Analytical hierarchy process (AHP) in decision-making for end-of-life of products

Abstract

The proposed EU Waste from Electronic and Electrical Equipment (WEEE) Directive sets targets for the reuse and recycling of EEE reaching end-of-life (EOL). How a producer determines which is the best strategy for his/her products (reuse, part reclamation, remanufacturing, recycling) is not addressed. The producer must meet targets in the WEEE Directive, consider other environmental regulations, and make sure he is economically efficient. Economic costs and values are expressed in money (Euro), environmental impacts in a multitude of units. It is extremely difficult (if not impossible) to express them all in a single unit. Therefore the decision as to choice of EOL strategy for WEEE is a difficult one, based as it is on a number of unlike criteria. A possible solution to this problem is AHP. It will not replace the decision-making process itself but it will generate information needed to make the decision and will present it in a structured way. The AHP is a method of solving problems that involve prioritisation of alternative solutions and is based on the evaluation of a set of criteria. Associated with the decision-making for WEEE, there are a number of criteria that a producer has to consider: damage to resources, damage to ecosystem, and economic efficiency. A hierarchical structure is formed by using the overall goal (choose strategy) as a root of the decision tree and making each major criterion a child. Each criterion in turn is detailed to provide additional descendants. At the lowest level of the tree are the end-of-life options (reuse, part reclamation, remanufacturing, recycling) assigned to every leaf node in the tree. The decision-maker makes pairwise comparisons at each level between criteria which have the same parent node and gives them relative weights. He/she will also judge all the EOL options against each other with respect to each of the criteria situated in the leaf nodes, and will give them relative weights. All these comparisons lead to priority vectors which are propagated through the hierarchy to arrive at a final priority vector for the set of end-of-life options. The decision maker is thus enabled to base his/her decision on various criteria to which he himself has assigned weightings.