Importância da Programação Linear na Determinação de Informações de Gestão - Caso Moave, Sa

A Investigação Operacional vem demonstrando ser uma valiosa ferramenta de gestão nos dias de hoje em que se vive num mercado cada vez mais competitivo. Através da Programação Linear pode-se reproduzir matematicamente um problema de maximização dos resultados ou minimização dos custos de produção com o propósito de auxiliar os gestores na tomada de decisão. A Programação Linear é um método matemático em que a função objectivo e as restrições assumem características lineares, com diversas aplicações no controlo de gestão, envolvendo normalmente problemas de utilização dos recursos disponíveis sujeitos a limitações impostas pelo processo produtivo ou pelo mercado. O objectivo geral deste trabalho é o de propor um modelo de Programação Linear para a programação ou produção e alocação de recursos necessários. Optimizar uma quantidade física designada função objectivo, tendo em conta um conjunto de condicionalismos endógenas às actividades em gestão. O objectivo crucial é dispor um modelo de apoio à gestão contribuindo assim para afectação eficiente de recursos escassos à disposição da unidade económica. Com o trabalho desenvolvido ficou patente a importância da abordagem quantitativa como recurso imprescindível de apoio ao processo de decisão. The operational research has proven to be a valuable management tool today we live in an increasingly competitive market. Through Linear Programming can be mathematically reproduce a problem of maximizing performance or minimizing production costs in order to assist managers in decision making. The Linear Programming is a mathematical method in which the objective function and constraints are linear features, with several applications in the control of management, usually involving problems of resource use are available subject to limitations imposed by the production process or the market. The overall objective of this work is to propose a Linear Programming model for scheduling or production and allocation of necessary resources. Optimizing a physical quantity called the objective function, given a set of endogenous constraints on management thus contributing to efficient allocation of scarce resources available to the economic unit. With the work has demonstrated the importance of the quantitative approach as essential resource to support the decision process.

Does Prison Substance Abuse Treatment Reduce Recidivism?Performance Audit Report Iowa Department of Corrections Licensed Substance Abuse Programs, May 25, 2007

The Iowa Department of Corrections faces a growing prison population expected to quickly exceed current capacities. Additionally, nine out of every ten offenders have a history of alcohol or drug problems often both. Research suggests that alcohol and drugs lead to criminal behavior, which lead offenders right back to prison creating a vicious circle and placing a financial and societal burden on the state. However, research also shows that substance abuse treatment can minimize criminal behavior, and offers a way to shut the revolving prison door. Substance abuse programming attempts to change offender thinking patterns and behavior in order to facilitate re-entry back into the community, lessen substance abuse relapse and reduce recidivism. Yet nearly 60% of offenders with identified needs are not treated, and many lacking treatment are high risk. Additionally, the percentage of offenders returning to prison varies significantly from program to program and some programs can not show they have reduced recidivism when compared to offender groups with substance abuse problems and receiving no treatment at all. All of which minimize the effect substance Abuse programming has in curbing prison population growth and reducing crime.

The investigation of deaths in custody: a qualitative analysis of problems and prospects.

The right to be treated humanely when detained is universally recognized. Deficiencies in detention conditions and violence, however, subvert this right. When this occurs, proper medico-legal investigations are critical irrespective of the nature of death. Unfortunately, the very context of custody raises serious concerns over the effectiveness and fairness of medico-legal examinations. The aim of this manuscript is to identify and discuss the practical and ethical difficulties encountered in the medico-legal investigation following deaths in custody. Data for this manuscript come from a larger project on Death in Custody that examined the causes of deaths in custody and the conditions under which these deaths should be investigated and prevented. A total of 33 stakeholders from forensic medicine, law, prison administration or national human rights administration were interviewed. Data obtained were analyzed qualitatively. Forensic experts are an essential part of the criminal justice process as they offer evidence for subsequent indictment and eventual punishment of perpetrators. Their independence when investigating a death in custody was deemed critical and lack thereof, problematic. When experts were not independent, concerns arose in relation to conflicts of interest, biased perspectives, and low-quality forensic reports. The solutions to ensure independent forensic investigations of deaths in custody must be structural and simple: setting binding standards of practice rather than detailed procedures and relying on preexisting national practices as opposed to encouraging new practices that are unattainable for countries with limited resources.

The achievable region approach to the optimal control of stochastic systems

The achievable region approach seeks solutions to stochastic optimisation problems by: (i) characterising the space of all possible performances(the achievable region) of the system of interest, and (ii) optimisingthe overall system-wide performance objective over this space. This isradically different from conventional formulations based on dynamicprogramming. The approach is explained with reference to a simpletwo-class queueing system. Powerful new methodologies due to the authorsand co-workers are deployed to analyse a general multiclass queueingsystem with parallel servers and then to develop an approach to optimalload distribution across a network of interconnected stations. Finally,the approach is used for the first time to analyse a class of intensitycontrol problems.

A randomized concave programming method for choice network revenue management

Models incorporating more realistic models of customer behavior, as customers choosing froman offer set, have recently become popular in assortment optimization and revenue management.The dynamic program for these models is intractable and approximated by a deterministiclinear program called the CDLP which has an exponential number of columns. However, whenthe segment consideration sets overlap, the CDLP is difficult to solve. Column generationhas been proposed but finding an entering column has been shown to be NP-hard. In thispaper we propose a new approach called SDCP to solving CDLP based on segments and theirconsideration sets. SDCP is a relaxation of CDLP and hence forms a looser upper bound onthe dynamic program but coincides with CDLP for the case of non-overlapping segments. Ifthe number of elements in a consideration set for a segment is not very large (SDCP) can beapplied to any discrete-choice model of consumer behavior. We tighten the SDCP bound by(i) simulations, called the randomized concave programming (RCP) method, and (ii) by addingcuts to a recent compact formulation of the problem for a latent multinomial-choice model ofdemand (SBLP+). This latter approach turns out to be very effective, essentially obtainingCDLP value, and excellent revenue performance in simulations, even for overlapping segments.By formulating the problem as a separation problem, we give insight into why CDLP is easyfor the MNL with non-overlapping considerations sets and why generalizations of MNL posedifficulties. We perform numerical simulations to determine the revenue performance of all themethods on reference data sets in the literature.

Beyond Smith's rule: An optimal dynamic index, rule for single machine stochastic scheduling with convex holding costs

Most research on single machine scheduling has assumedthe linearity of job holding costs, which is arguablynot appropriate in some applications. This motivates ourstudy of a model for scheduling $n$ classes of stochasticjobs on a single machine, with the objective of minimizingthe total expected holding cost (discounted or undiscounted). We allow general holding cost rates that are separable,nondecreasing and convex on the number of jobs in eachclass. We formulate the problem as a linear program overa certain greedoid polytope, and establish that it issolved optimally by a dynamic (priority) index rule,whichextends the classical Smith's rule (1956) for the linearcase. Unlike Smith's indices, defined for each class, ournew indices are defined for each extended class, consistingof a class and a number of jobs in that class, and yieldan optimal dynamic index rule: work at each time on a jobwhose current extended class has larger index. We furthershow that the indices possess a decomposition property,as they are computed separately for each class, andinterpret them in economic terms as marginal expected cost rate reductions per unit of expected processing time.We establish the results by deploying a methodology recentlyintroduced by us [J. Niño-Mora (1999). "Restless bandits,partial conservation laws, and indexability. "Forthcomingin Advances in Applied Probability Vol. 33 No. 1, 2001],based on the satisfaction by performance measures of partialconservation laws (PCL) (which extend the generalizedconservation laws of Bertsimas and Niño-Mora (1996)):PCL provide a polyhedral framework for establishing theoptimality of index policies with special structure inscheduling problems under admissible objectives, which weapply to the model of concern.

A new compact linear programming formulation for choice network revenue management

The choice network revenue management model incorporates customer purchase behavioras a function of the offered products, and is the appropriate model for airline and hotel networkrevenue management, dynamic sales of bundles, and dynamic assortment optimization.The optimization problem is a stochastic dynamic program and is intractable. A certainty-equivalencerelaxation of the dynamic program, called the choice deterministic linear program(CDLP) is usually used to generate dyamic controls. Recently, a compact linear programmingformulation of this linear program was given for the multi-segment multinomial-logit (MNL)model of customer choice with non-overlapping consideration sets. Our objective is to obtaina tighter bound than this formulation while retaining the appealing properties of a compactlinear programming representation. To this end, it is natural to consider the affine relaxationof the dynamic program. We first show that the affine relaxation is NP-complete even for asingle-segment MNL model. Nevertheless, by analyzing the affine relaxation we derive a newcompact linear program that approximates the dynamic programming value function betterthan CDLP, provably between the CDLP value and the affine relaxation, and often comingclose to the latter in our numerical experiments. When the segment consideration sets overlap,we show that some strong equalities called product cuts developed for the CDLP remain validfor our new formulation. Finally we perform extensive numerical comparisons on the variousbounds to evaluate their performance.

Solving two production scheduling problems with sequence-dependent set-up times

In today’s competitive markets, the importance of goodscheduling strategies in manufacturing companies lead to theneed of developing efficient methods to solve complexscheduling problems.In this paper, we studied two production scheduling problemswith sequence-dependent setups times. The setup times areone of the most common complications in scheduling problems,and are usually associated with cleaning operations andchanging tools and shapes in machines.The first problem considered is a single-machine schedulingwith release dates, sequence-dependent setup times anddelivery times. The performance measure is the maximumlateness.The second problem is a job-shop scheduling problem withsequence-dependent setup times where the objective is tominimize the makespan.We present several priority dispatching rules for bothproblems, followed by a study of their performance. Finally,conclusions and directions of future research are presented.

Estimating Bayesian decision problems with heterogeneous priors

In many areas of economics there is a growing interest in how expertise andpreferences drive individual and group decision making under uncertainty. Increasingly, we wish to estimate such models to quantify which of these drive decisionmaking. In this paper we propose a new channel through which we can empirically identify expertise and preference parameters by using variation in decisionsover heterogeneous priors. Relative to existing estimation approaches, our \Prior-Based Identification" extends the possible environments which can be estimated,and also substantially improves the accuracy and precision of estimates in thoseenvironments which can be estimated using existing methods.

Efficacy of brief motivational intervention at army conscription to address alcohol use and related problems: a randomized controlled trial

Objectives: The aim of this study was to evaluate the efficacy of brief motivational intervention (BMI) in reducing alcohol use and related problems among binge drinkers randomly selected from a census of 20 year-old French speaking Swiss men and to test the hypothesis that BMI contributes to maintain low-risk drinking among non-bingers. Methods: Randomized controlled trial comparing the impact of BMI on weekly alcohol use, frequency of binge drinking and occurrence of alcohol-related problems. Setting: Army recruitment center. Participants: A random sample of 622 men were asked to participate, 178 either refused, or missed appointment, or had to follow military assessment procedures instead, resulting in 418 men randomized into BMI or control conditions, 88.7% completing the 6-month follow-up assessment. Intervention: A single face-to-face BMI session exploring alcohol use and related problems in order to stimulate behaviour change perspective in a non-judgmental, empathic manner based on the principles of motivational interviewing (MI). Main outcome measures: Weekly alcohol use, binge drinking frequency and the occurrence of 12 alcohol-related consequences. Results: Among binge drinkers, we observed a 20% change in drinking induced by BMI, with a reduction in weekly drinking of 1.5 drink in the BMI group, compared to an increase of 0.8 drink per week in the control group (incidence rate ratio 0.8, 95% confidence interval 0,66 to 0,98, p = 0.03). BMI did not influence the frequency of binge drinking and the occurrence of 12 possible alcohol-related consequences. However, BMI induced a reduction in the alcohol use of participants who, after drinking over the past 12 months, experienced alcohol-related consequences, i.e., hangover (-20%), missed a class (-53%), got behind at school (-54%), argued with friends (-38%), engaged in unplanned sex (-45%) or did not use protection when having sex (-64%). BMI did not reduce weekly drinking in those who experienced the six other problems screened. Among non-bingers, BMI did not contribute to maintain low-risk drinking. Conclusions: At army conscription, BMI reduced alcohol use in binge drinkers, particularly in those who recently experienced alcohol-related adverse consequences. No preventive effect of BMI was observed among non-bingers. BMI is an interesting preventive option in young binge drinkers, particularly in countries with mandatory army recruitment.

Median problems in networks

The P-median problem is a classical location model par excellence . In this paper we, firstexamine the early origins of the problem, formulated independently by Louis Hakimi andCharles ReVelle, two of the fathers of the burgeoning multidisciplinary field of researchknown today as Facility Location Theory and Modelling. We then examine some of thetraditional heuristic and exact methods developed to solve the problem. In the third sectionwe analyze the impact of the model in the field. We end the paper by proposing new lines ofresearch related to such a classical problem.

Recursive contracts

We obtain a recursive formulation for a general class of contractingproblems involving incentive constraints. Under these constraints,the corresponding maximization (sup) problems fails to have arecursive solution. Our approach consists of studying the Lagrangian.We show that, under standard assumptions, the solution to theLagrangian is characterized by a recursive saddle point (infsup)functional equation, analogous to Bellman's equation. Our approachapplies to a large class of contractual problems. As examples, westudy the optimal policy in a model with intertemporal participationconstraints (which arise in models of default) and intertemporalcompetitive constraints (which arise in Ramsey equilibria).

Enforcement problems and secondary markets

There is a large and growing literature that studies the effects of weak enforcement institutions on economic performance. This literature has focused almost exclusively on primary markets, in which assets are issued and traded to improve the allocation of investment and consumption. The general conclusion is that weak enforcement institutions impair the workings of these markets, giving rise to various inefficiencies.But weak enforcement institutions also create incentives to develop secondary markets, in which the assets issued in primary markets are retraded. This paper shows that trading in secondary markets counteracts the effects of weak enforcement institutions and, in the absence of further frictions, restores efficiency.