A STOCHASTIC SIMULATION-BASED HYBRID INTERVAL FUZZY PROGRAMMING APPROACH FOR OPTIMIZING THE TREATMENT OF RECOVERED OILY WATER

In this paper, a stochastic simulation-based hybrid interval fuzzy programming (SHIFP) approach is developed to aid the decision-making process by solving fuzzy linear optimization problems. Fuzzy set theory, probability theory, and interval analysis are integrated to take into account the effect of imprecise information, subjective judgment, and variable environmental conditions. A case study related to oily water treatment during offshore oil spill clean-up operations is conducted to demonstrate the applicability of the proposed approach. The results suggest that producing a random sequence of triangular fuzzy numbers in a given interval is equivalent to a normal distribution when using the centroid defuzzification method. It also shows that the defuzzified optimal solutions follow the normal distribution and range from 3,000-3,700 tons, given the budget constraint (CAD 110,000-150,000). The normality seems to be able to propagate throughout the optimization process, yet this interesting finding deserves more in-depth study and needs more rigorous mathematical proof to validate its applicability and feasibility. In addition, the optimal decision variables can be categorized into several groups with different probability such that decision makers can wisely allocate limited resources with higher confidence in a short period of time. This study is expected to advise the industries and authorities on how to distribute resources and maximize the treatment efficiency of oily water in a short period of time, particularly in the context of harsh environments

A systematic grey-box modeling methodology via data reconciliation and SOS constrained regression

Producción CientíficaDeveloping the so-called grey box or hybrid models of limited complexity for process systems is the cornerstone in advanced control and real-time optimization routines. These models must be based on fundamental principles and customized with sub-models obtained from process experimental data. This allows the engineer to transfer the available process knowledge into a model. However, there is still a lack of a flexible but systematic methodology for grey-box modeling which ensures certain coherence of the experimental sub-models with the process physics. This paper proposes such a methodology based in data reconciliation (DR) and polynomial constrained regression. A nonlinear optimization of limited complexity is to be solved in the DR stage, whereas the proposed constrained regression is based in sum-of-squares (SOS) convex programming. It is shown how several desirable features on the polynomial regressors can be naturally enforced in this optimization framework. The goodnesses of the proposed methodology are illustrated through: (1) an academic example and (2) an industrial evaporation plant with real experimental data.Ministerio de Economía, Industria y Competitividad (grant DPI2016-81002-R

Prioritization of service quality dimensions for healthcare sector using analytic hierarchy process

Due to the unique nature among the service industries, the overriding objective of the healthcare sector is to provide error free, impeccable services to their patients and clientele. It is not an option rather a norm for the healthcare sector to uphold and maintain the level of service that is quintessential and benchmark in the service industries. However, service quality has different meanings to different people. In this paper, SERVQUAL has been considered as the main tool to measure service quality and Analytic Hierarchy Process (AHP) has been applied to prioritize the five dimensions of SERVQUAL for healthcare sector. In addition to this, AHP has been applied to prioritize the various items representing each of the dimensions. The results show that Reliability and Assurance are the two most important service quality dimensions in the healthcare sector. The priorities of dimensions and their items are also determined with respect to a selected demographic factors on the part of the respondents