Journal of Earth & Environmental Waste Management
ISSN: 3065-8799 | DOI: 10.64030/3065-8799
Jiaxuan Chen, Ling Zhou Shen and Jinchen Liu
In this article, we will discuss the optimization of Shanghai’s recycling collection program, with the core of the task as deciding among the choice of the alternatives. We will be showing a vivid and comprehensive application of the classical mathematical multi-criteria decision model: Analytical Hierarchy Process (AHP), using the eigenvector method. We will also seek the key criteria for the sustainability development of human society, by assessing the important elements of waste recycling. First, we considered the evaluation for a quantified score of the benefits and costs of recycling household glass wastes in Shanghai, respectively. In the evaluation of each score, we both adopted the AHP method to build a hierarchical structure of the problem we’re facing. We first identified the key assessment criteria of the evaluation, on various perspectives including direct money costs and benefits, and further environmental and indirect considerations. Then, we distributed questionnaires to our school science teachers, taking the geometric mean, to build the pairwise comparison matrix of the criterion. A consistency check is done by eigenvector method to ensure that the matrix is consistent for weight calculation. By finding the normalized
eigenvector of the matrix, we obtained our weight vector, which is used for the evaluation of the scores. After the theoretical
modeling works are done, we began collecting the essential datasets for the evaluation of each score, by doing research on the
official statistics, Internet information, market information and news reports. Sometimes, we proceed a logical pre-procession
of the data from other data, if the data wanted isn’t directly accessible. Then, we crucially considered the generalization of
our mathematical model. We considered from several perspectives, including the extension of assessment criteria, and the
consideration of the dynamic interdependency between the wastes, inside a limited transportation container. After using AHP
again, we finish with new weight vectors. A crucial and logical data collection process is done, with a min-max normalization.
Now, the data is ready for scoring by adding weights. A benefit and a cost score are both evaluated for each type of data,
with the theoretical maximum value of 1. By comparing the data’s final score, which is the difference between the benefit
and difference, we are eligible to make our final decisions. Our model can also be adapted to optimize the waste recycling
program for other cities.