Alejandro Alvarado-Iniesta

Optimization of the material flow in a manufacturing plant by use of artificial bee colony algorithm

To survive in todays competitive global market, companies must perform strategic changes in order to increase productivity, eliminating wasted materials, time, and effort. This study will examine how to optimize the time and effort required to supply raw material to different production lines in a manufacturing plant in Juarez, Mexico by minimizing the distance an operator must travel to distribute material from a warehouse to a set of different production lines with corresponding demand. The core focus of this study is similar to that of the Vehicle Routing Problem in that it is treated as a combinatorial optimization problem. The artificial bee colony algorithm is applied in order to find the optimal distribution of material with the aim of establishing a standard time for this duty by examining how this is applied in a local manufacturing plant. Results show that using this approach may be convenient to set standard times in the selected company.

Multiobjective optimization of torch brazing process by a hybrid of fuzzy logic and multiobjective artificial bee colony algorithm

This paper describes an application of a hybrid of fuzzy logic (FL) and multiobjective artificial bee colony algorithm (MOABC) for optimizing the torch brazing process of aluminum in the fabrication of condensers in the automotive manufacturing industry of Juarez, Mexico. This work aims to show how artificial intelligence is being applied in the manufacturing sector of Mexico for optimizing processes leading to cost reduction. The approach consists of using FL as surrogate model of the brazing process; after, MOABC is applied to find the nondominated solutions for leak rate which is a quality test of the condenser and production time. Results show the use of artificial intelligence is an excellent tool for optimizing manufacturing processes leading to improve productivity, mainly in the selected region, where this type of methodologies are fairly new in applicability.

MOORA under Pythagorean Fuzzy Set for Multiple Criteria Decision Making

The multiobjective optimization on the basis of ratio analysis (MOORA) method captures diverse features such as the criteria and alternatives of appraising a multiple criteria decision-making (MCDM) problem. At the same time, the multiple criteria problem includes a set of decision makers with diverse expertise and preferences. In fact, the literature lists numerous approaches to aid in this problematic task of choosing the best alternative. Nevertheless, in the MCDM field, there is a challenge regarding intangible information which is commonly involved in multiple criteria decision-making problem; hence, it is substantial in order to advance beyond the research related to this field. Thus, the objective of this paper is to present a fused method between multiobjective optimization on the basis of ratio analysis and Pythagorean fuzzy sets for the choice of an alternative. Besides, multiobjective optimization on the basis of ratio analysis is utilized to choose the best alternatives. Finally, two decision-making problems are applied to illustrate the feasibility and practicality of the proposed method.

Reliability Estimation for Accelerated Life Tests Based on a Cox Proportional Hazard Model with Error Effect

In this paper, a Cox proportional hazard model with error effect applied on the study of an accelerated life test is investigated. Statistical inference under Bayesian methods by using the Markov chain Monte Carlo techniques is performed in order to estimate the parameters involved in the model and predict reliability in an accelerated life testing. The proposed model is applied to the analysis of the knock sensor failure time data in which some observations in the data are censored. The failure times at a constant stress level are assumed to be from a Weibull distribution. The analysis of the failure time data from an accelerated life test is used for the posterior estimation of parameters and prediction of the reliability function as well as the comparisons with the classical results from the maximum likelihood estimation. Copyright

Lean Manufacturing: A Strategy for Waste Reduction

In this chapter, we present relevant information about lean manufacturing (LM), its beginnings, evolution, as well as the main LM techniques and tools. The main purpose of this chapter is to know the current state of LM methodology and describe the leading tools that have contributed to successful LM projects worldwide. For this purpose, we conducted a literature review of 130 scientific papers, dividing LM tools, and technologies in two periods to track their evolution. Then, we conducted an analysis based on odd ratio and hypothesis testing for the proportions of frequency in the use of LM tools and technologies. This literature review aims at supporting practitioners in understanding the implications of lean manufacturing and knowing the main T&T that have contributed, during the last 12 years, to waste reduction and increased productivity in the industrial sector.

Multi-objective Optimization of an Injection Molding Process

This study presents a hybrid of artificial neural network and NSGA-II for multi-objective optimization of a particular plastic injection molding process. The objectives to be optimized are: a dimension of the finished plastic product (product quality), the processing time (productivity), and the energy consumption (manufacturing cost). The data collection and results validation are made on a 330 ton plastic injection machine. The design variables considered are mold temperature, material temperature, injection time, packing pressure, packing pressure time, and cooling time. An artificial neural network is used to map the relationship between design variables and output variables. Then, NSGA-II is used to find the set of Pareto optimal solutions. The results show that the methodology gives the designer flexibility and robustness to choose different scenarios according to current design requirements in terms of quality, productivity and energy savings.

Expert System Development Using Fuzzy If–Then Rules for Ergonomic Compatibility of AMT for Lean Environments

Lean Manufacturing is a revolution that is impacting the manufacturing process—it isn’t just about using tools, or changing a few steps in our manufacturing processes—it’s about the complete change of businesses—how the supply chain operates, how the directors direct, how the managers manage, how employees—people—go about their daily work. Advanced Manufacturing Technology (AMT) is a field that offers opportunities to the enterprises to adopt a Lean Manufacturing posture. This chapter provides a description of the development of an expert system for the ergonomic compatibility evaluation of AMT for Lean Environments. This system allows the evaluation of tangible and intangible ergonomic compatibility attributes of a variety of alternatives of AMT based on a multiattribute fuzzy axiomatic design approach rules, so the evaluation is by mean of linguistic terms. As result, system offers the ergonomic compatibility content of all the alternatives and the expert system direct them to the alternative that best satisfies ergonomic attributes. The results of the expert system were validated by the ergonomic compatibility evaluation of AMT carried out by experts.

Alternatives Methodologies for Lean Manufacturing: Genetic Algorithm

Global competition has forced industries to be in a constant search of technology; however, any technology that involves an advantage is temporary. Nowadays, a computer is seen in every enterprise mostly with the objective of being an advantage and as a tool to facilitate tasks (besides entertaining). Thus, a question arises, is it the most modern and expensive computer a significant advantage by default? I guess not today. The development of computers has arguably been the most radical achievement in the history of science and technology. The first computer scientists were motivated in large part by visions of creating computers programs with intelligence, mainly focusing to modeling the brain, imitating human behavior, and simulating biological evolution. This matter had its takeoff in the early 1980s, mostly by the advances in computational power; with evolutionary computation as one of the first ones, where “genetic algorithm” is the most prominent example (Mitchell 1998). Therefore, the “best” technologies are not limited to the acquisition of modern equipment or expensive machinery, but also include the progress and implementation of intelligent computational approaches such as the Genetic Algorithm. Therefore, the aim of this chapter is to show some applications of Genetic algorithm in the manufacturing sector in order to obtain a lean environment. This chapter begins with a brief definition of what a Genetic Algorithm is, its basic elements, and some numerical examples in order to facilitate its application. Next, two cases taken from the manufacturing industry of Juarez, Mexico are illustrated.