End-of-Life Decision Making With Uncertain Product Return Quantity

2012 ◽  
Vol 134 (10) ◽  
Author(s):  
Sara Behdad ◽  
Aida Sefic Williams ◽  
Deborah Thurston

The management of end-of-life electronic waste (e-waste) attracts significant attention due to environmental concerns, legislative requirements, consumer interest in green products, and the market image of manufacturers. However, managing e-waste is complicated by several factors, including the high degree of uncertainty of quantity, timing of arrival, and quality of the returned products. This variability in the stream of returned end-of-life (EOL) products makes it difficult to plan for remanufacturing facility materials, equipment, and human resource requirements. The aim of this research is to tackle the uncertainty associated with the quantity of received used products. A stochastic programming model for waste stream acquisition systems (as opposed to market-driven systems) is introduced. The model considers the quantity of returned product as an uncertain parameter and determines to what extent the product should be disassembled and what is the best EOL option for each subassembly. The stochastic model is defined in a form of chance constrained programming and is then converted to a mixed integer linear programming. An example is provided to illustrate the application of the model for an uncertain stream of PCs (minus monitor and keyboard) received in a PC refurbishing company. The remanufacturer must then decide which proportion of disassembled modules should be processed given specific remanufacturing options.

Author(s):  
Sara Behdad ◽  
Aida Sefic Williams ◽  
Deborah Thurston

The management of end-of-life electronic waste (e-waste) attracts significant attention due to environmental concerns, legislative requirements, consumer interests in green products and market image of manufacturers. However, managing e-waste is complicated by some factors including the high degree of uncertainty of quantity, timing of arrival and quality of the returned products. The variability in the stream of returned end of life (EOL) products makes it difficult to plan for facility materials, equipment and human resource requirements. The aim of this research is to tackle the uncertainty associated with the quantity of received used products. A stochastic programming model for waste stream acquisition systems (compare to market driven systems) is introduced. The model considers the quantity of returned product as an uncertain parameter and determines to what extend the product should be disassembled and what is the best end of life option for each subassembly. The stochastic model is defined in a form of chance constrained programming and is then converted to a mixed integer linear programming. An example is provided to show the application of the model for an uncertain stream of CPUs received in a refurbishing company. Remanufacturers must then decide which proportion of disassembled modules should be processed given specific remanufacturing options.


2017 ◽  
Vol 6 (1) ◽  
pp. 56-85
Author(s):  
Javad Nematian ◽  
Seyed Salar Ghotb

Nowadays by growing concerns about environmental problems, businesses and industries are under pressure to decrease their negative impact on environment, consequently firms and industries have to reconsider about their activities and make their business compatible with environment. So industries should green their supply chains to optimize economic and environmental concerns, but because of uncertainty in the real world like inconsistency of world economy, the process of greening supply chains can be more complex. To optimize total costs and the unfavourable sides of supply chains simultaneously in an uncertain situation, this paper presents a multi-objective mixed integer programming with fuzzy random variables (FRVs) and by using fuzzy theory and fuzzy random chance-constrained programming (FRCCP), the proposed model is converted to deterministic model. This paper can be also suitable for decision making with optimistic, pessimistic and realistic notion. Finally, a numerical example is presented to illustrate the model.


2012 ◽  
Vol 468-471 ◽  
pp. 668-673 ◽  
Author(s):  
Hua Jiang ◽  
Zhi Gang Lu

An integrated supplier selection problem under fuzzy environment is studied in this paper. Firstly, the linear weight method is used to calculate the scores of suppliers according to their different attributes, such as: quality, service, warranty, delivery, reputation and position, which are assumed as fuzzy variables. Secondly, a fuzzy expected value programming model and a fuzzy chance-constrained programming model are proposed to select the best combination of the suppliers and determine the order quantities. A hybrid intelligent algorithm, based on fuzzy simulation, genetic algorithm and neural network, is used to solve the two models. Finally, a numerical example is given to illustrate the effectiveness of the proposed models.


Sign in / Sign up

Export Citation Format

Share Document