Chance constraint model is a way to

solve the uncertain problem which uses expected value, chance measure and realization probability to investigate the situation. Chance constraint Selumetinib ic50 model needs the distribution function of the uncertain element which is difficult to measure. Meanwhile, the distribution function cannot include all situations. The service quality will be affected by the negative scenarios, whose demand is beyond the distribution function. However, robust optimization model can largely avoid this dilemma. Both expected objective value and deviation between actual objective value and expected value are considered [8, 9]. The result can decrease the occurrence of negative scenarios. Robust optimization has

been used in network plan [10], routing optimization [11], scheduling problem [12], and so forth. In China, Wang and He [13] used chance constraint model to solve railway logistic center location problem. Sun et al. [14] applied the robust optimization on the feeder bus network timetable schedule problem. The main purpose of this paper is to provide robust optimization model of railway freight transport center location problem and a method to solve it. The location optimization model considers service coverage constraint. The adaptive clonal selection algorithm (ACSA) is combined with the Cloud Model (CM) called cloud adaptive clonal selection algorithm (C-ACSA) to solve the model. The outline of this paper is as follows: Section 2 introduces the robust optimization model of freight center location problem. In Section 3, a new algorithm is proposed. Finally, a numerical example is given to illustrate the application

of the model and algorithm. 2. Robust Optimization Model of Railway Freight Transport Center Location Problem (1) Decision Variables. Scenario specifies the realization of stochastic demand. And transport demand of the scenario is known. The objective of robust model is to find the location of railway freight transport centers and the assignment between centers and shippers in all scenarios. The location decision and assignment are treated as decision variables. Those are as follows. xijk equals 1 if shipper i is assigned to center j in scenario k. Otherwise, it equals 0. yjk equals 1 if a railway freight transport center is located at candidate center j in scenario k. Otherwise, it equals 0. (2) Objective Function (a) Objective Function of Deterministic Dacomitinib Model. Cost of location problem in scenario k includes two parts: the first is construction cost of railway freight transport centers; the second is transport cost between shippers and the centers. The objective function of scenario k is as follows: zk=μ1c∑i∈I ∑j∈Jhikdijxijk+μ2∑j∈JCjyjk, (1) where c is unit transport cost of transport demand from shipper to railway freight transport center. μ1 and μ2 are weight of transport cost and construction cost in objective function. They are defined in advance.