Vijitashwa Pandey

Varying lifecycle lengths within a product take-back portfolio

Product take-back and reuse is sometimes at odds with the rapidly evolving desires ofsome customers. For other customers, the environmental benefits of reuse more thancompensate for minor drawbacks. “Selling a service” (rather than a product) throughleasing enables the manufacturer to control the timing and quality of product take-backbut current methods assume a fixed leasing period. What is needed is a method for finetuning the time span of customers’life cycles in order to provide each market segment thecombination of features it most desires. This paper presents a new method for performinglong range product planning so that the manufacturer can determine optimal take-backtimes, end-of-life design decisions, and number of lifecycles. The method first determinesa Pareto optimal frontier over price, environmental impact and reliability using a geneticalgorithm. Then, a multiattribute utility function is employed to maximize utility acrossdifferent segments of the market and also across different lifecycles within each segment.Post-optimal studies help determine feasibility of component redesign in addition to partsconsolidation. The proposed method is illustrated through an example involving personalcomputers.

A distributed pool architecture for genetic algorithms

The genetic algorithm (GA) paradigm is a well-known heuristic for solving many problems in science and engineering. As problem sizes increase, a natural question is how to exploit advances in distributed and parallel computing to speed up the execution of GAs. This paper proposes a new distributed architecture for GAs, based on distributed storage of the individuals in a persistent pool. Processors extract individuals from the pool in order to perform the computations and then insert the resulting individuals back into the pool. Unlike previously proposed approaches, the new approach is tailored for distributed systems in which processors are loosely coupled, failure-prone and can run at different speeds. Proof-of-concept simulation results are presented indicating that the approach can deliver improved performance due to the distribution and tolerates a large fraction of crash failures.

Effective age of remanufactured products: An entropy approach

Product take-back and remanufacturing systems are difficult to implement cost effectively. One contributing factor is the complex nature of the inter-relationships among components of a product. Modeling of these relationships helps determine the product’s overall performance as a function of the performances of individual components. Reliability, a commonly used measure of performance, is a good measure of the physical failure rate, but it does not always reflect value degradation as experienced by customers or experts. As a result, it is difficult to define the effective performance of remanufactured products when some components are reused while others are not. Legislated take-back mandates across the world increasingly make it necessary to understand this perceived performance. In this paper we propose a method for combining customers’/experts’ assessments of value degradation using the maximum entropy principle. This value degradation information is then coupled with the components’ failure rate information. A method for modeling performance of a product that is comprised of components of different ages is presented. Overall performance is measured in units of time (effective age) by aligning with that of a product that has never been disassembled. We demonstrate the approach using a personal computer as example.

Copulas for Demand Estimation for Portfolio Reuse Design Decisions

Design for multiple product lifecycles with component reuse potentially improves profitability, customer satisfaction and environmental impact. However, deciding on the scope and the level of detail (granularity) to be considered in the design process can be challenging. Although a comprehensive model that takes into account all important issues would be immensely useful, modeling difficulties and computational intractability prevent their successful implementation. This paper extends the scope of a previously developed design decision tool for determining optimal end-of-lifecycle decisions. The single product case is extended to a product portfolio, which has been shown to capture more demand. Demand is explicitly considered and its modeling is accomplished with the use of copulas. An important result from statistics, Sklar’s theorem, provides a way to use data from existing product sales to estimate demand for currently nonexistent reused products. In addition, effective age calculations are updated. On the computational front, time-continuation and seeding is used for NSGA-II to converge to optima more quickly in the resulting larger problem. A personal computer case study illustrates the effect of different parameters such as portfolio size, the possibility of recycle, and limits on environmental impact (as opposed to mandated take-back).Copyright

Variability and Component Criticality in Component Reuse and Remanufacturing Systems

Different operations, such as take-back, cleaning, and repair, lead to high system variability rendering remanufacturing systems difficult to manage. Even when a product is successfully remanufactured, there remains the problem of customer perception of remanufactured products being not able to perform as well as new ones. The possibility of several different options (reusing, remanufacturing, and recycling) further compound the complexity of the information set that should be considered for effective remanufacturing. This paper develops a method that can be employed for making component level decisions that accounts for aforesaid issues. A metric is proposed that measures the randomness or variability imposed by a reuse alternative. A measure of effective age is also proposed, extending the lines of previous research. A washing machine example illustrates the method and how the two measures can be incorporated into a design decision model.

Metric for disassembly and reuse: Formulation and validation

Design for disassembly and reuse focuses on developing methods to minimize difficulty in disassembly for maintenance or reuse. These methods can gain substantially if the relationship between component attributes (material mix, ease of disassembly etc.) and their likelihood of reuse or disposal is understood. For products already in the marketplace, a feedback approach that evaluates willingness of manufacturers or customers (decision makers) to reuse a component can reveal how attributes of a component affect reuse decisions. This paper introduces some metrics and combines them with ones proposed in literature into a measure that captures the overall value of a decision made by the decision makers. The premise is that the decision makers would choose a decision that has the maximum value. Four decisions are considered regarding a component’s fate after recovery ranging from direct reuse to disposal. A method on the lines of discrete choice theory is utilized that uses maximum likelihood estimates to determine the parameters that define the value function. The maximum likelihood method can take inputs from actual decisions made by the decision makers to assess the value function. This function can be used to determine the likelihood that the component takes a certain path (one of the four decisions), taking as input its attributes, which can facilitate long range planning and also help determine ways reuse decisions can be influenced.Copyright

Metric for Disassembly and Reuse Decisions: Formulation and Validation

Design for disassembly and reuse focuses on developing methods to minimize difficulty in disassembly for maintenance or reuse. These methods can gain substantially if the relationship between component attributes (material mix, ease of disassembly etc.) and their likelihood of reuse or disposal is understood. For products already in the marketplace, a feedback approach that evaluates willingness of manufacturers or customers (decision makers) to reuse a component can reveal how attributes of a component affect reuse decisions. This paper introduces some metrics and combines them with ones proposed in literature into a measure that captures the overall value of a decision made by the decision makers. The premise is that the decision makers would choose a decision that has the maximum value. Four decisions are considered regarding a component’s fate after recovery ranging from direct reuse to disposal. A method on the lines of discrete choice theory is utilized that uses maximum likelihood estimates to determine the parameters that define the value function. The maximum likelihood method can take inputs from actual decisions made by the decision makers to assess the value function. This function can be used to determine the likelihood that the component takes a certain path (one of the four decisions), taking as input its attributes, which can facilitate long range planning and also help determine ways reuse decisions can be influenced.Copyright

Non-Dominated Strategies for Decision Based Design for Component Reuse

Component level reuse enables retention of value from products recovered at the end of their first lifecycle. Reuse strategies determined at the beginning of the lifecycle are aimed at maximizing this recovered value. Decision based design can be employed, but there are several difficulties in large scale implementation. First, computational complexities arise. Even with a product with a relatively small number of components, it becomes difficult to find the optimal component level decisions. Second, if there is more than one stakeholder involved, each interested in different attributes, the problem becomes even more difficult, due both to complexity and Arrow’s Impossibility Theorem. However, while the preferences of the stakeholders may not be known precisely, and aggregating those preferences poses difficulties, what is usually known is the partial ordering of alternatives. This paper presents a method for exploiting the features of a solution algorithm to address these difficulties in implementing decision based design. Heuristic methods including non-dominated sorting genetic algorithms (NSGA) can exploit this partial ordering and reject dominated alternatives, simplifying the problem. Including attributes of interest to various stakeholders ensures that the solutions found are practicable. One of the reasons product reuse has not achieved critical acceptance is because the three entities involved, the customers, the manufacturer and the government do not have a common ground. This results in inaccurate aggregating of attributes which the proposed method avoids. We illustrate our approach with a case study of component reuse of personal computers.Copyright

Varying Lifecycle Lengths Within a Portfolio for Product Take-Back

Product take back and reuse is sometimes at odds with the rapidly evolving desires of some customers. For other customers, the environmental benefits of reuse more than compensate for minor drawbacks. “Selling a service” (rather than a product) through leasing enables the manufacturer to control the timing and quality of product take-back, but current methods assume a fixed leasing period. What is needed is a method for fine-tuning the time span of the customer’s life cycle in order to provide each market segment the combination of features it most desires. This paper presents a new method for performing long range product planning so that the manufacturer can determine optimal take-back times, end-of-life design decisions, and number of lifecycles. The method first determines a Pareto optimal frontier over price, environmental impact and reliability using a genetic algorithm. Then, a multiattribute utility function is employed to maximize utility across different segments of the market, and also across different lifecycles within each segment. The proposed methodology is illustrated through an example involving personal computers.Copyright

Metric for disassembly and reuse

Design for disassembly and reuse focuses on developing methods to minimize difficulty in disassembly for maintenance or reuse. These methods can gain substantially if the relationship between component attributes (material mix, ease of disassembly etc.) and their likelihood of reuse or disposal is understood. For products already in the marketplace, a feedback approach that evaluates willingness of manufacturers or customers (decision makers) to reuse a component can reveal how attributes of a component affect reuse decisions. This paper introduces some metrics and combines them with ones proposed in literature into a measure that captures the overall value of a decision made by the decision makers. The premise is that the decision makers would choose a decision that has the maximum value. Four decisions are considered regarding a component’s fate after recovery ranging from direct reuse to disposal. A method on the lines of discrete choice theory is utilized that uses maximum likelihood estimates to determine the parameters that define the value function. The maximum likelihood method can take inputs from actual decisions made by the decision makers to assess the value function. This function can be used to determine the likelihood that the component takes a certain path (one of the four decisions), taking as input its attributes, which can facilitate long range planning and also help determine ways reuse decisions can be influenced.Copyright