دانلود مقاله ISI انگلیسی شماره 70444
ترجمه فارسی عنوان مقاله

جستجوی محله متغیر برای مشکل طراحی شبکه پس از فروش دو هدفه: روش تجزیه و تحلیل چشم انداز تناسب اندام

عنوان انگلیسی
Variable neighborhood search for the bi-objective post-sales network design problem: A fitness landscape analysis approach
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
70444 2014 15 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Computers & Operations Research, Volume 52, Part B, December 2014, Pages 300–314

ترجمه کلمات کلیدی
لجستیک معکوس؛ طراحی شبکه؛ ارائه دهنده تدارکات حزب سوم؛ بهینه سازی چند هدفه؛ جستجوی محله متغیر؛ تجزیه و تحلیل چشم انداز
کلمات کلیدی انگلیسی
Reverse logistics; Network design; Third party logistics provider; Multi-objective optimization; Variable neighborhood search; Landscape analysis
پیش نمایش مقاله
پیش نمایش مقاله  جستجوی محله متغیر برای مشکل طراحی شبکه پس از فروش دو هدفه: روش تجزیه و تحلیل چشم انداز تناسب اندام

چکیده انگلیسی

Post-sales services are important markets in electronics industry due to their impact on marginal profit, market share, and their ability to retain customers. In this study, designing a multi-product four-layer post-sales reverse logistics network operated by a 3PL is investigated. A bi-objective MILP model is proposed to minimize network design costs as well as total weighted tardiness of returning products to customers. To solve the proposed model, a novel multi-start variable neighborhood search is suggested that incorporates nine neighborhood structures and three new encoding–decoding mechanisms. In particular, a fitness landscape measure is employed to select an effective neighborhood order for the proposed VNS. Extensive computational experiments show the effectiveness of the proposed heuristic and the three encoding–decoding mechanisms. The proposed method finds significantly better Pareto optimal sets in comparison with the original Priority method based on the number and the quality of obtained Pareto optimal solutions. In addition, it shows high efficiency by finding near-optimal solutions for the single objective versions of the problem.