Planning Problems in Intermodal Freight Transport ...

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sufficient number of container pools. The authors do not integrate loaded and empty container flow decisions in a single model. Lin and Cheng [47] study a network design problem of a door-to-door express service. An air-ground intermodal carrier provides a delivery service in a hierarchical hub-and-spoke network. The network consists of multiple clusters. Local cluster centres are connected to their own hub through a secondary route. Each hub is connected to other hubs through a primary route. Large trucks or aircrafts are used on primary routes, smaller trucks or aircrafts on secondary routes. The problem is to determine fleet size, routes and schedules for both primary and secondary trucks or aircrafts simultaneously, with the objective to minimize the sum of fixed and operating costs while meeting the desired service level. The authors formulate the problem as an integer program in a route-space directed network. The binary program is solved through an implicit enumeration algorithm that contains an embedded least time path sub-problem. Finally, pricing strategy decisions have to be considered at the tactical planning level. Li and Tayur [48] develop a tactical planning model for intermodal rail transport that jointly considers operations planning and pricing decisions. In the operations-planning subproblem, freight routing, train routing and train assignment are considered simultaneously. Train routes, frequency of service and number of locomotives and flatcars used on each route need to be determined. The combined problem is formulated as a nonlinear programming model. It is solved to optimality through a decomposition that exploits the structure of the subproblems. The model is developed for the intermodal transport of trailers, but may be easily extended to intermodal transport of containers. Two other papers on pricing strategy decisions are given by Yan et al. [49] and Tsai et al. [50]. Yan et al. [49] develop a framework for estimating the opportunity costs for all services in trailer-on-flatcar operations. These opportunity costs are to be taken into account when setting the price level of intermodal transport. The framework
is based on a network model, formulated as a linear network flow problem with side constraints. A mathematical program is formulated to address this problem incorporating an efficient algorithm for approximating better the reduced costs. The algorithm combines the use of Langrangian relaxation with a minimum cost algorithm and a shortest path algorithm. Tsai et al. [50] construct two models to determine an optimal price level and service level for intermodal transport in competition with truck transport. The authors consider the whole intermodal chain, contrary to Yan et al. [49] who only consider rail haul. The models take into account not only carriers’ pricing behaviour (supply side) but also shippers’ mode choice behaviour (demand side). Solutions to find an equilibrium are pursued by a mathematical programming approach. The objective is to optimise intermodal profit within some constraints, which include shippers’ mode choice behaviour, non-negativity of carrier price and cargo amounts and intermodal volume constraints. 5. OPERATIONAL PLANNING Important operational decisions are the scheduling of services, empty vehicle distribution and repositioning, crew scheduling and allocation of resources. The main issues are similar to those at the tactical decision level. However, while tactical planning is concerned with ‘where’ and ‘how’ issues (selecting services of given types and traffic routes between spatial locations), operational planning is interested in ‘when’ issues (when to start a given service, when a vehicle arrives at a destination or at an intermediary terminal, etc.).(Crainic and Laporte [4]) 5.1 Drayage Operator
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