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In general, reverse logistics network design has been driven by a need to reduce costs and to improve customer service without considering its environmental impact. In this paper, we address a reverse logistics network design problem regarding carbon emission. The problem is formulated as a bi-objective, mixed-integer, and nonlinear programming model under various operation technologies and transport modes in the truck tire remanufacturing industry. An improved non-dominated sorting genetic algorithm II (NSGA-II) solves this NP-hard problem with bi-objectives. The numerical cases demonstrate the validation of the proposed model and the advantage of improved NSGA-II over the basic NSGA-II. Furthermore, we conducted extensive sensitivity analysis, and several managerial insights are derived.
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Yuchi et al. (2019) studied this question.
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