Low carbon supplier selection using a hesitant fuzzy linguistic span method integrating the analytic network process

Abstract

Nowadays, environmental pollution and energy shortage are attracting increasing attention of scholars and practitioners. To get social identity and achieve the goal of profit maximization for enterprises in the supply chain, the low carbon operation is a necessary path. In low carbon supply chain management, the supplier selection is one of the most important problems, which can be regarded as a typical multiple criteria decision-making problem with respect to multiple indicators from different dimensions. With the increasing complexity of the social and economic environment, it is difficult for an evaluator to seize all information for all criteria and alternatives. Thus, multiple experts are involved in this evaluation process. A large majority related literature only focus on how to choose multiple criteria decision making methods to solve supplier selection problems. However, not all experts are familiar with all aspects regarding the alternatives and criteria, which makes the original information questionable. To fill this gap, this study proposes a model integrating the social participatory allocation network (SPAN) and the analytic network process under the hesitant fuzzy linguistic environment. The SPAN introduces a delegated evaluation mechanism which allows experts to delegate other experts that they trust to evaluate the alternatives for them. With this mechanism, we can solve the problem even though the experts are not familiar with some specific fields. The analytic network process is used to derive the weights of criteria in the case that there are some interrelationships among the criteria. To better deal with the imprecise and uncertain information, the hesitant fuzzy linguistic term set is adopted to represent the quality information with linguistic expressions. An illustrative example concerning low carbon supplier selection of a solar power company is presented to verify the practicability of the proposed model.