A Novel Technique for Solving Multiobjective Fuzzy Linear Programming Problems

This study considers multiobjective fuzzy linear programming (MFLP) problems in which the coefficients in the objective functions are triangular fuzzy numbers. The study proposing a new technique to transform MFLP problems into the equivalent single fuzzy linear programming problem and then solving it via linear ranking function using the simplex method, supported by numerical example

Minimización de costos de producción y de contaminantes químicos en una planta de fundición de estaño

The investigation consisted in the design of a mathematical model that allows the reduction of production costs in a tin foundry plant, which would lead to the reduction of pollutants. The model proposed by Kim and Lewis (1987), who was adapted to the company under study, was taken as reference. The situation that motivated this investigation was the insufficient quantitative techniques in the production programming around foundry operations, where the effect a batch has over others is not taken into account. After testing the model, the results indicated estimated savings of S/.3 314 964 per year, an estimated saving of 23% in the use of the melting furnace and a decrease in the content of pollutants.La investigación consistió en el diseño de un modelo matemático que permitiera reducir los costos de producción en una planta de fundición de estaño, lo cual permitiría la reducción de contaminantes. Se tomó como referencia el modelo de Kim y Lewis (1987), que fue adaptado a la empresa bajo estudio. La problemática que motivó esta investigación provino de las insuficientes técnicas cuantitativas para realizar la programación de producción en fundiciones, al no tomar en cuenta el efecto del trabajo de un lote sobre otros lotes. Luego de probar el modelo, los resultados indicaron un ahorro estimado de S/.3 314 964 anuales, de 23% en el uso del horno y un menor contenido de contaminantes químicos

Brass alloy blending problem from quality and cost perspectives: A multi-objective optimization approach

WOS:000595657400032Brass alloy is a composition of copper and zinc and it also includes lead, iron, tin, aluminum, nickel, antimony if necessary. One of the basic problems in brass casting is to determine which pure and scrap materials will be mixed at what quantities; this problem is known as the blending problem. The ingredient ratios of pure materials are exactly known, however they are expensive. The scrap materials are cheaper than the pure ones with varying ingredient ratios. Stochastic mathematical models aiming to minimize blend cost have been developed in the literature. In the solutions of these models, some of the ingredient ratios exactly equal to the specification limits. Because of the variation, some of them may violate the specification limits and cause quality problems in the actual blends. There is only one study in the literature to solve the quality problem by maximizing the process capability index. However, the blend cost increases when the process capability index maximized. In this study, a multiobjective stochastic mathematical model, which aims both to minimize blend cost and to maximize process capability index, has been developed. The developed model has been converted to a deterministic non-linear counterpart by using chance-constrained programming. Then, fuzzy programming is used to transform the multiobjective model into a single objective one. A solution procedure has been proposed to use it effectively in real life applications. The developed model and solution procedure have been tested by the data supplied from a brass factory. The solution of the numerical example has shown that the developed model and solution procedure can be used successfully in real life applications

The double pivot simplex method

The simplex method, created by George Dantzig, optimally solves a linear program by pivoting. Dantzig’s pivots move from a basic feasible solution to a different basic feasible solution by exchanging exactly one basic variable with a nonbasic variable. This paper introduces the double pivot simplex method, which can transition between basic feasible solutions using two variables instead of one. Double pivots are performed by identifying the optimal basis in a two variable linear program using a new method called the slope algorithm. The slope algorithm is fast and allows an iteration of DPSM to have the same theoretical running time as an iteration of the simplex method. Computational experiments demonstrate that DPSM decreases the average number of pivots by approximately 41% on a small set of benchmark instances

Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model

[EN] Lack of homogeneity in the product (LHP) appears in some production processes that confer heterogeneity in the characteristics of the products obtained. Supply chains with this issue have to classify the product in different homogeneous subsets, whose quantity is uncertain during the production planning process. This paper proposes a generic framework for reviewing in a unified way the literature about production planning models dealing with LHP uncertainty. This analysis allows the identification of similarities among sectors to transfer solutions between them and gaps existing in the literature for further research. The results of the review show: (1) sectors affected by LHP inherent uncertainty, (2) the inherent LHP uncertainty types modelled, and (3) the approaches for modelling LHP uncertainty most widely employed. Finally, we suggest a conceptual model reflecting the aspects to be considered when modelling the production planning in sectors with LHP in an uncertain environment.This research was initiated within the framework of the project funded by the Ministerio de Economía y Competitividad [Ref. DPI2011-23597] entitled ‘Methods and models for operations planning and order management in supply chains characterised by uncertainty in production due to the lack of product uniformity’ (PLANGES-FHP) already finished. After, the project leading to this application has received funding from the European Union’s research and innovation programme under the H2020 Marie Skłodowska-Curie Actions with the grant agreement No 691249, Project entitled ’Enhancing and implementing Knowledge based ICT solutions within high Riskand Uncertain Conditions for Agriculture Production Systems’ (RUC-APS).Mundi, I.; Alemany Díaz, MDM.; Poler, R.; Fuertes-Miquel, VS. (2019). 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Nonlinear Optimization for Managing Occupational Exposure Risks in the Nanomaterial Manufacturing Workplace under Uncertainty

Critical environmental and human health concerns are associated with the rapidly growing fields of nanotechnology and Engineered nanomaterials (ENMs). The main risk arises from occupational exposure via chronic inhalation of nanoparticles. This research presents a fuzzy chance-constrained nonlinear programming (FCCNLP) optimization approach, which is developed to maximize the nanomaterial production and minimize the risks of workplace exposure to ENMs. The FCCNLP method integrates fuzzy mathematical programming (FMP) and chance-constrained programming (CCP) into nonlinear programming (NLP) optimization framework, and could be used to deal with uncertainties expressed as not only probability distributions and fuzzy values associated with components of constraints but ambiguity of the objective function as well. The FCCNLP method was examined through a single-walled carbon nanotube (SWNT) manufacturing process. Solutions of the compromise decision alternatives associated with different risk levels of relaxed constraint violations were obtained. This study confirmed that a high level control strategy through strict occupational exposure limits (OELs) combined with a high enforcement of OELs would lower the nanomaterial exposure risks to workers. The related cost and nanomaterial production have also been optimized for different operational scenarios under multi-layer system uncertainties. The results were helpful for decision makers to identify desirable schemes under uncertainties to maximize the economic benefits and ensure workplace safety through minimizing the nanomaterial-related health risks. The developed technology has technical novelty to help finding cost-effective measures for the sustainable development of nanotechnology

Time-dependent evaluation of upgrading technologies for recycling

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 151-160).As consumption in the US grows, so does concern about sustainable materials usage. Increasing recycling is a key component within a broad arsenal of strategies for moving towards sustainable materials usage. There are many barriers to increasing recycling; one that is problematic is compositional uncertainty in the scrap stream. Repeated recycling compounds this problem through the accumulation of tramp elements in the material stream over time. Pertaining to the available operational and technological strategies that exist to mitigate accumulation, this thesis addresses the following questions: 1) How effective are these strategies at mitigating accumulation? 2) Under what conditions do upgrading technologies provide a cost efficient and environmentally effective improvement to the composition of recycled scrap streams? To answer these, a method was developed combining dynamic material flow analysis with optimal allocation of those materials into production portfolios using blending models. This methodology thus captured 1) the flow of EOL scraps, 2) how the economics of production are affected by changes in technology, and 3) a characterization of how recycling parameters influence accumulation in recycled streams. Using this methodology, optimal allocation was found to be an effective strategy for mitigating accumulation, for example, iron in the scrap stream was 69% less when compared to the value projected by conventional statistical methods. Two upgrading technology cases were examined using the time-dependent methodology developed: shredding, sorting, and dismantling of aerospace scraps and fractional crystallization.(cont.) Case results indicate that the time-dependent value of these technologies relies on whether or not the scrap stream is compositionally or availability constrained. These values were compared to analysis that does not consider repeated recycling (time-independent). Results show that undervaluing will occur in a regime where scrap availability is constrained and there is significant compositional accumulation occurring, a regime that may very well represent the reality faced by aluminum secondary producers in the US.by Gabrielle G. Gaustad.Ph.D