Evaluation of Evolutionary Algorithms for Multi-objective Train Schedule Optimization

Chang, C.S.; Kwan, C.M.

doi:10.1007/978-3-540-30549-1_69

Cited by 15 publications

(8 citation statements)

References 7 publications

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“…Also, one of the most beneficial solutions for trains schedule optimizations are the Pareto-based methods (Ghoseiri et al, 2004;Chang and Kwan, 2005). Therefore, in this study, NSGA-II (Deb et al, 2002) is used to solve the subway scheduling problem.…”

Section: Optimization Procedure: Results and Discussionmentioning

confidence: 99%

“…Also, Sels et al (2013) introduced an objective function for the total passenger travel times in subway systems in order to overcome the lack of mathematical modelling in a sustainable scheduling. Furthermore, Chang and Kwan (2005) evaluated the performance of the evolutionary algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) in trains schedule optimization problems.…”

Section: Related Workmentioning

confidence: 99%

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Modelling Temporal Schedule of Urban Trains Using Agent-Based Simulation and Nsga2-Based Multiobjective Optimization Approaches

Sahelgozin

Alimohammadi

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Increasing distances between locations of residence and services leads to a large number of daily commutes in urban areas. Developing subway systems has been taken into consideration of transportation managers as a response to this huge amount of travel demands. In developments of subway infrastructures, representing a temporal schedule for trains is an important task; because an appropriately designed timetable decreases Total passenger travel times, Total Operation Costs and Energy Consumption of trains. Since these variables are not positively correlated, subway scheduling is considered as a multi-criteria optimization problem. Therefore, proposing a proper solution for subway scheduling has been always a controversial issue. On the other hand, research on a phenomenon requires a summarized representation of the real world that is known as Model. In this study, it is attempted to model temporal schedule of urban trains that can be applied in Multi-Criteria Subway Schedule Optimization (MCSSO) problems. At first, a conceptual framework is represented for MCSSO. Then, an agent-based simulation environment is implemented to perform Sensitivity Analysis (SA) that is used to extract the interrelations between the framework components. These interrelations is then taken into account in order to construct the proposed model. In order to evaluate performance of the model in MCSSO problems, Tehran subway line no. 1 is considered as the case study. Results of the study show that the model was able to generate an acceptable distribution of Pareto-optimal solutions which are applicable in the real situations while solving a MCSSO is the goal. Also, the accuracy of the model in representing the operation of subway systems was significant.

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Section: Optimization Procedure: Results and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Modelling Temporal Schedule of Urban Trains Using Agent-Based Simulation and Nsga2-Based Multiobjective Optimization Approaches

Sahelgozin

Alimohammadi

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…For examp problem [30,31], machine sch scheduling [34], and vehicle routin suggested different ways to encode permutation) to PSO particle. For swarm optimization (DPSO) [17 onsists of particles flying space.…”

Section: C2 Pso For No-wait Ffsspmentioning

confidence: 99%

Metaheuristics for two-stage no-wait flexible flow shop scheduling problem

Ghaleb

Suryahatmaja

Al-Harkan

2015

2015 International Conference on Industrial Engineering and Operations Management (IEOM)

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The flexible flow-shop scheduling problem (FFSSP) is an important branch of production scheduling, the flexible flow shop is a combination of two well-known machine environments, which are flow shops and parallel machines, and it is well known that this problem is NP-hard. The no-wait requirement is a phenomenon that may occur in flow shops, and it's prevent the jobs to wait between two successive machines (or stages), jobs must be processed from the start to finish, without any interruption on machines (or stages) or between them. Many different approaches have been applied to FFSSP with no-wait. In this research, two-stage no-wait flexible flow shop scheduling problem (NWFFSSP) has been solved using two meta-heuristics, which are Tabu Search (TS) and Particle Swarm Optimization (PSO). We solved the NWFFSSP with minimum makespan as a performance measure. The performance of the proposed algorithms are studied and compared with previous research results using the same problem data. The results of the study proposes the effective algorithm.

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“…In experiments for 4, 7, and 10 different criteria, the approach was shown to outperform multiple existing algorithms, including the popular NSGA-III [8,10]. GA approaches are commonly used for solving a wide range of problems ranging from multi-objective train scheduling [11] to continuous process improvement [12].…”

Section: Introductionmentioning

confidence: 98%

Solving Non-Permutation Flow Shop Scheduling Problem with Time Couplings

2021

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In this paper, a non-permutation variant of the Flow Shop Scheduling Problem with Time Couplings and makespan minimization is considered. Time couplings are defined as machine minimum and maximum idle time allowed. The problem is inspired by the concreting process encountered in industry. The mathematical model of the problem and solution graph representation are presented. Several problem properties are formulated, including the time complexity of the goal function computation and block elimination property. Three solving methods, an exact Branch and Bound algorithm, the Tabu Search metaheuristic, and a baseline Genetic Algorithm metaheuristic, are proposed. Experiments using Taillard-based problem instances are performed. Results show that, for the Tabu Search method, the neighborhood based on the proposed block property outperforms other neighborhoods and the Genetic Algorithm under the same time limit. Moreover, the Tabu Search method provided high quality solutions, with the gap to the optimal solution for the smaller instances not exceeding 2.3%.

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Evaluation of Evolutionary Algorithms for Multi-objective Train Schedule Optimization

Cited by 15 publications

References 7 publications

Modelling Temporal Schedule of Urban Trains Using Agent-Based Simulation and Nsga2-Based Multiobjective Optimization Approaches

Modelling Temporal Schedule of Urban Trains Using Agent-Based Simulation and Nsga2-Based Multiobjective Optimization Approaches

Metaheuristics for two-stage no-wait flexible flow shop scheduling problem

Solving Non-Permutation Flow Shop Scheduling Problem with Time Couplings

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