Abstract
Urban rail crew scheduling problem is to allocate train services to crews based on a given train timetable while satisfying all the operational and contractual requirements. In this paper, we present a new mathematical programming model with the aim of minimizing both the related costs of crew duty and the variance of duty time spreads. In addition to iincorporating the commonly encountered crew scheduling constraints, it also takes into consideration the constraint of arranging crews having a meal in the specific meal period of one day rather than after a minimum continual service time. The proposed model is solved by an ant colony algorithm which is built based on the construction of ant travel network and the design of ant travel path choosing strategy. The performances of the model and the algorithm are evaluated by conducting case study on Changsha urban rail. The results indicate that the proposed method can obtain a satisfactory crew schedule for urban rails with a relatively small computational time.
Highlights
Urban rail crew schedule optimization problem (URCSOP) aims to find the lowest cost crew schedule covering all trips of trains specified in a train timetable subject to various constraints such as crew rest and meal time requirements
The set covering problem allows crews to take a trip to another station for starting their duties. Considering this case will lead to more duty time spread and reduce crew service efficiency, this paper models the URCSOP as the set partitioning problem
We proposed an urban rail crew schedule optimization model after analyzing its optimization objectives and all types of constraints
Summary
Urban rail crew schedule optimization problem (URCSOP) aims to find the lowest cost crew schedule covering all trips of trains specified in a train timetable subject to various constraints such as crew rest and meal time requirements. The URCSOP has still become one of the most important topics in urban rail transport because crew cost is the main variable operation expenses, and a small improvement to the crew schedule essentially leads to accumulated savings producing large annual cost savings for urban rail enterprise. URCSOP was more frequently formulated as either set covering problem or set partitioning problem with binary integral decision variables, and each decision variable represents whether or not a duty is chosen as the service for a crew Both problems were solved by either an exact or a heuristic method, or their combination. Column-generation-based methods have higher solving efficiency than other exact approaches, and they are widely used to solve the allocation problems, but as stated in study [7], they are not efficient for solving URCSOP whose decision variables are only determined as 0 or 1, rather than any real number
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