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 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.
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.
In this paper, we consider the problem of determining the optimal number of returned products to disassemble to fulfill the demand for a specified number of parts. This is known as the disassembly-to-order (DTO) problem. The deterministic yield version of this problem has been addressed in the literature. Recently, the stochastic yield version of this problem with a single objective has also been reported in the literature. In this paper, we extend the methodology to include multiple objectives. To this end, we model the DTO problem using integer goal programming. The stochastic problem is solved by transforming it into its deterministic equivalent problem. This conversion is accomplished by considering the specific structures of the products with one core and one part (“one-to-one structure”) and apply it to handle the products with one core and multiple parts (“one-to-many structure”). For these special cases it is possible to solve the stochastic problem analytically so that valuable insights can be gained by comparing the stochastic and deterministic solutions. This will help us to determine effective deterministic yield equivalents. We present a case example to illustrate the methodology.
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