Research Article | Open Access
Yi Zhao, Qingwan Xue, Zhichao Cao, Xi Zhang, "A Two-Stage Chance Constrained Approach with Application to Stochastic Intermodal Service Network Design Problems", Journal of Advanced Transportation, vol. 2018, Article ID 6051029, 18 pages, 2018. https://doi.org/10.1155/2018/6051029
A Two-Stage Chance Constrained Approach with Application to Stochastic Intermodal Service Network Design Problems
Compared with traditional freight transportation, intermodal freight transportation is more competitive which can combine the advantages of different transportation modes. As a consequence, operational research on intermodal freight transportation has received more attention and developed rapidly, but it is still a young research field. In this paper, a stochastic intermodal service network design problem is introduced in a sea-rail transportation system, which considers stochastic travel time, stochastic transfer time, and stochastic container demand. Given candidate train and ship services, we develop a two-stage chance constrained programming model for this problem with the objective of minimising the expected total cost. The first stage allows for the selection of operated services, while the second stage focuses on the determination of intermodal container routes where capacity and on-time delivery chance constraints are presented. A hybrid heuristic algorithm, incorporating sample average approximation and ant colony optimisation, is employed to solve this model. The proposed model is applied to a realistic intermodal sea-rail network, which demonstrates the performance of the model and algorithm as well as the influence of stochasticity on transportation plans. Hence, the proposed methodology can improve effectively the performance of intermodal service network design scheme under stochastic conditions and provide managerial insights for decision-makers.
As a vital component of logistics and economy, intermodal freight transportation (IFT) facilitates international trade among most countries in the world. With the growth of international shipping, IFT is playing a more and more significant role in global transportation. Generally, the IFT is defined as the transportation of a load from its origin to its destination by a sequence of at least two different modes of transportation , where the load is transported in one and the same transportation unit . Based on previous work [3, 4], the research issues on IFT can be summarised as five categories: intermodal transportation policy , intermodal network design , intermodal service network design , intermodal routing, and empty container reposition .
This paper focuses on the intermodal service network design and, more specifically, the stochastic intermodal service network design (SISND) in a sea-rail network, which is a type of service network design (SND). The SISND is defined as to determine the intermodal services, the specification of terminal operations, and the routing of container demands , which is associated with the tactical planning level . Table 1 lists the key literature on service network design problems which consider stochastic characteristics.
According to the transportation mode considered, the literature on SND can be sorted into two categories, i.e., unimodal SND and intermodal SND. In the early development of unimodal SND , the SND model was probably first constructed for railway transportation, in which freight routing, block policy, makeup policy, and classification workload allocation were addressed simultaneously. Zhu et al.  proposed a two-layer space-time network to depict the operation and decision in a railway transportation system. The railway SND was formulated as a mixed integer programming model and then solved by a tabu search heuristic. Zhu  and Zhu et al.  extended this work and developed a three-layer space-time network to deal with scheduled SND for railway freight transportation. A metaheuristic integrating ellipsoidal search and slope scaling was introduced to solve the problem. Later on, some literature is concerned with SND taking into consideration the asset management [12–16] such as locomotives, railcars, cranes, and crews.
In addition to railway transportation, there are also articles concentrating on maritime and other SND. For example, Lai and Lo  studied ferry SND involving fleet size optimisation, routing, and scheduling. The model was formulated as a network flow problem with multiple origin-destination pairs, with the aim of balancing the operator cost and passenger cost. Meng and Wang  and Shintani et al.  both considered the liner shipping SND incorporating empty container reposition, while Huang et al.  presented a mixed integer linear programming model considering liner SND, fleet deployment, and empty container reposition. Additionally, Armacost et al.  focused on the express shipment SND in an overnight air network, which considered aircraft route design, aircraft assignment, and package routing. The model was tested on the UPS Next Day Air delivery network to demonstrate its performance.
Compared with the unimodal transportation, intermodal transportation has a large number of advantages such as faster transhipment, lower cost, increased flexibility, higher productivity, and improved safety . As a result, intermodal SND has recently received more and more attention. The pioneering research of Crainic and Rousseau  established a general modelling framework for multimodal freight SND based on a network optimisation model. The problem was solved by decomposition and column generation principles. Meng et al.  presented a linear programming model to formulate the intermodal liner shipping SND in an inland and maritime network. The model considered the laden container and the empty container separately and captured several important issues including liner service design, laden container routing, and empty container reposition. Riessen et al.  proposed a model based on a path-based formulation and a minimum flow network formulation to combine the self-operated service and subcontracted service to address the intermodal SND within European gateway services network. Moreover, Andersen et al.  analysed the consequence of collaborating service synchronisation removing border operations and investigated a more comprehensive model which integrated SND, vehicle management, and fleet coordination.
All the literature mentioned above considers the deterministic SND. However, in practice, IFT is subject to a variety of uncertain factors. For example, Meng et al.  reviewed the research on containership routing and scheduling problems and indicated that there are too many uncertainties in containerised maritime transportation, such as container demand , port time , and travel time . Yang et al.  constructed a weighted min-max chance constrained model to solve the train routing problem for achieving a minimal transportation cost, in which the demand, transportation cost, and transportation capacity were treated as fuzzy variables. Furthermore, Milenković and Bojović  investigated rail freight car fleet sizing problem by considering the fuzziness and randomness of freight demand. In the railway system, travel time of freight trains is frequently affected by passenger trains due to the relatively lower priority of freight trains, which contributes to uncertainty; meanwhile, the freight demand also fluctuates over space and time. Similarly, in the maritime system, stochasticity at sea and port poses a big challenge for liner shipping companies because of unexpected weather and variable operation efficiency.
Obviously, modelling these components by their expected values cannot capture the characteristics of real-life problems. In some cases, the optimal solution acquired under deterministic conditions may lead to a poor or even infeasible design, due to various stochastic factors. Therefore, it is essential to incorporate the stochasticity of freight demand, travel time, and terminals transfer time in the IFT SND. As a consequence, how to tackle the stochastic demand and time parameters (such as travel time and transfer time) has become one of the most significant challenges faced by freight companies. So far, research on stochastic SND is limited to Lium et al. [32, 33] for railway transportation, An and Lo  for maritime transportation, Demir et al. , Hrušovský et al.  and Lanza  for intermodal transportation, and Bai et al.  for other types of transportation. Specifically, Lium et al.  introduced the stochastic freight demand into SND formulation and investigated the difference between solutions under deterministic and stochastic conditions. An and Lo  established a model for ferry SND with uncertain demand under user equilibrium flows, in which regular and ad hoc services were taken into account. Demir et al.  developed a stochastic intermodal mixed integer programming model for the green intermodal SND with uncertain travel time and uncertain demand. The objective was to minimise the weighted sum of transportation cost, late delivery cost, and CO2 emissions cost. Sample average approximation (SAA) method was used to solve this problem. For stochastic SND of other transportation types, Bai et al.  described a two-stage stochastic model for stochastic freight delivery SND with vehicle rerouting, in which the stochasticity of demand was captured.
SND is a typical NP-hard problem. Thus, highly efficient algorithms are needed for solving SISND and generating a practical transportation plan. There has been extensive research on various algorithms to solve stochastic SND. Hoff et al.  developed a metaheuristic based on neighbourhood search for stochastic SND by integrating exact and heuristic methods, while Crainic et al.  introduced a metaheuristic with the progressive hedging algorithm to divide their stochastic problem into several deterministic problems. Hrušovský et al.  proposed a hybrid methodology framework combining simulation and optimisation approaches. The methodology was implemented on real-life instances to illustrate its advantages. Although more time-consuming, the stochastic programming model can provide more flexibility and robustness for planners to deal with uncertain and fuzzy information. However, to the best of our knowledge, no research has considered stochastic travel time, stochastic transfer time, and stochastic container demand simultaneously in IFT.
The rest of this paper is structured as follows. Section 2 describes the SISND problem with stochastic travel time, transfer time, and container demand. Section 3 formulates this problem as a two-stage chance constrained programming problem. Section 4 presents the proposed solution algorithm involving SAA method and ant colony optimisation, while Section 5 implements the methodology on a real-life intermodal network and discusses the computational results. The conclusions are drawn in Section 6.
2. Problem Description
Based on the characteristics of sea-rail IFT system, the SISND problem in a sea-rail network is complicated in three aspects. First, compared with traditional freight transportation, the goods transported by containers is more time-sensitive and perishable. Hence, besides transportation cost, the SISND problem is also required to consider transportation time (e.g., delivery time at destinations), which may contribute to late delivery penalty cost. Second, both train and ship services have their service paths, capacities, operation costs, and travel times. Therefore, the coordination of individual rail and ship services has to be considered, which makes the SISND problem more complex. Third, stochastic times and demands may decrease the performance of a transportation plan and sometimes may even make it infeasible, which further increases the difficulties in achieving a robust transportation plan. In response to the complexities mentioned above, we introduce the SISND problem in this paper to minimise the expected total cost, by designing the optimal intermodal service and specifying services for each container demand from its origin to its destination, where stochastic time parameters and demands are considered.
To illustrate the problem, we first consider a simple sea-rail intermodal network with three railway stations A, B, and C, two intermodal hubs D and E where containers are transhipped from trains to ships, and one destination F, as shown in Figure 1.
The container demands are distinguished by the original stations, destination ports, equivalent volumes, and due times. Let , , denote the container demands transported from nodes A, B, and C to node F, respectively, as well as and the container demands from A to B and C, respectively. In order to transport container demands, train and ship services are operated. Each service s is characterised by the origin node, intermediate stops, destination node, travel time, capacity, and fixed operating cost. All container demands need to be served by train, and some of them are then served by ship from inland to port F. Since railway transportation is a type of consolidated based transportation, different train service combinations should be considered. According to the intermodal network in Figure 1, there are four potential train service designs and 108 possible intermodal routes for transporting these container demands, as shown in Table 2.
For freight companies, the estimated container demand is usually used to generate the transportation plan. However, it cannot reflect the variability of the real world. The fluctuation of container demand has a significant impact on routing container shipment and can even lead to an infeasible routing plan in some cases. In this case, the capacity chance constraints regarding such stochastic container demands are required. For instance, assuming that the container demands and both select Ship 1 and service BD, (1) imposes that the total container volume cannot exceed the capacity of service Ship 1 with the probability of at least ,where and denote the volume of and , respectively, denotes the capacity of service Ship 1, and denotes the service frequency. Furthermore, the chance constraints with respect to arc capacity and node transfer capacity are also essential and shown in (2) and (3), respectively, where and denote the capacity of arc BD and the transfer capacity of node D, respectively. For each container demand, there is a due time at destination ports. Late delivery is allowed but will incur penalty. For example, we assume that the optimal intermodal route of isLet denote the due time of , and the travel time of train services AB and BD, respectively, the travel time of ship service Ship 1, and and the transfer time at nodes B and D, respectively. For container demand , it needs to transfer from service AB to service BD, and then from service BD to service Ship 1. Thus, when the travel time and transfer time are both stochastic, the on-time delivery chance constraints have to be considered as well in (5), which requires arriving at the destination port with a probability of no less than .Based on the constraints introduced in (1), (2), (3), and (5), a two-stage chance constrained programming model for the SISND problem with random variables is constructed in Section 3. This SISND formulation is then solved in Section 4 by an SAA method which yields a robust design intermodal service network and a reliable intermodal route plan for each container demand.
3. A Two-Stage Chance Constrained SISND Problem with Stochastic Time and Demand Variables
In this section, we depict the two-stage chance constrained optimisation model for the SISND problem, which is used for the selection of intermodal services and route plans for container demands. Specifically, Section 3.1 defines the notations to be used in the remainder of the article, based on which Section 3.2 provides the formulation for the SISND problem.
This section lists notations used in Table 3, including indices, sets, input parameters, auxiliary parameters, and decision variables.
3.2. Mathematical Formulation
In this paper, we formulate the SISND problem in a sea-rail intermodal network as a two-stage chance constrained programming model, which makes service design decisions and a series of resource decisions to allocate container demands. The sea-rail intermodal network is represented by a directed graph , where V stands for the set of nodes and A the set of arcs.
Our problem is formulated based on the following assumptions.
Assumption 1. Each container demand can be transported by only one service path.
Assumption 2. All container demands can arrive later than the due times but will incur penalty cost which is proportional to the delay time and the demand volume.
Assumption 3. The railway transportation cost and travel time on arcs are proportional to the arc distance.
Assumption 4. Only direct train services are considered. Thus, container demands can be transported directly to their destinations by one direct service without reclassification at intermediate stations. Alternatively, container demands can also be sent by a sequence of direct services.
The objective function is the expected total cost which includes fixed cost, variable cost, transfer cost, and late delivery penalty cost.
(i) Fixed cost consists of crew cost, locomotives cost , administration cost , and other resources cost. It is formulated by (6), where the first term represents the fixed cost for operating train services while the second term for operating ship services: (ii) Variable cost is relevant to the fuel consumption, infrastructure fees, etc. and is formulated in (iii) The transfer cost is made up of the unloading, transportation, and loading cost during the transfer process as given in (iv) Late delivery penalty cost is incurred when the container demand does not arrive at the destination on time due to the stochasticity of travel time and transfer time. The penalty cost is proportional to the delay time as given inThe total cost for each unit of container demand is then the sum of variable cost, transfer cost, and late delivery penalty cost.The two-stage SISND problem is to minimise the expected total cost, where the first stage minimises the fixed cost to operate services and the second stage minimises the transportation cost for all container demands.
P0wheresubject to the following constraints.
(a) Flow Conservation ConstraintsEquations (13)-(15) enforce flow conservation at origin, intermediate, and destination nodes, respectively. Equations (16) and (17) enforce flow conservation for train and ship services, respectively. Equation (18) enforces flow conservation for arcs.
(b) Capacity Chance ConstraintsEquations (19) and (20) ensure that the flows via train and ship are within their capacities with a possibility of at least . Similarly, (21) limits the flow on arcs, while (22) restricts transfer workload at intermediate nodes.
(c) On-Time Delivery Chance Constraints
Equation (23) ensures that each container demand can arrive at the destination port before its due time with a possibility of at least .
(d) Decision Variables ConstraintsEquations (24)-(26) ensure that only one intermodal container route comprising several train services, one intermodal transfer hub, and one ship service can be selected to transport each container demand. Equations (27) and (28) specify that service s cannot be used to transport containers if it is not operated, while (29) represents that service s must be selected before allowing for its service frequency. Equation (30) enforces that the container demand cannot transfer at an intermodal transfer hub if the ship service departing from this hub is not operated.
4. The Solution Algorithm for the Two-Stage Chance Constrained SISND Problem
This section is dedicated to explaining the hybrid heuristic algorithm we propose for solving the aforementioned two-stage chance constrained SISND problem. The algorithm consists of two parts: (1) the SAA method for converting the stochastic problem to deterministic sample average approximation problems, by replacing the original distribution of random variables with an empirical distribution obtained from a random sample, and (2) the ant colony optimisation (ACO) algorithm for solving the converted problems.
4.1. Sample Average Approximation Method
Although chance constrained problems have been studied for almost 60 years, they are still difficult to solve numerically, even for simple problems. One reason is that the feasibility of a solution is hard to check because of the difficulty of computing chance constraints. The other reason is that the feasible region defined by chance constraints is not convex generally .
In the chance constrained problem (11)-(30), the expectation in the objective function and the chance constraints are very difficult to calculate, even for simple function forms. In this paper, we apply the SAA method to solve our SISND problem with chance constraints, which is a mature approach to solve stochastic optimisation problems . The SAA scheme approximates the expected objective function and chance constraints by the corresponding sample average function based on Monte Carlo simulation . In detail, let be an independent sample which comprises N realisations of the random vector according to the probability distributions of random variables, i.e.,where , , , are the values of all random variables; then is approximated by . The chance constraints are also approximated in a similar way as follows. Denote function asThen the probability in (19) is approximated asThus, the two-stage chance constrained programming model P0 can be converted to the following SAA problem P1:
P1subject to (13)-(18), (24)-(30), and (35)-(39):By generating M independent samples, each containing N realisations of , we can formulate M associated SAA problems. By solving the SAA problem for each sample, we get their optimal solutions, denoted by , , and treat them as candidate solutions for P0. Without loss of generality, we assume that the corresponding optimal values of the objective function, denoted by , , are rearranged as . Thus, yields the lower bound of the objective function of P0 suggested by Luedtke and Ahmed , which, when , , is valid with a confidence level inIn addition, each candidate solution is checked by a posteriori analysis to see whether the constraints are satisfied . Here we generate an independent test sample containing realisations of the random vector , i.e., . For all candidate solutions, the possibilities of chance constraints are recalculated by using the test sample, based on which feasible solutions to P0 are derived. For any feasible solution , the upper bound stated by Verweij et al.  for the optimal value of P0 can be estimated byFrom the above M candidate solutions, we choose the one which is feasible for P0 and has the smallest estimated objective value of P0 as the optimal solution, denoted by . The quality of the optimal solution can be evaluated by the optimality gap (i.e., the difference between optimal value and lower bound) calculated in (42) as follows:where is recomputed by using the test sample with size and provides a lower bound as mentioned above.
4.2. Ant Colony Optimisation for SAA Problem
The deterministic SAA problem P1 converted from the SISND problem is still NP-hard. In this subsection, we employ ACO algorithm to solve the SAA problems. ACO is a heuristic algorithm for solving combinatorial optimisation problems , which is first proposed by Dorigo et al.  and applied to the travelling salesman problems (TSP). Recently, ACO has been widely applied to different research fields such as vehicle routing , traffic signal plan , reactive power management , and economic dispatch . The details about this algorithm are described as follows.
We put initial pheromone trails on each service. A probability function in (43) is defined to select the service to be operated:where is the probability of operating service s by ant k, the set of services not selected by ant k, α the parameter to regulate the influence of pheromone trail , and the intensity of pheromone trail on service s.
Similarly, intermodal container route of each demand is also constructed by ACO algorithm. After determining the services to operate, a probability function in (44) is defined to select the service used to transport container demand p:where is the probability of choosing service s to transport container demand p by ant k, is the set of services not selected to transport container demand p by ant k, α is the parameter to regulate the influence of pheromone trail , β is the parameter to regulate the influence of heuristic information , is the intensity of pheromone trail on container demand p transported by service s, and is the heuristic information of container demand p transported by service s, where is the route cost of service s.
In the process of searching the optimal solution, pheromone trails on services change dynamically iteration by iteration. Pheromone trails are updated based on evaporation rate and increase of pheromone trail as follows: where is pheromone trail evaporation rate is pheromone trail on service s at iteration T is increase of pheromone trail on service s at iteration T-1 is pheromone trail on container demand p transported by service s at iteration T is increase of pheromone trail on container demand p transported by service s at iteration T-1
As given in (45), if service s is operated, the pheromone trail on this service is increased by , where is a predefined coefficient to adjust the effect of increasing pheromone trail  and is the total cost calculated by ant k. Otherwise, if service s is not operated, the increased pheromone trail is zero. The way of updating pheromone trails in (46) is similar to that of (45).
4.3. A Hybrid Heuristic Algorithm
As a metaheuristic search method, ACO has a high efficiency in solving combinatorial optimisation problems. Hence, in this paper, the SAA method and ACO algorithm are integrated to develop a hybrid heuristic algorithm for solving the two-stage chance constrained programming model, where SAA is used to simulated stochastic travel time, transfer time, and container demand, and ACO is employed to yield the optimal service design and intermodal container routes. The procedure of the hybrid heuristic algorithm is illustrated in Figure 2.
5. Numerical Example
In this section, we use a practical sea-rail intermodal network to demonstrate the two-stage chance constrained programming model, and to assess the proposed hybrid heuristic algorithm for solving the SISND problem with stochastic time parameters and container demands. We also compare the results under deterministic and stochastic conditions in Section 5.1 and investigate the effect of stochastic factors on optimal solutions and the performance of the solutions in Section 5.2.
5.1. Case Study
The case study is on a realistic sea-rail intermodal network from China to Singapore. As depicted in Figure 3, this intermodal network comprises 17 railway stations, 1 destination port, and 2 intermodal transfer hubs where containers can be transhipped from train services to ship services.
It is assumed that 12 container demands need to be transported, including inland demands and container demands, and their details are given in Table 4. To transport these container demands, 42 train services and 6 ship services are available, which are listed in Tables S1-S2 in the Supplementary Materials. Each service is characterised by its origin, destination, service path, service distance, service time, fixed cost, and variable cost. Unit transfer cost and unit penalty cost are assumed to be 25 (US/TEU) and 50 (US/TEU/day), respectively. In addition, confidence levels and are both set as 0.9.
The heuristic algorithm incorporating SAA method and ACO is coded in MATLAB R2012a. The programme is carried out on a desktop PC with a core i5 2.50GHz processor and 4GB RAM.
We first test the case with deterministic parameters. The optimal operated services and intermodal container routes in this deterministic case are shown in Table 5, leading to a total system cost of 184,275.
We then test the case with stochastic parameters, where a multiplier is introduced to describe the variability of the stochastic travel times of trains and ships. In this problem, the travel time on a railway arc a follows a normal distribution, i.e., with the mean travel time and