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Abstract:With the increasing environmental awareness, apparel manufacturers have begun to consider environmental issues in supplier evaluation and selection. It is crucial to assess suppliers based on their environmental performance along with other criteria for supplier selection. This paper addresses the green supplier evaluation and selection problem in global apparel manufacturing by developing a methodological framework for green supplier evaluation and selection based on the triple bottom line principle and a fuzzy multi-criteria decision-making (MCDM) model. First, a green supplier evaluation criteria hierarchy based on the triple bottom line principle is established based on comprehensive literature review, on-site investigation and policy analysis. Then, a fuzzy MCDM model is presented to evaluate and select the best material supplier. Finally, a sensitivity analysis is conducted to verify the effectiveness of the proposed framework. Results show that the proposed framework can handle green supplier evaluation and selection in apparel manufacturing effectively.

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Link travel speeds in road networks are fundamental data in many research areas of traffic, transportation, and logistics. To support the research in these areas, we develop a dataset, containing the travel speeds on each road link and in different time periods together with the real road network map. The dataset is collected from a representative megacity in Western China, Chengdu. The road network of this city involves different urban road network structures. The dataset shows the realistic variations and randomness of urban link travel speeds. This enables the research of real data-driven decision-making problems in traffic, transportation and logistics areas.

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Implementing green projects is critical to achieve the green and sustainable development goal. This paper investigates a green project planning with the realistic consideration of multiple objectives including minimizing the total cost and maximizing the total emission reduction. The realistic multi-objective problem in engineering optimization aims to find a best solution for real-world use instead of finding a set of Pareto-optimal solutions. To handle this problem, we develop a weight sets-based multi-objective evolutionary optimization approach to find the best solution for realistic use. The approach integrates a single-objective evolutionary optimization process, novel solution encoding and decoding heuristics, and a non-dominated sort technique. Based on real-world data from a seaport in China, experiments were conducted to validate the proposed optimization approach. Results show that the proposed approach can effectively solve the real-world multi-objective green project planning problem because the solution found by our approach is one of the Pareto optimal solutions generated by the NSGA-II.