Sergio Maturana

A robust optimization approach to wine grape harvesting scheduling

Optimization models are increasingly being used in agricultural planning. However, the inherent uncertainties present in agriculture make it difficult. In recent years, robust optimization has emerged as a methodology that allows dealing with uncertainty in optimization models, even when probabilistic knowledge of the phenomenon is incomplete. In this paper, we consider a wine grape harvesting scheduling optimization problem subject to several uncertainties, such as the actual productivity that can be achieved when harvesting. We study how effective robust optimization is solving this problem in practice. We develop alternative robust models and show results for some test problems obtained from actual wine industry problems.

Improving Planning Reliability and Project Performance using the Reliable Commitment Model.

Commitment planning reliability at an operational level is a key factor for improving project performance. In the last 15 years, the Last Planner System, a production planning and control system based on lean production principles, has improved commitment planning reliability in the construction industry. However, many construction decision makers continue to rely on their experience and intuition when planning their commitments, which hinders their reliability. The reliable commitment model (RCM) is proposed to improve commitment planning reliability at the operational level by using statistical models. RCM is an operational decision-making tool based on lean principles that supports short-term forecasting commitment planning using common-site information such as workers, buffers, and plans. RCM was tested in several case studies, demonstrating its production forecasting capabilities and its ability to help increase commitment planning reliability and improve project performance. RCM also supports worklo...

A Mixed-Integer Programming Model for Gas Purchase and Transportation

The natural gas supply chain involves three main agents: producers, transportation companies, and local distribution companies (LDCs). We present a MIP model that is the basis for a decision support system developed for a Chilean LDC. This model takes into account many of the complexities of the purchasing and transportation contracts to help optimize daily purchase and transportation decisions in the absence of local storage facilities. The model was solved to optimality within a reasonable time. We show how the model handles several contractual issues and give some insights for the case when demand scenarios are used to deal with uncertainty.

A Multiple Stage Approach for Performance Improvement of Primary Healthcare Practice

Chilean primary healthcare practice is analyzed using a Data Envelopment Analysis (DEA) multiple stage approach. We estimate the efficiency level of 259 municipalities nationwide. Since the efficiency score by itself is of limited value for decision making, we use a multivariate tool to help explain the effect of relevant factors. First, we use a cluster analysis to homogenize the units under study. Second, we use DEA to estimate the efficiency levels, which varies from 61% to 71% for urban municipalities, and from 51% to 56% in rural ones. Third, we use bootstrap to estimate confidence intervals for the efficiency scores, and a Biplot method to identify adequate variables to include in the Tobit Model, which is our last stage. We identify six factors associated with rural municipalities’ operational efficiency, and two with urban ones. Knowing the efficiency level of municipalities can help determine ways to improve their efficiency.

Scheduling production for a sawmill: A comparison of a mathematical model versus a heuristic

Sawmill production scheduling is complex. It involves determining which logs to process taking into account the diameter, length, and grade of each log, on one hand, and the finished products that are needed to fulfill the orders, on the other. The cutting pattern determines which products are generated and also the yield, which is how much of the volume of the log ends as finished products. We used two approaches to solve the problem: a mathematical model, which calculates the volume and the schedule of various types of logs, and a heuristic that solves the problem by systematically applying the criteria used by a sawmill programmer in southern Chile. We compare the results of using both approaches under various scenarios in a 6week planning horizon, with the mathematical model showing a superior performance in almost all instances except two, were both found the optimal solution. The proposed mathematical model can be solved in a relatively short time, which makes it a suitable basis of a practical optimization-based decision support system.

Issues in the design of modeling languages for mathematical programming

Abstract Modeling languages have become one of the most important tools in helping to make Mathematical Programming technology easier to use. Having proven its usefulness, more attention is now being given to the improvement of the design and implementation of these languages. This paper reviews some of the main issues that arise in the design of modeling languages and which can be applied to any algebraic modeling language to help evaluate its strengths and weaknesses. Existing modeling languages are used to illustrate these issues.

Site Management of Work-in-Process Buffers to Enhance Project Performance Using the Reliable Commitment Model: Case Study

Buffers have been commonly used as a production strategy to protect construction processes from the negative impact of variability. Construction practitioners and researchers have proposed several buffering approaches for different production situations and contexts. However, these solutions have been impractical for managing buffers. To overcome this, this study proposes a new site methodology for managing work-in-process (WIP) buffer in repetitive projects, on the basis of the reliable commitment model (RCM). RCM is a decision-making tool based on lean principles, which uses statistical models to develop more reliable work plans at the operational level. RCM helps to manage WIP buffer in work plans by using site information and planning reliability indicators that result in improved project performance, such as labor productivity and process progress. A repetitive building project was used as a case study. The main finding was that labor productivity, process progress, and waiting times improved when us...

Design and implementation of an optimization-based decision support system generator

Abstract Decision makers today need to be able to rapidly find good solutions to increasingly complex problems. Optimization-based decision support systems (OBDSSs) can help decision makers to meet this challenge. Building such systems, however, is expensive and time consuming. In this paper we present the design of an OBDSS generator that greatly automates the development process by integrating a model specification with a database, a solver, and a graphical user interface (GUI). The generator automatically creates the database for storing the instantiating data, the GUI, and the solver interface. With this generator, an OR/MS analyst can build an OBDSS faster and more efficiently than using traditional tools. The resulting OBDSSs can be used by decision makers with little knowledge of the underlying OR/MS technology. We also discuss our experience using the generator to develop production planning and truck dispatching OBDSSs for several firms in the context of a three-year R&D project.

A possibilistic decision support system for imprecise mathematical programming problems

Abstract An approach for decision support under uncertainty based on a nondeterministic possibilistic approach is considered. Problem uncertainties are defined by fuzzy numbers and characterized by membership functions. A method for the efficient numerical implementation of the proposed decision support system is presented. The method is basically an algebraic process which can be implemented for general decision making problems. A set of representative samples of the solution behavior can be obtained directly from the formulation. At the same time, information about the global effect of the problem uncertainties on the optimal solution can be evaluated immediately from the analysis. The effectiveness of the method is illustrated by the solution of a programming model that represents the logistics management of the sulfuric acid business in Chile. Numerical results show the usefulness and potential of the proposed possibilistic approach.

Fraudes a los sistemas de salud en Chile: un modelo para su detección

OBJECTIVES To develop a model for detecting cases of organized fraud in Chile based on data from the legal forms for medically authorized leave (formulario legal de licencia médica curativa-MAL) and to establish the relevance of this data to fraud detection. METHODS A binomial logistic regression model was employed using four variables from the MAL form, a national requirement for illness-related work absences: the number of legal absences taken by a single person, the number of days authorized by the prescribing doctor, the total cost per illness, and a dichotic variable reflecting whether or not the diagnosis is one that can be proven. The analysis involved 4,079 MAL forms that had been submitted in 2003 to a private health provider and of which 356 were already identified as fraudulent by a panel of medical fraud experts. RESULTS The model successfully identified 99.71% of the fraudulent medical authorizations and 99.86% of the non-fraudulent, according to the criteria of the panel of fraud experts. Three of the variables employed had statistically-significant independent predictive power. The positive predictive value of the proposed model was 98.59%, while its negative predictive value was 99.97%. CONCLUSIONS The binomial logistic model that was developed uses four variables that are common to all MAL forms in use by Chiles public as well as private insurers, permitting separation of fraudulent from non-fraudulent requests to be more accurate, more timely, and at a cost lower that of an expert panel.