Finding an efficient way for determining a near-optimum disassembly sequence for complex products is becoming an important challenge for many industries, given the increasing environmental awareness of both governments and society. As a first approach, mathematically exact methods can be used to deal with this problem. But when disassembly costs that are dependent on the sequence and the number of components inside the product structure are prohibitive, heuristics or artificial intelligence-based methods are normally much more suitable to fulfill industry requirements. Nevertheless, when the size of the instance is very large, sequential algorithms are too slow. In this paper, a multi-start, greedy heuristic is defined and tested on a sample of products previously developed to measure the performance of a Scatter Search metaheuristic dealing with the same problem. The performance of the new algorithm was demonstrated to be competitive when compared with the one done under Scatter Search. It is also notably faster especially as the number of components inside the product structure increases.

In this paper, we concentrate on the disassembly-to-order (DTO) system, where end-of-life (EOL) products are taken back from last users to be disassembled to fulfill the demands for components and materials. The objective is to determine the number of EOL products that would be needed to maximize the profit and minimize the costs of the system. The conditions of EOL products are not always certain, which makes the problem difficult. We use a heuristic approach which transforms the stochastic disassembly yields into their deterministic equivalents and use a multi-criteria decision-making technique to solve the problem. In addition, we take the products' ages (and thus their deterioration) into account to determine their yield rates (e.g., older products tend to have lower yield rates for usable components) and generate the DTO plans for multiple periods. A numerical example is considered to illustrate the implementation of the approach.

The automobile manufacturing industry plays a very important role in a country's economy. The importance of automobile manufacturing industry lies in its sheer size and complexity in terms of the direct and indirect influence it commands across many other industries. While millions of people are employed in the automobile manufacturing industry, it is estimated that more than two and half times that number are employed in the auxiliary companies that supply parts to the automobile manufacturing companies. The auxiliary companies represent a group of businesses of various sizes, types, and geographical locations, producing a vast variety of products ranging from the very simple to the extremely intricate. In this study, the current environmental practices of management in the core Spanish auxiliary companies that do business with the automobile manufacturing industry (and thus form a large part of the automobile manufacturing industry's supply chain) are investigated. We show that while automobile manufacturing companies are under scrutiny to become more and more environmentally friendly, not only at their manufacturing stage but also at their products' useful and EOL stages, there appears to be no such burden on the auxiliary companies. Our conclusion is based on an elaborate survey conducted during the fall of 2004 of Spanish auxiliary companies with questions about the characteristics, environmental practices and reverse logistics related activities carried out by the companies.

The use of household appliances continues to rise every year. A significant number of End-Of-Life (EOL) appliances are generated because of the introduction of newer models that are more attractive, efficient and affordable. Others are, of course, generated when they become non-functional. Many regulations encourage recycling of EOL appliances to reduce the amount of waste sent to landfills. In addition, EOL appliances offer the appliance manufacturing and remanufacturing industries a source of less expensive raw materials and components. For this reason product recovery has become a subject of interest during the past decade. In this paper, we study the disassembly line for appliance disassembly. We discuss and incorporate some of the complications that are inherent in disassembly line including product arrival, demand arrival, inventory fluctuation and production control mechanisms. We show how to overcome such complications by implementing a multi-kanban system in the appliance disassembly line setting. The multi-kanban system (MKS) relies on dynamic routing of kanbans according to the state of the system. We investigate the multi-kanban mechanism using simulation and explore the effect of product mix on performance of the traditional push system (TPS) and MKS in terms of controlling the system's inventory while attempting to achieve a decent customer service level.

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.

Maintaining competitiveness in an environment where price and quality differences between competing products are disappearing depends on the company's ability to reduce costs and supply time. Timely responses to rapidly changing market conditions require an efficient Supply Chain Management (SCM). Outsourcing logistics to third-party logistics service providers (3PLs) is one commonly used way of increasing the efficiency of logistics operations, while creating a more "core competency focused" business environment. However, this alone may not be sufficient. Due to recent environmental regulations and growing public awareness regarding environmental issues, 3PLs need to be not only efficient but also environmentally benign to maintain companies' competitiveness. Even though an efficient and environmentally benign combination of 3PLs can theoretically be obtained using exhaustive search algorithms, heuristics approaches to the selection process may be superior in terms of the computational complexity. In this paper, a hybrid approach that combines a multiple criteria Genetic Algorithm (GA) with Linear Physical Weighting Algorithm (LPPW) to be used in efficient and environmentally benign 3PLs is proposed. A numerical example is also provided to illustrate the method and the analyses.

In this paper, we employ fuzzy AHP methodology for selecting potential recovery facilities in a closed-loop supply chain. This methodology utilizes triangular fuzzy numbers for pair-wise comparisons and the extent analysis method for the synthetic extent value of the fuzzy pair-wise comparisons and principle of comparison of fuzzy numbers to derive the weight vectors to address the criticism traditional AHP often faces due to its unbalanced scale of judgments and inability to handle inherent uncertainty in carrying out pair-wise comparisons. A numerical example is considered to illustrate the methodology.

In recent years, relating organization's attitude towards sustainable development, environmental management is gaining an increasing interest among researchers in supply chain management. With regard to a long term requirement of a shift from a linear economy towards a cycle economy, businesses should be motivated to embrace change brought about by consumers, government, competition, and ethical responsibility. To achieve business goals and objectives, a company must reply to increasing consumer demand for "green" products and implement environmentally responsible plans. Reverse logistics is an activity within organizations delegated to the customer service function, where customers with warranted or defective products would return them to their supplier. Emergence of reverse logistics enables to provide a competitive advantage and significant return on investment with an indirect effect on profitability. Many organizations are hiring third-party providers to implement reverse logistics programs designed to retain value by getting products back. Reverse logistics vendors play an important role in helping organizations in closing the loop for products offered by the organizations. In this regard, the selection of third-party providers issue is increasingly becoming an area of reverse logistics concept and practice. This study aims to assist managers in determining which third-party logistics provider to collaborate in the reverse logistics process with an alternative approach based on an integrated model using neural networks and fuzzy logic. An illustrative case study is discussed and the best provider is identified through the solution of this model.

This paper deals with disassembly sequencing problems subjected to sequence dependent disassembly costs. We present a heuristic and an iterative method based on partial branch and bound concept to solve such problems. Since heuristic methods intrinsically generate suboptimum solutions, we compared the heuristically obtained solutions with the exact solutions to see if they are reasonably good or not. This process, however, is limited to small or perhaps medium sized problems only as the required CPU time for exact methods tends to increase exponentially with the problem size. For the problems tested, we observed that the methods described in this paper generate surprisingly good results using almost negligible amount of CPU time.

Disassembly takes place in remanufacturing, recycling, and disposal, with a line being the best choice for automation. The disassembly line balancing problem seeks a sequence which: is feasible, minimizes workstations, and ensures similar idle times, as well as other end-of-life specific concerns. Finding the optimal balance is computationally intensive due to exponential growth. Combinatorial optimization methods hold promise for providing solutions to the disassembly line balancing problem, which is proven here to belong to the class of unary NP-complete problems. Probabilistic (ant colony optimization) and uninformed (H-K) search methods are presented and compared. Numerical results are obtained using a recent case study to illustrate the search implementations and compare their performance. Conclusions drawn include the consistent generation of near-optimal solutions, the ability to preserve precedence, the speed of the techniques, and their practicality due to ease of implementation.

Product and material recovery relies on the disassembly process to separate target components or materials from the end-of-life (EOL) products. Disassembly line is especially effective when products in large quantity are disassembled. Unlike an assembly line, a disassembly line is more complex and is subjected to numerous uncertainties including stochastic and multi-level arrivals of component demands, stochastic arrival times for EOL products, and process interruption due to equipment failure. These factors seriously impair the control mechanism in the disassembly line. A common production control mechanism is the traditional push system (TPS). TPS responds to the aforementioned complications by carrying substantial amounts of inventories. An alternative control mechanism is a newly developed multi-kanban pull system (MKS) that relies on dynamic routing of kanbans, which tends to minimize the system's inventories while maintaining demand serviceability. In this paper we explore the impact of sudden breakdown of server on the performance of a disassembly line. We compare the overall performances of the TPS and MKS by considering two scenarios. We present the solution procedure and results for these cases.

In this paper, we compare the impact of different disassembly line balancing (DLB) algorithms on the performance of our recently introduced Dynamic Kanban System for Disassembly Line (DKSDL) to accommodate the vagaries of uncertainties associated with disassembly and remanufacturing processing. We consider a case study to illustrate the impact of various DLB algorithms on the DKSDL. The approach to the solution, scenario settings, results and the discussions of the results are included.

Alternative disposal methods for food and other organic manufacturing waste streams are increasingly being investigated. Direct shipping, blending, extrusion, pelleting, and drying are commonly used to produce finished human food, animal feed, industrial products, and components ready for further manufacture. This paper discusses a new initiative whose goal is to develop a computer model based on analytical methods used for disassembly planning and demanufacturing modeling, but applied to organic processing waste streams. Upon completion, the simulation model discussed here will be used to analyze various liquid, sludge, and solid byproduct streams in order to determine optimal reprocessing avenues for specific manufacturing firms.

As manufacturing industries become more cognizant of the ecological effects that their firms have on the surrounding environment, their waste streams are increasingly becoming viewed not only as materials in need of disposal, but also as resources that can be reused, recycled, or reprocessed into valuable products. Within the food processing sector are many examples of various liquid, sludge, and solid biological and organic waste streams that require remediation. Alternative disposal methods for food and other bio-organic manufacturing waste streams are increasingly being investigated. Direct shipping, blending, extrusion, pelleting, and drying are commonly used to produce finished human food, animal feed, industrial products, and components ready for further manufacture. Landfilling, the traditional approach to waste remediation, however, should not be dismissed entirely. It does provide a baseline to which all other recycling and reprocessing options should be compared. This paper discusses the implementation of a computer model designed to examine the economics of landfilling bio-organic processing waste streams. Not only are these results applicable to food processing operations, but any industrial or manufacturing firm would benefit from examining the trends discussed here.

Manufacturers in many of today's industries are faced with product shelf life counted in months. Traditionally, this has made it very difficult to make a life cycle assessment (LCA) of a product, since the product would be obsolete by the time the LCA was completed. A new concept in LCA that allows specialists in things other than LCA to rapidly create both a model and generate "what-if" scenarios will allow even manufacturers of short shelf life products take advantage of the benefits of LCA. These industry-specific "wizards" are built around a manufacturing process and can be rapidly updated or customized to a particular manufacturer or process type. Results can be used internally for decision-making and can also enable manufacturers submit information for environmentally preferable purchasing, eco-labels, etc.

This paper deals with a cost management problem of a remanufacturing system with stochastic demand. We model the system with consideration for two types of inventories. One is the actual product inventory in the factory. The other is the virtual inventory that is being used by the customer. For this virtual inventory, it should be required to consider an operational cost that we need in order to observe and check the quantity of the inventory. We call this the virtual inventory cost and model the system by including it. We define the state of the remanufacturing system by the two inventory levels. It is assumed that the cost function is composed of various cost factors such as holding, backlog and manufacturing costs. We obtain the optimal policy that minimizes the expected average cost per period. Numerical results reveal the effects of the factors on the optimal policy.

The main objective of a product recovery facility (PRF) is to disassemble end-of-life (EOL) products and sell the reclaimed components for reuse and recovered materials in second-hand markets. Variability in the inflow of EOL products and fluctuation in demand for reusable components contribute to the volatility in inventory levels. To stay profitable the PRFs ought to manage their inventory by regulating the price appropriately to minimize holding costs. This work presents two deterministic pricing models for a PRF bounded by environmental regulations. In the first model, the demand is price dependent and in the second, the demand is both price and time dependent. The models are valid for single component with no inventory replenishment sale during the selling horizon . Numerical examples are presented to illustrate the models.

Analytic Hierarchy Process (AHP) has been employed by researchers for solving multi-criteria analysis problems. However, AHP is often criticized for its unbalanced scale of judgments and failure to precisely handle the inherent uncertainty and vagueness in carrying out the pair-wise comparisons. With an objective to address these drawbacks, in this paper, we employ a fuzzy approach in selecting potential recovery facilities in the strategic planning of a reverse supply chain network that addresses the decision maker's level of confidence in the fuzzy assessments and his/her attitude towards risk. A numerical example is considered to illustrate the methodology.

Remanufacturing is rapidly becoming a very important element in the economies of the world. Products such as washing machines, clothes driers, automobile parts, cell phones and a wide range of consumer durable goods are being reclaimed and sent through processes that restore these products to levels of operating performance that are as good or better than their new product performance. The operations involved in the remanufacturing process add several new dimensions to the work that must be performed. Disassembly is an operation that rarely appears on the operations chart of a typical production facility. The inspection and test functions in remanufacturing most often involve several more tasks than those involved in the first time manufacturing cycle. A close evaluation of most any remanufacturing operation reveals several points in the process in which parts must be cleaned, tested and stored. Although several researchers have focused their work on optimizing the disassembly function and the inspection, test and store functions, very little research has been devoted to studying the impact of the facilities design on the effectiveness of the remanufacturing process. The purpose of this paper will be to delineate the differences between first time manufacturing operations and remanufacturing operations for durable goods and to identify the features of the facilities design that must be considered if the remanufacturing operations are to be effective.

This paper provides an efficiency analysis of practices in Solid Waste Management of manufacturing companies in Wales. We apply data envelopment analysis (DEA) to a data set compiled during the National Waste Survey Wales 2003. We explore the relative performance of small and medium sized manufacturing enterprises (SME; 10-250 employees) in Wales. We determine the technical and scale environmental and economic efficiencies of these organizations. Our evaluation focuses on empirical data collected from companies in a wide diversity of manufacturing industries throughout Wales. We find significant differences in industry and size efficiencies. We also find correlations that exist among environmental and economic efficiencies. These variations show that improvements can be made using benchmarks from similar and different size industries. Further pursuit of an investigation of possible reasons for these differences is recommended.

Green house gas (GHG) emissions have been tied to global climate change. One popular policy instrument that seems to have gained credibility with explicit mention of its application in the Kyoto Protocol is the use of permit trading and cap-and-trade mechanisms. Organizations functioning within this environment will need to manage their resources appropriately to remain competitive. Organizations will either have the opportunity to purchase emissions credits (offsets) from a market trading scheme or seek to reduce their emissions through different measures. Some measures may include investment in new technologies that will reduce their reliance on GHG emitting practices. In many countries, large organizations and institutions generate their own power to operate their facilities. Much of this power is generated (or bought) from GHG producing technology. Specific renewable energy sources such as wind and solar photovoltaic technology may become more feasible alternatives available to a large percentage of these organizations if they are able to take advantage and incorporate the market for GHG emissions trading in their analyses. To help organizations evaluate investment in these renewable energy technologies we introduce a real options based model that will take into consideration uncertainties associated with the technology and those associated with the GHG trading market. The real options analysis will consider both the stochastic (uncertainty) nature of the exercise price of the technology and the stochastic nature of the market trading price of the GHG emissions.

This paper studies a servo-valve controlled hydraulic cylinder system which is mostly used in industrial applications such as robotics, computer numerical control (CNC) machines and transportations. The system model consists of combination of two models: The first model involves nonlinear flow equations of the servo-valve, which are widely available in the literature. The second model employed in the system is a tailored asymmetric cylinder model. A fourth order nonlinear system model is then obtained by combining these two models. Two different neural network control algorithms are applied to the system. The first algorithm is "Neural Network Predictive Control (NNPC)," which employs identified neural network model to predict the future output of the system. The second algorithm is "Nonlinear Autoregressive Moving Average (NARMA-L2)" control, which transforms nonlinear system dynamics into linear system dynamics by eliminating the nonlinearities. On the simulation, NNPC and NARMA-L2 control are applied to the system model by using Matlab's Simulik simulation package and position control of the system is realized. A discussion regarding the advantages and disadvantages of the two control algorithms are also provided in the paper.

The return flow of remanufacturing processes is highly variable in terms of the quality of returned products. This fact causes higher variability in remanufacturing processing times. In order to decrease the variability in the process the returns can be inspected and classified according to their quality following their arrival. However this classification will add an extra cost to the remanufacturing process. In this study we investigate the pros and cons of such a classification.

In hybrid production systems when both remanufacturing and new product manufacturing activities are performed at the same facility used parts can be remanufactured at the same shop floor as the new products. Here the quality difference between used and new parts being processed impacts both the output quality and the efficiency of shared machines by causing quicker than normal wear of tools. In this study, in order to capture this impact we first look at a one machine system that manufactures both classes of products with distinct process related failure rates, then a two machine one buffer tandem system with infinite buffer capacity and suggest a method to approximately compute the throughput rate of this system.