Disassembly has recently gained attention in the literature due to its role in product recovery. Disassembly activities take place in various recovery operations including remanufacturing, recycling, and disposal. The disassembly line is the best choice for automated disassembly of returned products. It is therefore important that the disassembly line be designed and balanced so that it works as efficiently as possible. However, finding the optimal balance is computationally intensive with exhaustive search quickly becoming prohibitively large, even for relatively small products, due to exponential growth. In this paper, complexity theory is reviewed and used to prove that the DISASSEMBLY LINE BALANCING PROBLEM is NP-complete, unary NP-complete, and NP-hard, necessitating specialized solution methodologies, including those from the field of combinatorial optimization.

Product recovery is a new trend that many manufacturers practice to minimize the fast depletion of virgin resources and to realize economical benefits from recovering end-of-life (EOL) products. However, the practice of recovering components and materials is challenging as it often requires disassembly. There are many distinctive complications associated with the disassembly process. One of the complications stems from the disassembly line balancing problem (DLBP). DLBP has recently been actively researched in the literature and several heuristic models have been introduced to provide near optimal work contents at each workstation of the disassembly line. However, due to the disparity between demands for parts and their yields, there are many inventory problems that arise during the disassembly line balancing process. In this paper, we identify the issue of unbalanced inventories generated at various workstations of a disassembly line and discuss how to overcome this. A case example involving a personal computer (PC) is considered for discussion. In order to provide a full analysis of the problem, measures of performances are defined. Measures of performances reflect the state of the system and the ability to meet the demand while maintaining consistent flow of parts. We also discuss and compare various issues associated with the assembly systems and the disassembly systems. While it is clear that the inventory issues surrounding the disassembly line offer a new challenge, the understating that we have gained from solving the traditional inventory problems, nevertheless, provide helpful insights in overcoming this new challenge.

In this paper, we apply the Dynamic Kanban System for Disassembly Line (DKSDL) to an industrial setting. The product considered is a second-generation industrial voice recognition client unit. The company manufacturing this device has moved on to the next generation device, which has more memory, faster CPU and a standardized OS support. There are many of the older generation clients in the customer base, which are being upgraded to the new generation resulting in a large number of the older units at hand. These units are being returned to the manufacturing facility to be processed. The casing and some other components are being used in the new design and can be disassembled and stocked as refurbished items to be used in returned products processing. The resulting DKSDL for this disassembly process is modeled using simulation and the results are discussed.

We solve the disassembly-to-order (DTO) problem by using Evolutionary Computation. DTO is a system where a variety of returned products are disassembled to fulfill the demand for specified numbers of components and materials. The main objective is to determine the optimal number of take-back EOL products for the DTO system which satisfy the desirable criteria of the system. One of the most widely used forms of Evolutionary Computation is Genetic Algorithm (GA). GA, which has the capability to improve a set of solutions over evolutionary steps, is used to generate optimal number of take-back EOL products. Moreover, linear physical programming (LPP), which has key features to entirely remove the decision maker (DM) from the process of choosing weights and to handle the vagueness of aspiration levels, is used to calculate fitness values in the GA process. A numerical example is considered to illustrate the methodology.

A way to implement the pull system in a disassembly line is to use a multi-kanban model. The model employs several types of kanbans attached to both components and subassemblies. The heart of the system lies in the kanban routing mechanism which allows routing of kanbans in multi-directions based on real time conditions. This mechanism creates minimum amount of residual inventory while satisfying varying demand levels. It also helps regulate the requests for subassembly from upstream workstations when a breakdown occurs at a workstation. This reduces blockage and starvation of subassemblies at workstations other than the broken workstation. In this paper, we discuss the difficulties involved in utilizing the multi-kanban mechanism. We thoroughly investigate several scenarios of the disassembly line setting including a scenario with common products, a scenario with component discriminating demand, a scenario in the presence of products with multiple precedence relationships, and a scenario with workstation breakdowns. These scenarios represent various disassembly environments that a facility may face when dealing with the disassembly of both single and multiple products on a single line. In each scenario, we examine effectiveness of the multi-kanban model using three performance measures, viz., the inventory level, the level of satisfied demand, and the customer waiting time. We compare these results with the ones generated from the same line that employs a traditional push system. Using simulation, we demonstrate that the overall performance of the disassembly line using multi-kanban mechanism outperforms the disassembly line with the traditional push system.

In this paper, Genetic Algorithm (GA) is used to solve the disassembly-to-order (DTO) problem. DTO is a system where a variety of returned products are disassembled to fulfill the demand for specified numbers of components and materials. The main objective is to determine the optimal number of take-back EOL (end-of-life) products for the DTO system which satisfy the desirable criteria of the system. We implement the Weighted Fuzzy Goal Programming (WFGP) to calculate the fitness values in GA process. We also consider product deterioration which affects the yield rates (e.g., older products tend to have lower yield rates for usable components) and use heuristic procedure to transform the stochastic disassembly yields into their deterministic equivalents. A numerical example is also considered.

We consider the disassembly sequencing problem subjected to sequence dependent disassembly costs. Because most of the available methods that deal with such problems rely on metaheuristic and heuristic methods, it is desirable to have exact methods available that can at least be applied to medium sized problems to determine if the heuristically obtained solutions are acceptable or not. The conventional integer linear programming (ILP) approaches become unmanageable even for modest product complexity cases. In this paper, we propose an iterative method that repeatedly solves a binary integer linear programming (BILP) problem. The method converges quickly for medium sized problems. We use the proposed method to solve a cell phone problem from practice, consisting of 25 components that are represented, according to a set of precedence relationships, via a disassembly precedence graph.

Interpretive structural modeling (ISM) has been applied to a number of organizational decision problems. It is a technique that is helpful to managers in organizing the relationships among a series of factors that may influence their decision. The decision support technique has a long history dating back to the late 1960's and early 1970's. Yet, its application and investigation by researchers to a variety of topical areas has not occurred until very recently. This lack of application is especially true for investigation into topics related to environmentally conscious manufacturing (ECM). We find that the barriers to this organizational practice can be further investigated and analyzed with ISM. This paper seeks to provide an illustrative example of this procedure. More complete evaluation and implementation of this technique is recommended for its validation.

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 coproducts. Within the food and biological processing sector are many examples of various liquid, sludge, and solid waste streams that require remediation. Alternative disposal methods for these organic manufacturing waste streams are increasingly being investigated. Even though high-value extrusion, pelleting, and drying are commonly used to produce finished human foods, animal feeds, industrial products, and components ready for further manufacture, blending and shipping is a commonly used approach to waste utilization, especially when these materials are used as animal feeds. This paper discusses the implementation of a computer model that examines the economics of blending organic waste streams with other carrier materials, to improve nutrient content as well as augment storage and handling properties. Not only are these results applicable to food processing operations, but any industrial or manufacturing firm could benefit from examining the trends discussed here.

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 as materials in need of disposal, but rather as resources that can be reused, recycled, or reprocessed into valuable products. Within the food processing sector are many examples of biological and organic processing residues. Alternative disposal methods for these manufacturing waste streams are increasingly being investigated. Direct shipping, blending, extrusion, pelleting, and drying are commonly used to produce finished human foods, animal feeds, industrial products, and components ready for further manufacture. Computer modeling and simulation can aid in these value-added endeavors. This paper discusses a strategy that can be used when constructing computer models for bio-organic processing streams. Not only do macroscale process flows need to be considered, but individual constituents, on a micro-scale, are also essential in order to develop appropriate models. The development heuristics and hierarchies discussed here can be applied to various liquid, sludge, or solid materials when simulating processing and reprocessing avenues for specific manufacturing process streams. Thus this methodology is applicable to food processing operations, but many other industrial and manufacturing firms could also benefit from instituting the principles described here.

In this paper, we introduce an approach to combine material flow analysis (MFA) with data envelopment analysis (DEA) in order to evaluate resource efficiency of different stages in the material cycles in various countries. The approach is exemplified for the end-of-life stage of the anthropogenic iron cycle, comparing data of 48 countries. The proposed performance criteria are defined through a common MFA system definition so that the model could also be expanded to indicate the overall efficiency of the system, while emphasizing the relative importance of the different stages. Steps and implementation of the proposed methodology are explained with the help of a case study for iron.

Independent and small scale product recovery facilities (PRFs) often struggle to achieve profits when faced with inconsistent inflows of discarded products, varying demand patterns for recovered components, and stringent environmental regulations. Inconsistent inflows coupled with the varying demand cause undue fluctuations in inventory levels and frequently affect costs involved in product recovery operations. An effective pricing strategy can stabilize the fluctuations in demand and consequently can allow PRFs to control inventory levels. This research determines the prices of reusable and recyclable components and acquisition price of discarded products that allow PRFs to simultaneously maximize their financial returns and minimize the product recovery costs. Genetic algorithms and analytic hierarchy process are employed to solve this multi-criteria decision making problem.

This paper proposes a new performance evaluation approach to an inventory management system based on an environmental conscious manufacturing system such as a remanufacturing system with consideration for Life Cycle Assessment (LCA). We here formulate an inventory system with single item based on newsboy problem. The system is evaluated by the total cost that includes the holding, the backlogged, the disposal and the CO₂ penalty costs. In this approach, we consider two types of inventories: one is the actual product inventory in a factory whereas the other is the LCA inventory that denotes CO₂ emission for all the life cycle of the product. This model also includes disposal and recycle rates. Using the suggested model, we can obtain the total inventory cost with consideration for the environment. Numerical examples are considered to illustrate the implementation of the methodology.

Rapid technological developments and the growing desire of customers to acquire latest technology has led to a new environmental problem "waste", comprising of both end-of-life products and used products that are disposed prematurely. As a result, both consumer and government concerns for the environment are driving many original equipment manufacturers (OEM) to engage in additional series of activities stemming from the reverse supply chain. The combination of forward/traditional supply chain and reverse supply chain forms the closed-loop supply chain. Contrary to a traditional/forward supply chain, a closed-loop supply chain involves more variability. In this paper, we explore the use of Motorola's Six Sigma methodology to achieve better synchronization in a closed-loop supply chain network by tailoring the individual processes in a way that maximizes the overall delivery performance. A numerical example is considered to illustrate the approach.

Traditionally, in supply chain literature, the supplier selection problem is treated as an optimization problem that requires formulating a single objective function. However, not all supplier selection criteria can be quantified, as a result of which, only a few quantitative criteria are included in the problem formulation. To this end, in this paper, we develop an integrated analytic network process (ANP) and preemptive goal programming (PGP) based multi-criteria decision making methodology to address the qualitative and quantitative criteria that influence the supplier selection problem in a closed-loop supply chain network (CLSC). While the ANP methodology aids in determining qualitatively the supply chain strategy by evaluating the suppliers with respect to several criteria, the PGP methodology uses the ANP ratings as inputs and aids in mathematically determining the optimal quantities to be ordered from the suppliers.

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.

Reverse logistics is becoming a very important and necessary part of any business wanting to excel and move forward. One important aspect of reverse logistics is product returns. It is becoming essential to make sound decisions at all levels; strategic, tactical and operational, concerning the return flow of products. Thus, most firms have begun to explore the possibility of managing product returns in a more cost efficient manner. However, few studies have addressed the problem of determining the number and location of centralized return centers (i.e., reverse consolidation points) where returned products from retailers or end customers were collected, sorted, and consolidated into a large shipment destined for manufacturers or distributors' repair facilities. To fill the void in such a line of research, this paper proposes a nonlinear integer programming model that is subsequently transformed into an equivalent mixed integer linear programming model.

While the strategic planning of a supply chain, which is typically a long range planning, deals with the design aspect of the supply chain (what products should be processed/produced in what facilities etc.), tactical planning is typically a medium-range planning that involves the optimization of flow of goods and services across the supply chain. In this paper, we present a multi-criteria optimization model for the strategic and tactical planning of a closed-loop supply chain under uncertainty, where the aspiration levels for various goals are more likely to be in the "approximately more/less than" and/or "more/less is better" form. We use fuzzy goal programming technique to solve the problem. When solved, the model identifies simultaneously the most economical used-product to re-process in the supply chain, the efficient production facilities and the right mix and quantity of goods to be transported across the supply chain. A numerical example is considered to illustrate the methodology.

Both consumer and government concerns for the environment are driving many original equipment manufacturers (OEM) to engage in additional series of activities stemming from the reverse supply chain. The combination of forward/traditional supply chain and reverse supply chain forms the closed-loop supply chain. Apart from its efficient design, the success of a closed-loop supply chain network depends on its marketing strategy as well. Hence, it is important that the planned marketing strategy be evaluated with respect to the drivers of public participation in the network. To this end, we identify the important drivers of public participation and propose a fuzzy Quality Function Deployment based methodology and method of total preferences to evaluate the marketing strategy of a closed-loop supply chain with respect to those drivers. A numerical example is considered to illustrate the methodology.

The increasing awareness of sustainable development concept and its economic benefits are making environmentally proactive companies to consider how they can achieve eco-efficiency improvement through material exchange and by partnering with academic, governmental and non-governmental agencies. This paper reports the experiences and achievements of a tripartite partnership initiated by the author with a number of companies in Calgary and a Calgarian NGO. The network is a form of eco-industrial network that is being developed to benefit the participating companies and to develop industrial ecology students' skill in eco-industrial network modeling. The paper highlights the initial difficulties, how they were overcome and a conceptual model developed for assessing the sustainability of the material exchange loop. The preliminary results obtained revealed that the companies are enthusiastic in taking part if it will help them achieve waste management cost reduction, improvement in their corporate environmental performance and corporate goodwill, and protection of their proprietary information. It also reveals that such corporate exposure to students develop their skills in balancing their academic view with what works in the corporate world.

This paper presents a design concept originally developed to suit the needs of the agro-industrial sector in the developing economies. It also highlights how this concept fits into other green design paradigms and to the goals of industrial ecology. The need to design for multi-lifecycles arose from the need for durable, easily maintained agri-processing machines in these economies. Most of the available machines are typically imported from countries of entirely different technological, climatic and socio-cultural conditions. Many become unmanageable after only a few years of use because of lack of technical know-how. Consequently, they become environmental problems and sources of economic drain for farmers, processors, regional and municipal authorities. There is therefore a need to develop a design concept that considers all prevalent local techno-economic and socio-cultural conditions, as well as develop design features that promote multi-lifecycle use of such agri-industrial machinery. This design concept incorporates DfX paradigms such as design for modularity, cost, assemblability, manufacturability, disassemblability, maintainability, reusability, and remanufacturability. This concept has been used to design and develop a cassava processing machine. The performance evaluation of the machine compares with the imported ones. By incorporating all the aforementioned DfXs, this design concept promotes resource use optimization, pollution prevention and cost minimization which are among the goals of industrial ecology. It is believed that this design concept can be applied to other areas of need in the industrial and agricultural sectors, and that using this design concept will go far in complementing various efforts aimed at reducing total environmental impact of our industrial activities.

The growing social concern about the environment makes the reverse logistics activities to become the focus of numerous businesses opportunities. The present study tries to identify which are the most important motivations and the profits obtained by the firms which implement reverse logistics activities. To corroborate this, an empirical study was carried out in the Spanish auxiliary automotive industry obtaining that the profits are possible to classify in two big groups: economical and ecological profits. A 'green' image has become an important marketing element. This development has stimulated a number of companies to explore options for take-back and recovery of their products. Overhauled products may be used as spares or sold on secondary markets while requiring only a small fraction of the original production costs for repair so this is and important economical profits. It has been used EQS as software tool in order to verify the considered hypothesis.

The concept of sustainability has become increasingly important for organisations and has permeated a number of managerial and organisational decisions. Sustainability, as defined by its 'triple-bottom line' factors of economic, environmental and social dimensions, is the underlying framework we use to develop and apply a strategic justification tool for project evaluation with sustainability implications. An activity based management methodological framework is used as a vehicle to frame decisions around using corporate sustainability. An illustrative application of this technique investigates how an organisation would select from one of two competing reverse logistics providers. This process requires that we introduce issues relevant to three major sustainability factors (and their sub factors) and how they are influenced by a reverse logistics provider decision. The dual contribution of this paper includes investigating the design and development of the strategic sustainability evaluation framework and the linkage of reverse logistics to economic, environmental and social sustainability dimensions.