A constraint programming-based approach to the crew scheduling problem of the Taipei mass rapid transit system

Han, Anthony F.; Li, Elvis C.

doi:10.1007/s10479-014-1619-1

Cited by 15 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several papers published so far tackled the subject of automation in railway crew scheduling. One commonly used approach is to employ complete enumeration to generate all possible candidates that satisfy the given constraints and, afterward, apply integer linear programming to solve the problem [8] of crew scheduling in transit systems. Additionally, heuristic methods are widely adopted.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Component of the FERODATA AI Engine to Optimize the Assignment of Rail Freight Locomotive Drivers

Brezulianu,

Geman,

Popa

2023

Applied Sciences

View full text Add to dashboard Cite

The optimization of locomotive drivers’ scheduling in rail freight transportation comes as a necessity for minimizing economic expenses and training investments. The Ferodata AI engine, an artificial intelligence (AI)/machine learning (ML) software module, developed by our team, has integrated a supervised random forest model that automatically assigns conductors to freight transportation orders based on the data about locomotive driver’s tiredness score, distance of the driver to the departure point of a transportation order, driver availability, and circulation history. The model proposed by us obtained very good performance metrics on the train set (accuracy: 95%, AUC: 0.9905) and reasonably good and encouraging performance on the test set (accuracy: 84%, AUC: 0.8357). After rigorous testing and validation on external and larger datasets, the automated optimization of locomotive driver assignments could bring operational efficiency, cost savings, regulatory compliance, and improved safety to scheduled rail freight transports.

show abstract

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Component of the FERODATA AI Engine to Optimize the Assignment of Rail Freight Locomotive Drivers

Brezulianu,

Geman,

Popa

2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…According to the growing needs for high‐speed and high‐density train operations that are punctual and safe, rescheduling has become so complicated that it cannot be done without using a computer‐based support system . Reassigning of train crews is a computationally complex problem, and various algorithms for solving it by using optimization techniques incorporating heuristic methods have been reported .…”

Section: Introductionmentioning

confidence: 99%

Interactive system with explanation function for real‐time railway crew rescheduling

Yamada

Sato

Tomiyama

et al. 2017

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

To win a user's trust while railway crew rescheduling by interactively explaining the reason(s) for the solution calculated by a computer, we propose a schema for describing crew rescheduling patterns in an 'if-then-because' format, an improved method for controlling user interaction based on the patterns, and a cognitive model of the user in a state-transition form. An improved system developed using the proposed method generates crew schedules automatically by using a rule base comprising 30 patterns. Characteristic of the improved system is the use of 'because' information for explaining the computing process. System tests with an actual schedule for crews and trains in a railway line demonstrate that interactive processing with the help of an explanation function better visualizes the computing process. We evaluate our improved system by think-aloud testing. Protocol analysis of verbal comments revealed that the user state was transited in accordance with our design intent that the user state for interpreting 'because' information plays a hub role in the decision making: users spent 28% of their time interpreting 'because' information. Questionnaire survey results show that contribution of the explanation is 34%. Interactive rescheduling with the help of an explanation function is practical for real-time railway crew rescheduling.

show abstract

“…To the best of the authors' knowledge, the hybrid CP/ IP approach has not been applied to the railroad crew scheduling problem for real-world applications. Nevertheless, this hybrid approach has been proved to be successful in the applications in some related fields, for example, bus driver scheduling, MRT crew scheduling, and maintenance crew scheduling (21)(22)(23). Therefore, this paper reports the first application of using CP and IP to solve the railroad crew scheduling problem with a case from the Kaohsiung depot of the Taiwan Railways Administration (TRA).…”

mentioning

confidence: 99%

Applying Constraint Programming and Integer Programming to Solve the Crew Scheduling Problem for Railroad Systems: Model Formulation and a Case Study

Chen¹,

Jong²,

Han

2021

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Crew scheduling is one of the crucial processes in railroad operation planning. Based on current regulations and working and break time requirements, as well as the operational rules, this process aims to find a duty arrangement with minimal cost that covers all trips. Most past studies considered this subject for railroad systems as an optimization problem and solved it with mathematical programming-based methods or heuristic algorithms, despite numerous logical constraints embedded in this problem. Few studies have applied constraint programming (CP) approaches to tackle the railroad crew scheduling problem. This paper proposes a hybrid approach to solve the problem with a CP model for duty generation, and an integer programming model for duty optimization. These models have been applied to the Kaohsiung depot of Taiwan Railways Administration, the largest railroad operator in Taiwan. The encouraging results show that the proposed approach is more efficient than the manual process and can achieve 30% savings of driver cost. Moreover, the approach is robust and provides flexibility to easily accommodate related operational concerns such as minimizing the number of overnight duties. Thus, this hybrid two-phase approach seems to have the potential for applications to the railroad crew scheduling problems outside Taiwan.

show abstract

A constraint programming-based approach to the crew scheduling problem of the Taipei mass rapid transit system

Cited by 15 publications

References 21 publications

Artificial Intelligence Component of the FERODATA AI Engine to Optimize the Assignment of Rail Freight Locomotive Drivers

Artificial Intelligence Component of the FERODATA AI Engine to Optimize the Assignment of Rail Freight Locomotive Drivers

Interactive system with explanation function for real‐time railway crew rescheduling

Applying Constraint Programming and Integer Programming to Solve the Crew Scheduling Problem for Railroad Systems: Model Formulation and a Case Study

Contact Info

Product

Resources

About