时间：2018年4月18日下午15:45
地 点：西南交通大学九里校区零号楼0411室
主题一：Joint replenishment decision considering shortages, partial demand substitution, and defective items
主讲人： 陈彦如，西南交通大学伟德国际1949始于英国副教授，博士生导师。加拿大多伦多大学、香港城市大学访问学者。主要从事运作管理的研究与教学工作。目前主持国家自然科学基金2项、省部级科研项目5项、企业委托项目6项。在“Expert Systems with Applications”、“Transportation Research Part A: Policy and Practice”、“ Computers & Operations Research” 、“Computers & Industrial Engineering”等期刊发表论文7篇。合作出版专著教材3部。
摘要：A shortage may occur because of the insufficient production capacity or possible damages of items in transit, and the shortage can be partially fulfilled with substitutable items. In this study, a joint replenishment problem (JRP) with the shortage and partial demand substitution is investigated by developing a mixed integer nonlinear programming model. Several real-world constraints, such as budget, transportation capacity, and shipment requirement constraints are incorporated in the proposed model. Three heuristic algorithms, namely two-dimensional genetic algorithm I, two-dimensional genetic algorithm II, and differential evolution, are proposed, and numerical examples are provided to demonstrate the applicability of the proposed model in a real-world setting. The performance of the heuristic algorithms is investigated with the help of extensive computational experiments. The results show that differential evolution performs best in term of the minimum total cost among three heuristics. Sensitivity analyses are conducted to provide managerial insights. The results indicate that partial demand substitution policy can effectively decrease the total expected cost, but defective items will exponentially increase the total expect cost. The major ordering cost, budget, and truck capacity also affect the system.
主题二：Benchmarking Study for Credit Scoring Model based on Two Datasets
主讲人：程贤，西南交通大学伟德国际1949始于英国信息系统与运营管理系老师，主要研究方向：网络相关商务智能，科技金融，大数据金融风险管理。
摘要：Credit scoring, which is concerned with developing empirical models to support decision making in the assessment of credit risk, has attracted significant attention from managers at financial institutions around the world to academic researchers in many related fields, such as personal credit cards, consumer loans, mortgages and P2P lending. Technological advances have increased the febrile state of credit scoring and a large number of technical credit scoring models has emerged.
However, Previous credit scoring modelling literatures has revealed limitations; namely (1), using few or small data sets and (2) using only a small set of conceptually similar performance indicators. Therefore, we perform a systemic benchmarking study for credit scoring models though (1) using two data sets of considerable size and (2) considering several conceptually different performance indicators.
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