KEYWORDS: Compound parabolic concentrators, Tolerancing, Manufacturing, Signal processing, Raw materials, Transform theory, Process control, Physical phenomena, Minerals, Air contamination
Six-Sigma is a newer version of Total Quality Management (TQM), and its fundamental principle is to reduce defects in processes. The traditional approach to calculate the value of n for an n-sigma process can be confusing to prospective six-sigma practitioners, because the three values of interest (viz., process capability ratio, process capability index, and n) are always different. In this paper, we present a new formula that is less confusing, and yet serves the purpose of checking how good a given process is. We apply this formula for a crucial issue (selection of potential recovery facilities) identified in the literature for reverse supply chain design. The CPC chart in the literature for selection of potential suppliers uses the process capability index alone. Since the process capability ratio too is required for judging the quality of a facility, we use the new formula for building a chart for selection of potential recovery facilities.
The growing desire of consumers to acquire the latest technology (both at home and in the workplace), along with the rapid technological development of new products, has led to a new environmental problem: waste. The only way to tackle this problem is design and implementation of reverse supply chains. Implementation of an efficient reverse supply chain requires coordination among a number of parties, such as the collector, the dismantler, the shredder, and the recycler. In this paper, we identify four different scenarios of homogeneous and heterogeneous products, and formulate some potential interactions between the collector and the dismantler, for each of those scenarios.
The designing of a reverse supply chain must involve selection of collection centers and recovery facilities that have sufficient success potentials. These success potentials depend heavily on the participation of the following three important groups who have multiple, conflicting, and incommensurate criteria for evaluation, and so, the potentials must be evaluated based on the maximized consensus among those groups: (i) Consumers (whose primary concern is convenience), (ii) Local government officials (whose primary concern is environmental consciousness), and (iii) Supply chain company executives (whose primary concern is profit). In this paper, we propose a three-phase multi-criteria group approach to select collection centers as well as recovery facilities, of sufficient success potentials. In the first phase of the approach, we identify important criteria for evaluation of the alternatives (collection centers as well as recovery facilities) by each of the above three groups. In the second phase, we give weights to the criteria of each group using the eigen vector method, and then, employ the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to find the success potential of each alternative, as evaluated by that group. Then, in the third and final phase, we use Borda’s choice rule that, for each alternative, combines individual success potentials into a group success potential.
It is difficult to obtain information regarding compositions and remaining life periods of used products. Hence, they often undergo partial or complete disassembly for subsequent re-processing (remanufacturing and/or recycling). However, researchers are now studying sensor embedded products (SEPs), the composition and remaining life of which can be obtained at the end of their use from sensors. This paper addresses decision-making regarding the futurity of an SEP at its end of use: whether to disassemble it for subsequent recycling/remanufacturing or to repair it for subsequent sale on a second-hand market. We identify some important factors that must be considered before making a decision. Using a numerical example, we propose a simple approach that employs Bayesian updating and fuzzy set theory to aid the decision-making process.
The cost-benefit analysis of data associated with re-processing of used products often involves the uncertainty feature of cash-flow modeling. The data is not objective because of uncertainties in supply, quality and disassembly times of used products. Hence, decision-makers must rely on “fuzzy” data for analysis. The same parties that are involved in the forward supply chain often carry out the collection and re-processing of used products. It is therefore important that the cost-benefit analysis takes the data of both new products and used products into account. In this paper, a fuzzy cost-benefit function is proposed that is used to perform a multi-criteria economic analysis to select the most economical products to process in a closed-loop supply chain. Application of the function is detailed through an illustrative example.
Although there are many quantitative models in the literature to design a reverse supply chain, every model assumes that all the recovery facilities that are engaged in the supply chain have enough potential to efficiently re-process the incoming used products. Motivated by the risk of re-processing used products in facilities of insufficient potentiality, this paper proposes a method to identify potential facilities in a set of candidate recovery facilities operating in a region where a reverse supply chain is to be established. In this paper, the problem is solved using a newly developed method called physical programming. The most significant advantage of using physical programming is that it allows a decision
maker to express his preferences for values of criteria (for comparing the alternatives), not in the traditional form of weights but in terms of ranges of different degrees of desirability, such as ideal range, desirable range, highly desirable range, undesirable range, and unacceptable range. A numerical example is considered to illustrate the proposed method.
Collectors of discarded products seldom know when those products were bought and why they are discarded. Also, the products do not indicate their remaining life periods. So, it is difficult to decide if it is “sensible” to repair (if necessary) a particular product for subsequent sale on the second-hand market or to disassemble it partially or completely for subsequent remanufacture and/or recycle. To this end, we build an expert system using Bayesian updating process and fuzzy set theory, to aid such decision-making. A numerical example demonstrates the building approach.
It has become common for manufacturing facilities involved in production of new products to also carry out collection and re-processing of used products. While environmental consciousness has become an obligation to the facilities in the production of new products due to governmental regulations and public perspective on environmental issues, potentiality of the facilities to re-process used products directly affects the profitability of the facilities. Although many papers in the literature deal with performance evaluation of facilities, none of them address these two factors. To this end, a TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) approach, which evaluates production facilities in
terms of both environmental-consciousness and potentiality, is proposed. Furthermore, since most of the criteria that fall
under these two factors are intangible, triangular fuzzy numbers (TFNs) are employed to rate them in the evaluation process. A numerical example demonstrates the feasibility of the proposed method.
Disassembly line is, perhaps, the most suitable way for the disassembly of large products or small products in large quantities. In this paper, we address the disassembly line balancing problem (DLBP) and the challenges that come with it. The objective ofbalancing the disassembly line is to utilize the disassembly line in an optimized fashion while meeting the demand for the parts retrieved from the returned products. Although, the traditional line balancing problem for assembly has been studied for a long time, so far, no one has formally talked about the DLBP. In this work, our primary objective is to address the DLBP related issues. However, we also present a heuristic to demonstrate how several important factors in disassembly can be incorporated into the solution process of a DLBP. An example is considered to illustrate the use of the heuristic.
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