multiple objective modeling

According to the importance of oil, gas and petrochemical industries and their related supply chain risks, this research with the aim of identifying logistics outsourcing risks, and proposing suitable risk management strategies is done. At first, logistics outsourcing to third party logistics (3PLs) providers were identified and classified in seven categories: strategic, financial, operational, design related, quality related, economic, changes in policies and government alteration. Main data gathering tools were questionnaire and interview. In data analysis phase, firstly, fuzzy cognitive mapping (FCM) was employed for analyzing risks and their interrelationships to determine their relative weight based on centrality measure and secondly, a combination of house of quality (HOQ) and multiple objective mathematical programming model were utilized to propose relevant risk mitigation strategies. Research findings show that the risk of unresponsiveness to customer needs is the most important one, following by disruption risks in operations, and uncontrollability on logistics operations. Further, requesting letter of credit from 3PL providers, creating black list, defining very strict quality standards and limits are among the most important preventive mitigation strategies. 3PLs ranking system design, using mixed contracts, holding tenders, and special attention to local suppliers have been identified as the most important flexibility strategies to mitigate risks. Finally, the most important process strategies to risk management were identified as improving the credit of company, establishing long-term relationships with 3PLs, enhancing resilience, and demand prediction and management system.