers a broad overview of the supply chain management discipline, using a simulation game by Responsive Learning Technologies to provide students with hands‐on experience designing and managing a supply chain. In this teaching brief, I give an overview of the game and present the formulation of an accompanying mixed‐integer linear programming model to design an optimal supply chain that meets customers, demand while maximizing company profit. Students use a spreadsheet template to develop their own implementation of the model and then apply the optimal solution during the simulation. Consequently, they gain an understanding of the role of network optimization in supply chain management. I conclude with recommendations for management of game activities using this model and a brief discussion of some of the issues to be monitored as the game is played.

A hybrid prediction model for wind speed using support vector machine and genetic programming in conjunction with error compensation

Wind energy resources are a clean renewable energy source. Accurate prediction of wind speed has important theoretical significance and practical value for sustainable use of wind energy, energy planning, and economic development. This paper proposes a hybrid model for prediction of wind speed using support vector machine and genetic programming models, with error compensation for the prediction residuals. Wind speed and the residuals generated using the hybrid model are simulated and predicted. The proposed model is applied to monthly average wind speed data observed over the period January 2005–December 2014 from the Tuoli and Hetian stations in Xinjiang, China. For each time series, eight prediction schemes, including independent support vector machine model and genetic programming model and their combination, were tried. At the same time, phase space reconstruction method was used to select model input factors. The results clearly show the performance differences of the eight wind speed prediction schemes in the prediction effect of the two time series. The combination of the support vector machine wind speed prediction model and the genetic programming residual prediction model has the best prediction effect, with a correlation coefficient value above 0.9, mean square value below 0.1, and mean absolute error value below 0.2. This combined model is found to be quite efficient and, therefore, the proposed approach is highly suitable to predict the monthly average wind speed.

Scheduling-based power capping in high performance computing systems

Supercomputer installed capacity worldwide increased for many years and further growth is expected in the future. The next goal for high performance computing (HPC) systems is reaching Exascale. The increase in computational power threatens to lead to unacceptable power demands, if future machines will be built using current technology. Therefore reducing supercomputer power consumption has been the subject of intense research. A common approach to curtail the excessive power demands of supercomputers is to hard-bound their consumption, power capping. Power capping can be enforced by reactively throttling system performance when the power bound is hit, or by scheduling workload in a proactive fashion to avoid hitting the bound. In this paper we explore the second approach: our scheduler meets power capping constraints and minimizes quality-of-service (QoS) disruption through smart planning of the job execution order. The approach is based on constraint programming in conjunction with a machine learning module predicting the power consumptions of HPC applications. We evaluate our method on the Eurora supercomputer, using both synthetic workloads and historical traces. Our approach outperforms the state-of-the-art power capping techniques in terms of waiting time and QoS, while keeping schedule computation time under control.

Kliniczne zastosowanie multimodalnego zestawu testów elektrofizjologicznych w celu przewidywania optymalnych poziomów odpowiedzi behawioralnych u użytkowników implantów ślimakowych

ObjectivesIndications for cochlear implantation have expanded to include very young children and those with syndromes or multiple handicaps. In such cases programming the implant based on behavioral responses may be tedious, wherein matching effective and appropriate measurable auditory percepts (maps) and becomes the key issue in the rehabilitation program. In ‘difficult to map’ scenarios, objective measures become paramount for predicting optimal current levels to be set in the map. We aimed (a) to study the trends in multi-modal electrophysiological tests and behavioral responses sequentially over the first year of implant use, (b) to generate normative data from the above, (c) to correlate the multi-modal electrophysiological thresholds levels with behavioral comfort levels, and (d) to create predictive formulas for deriving optimal comfort levels (if unknown), using linear and multiple regression analysis.Materials and MethodsThis prospective study included 10 profoundly hearing impaired children aged 2 to 7 years with normal inner ear anatomy and no additional handicaps. They received the Advanced Bionics HiRes 90K implant with Harmony speech processor and used the HiRes-P with Fidelity 120 strategy. They underwent impedance telemetry, neural response imaging, electrically evoked stapedial response telemetry, and electrically evoked auditory brainstem response tests at 1, 4, 8, and 12 months of implant use, in conjunction with behavioral mapping. Trends in electrophysiological and behavioral responses were analysed using paired t-tests. Using Pearson’s correlation method, electrode-wise correlations were derived for NRI thresholds versus M-levels, and offset-based (apical, mid-array, and basal array) correlations for EABR and ESRT thresholds versus M-Levels were calculated over time. These were used to derive predictive formulae by linear and multiple regression analysis. Such statistically predicted M-levels were compared with the behaviorally recorded M-levels among the cohort, using Cronbach’s alpha reliability test method for confirming the efficacy of this method.ResultsNRI, ESRT, and EABR thresholds showed statistically significant positive correlations with behavioral M-levels, which improved with implant use over time. These correlations were used to derive predicted M-levels using regression analysis. Such predicted M-levels were found to be close to the actual behavioral M-levels recorded among this cohort and proved to be statistically reliable.ConclusionsThe study has explored the trends and correlations between electrophysiological tests and behavioral responses, recorded over time among a cohort of cochlear implantees. It provides a statistical method which may be used as a guideline to predict optimal behavioral levels in difficult situations among future implantees. In ‘difficult to map’ scenarios, the best outcomes will come from following a protocol of sequential behavioral programming in conjunction with electrophysiological correlates.

On optimal control policy for probabilistic Boolean network: a state reduction approach

BackgroundProbabilistic Boolean Network (PBN) is a popular model for studying genetic regulatory networks. An important and practical problem is to find the optimal control policy for a PBN so as to avoid the network from entering into undesirable states. A number of research works have been done by using dynamic programming-based (DP) method. However, due to the high computational complexity of PBNs, DP method is computationally inefficient for a large size network. Therefore it is natural to seek for approximation methods.ResultsInspired by the state reduction strategies, we consider using dynamic programming in conjunction with state reduction approach to reduce the computational cost of the DP method. Numerical examples are given to demonstrate both the effectiveness and the efficiency of our proposed method.ConclusionsFinding the optimal control policy for PBNs is meaningful. The proposed problem has been shown to be . By taking state reduction approach into consideration, the proposed method can speed up the computational time in applying dynamic programming-based algorithm. In particular, the proposed method is effective for larger size networks.

Optimization of Continuous Post-Tensioned Concrete Bridge Girders of Non-Uniform Depth

Post-tensioned single-cell concrete box girders of variable depth are used in continuous bridges to achieve both economy and aesthetics. In this paper, the minimum prestressing for a given two- or three-span continuous bridge deck profile is achieved through a constrained optimization procedure using linear programming in conjunction with gradient method of optimization. The constrained nonlinear problem is solved by transforming it to a linear one by introducing a new design variable which proportions long (full length) and short tendons. A gradient search technique is used to solve iteratively the optimization problem. The design variables include interior to exterior span ratio (for three-span), the depth profile of the cross-section, tendon profile, and prestressing force. The total cost of the member is taken as the cost of structural materials (concrete and prestressing steel), excluding the cost of formwork. The design constraints are the prescribed limits on the allowable stresses, ultimate strength, and serviceability requirements as per ACI 343R-95. A purpose-built computer program has been developed to readily determine the optimum prestressing for a given bridge deck profile. From solutions of a number of hypothetical bridge girders using AASHTO H20 bridge loading, several useful results related to design are obtained. Results show that for a symmetrical three-span and two-span continuous bridge girder, the optimum prestressing consists of an optimum arrangement of both long and short tendons, and for a three-span girder of prescribed length, the optimum design is attained at an optimum ratio of interior to exterior span with 1.35 to 1.45.

Blocking Mixed-Level Factorials withSAS

Orthogonal array screening designs for mixed-level or asymmetric factorials have recently become popular. Tables of designs and software for creating these designs are readily available to practitioners. However, confounded block designs for mixed-level factorials are not as popular partly due to the fact that software for creating these designs has not been well publicized. Classical methods for creating confounded-block mixed-level factorials normally described in textbooks utilize modular arithmetic or finite fields. In the recent literature optimal design theory has also been proposed as method for creating these designs. Although no examples are shown in the online documentation, both classical and optimal confounded-block mixed-level factorials can be easily created using SAS data step programming in conjunction with proc plan, proc factex or proc optex. In this article we show examples of creating these designs in SAS, and we compare the properties of designs created by classical methods and optimal design theory.

Mortality Rates, Prevalence of Malnutrition, and Prevalence of Lost Pregnancies among the Drought-Ravaged Population of Tete Province, Mozambique

Tete Province, Mozambique has experienced chronic food insecurity and a dramatic fall in livestock numbers due to the cyclic problems characterized by the floods in 2000 and severe droughts in 2002 and 2003. The Province has been a beneficiary of emergency relief programs, which have assisted > 22% of the population. However, these programs were not based on sound epidemiological data, and they have not established baseline data against which to assess the impact of the programs. The objective of this study was to document mortality rates, causes of death, the prevalence of malnutrition, and the prevalence of lost pregnancies after 2.5 years of humanitarian response to the crisis. A two-stage, 30-cluster household survey was conducted in the Cahora Bassa and Changara districts from 22 October to 08 November 2004. A total of 838 households were surveyed, with a population size of 4,688 people. Anthropometric data were collected among children 6-59 months of age. In addition, crude mortality rates (CMRs), under five mortality rates (U5MRs), causes of deaths, and prevalence of lost pregnancies were determined among the sample population. The prevalence of malnutrition was 8.0% (95% confidence interval (CI) = 6.2-9.8%) for acute malnutrition, 26.9% (95% CI = 24.0-29.9%) for being underweight, and 37.0% (95% CI = 33.8-40.2%) for chronic malnutrition. Boys were more likely to be underweight than were girls (odds ratio (OR) = 1.34; 95% CI = 1.00, 1.82; p < 0.05) after controlling for age, household size, and food aid beneficiary status. Similarly, children 30-59 months of age were significantly less likely to suffer from acute malnutrition (OR = 0.45; 95% CI = 0.26, 0.79; p < 0.01) and less likely to be underweight (OR = 0.37; 95% CI = 0.27, 0.51; p < 0.01) than children 6-29 months of age, after adjusting for the other, aforementioned factors. The proportion of lost pregnancies was estimated at 7.7% (95% CI = 4.5-11.0%). A total of 215 deaths were reported during the year preceding the survey. Thirty-nine (18.1%) children < 5 years of age died. The CMR was 1.23/10,000/day (95% CI = 1.08-1.38), and an U5MR was 1.03/10,000/day (95% CI = 0.71-1.35). Diarrheal diseases, malaria, tuberculosis, and human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) accounted for more than two-thirds of all deaths. The observed CMR in Tete Province, Mozambique is three times higher than the baseline rate for sub-Saharan Africa and 1.4 times higher than the CMR cut-off point used to define excess mortality in emergencies. The current humanitarian response in Tete Province would benefit from an improved alignment of food aid programming in conjunction with diarrheal disease control, HIV/AIDS, and malaria prevention and treatment programs. The impact of the food programs would be improved if mutually acceptable food aid program objectives, verifiable indicators relevant to each objective, and beneficiary targets and selection criteria are developed. Periodic re-assessments and evaluations of the impact of the program and evidenced-based decision-making urgently are needed to avert a chronic dependency on food aid.

Life cycle cost analysis based optimal maintenance and rehabilitation for underground infrastructure management

This study presents a sanitary sewer management decision-making framework incorporating demand forecasting and life cycle cost analysis. The framework provides the asset managers with an alternative approach in sewer management. It is designed to allow asset managers to better allocate limited funds for maintenance and rehabilitation by identifying possible problematic sewers and devising a maintenance plan to prevent costly sewer failures. Sewer demand forecasting model is developed using an artificial neural network. The forecasted sewer demand is then used to identify “critical” areas, where the current hydraulic capacity is less than the forecasted sewer demand. In such areas, an optimal maintenance and rehabilitation strategy is developed through the application of probabilistic dynamic programming in conjunction with Markov chain deterioration modeling.

Learning tactical human behavior through observation of human performance

It is widely accepted that the difficulty and expense involved in acquiring the knowledge behind tactical behaviors has been one limiting factor in the development of simulated agents representing adversaries and teammates in military and game simulations. Several researchers have addressed this problem with varying degrees of success. The problem mostly lies in the fact that tactical knowledge is difficult to elicit and represent through interactive sessions between the model developer and the subject matter expert. This paper describes a novel approach that employs genetic programming in conjunction with context-based reasoning to evolve tactical agents based upon automatic observation of a human performing a mission on a simulator. In this paper, we describe the process used to carry out the learning. A prototype was built to demonstrate feasibility and it is described herein. The prototype was rigorously and extensively tested. The evolved agents exhibited good fidelity to the observed human performance, as well as the capacity to generalize from it.

Perturbation-based stochastic FE analysis and robust design of inelastic deformation processes

The study addresses the perturbation-based stochastic finite element analysis and the robust design optimization of deformation processes of inelastic solids. The perturbation equations for the stochastic moment analysis of both steady-state and non-stationary processes are presented. An iteration scheme based on the secant system operator is given for the solution of the perturbation equations in case that a tangent matrix is not available. The robust design of deformation processes is stated as a two-criteria optimal design problem that attempts best mean properties of the outcome at minimum variability. The task is solved using optimization techniques based on sequential quadratic programming in conjunction with the stochastic finite element analysis. The proposed method is applied to the design of an extrusion die for robustness with respect to friction variability, and to a workpiece preform design problem. The numerical results show the potential of the method for applications regarding the design of robust deformation processes.

Application of dynamic programming and simulation for sewer management

This paper proposes a decision‐making framework to assist asset managers in decision making regarding sewer maintenance/rehabilitation (M&amp;R) plans under constraints of limited access to sewer condition data. It discusses the application of probabilistic dynamic programming in conjunction with a Markov chain model to analyze the life cycle cost of combined sewer systems. M&amp;R issues have traditionally been addressed with a crisis‐based approach, but this study contributes to sewer infrastructure management efforts in developing a management system based on life cycle cost analysis. The framework includes the optimal M&amp;R techniques for sewer projects and the optimal times of application. The role of simulation is also explored to obtain the variability of the total cost. By knowing the expected costs and their variabilities, a deeper understanding of life cycle costs of sewer infrastructure can be obtained. The model’s capability is enhanced further by testing its sensivitity to varying discount and inflation rates.

OPTIMUM POST-TENSIONING FOR THREE-SPAN CONTINUOUS SLAB-TYPE BRIDGE DECKS

An optimization procedure is prescribed in this paper to find the minimum prestressing force and the corresponding tendon profile required for three-span continuous slab-type bridge decks using a combination of both long and short tendons. The constrained nonlinear problem is solved by first transforming it into a linear one with the introduction of a new design variable which defines the proportion of long and short tendons. The problem is then solved iteratively by linear programming in conjunction with a gradient search technique. The results show that for a three-span continuous bridge deck with the interior span longer than the exterior one, the optimum prestressing consists of a combination of both long and short tendons. For the three-span bridge of a prescribed length, the procedure determines the optimal ratio of the interior to the exterior span for which the required prestressing force is minimum.

Optimization of electrical distribution networks fed by conventional and renewable energy sources

This paper provides a methodology for the optimization of an existing electrical distribution network when upgraded by renewable energies. The contribution of renewable energy in electricity generation is decided upon through both network design optimization and proper load management whereby applications that can be satisfied by non-electrical means are separated from the main load. The remaining load will then be satisfied by an optimal mix of renewable energy which will be injected to the existing grid. The proposed problem will be formulated using multiobjective linear programming in conjunction with fuzzy logic. It will be shown that optimization using fuzzy logic can provide decision makers with more flexibility that would assist them in the allocation of various energy resources to optimally meet the various end uses and solve the problem of renewable energy connection to existing distribution networks. Copyright © 1999 John Wiley & Sons, Ltd.

Unification of robust design and goal programming for multiresponse optimization-a case study

Fixing the levels of input process parameters to meet a required specification of output is a common process quality control problem. Especially when the output has many quality characteristics, and each of these quality characteristics has to satisfy a given specification, difficulties may arise. One such problem was encountered in an injection moulding process. This process was optimized using Taguchi's Robust Design methodology. Details of the process, problems encountered and outcome of optimization are presented in this paper. The optimization study using Taguchi's methodology revealed that the optimum conditions obtained for one response are not completely compatible with those of other responses. So trade-offs were made in selection of levels for factors using engineering judgement. This increases the uncertainty in the decision making process. In this paper, an approach is presented to optimize multiresponses simultaneously using goal programming in conjunction with Taguchi's methodology. Details of modelling, analysis and inferences obtained with relevance to the case are presented. This study revealed that the optimum conditions obtained using goal programming in conjuction with Taguchi's methodology have better goal attainment properties compared to Robust design. To understand goal attainment behaviour of output characteristics for various process conditions, a detailed sensitivity analysis was also conducted. The outcome of this analysis is also discussed in this paper. © 1997 John Wiley & Sons, Ltd.

The present status of electron probe microanalysis

Few other analytical methods have expanded so rapidly as electron probe microanalysis during the past 10 years. The number of elements that can be determined or detected extends from beryllium to uranium and concentrations in the range 0.1—100 per cent can be dealt with. This has been made possible by the introduction of new analysing crystals and counting systems. The application of computer programming in conjunction with the various microprobe signals has allowed the development of valuable new methods for structural analysis. Even in commercial instruments, the electron beam can now be focused down to a diameter of 0•15 tm, hence the resolution of backscattered or secondary electron images is excellent and a large amount of information can be gained on the topography and morphology of solid substances. When integrators with proper discrimination are used, new information on diffusion systems, etc., in solid materials can be obtained. With all these possibilities, the position of electron microprobe analysis is now very strong. In addition to the use of x-rays for quantitative work, many other signals arising from the interaction of electrons with the target can be utilized.