The development of e-commerce has led to a surge in the number of online shopping parcels. However, given the lack of scale effect, last mile delivery is inefficient, expensive, and produces a considerable amount of carbon emissions, which has become an obstacle to the development of a sustainable economy. This work proposes a traveling salesman problem with carbon emission reduction in last mile delivery. The proposed problem aims to reduce the total costs and carbon emissions of last mile delivery by deciding on the allocation of parcel lockers while scheduling delivery routes. In addition, we take the customer self-collection intention into consideration and translate it into self-collection costs, which are included in the objective. An iterated local search (ILS) algorithm is proposed, and four new local search operators are designed to improve customer allocation. The proposed method is tested on a set of scattered and clustered instances, including a real-world instance. The computational results show the superiority and competitiveness of the proposed algorithm.INDEX TERMS Sustainable economy, carbon emission, parcel lockers, customer pickup, last mile, ILS.
Extant research has examined the effects of amount‐off discounts on consumer responses, but the empirical findings are inconsistent. This study presents a meta‐analysis of 19 studies and 86 effect sizes (N = 12,003) calibrating the effects of discount frames on consumer responses. Amount‐off discounts are found to lead to positive consumer responses through positive changes in attitudes. Analyzing various moderators’ impacts shows that amount‐off discounts have different effects depending on the product price level, the product type, and the price promotion mode. For instance, amount‐off discounts tend to be more effective than percentage‐off discounts when the price is high, when the product type is material, and when the price promotion mode is a discount.
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