S. Hamacher

This paper presents a Genetic Algorithm-based method to optimize the production schedule of the fuel oil and asphalt section in a petroleum refinery. Two Genetic Algorithm models were developed to establish the sequence and size of all production shares. A special mutation operator was also proposed to minimize the number of changes in the production. A multi-objective fitness evaluation technique was also incorporated to the Genetic Algorithm models. The obtained results confirm that the proposed Genetic Algorithm models, associated with the multi-objective energy minimization method, are able to solve the scheduling problem, optimizing the refinery’s operational objectives.

La evaluación de los proveedores es importante para las organizaciones debido al papel preponderante que éstos cumplen en la dinámica de las cadenas de suministros y a la importancia estratégica que las funciones de compra tienen a raíz de la tercerización de los procesos que no forman parte del know-how de la empresa. No obstante, la literatura académica carece de trabajos concernientes al diseño e implementación de sistemas de medición de desempeño de proveedores (SMDP). En ese contexto, este artículo presenta los factores más importantes del diseño de un SMDP, resaltando, a través de un caso de estudio, los cuidados y recomendaciones que deben ser considerados. Además del detalle del método y los resultados de su aplicación, el artículo presenta el análisis de los procesos y atributos que definen el diseño del SMDP propuesto.

... Gabriela Ribas Adriana Leiras Silvio Hamacher Núcleo de Excelência em Otimização – NExO Departamento de Engenharia Industrial – DEI Pontifícia Universidade Católica – PUC-Rio +55 21 3527-2111 gabiribas@gmail.com aleiras ... valores no intervalo ˆ ˆ [ , ] ij ij ij ij aaaa . ...

ABSTRACT This paper addresses the solution of a two-stage stochastic programming model for an investment planning problem applied to the petroleum products supply chain. In this context, we present the development of acceleration techniques for the stochastic Benders decomposition that aim to strengthen the cuts generated, as well as to improve the quality of the solutions obtained during the execution of the algorithm. Computational experiments are presented for assessing the efficiency of the proposed framework. We compare the performance of the proposed algorithm with two other acceleration techniques. Results suggest that the proposed approach is able to efficiently solve the problem under consideration, achieving better performance in terms of computational times when compared to other two techniques.

ABSTRACT We present a scenario decomposition framework based on Lagrangean decomposition for the multi-product, multi-period, supply investment planning problem considering network design and discrete capacity expansion under demand uncertainty. We also consider a risk measure that allows to reduce the probability of incurring in high costs while preserving the decomposable structure of the problem. To solve the resulting large-scale two-stage mixed-integer stochastic linear programming problem we propose a novel Lagrangean decomposition scheme, and compare different formulations for the non-anticipativity conditions. In addition, we present a new hybrid algorithm for updating the Lagrangean multiplier set based on the combination of cutting-plane, subgradient and trust-region strategies. Numerical results suggest that different formulations of the non-anticipativity conditions have a significant impact on the performance of the algorithm. Moreover, we observe that the proposed hybrid approach has superior performance in terms of faster computational times when compared with the traditional subgradient algorithm.