Resumo em Inglês:

ABSTRACT Sustainable Supply Chain is nowadays an increasing area of concern for both academia and industry that aims to design, plan and operate supply chains that guarantee market needs while considering not only profit objectives but also environmental and social concerns in a solution of compromise. Being supply chains complex systems the pursuing of such compromise solution calls for the use of decision tools that can support decision makers. Such tools should be based on quantitative models where Operational Research (OR) methods ought to be explored and where optimization, in particular, has a role to play. In this paper, the main characteristics of such optimization models, focusing on the design and planning of sustainable supply chains are discussed and the main issues to be considered when addressing such problems are identified. A framework, SusFrame, developed to guide practitioners and researchers in the development of optimization models for the design and planning of sustainable supply chains, is presented and applied to the solution of a set of case-studies, demonstrating the large applicability of such tool and how optimization methods can be used to help the sustainable supply chain decision process.

Resumo em Inglês:

ABSTRACT Dengue Fever, Zika and Chikungunya are febrile infectious diseases transmitted by the Aedes species of mosquito with a high rate of mortality. The most common vector is Aedes aegypti. According to World Health Organization outbreaks of mosquito-borne illnesses are common in the tropical and subtropical climates, as there are currently no vaccines to protect against Dengue Fever, Chikungunya or Zika diseases. Hence, mosquito control is the only known method to protect human populations. Consequently, the affected countries need urgently search for better tools and sustained control interventions in order to stop the growing spread of the vector. This study presents an optimization model, involving chemical, biological and physical control decisions that can be applied to fight against the Aedes mosquito. To determine solutions for the optimization problem a genetic heuristic is proposed. Through the computational experiments, the algorithm shows considerable efficiency in achieving solutions that can support decision makers in controlling the mosquito population.

Resumo em Inglês:

ABSTRACT This paper aims to analyse the ordering of Technology Transfer Offices’ (TTO) positions regarding innovation indicators using multicriteria analysis as a tool. The purpose is to identify and analyse the TTOs’ strengths and weaknesses in a specific higher education institution in order to understand their performance via ranking, for obtaining revenues from intellectual property assets possibilities developed by them. The use of a methodology for the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), using two normalizing methods, was preceded by a treatment of this qualitative data with the Measuring Attractiveness by a Category Based Evaluation Technique (MACBETH). It is based on an applied questionnaire on TTO’s for recognizing the status of the technology transfer condition to developing nations, focusing on an approach to the institutionalization of TTO’s within a national context. The results of these technical applications bring together novel approaches to the processing of data using both methods to support multicultural decisions (using qualitative and quantitative data). These are initially arranged in different ways and present better analytical conditions in respect to the presented technological development. The first combination between TOPSIS and MACBETHS, with two normalized methods, and associated with a Pearson analysis between the criteria.

Resumo em Inglês:

ABSTRACT Chemical Reaction Optimization (CRO) is a metaheuristic for solving optimization problems, which mimics the interactions between molecules in a chemical reaction with the purpose of achieving a stable, low-energy state. In the present work, we utilize the CRO metaheuristic to solve, in an efficient manner, the capacitated p-median problem, in order to locate service stations. Results from solving small to medium-sized problems available in the literature, with up to 724 notes and 200 medians, are compared to their optimal or best-known values. Results show that CRO results are comparable, in terms of accuracy and execution time, to many existing successfulmetaheuristics, as well as exact and hybrid methods, having exceeding those in some cases.

Resumo em Inglês:

ABSTRACT Oil and gas resources have been considered valuable assets, associated with potential conflicts due to distinct interests of many agents involved in their exploration, such as producing and consuming countries, governments and companies. These conflicts can show up under many situations and market conditions, such as partnerships, joint development, optimal outputs and reserve maximization. Game theory is known as a methodology that improves the decision-making processes by better understanding the players’ specific motivations, strategic interactions and payoff estimation. A widely used framework for modeling social and economic phenomena is the 2 x 2 strategic games, of which include classical forms such as Prisoner’s Dilemma, Stag Hunt, and Battle of Sexes. Therefore, this paper proposes to examine relevant realistic and real-world cases of the oil and gas industry in the form of 2 x 2 strategic games, aiming to investigate game theory approaches to aid in the discussion and resolution of the main dilemmas faced.

Resumo em Inglês:

ABSTRACT This paper addresses the Traveling Salesman Problem with Priority Prizes (TSPPP), an extension of the classical TSP in which the order of the node visits is taken into account in the objective function. A prize p ki is received by the traveling salesman when node i is visited in the k-th order of the route, while a travel cost c ij is incurred when the salesman travels from node i to node j . The aim of the TSPPP is to find the maximum profit n-node tour. The problem can be seen as a TSP variant with a more general objective function, aiming at solutions that in some way consider the quality of customer service and the delivery priorities and costs. A natural representation for the TSPPP is here grounded in the point of view of Koopmans and Beckmann approach, according to which the problem is seem as a special case of the quadratic assignment problem (QAP). Given the novelty of this TSP variant, we propose different mixed integer programming models to appropriately represent the TSPPP, some of them based on the QAP. Computational experiments are also presented when solving the MIP models with a well-known optimization software, as well as with a tabu search algorithm.

Resumo em Inglês:

ABSTRACT This study presents a new approach for the definition of weight restrictions in Data Envelopment Analysis (DEA) for the one output, multiple inputs case, using the results of a Linear Regression model (LRM) developed with the same DEA variables. Thus, the limits of Wong-Beasley and Cone Ratio methods are chosen without interference from a decision maker, with DEA weight search intervals defined from the estimated standardized coefficients of a linear regression (which represent the statistical importance of the inputs for the definition of the DEA efficiency scores). As an example, weight restrictions for a DEA model (Constant Returns to Scale (CRS)) were obtained through the unrestricted, Wong-Beasley and Cone Ratio methods applied to a dataset consisting of hospital admissions (output), number of beds and number of health professionals (inputs) in the year 2016; and rankings were compared by a Spearman correlation procedure. The regression model had R 2 = 0.89 with coefficients 0.43 (professionals) and 0.54 (beds); and the Spearman correlation among rankings was at least RS2 = 0.84. In conclusion, rankings were consistent and interpretable, and the approach circumvents the need for a subjective intervention by a decision maker when defining weight restrictions in DEA.

Resumo em Inglês:

ABSTRACT This study was developed to maximize revenue from commercialization of sugar by optimizing the crop harvest within continuous scheduling periods subject to constraints on processing capacity and minimum proportion of farm area to be harvested. Specific constraints were developed to guarantee the continuity of the harvesting calendar per farm. The mathematical model employed is based on a Mixed Integer Linear Programming structure (MILP). Scenario analyses were conducted with the proportion of the farm to be harvested altered. An important finding was that a harvest scheduling pattern emerged as the proportion of the farm’s land to be harvested increased, being this pattern broken when the minimum harvested area increased to 33% of the farm’s cultivable area. Results are also useful for the evaluation of the feasibility of movement of equipment during harvesting operations.

Resumo em Inglês:

ABSTRACT This paper addresses the different methods of estimation of the unknown parameters of a two-parameter unit-logistic distribution from the frequentist point of view. We briefly describe different approaches, namely, maximum likelihood estimators, percentile based estimators, least squares estimators, maximum product of spacings estimators, methods of minimum distances: Cramér-von Mises, AndersonDarling and four variants of Anderson-Darling. Monte Carlo simulations are performed to compare the performances of the proposed methods of estimation for both small and large samples. The performances of the estimators have been compared in terms of their relative bias, root mean squared error, average absolute difference between the theoretical and empirical estimate of the distribution functions and the maximum absolute difference between the theoretical and empirical distribution functions using simulated samples. Also, for each method of estimation, we consider the interval estimation using the Bootstrap confidence interval and calculate the coverage probability and the average width of the Bootstrap confidence intervals. Finally, two real data sets have been analyzed for illustrative purposes.