Elif Kongar

A MULTI-CRITERIA DECISION MAKING APPROACH FOR DISASSEMBLY-TO-ORDER SYSTEMS

In this paper, we present a multi-criteria optimization model of a disassembly-to-order system to determine the best combination of the number of each product type to be taken back at the end-of-life and disassembled to meet the demand for items and materials retrieved from them under a variety of physical, financial and environmental constrains so as to achieve the preemptive goals of maximum total profit, maximum sales from materials, minimum number of disposed items, minimum number of stored items, minimum cost of disposal and minimum cost of preparation, in that order. When solved, the model provides the number of reused, recycled, stored and disposed items as well as the values of a host of other performance measures. A case example is presented to illustrate the models implementation.

Solving the disassembly-to-order problem using linear physical programming

This article focuses on a disassembly-to-order system for end-of-life (EOL) electronic products. The EOL system embodies a variety of cost and revenue functions, starting from the take-back step, till the disassembled items and recycled materials are sent to customers. Storage and proper disposal options are also included in the model with the appropriate financial measures. A thorough solution methodology, involving these functions in an environmentally conscious manner, is provided based on the linear physical programming algorithm. The algorithm embodies various environmental, managerial and financial targets, which may be either quantitative or qualitative. The introduction of intangible factors allows the model to obtain desirable levels of important targets viz., environmental damage and customer satisfaction. The model also fulfils the demand for items and materials without backorders.

A Robotic-Driven Disassembly Sequence Generator for End-Of-Life Electronic Products

In this study, we propose an intelligent automated disassembly cell for online (real time) selective disassembly. The cell is composed of an industrial robotic manipulator, a camera, range sensing and component segmentation visual algorithms. The cell prototype allows for robotic sensory-driven disassembly under uncertainty. An online genetic algorithm model for selective disassembly is also proposed for optimal and near/optimal disassembly sequencing.

Modeling the effects of pelleting on the logistics of distillers grains shipping

The energy security needs of energy importing nations continue to escalate. It is clear that biofuels can help meet some of the increasing need for energy. Theoretically, these can be produced from a variety of biological materials, including agricultural residues (such as corn stover and wheat straw), perennial grasses, legumes, algae, and other biological materials. Currently, however, the most heavily utilized material is corn starch. Industrial fuel ethanol production in the US primarily uses corn, because it is readily converted into fuel at a relatively low cost compared to other biomass sources. The production of corn-based ethanol in the US is dramatically increasing. As the industry continues to grow, the amount of byproducts and coproducts also increases. At the moment, the nonfermentable residues (which are dried and sold as distillers dried grains with solubles--DDGS) are utilized only as livestock feed. The sale of coproducts provides ethanol processors with a substantial revenue source and significantly increases the profitability of the production process. Even though these materials are used to feed animals in local markets, as the size and scope of the industry continues to grow, the need to ship large quantities of coproducts grows as well. This includes both domestic as well as international transportation. Value-added processing options offer the potential to increase the sustainability of each ethanol plant, and thus the industry overall. However, implementation of new technologies will be dependent upon how their costs interact with current processing costs and the logistics of coproduct deliveries. The objective of this study was to examine some of these issues by developing a computer model to determine potential cost ramifications of using various alternative technologies during ethanol processing. This paper focuses specifically on adding a densification unit operation (i.e., pelleting) to produce value-added DDGS at a fuel ethanol manufacturing plant. We have examined the economic implications of pelleting DDGS for varying DDGS production rates (100-1000 tons/d) and pelleting rates (0-100%), for a series of DDGS sales prices (

A multiple objective tabu search approach for end-of-life product disassembly

50-

Disassembly Sequencing Using Tabu Search

200/ton). As the proportion of pelleting increases, the cost of transporting DDGS to distant markets drastically declines, because the rail cars can be filled to capacity. For example, at a DDGS sales price of

A novel IT infrastructure for reverse logistics operations of end-of-life pharmaceutical products

50/ton, 100% pelleting will reduce shipping costs (both direct and indirect) by 89% compared to shipping the DDGS in bulk form (i.e., no pelleting), whereas at a DDGS sales price of

An Online Genetic Algorithm for Automated Disassembly Sequence Generation

200/ton, it will reduce costs by over 96%. It is clear that the sustainability of the ethanol industry can be improved by implementing pelleting technology for the coproducts, especially at those plants that ship their DDGS via rail.

A holistic approach for performance evaluation using quantitative and qualitative data

This paper addresses the disassembly-to-order (DTO) and the disassembly sequence planning (DSP) problems and employs a multiple objective tabu search (MOTS) algorithm to solve them. The focus of the study is on the disassembly operations of end-of-life (EOL) products taken back from their last users (owners). The proposed approach aims at disassembling the EOL products for reusing, recycling, storing and properly disposing them in an environmentally benign manner. Thus, the model controls and restricts the environmental damage while increasing the value captured from the EOL products. These goals are achieved in an economically viable manner. The proposed model employs two independent modules, namely, the DTO module and the DSP module. The DTO module embodies multiple objectives and provides various options for the number of items to be disassembled from each EOL product for resale, recycling, storage and proper disposal, along with the numbers and types of EOL products to be taken back. The module considers both environmental and financial objectives while meeting the demand expectations. The DSP module provides multiple feasible disassembly sequences for the EOL products. The module considers and preserves the precedence relationships among the items while fulfilling multiple objectives.

Data Envelopment Analysis Approach to Compare the Environmental Efficiency of Energy Utilization

End-of-life disassembly has developed into a major research area within the sustainability paradigm, resulting in the emergence of several algorithms and structures proposing heuristics techniques such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Neural Networks (NN). The performance of the proposed methodologies heavily depends on the accuracy and the flexibility of the algorithms to accommodate several factors such as preserving the precedence relationships during disassembly while obtaining near- optimal and optimal solutions. This paper improves a previously proposed Genetic Algorithm model for disassembly sequencing by utilizing a faster metaheuristic algorithm, Tabu search, to obtain the optimal solution. The objectives of the proposed algorithm are to minimize (1) the traveled distance by the robotic arm, (2) the number of disassembly method changes, and (3) the number of robotic arm travels by combining the identical-material components together and hence eliminating unnecessary disassembly operations. In addition to improving the quality of optimum sequence generation, a comprehensive statistical analysis comparing the previous Genetic Algorithm and the proposed Tabu Search Algorithm is also included