Abstract




 
   

IJE TRANSACTIONS A: Basics Vol. 30, No. 10 (October 2017) 1526-1537    Article in Press

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  A BI-OBJECTIVE STOCHASTIC OPTIMIZATION MODEL FOR HUMANITARIAN RELIEF CHAIN BY USING EVOLUTIONARY ALGORITHMS
 
N. Javadian, S. Modarres and A. Bozorgi
 
( Received: March 07, 2017 – Accepted in Revised Form: July 07, 2017 )
 
 

Abstract    Due to the increasing amount of natural disasters such as earthquakes and floods and unnatural disasters such as war and terrorist attacks, Humanitarian Relief Chain (HRC) is taken into consideration of most countries. Besides, this paper aims to contribute humanitarian relief chains under uncertainty. In this paper, we address a humanitarian logistics network design problem including local distribution centers (LDCs) and multiple central warehouses (CWs) and develop a scenario-based stochastic programming (SBSP) approach. Also, the uncertainty associated with demand and supply information as well as the availability of the transportation network's routes level after an earthquake are considered by employing stochastic optimization. While the proposed model attempts to minimize the total costs of the relief chain, it implicitly minimize the maximum travel time between each pair of facility and the demand point of the items. Additionally, a data set derived from a real disaster case study in the Iran area, and to solve the proposed model a exact method called ɛ-constraint in low dimension along with some well-known evolutionary algorithms are applied. Also, to achieve good performance, the parameters of these algorithms are tuned by using Taguchi method. In addition, the proposed algorithms are compared via four multi-objective metrics and statistically method. Based on the results, it was shown that: NSGA-II shows better performances in terms of SNS and CPU time, meanwhile, for NPS and MID, MRGA has better performances. Finally, some comments for future researches are suggested.

 

Keywords    Uncertainty, Robust Optimization, Emergency Logistics, Relief Chain, evolutionary algorithms

 

چکیده    با توجه به افزایش میزان بلایای طبیعی مانند زلزله و سیل و بلایای غیر طبیعی مانند جنگ و حملات تروریستی، موضوع زنجیره امدادی بشردوستانه مورد نظر بسیاری از کشورها قرار گرفته است. بنابراین، هدف این مقاله کمک به زنجیره امدادی بشر دوستانه تحت عدم قطعیت می باشد. در این پژوهش، یک مساله طراحی شبکه لجستیک بشردوستانه شامل مراکز توزیع محلی (LDC) و انبارها مرکزی متعدد (CWS) و توسعه یک رویکرد تصادفی مبتنی بر سناریو (SBSP) ارائه شده است. همچنین، عدم قطعیت در ارتباط با تقاضا و اطلاعات تامین و نیز در دسترس بودن سطوح مسیرهای شبکه حمل و نقل بعد از زلزله با ارائه بهینه سازی غیرقطعی در نظر گرفته شده است. اهداف مدل ارائه شده تلاش برای به حداقل رساندن کل هزینه های زنجیره امداد و ضمنا به حداقل رساندن حداکثر زمان مسیر بین هر جفت از امکانات و نقاط تقاضا از اقلام است. علاوه بر این، یک مجموعه داده حاصل از یک مطالعه موردی فاجعه واقعی در ایران استفاده شده است و برای حل مدل پیشنهادی برخی از الگوریتم های تکاملی شناخته شده به همراه روش اپسیلن محدودیت در ابعاد کوچک استفاده می شوند. همچنین برای دستیابی به عملکرد بهتر، پارامترهای الگوریتم ها با استفاده از روش تاگوچی تنظیم شده اند. همچنین الگوریتم های پیشنهادی نسب به سنجه های استاندارد چند هدفه با هم مقایسه شده اند، که از لحاظ شاخص های SNS و زمان الگوریتم NSGA-II عملکرد بهتری داشته است و از لحاظ شاخص های MID و تعداد نقاط پارتو الگوریتم NRGA بهتر بوده است. در نهایت پیشنهاداتی برای تحقیقات آتی ارائه شده است.

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