scholarly journals Solution of Multi-Crew Depots Railway Crew Scheduling Problems: The Chinese High-Speed Railway Case

2022 ◽  
Vol 14 (1) ◽  
pp. 491
Author(s):  
Chunxiao Zhao ◽  
Junhua Chen ◽  
Xingchen Zhang ◽  
Zanyang Cui
Keyword(s):  
Genetic Algorithm ◽  
Development Strategy ◽  
High Speed ◽  
Large Scale ◽  
Mathematical Formulation ◽  
Solution Space ◽  
Working Time ◽  
Crew Scheduling ◽  
Scheduling Problems ◽  
Improved Genetic Algorithm

This paper presents a novel mathematical formulation in crew scheduling, considering real challenges most railway companies face such as roundtrip policy for crew members joining from different crew depots and stricter working time standards under a sustainable development strategy. In China, the crew scheduling is manually compiled by railway companies respectively, and the plan quality varies from person to person. An improved genetic algorithm is proposed to solve this large-scale combinatorial optimization problem. It repairs the infeasible gene fragments to optimize the search scope of the solution space and enhance the efficiency of GA. To investigate the algorithm’s efficiency, a real case study was employed. Results show that the proposed model and algorithm lead to considerable improvement compared to the original planning: (i) Compared with the classical metaheuristic algorithms (GA, PSO, TS), the improved genetic algorithm can reduce the objective value by 4.47%; and (ii) the optimized crew scheduling plan reduces three crew units and increases the average utilization of crew unit working time by 6.20% compared with the original plan.

scholarly journals Genetic Optimization of Energy- and Failure-Aware Continuous Production Scheduling in Pasta Manufacturing

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 297 ◽  
Author(s):  
Ke Shen ◽  
Toon Pessemier ◽  
Xu Gong ◽  
Luc Martens ◽  
Wout Joseph
Keyword(s):  
Genetic Algorithm ◽  
Production Scheduling ◽  
Large Scale ◽  
Optimization Problems ◽  
Continuous Production ◽  
Optimal Schedule ◽  
Memory Search ◽  
Search Problem ◽  
Scheduling Problems ◽  
Improved Genetic Algorithm

Energy and failure are separately managed in scheduling problems despite the commonalities between these optimization problems. In this paper, an energy- and failure-aware continuous production scheduling problem (EFACPS) at the unit process level is investigated, starting from the construction of a centralized combinatorial optimization model combining energy saving and failure reduction. Traditional deterministic scheduling methods are difficult to rapidly acquire an optimal or near-optimal schedule in the face of frequent machine failures. An improved genetic algorithm (IGA) using a customized microbial genetic evolution strategy is proposed to solve the EFACPS problem. The IGA is integrated with three features: Memory search, problem-based randomization, and result evaluation. Based on real production cases from Soubry N.V., a large pasta manufacturer in Belgium, Monte Carlo simulations (MCS) are carried out to compare the performance of IGA with a conventional genetic algorithm (CGA) and a baseline random choice algorithm (RCA). Simulation results demonstrate a good performance of IGA and the feasibility to apply it to EFACPS problems. Large-scale experiments are further conducted to validate the effectiveness of IGA.

scholarly journals A GRASP Approach for Solving Large-Scale Electric Bus Scheduling Problems

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6610
Author(s):  
Raka Jovanovic ◽  
Islam Safak Bayram ◽  
Sertac Bayhan ◽  
Stefan Voß
Keyword(s):  
Public Transport ◽  
High Speed ◽  
Large Scale ◽  
Integer Program ◽  
Mixed Integer ◽  
Transport Systems ◽  
Mixed Integer Program ◽  
Scheduling Problems ◽  
Electric Bus ◽  
Battery Capacity

Electrifying public bus transportation is a critical step in reaching net-zero goals. In this paper, the focus is on the problem of optimal scheduling of an electric bus (EB) fleet to cover a public transport timetable. The problem is modelled using a mixed integer program (MIP) in which the charging time of an EB is pertinent to the battery’s state-of-charge level. To be able to solve large problem instances corresponding to real-world applications of the model, a metaheuristic approach is investigated. To be more precise, a greedy randomized adaptive search procedure (GRASP) algorithm is developed and its performance is evaluated against optimal solutions acquired using the MIP. The GRASP algorithm is used for case studies on several public transport systems having various properties and sizes. The analysis focuses on the relation between EB ranges (battery capacity) and required charging rates (in kW) on the size of the fleet needed to cover a public transport timetable. The results of the conducted computational experiments indicate that an increase in infrastructure investment through high speed chargers can significantly decrease the size of the necessary fleets. The results also show that high speed chargers have a more significant impact than an increase in battery sizes of the EBs.

scholarly journals Genetic Algorithm-Based Variable Value Control Method for Solving the Ground Target Attacking Weapon-Target Allocation Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Chao Wang ◽  
Guangyuan Fu ◽  
Daqiao Zhang ◽  
Hongqiao Wang ◽  
Jiufen Zhao
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Control Method ◽  
Search Space ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Mutation Strategy ◽  
Ground Target ◽  
Value Range ◽  
Target Allocation

Key ground targets and ground target attacking weapon types are complex and diverse; thus, the weapon-target allocation (WTA) problem has long been a great challenge but has not yet been adequately addressed. A timely and reasonable WTA scheme not only helps to seize a fleeting combat opportunity but also optimizes the use of weaponry resources to achieve maximum battlefield benefits at the lowest cost. In this study, we constructed a ground target attacking WTA (GTA-WTA) model and designed a genetic algorithm-based variable value control method to address the issue that some intelligent algorithms are too slow in resolving the problem of GTA-WTA due to the large scale of the problem or are unable to obtain a feasible solution. The proposed method narrows the search space and improves the search efficiency by constraining and controlling the variable value range of the individuals in the initial population and ensures the quality of the solution by improving the mutation strategy to expand the range of variables. The simulation results show that the improved genetic algorithm (GA) can effectively solve the large-scale GTA-WTA problem with good performance.

scholarly journals Optimal Sensor Placement for Health Monitoring of High-Rise Structure Based on Genetic Algorithm

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Ting-Hua Yi ◽  
Hong-Nan Li ◽  
Ming Gu
Keyword(s):  
Genetic Algorithm ◽  
Health Monitoring ◽  
Large Scale ◽  
Sensor Placement ◽  
Optimal Placement ◽  
Improved Genetic Algorithm ◽  
Optimal Sensor Placement ◽  
Selection Scheme ◽  
Dual Structure ◽  
Coding Method

Optimal sensor placement (OSP) technique plays a key role in the structural health monitoring (SHM) of large-scale structures. Based on the criterion of the OSP for the modal test, an improved genetic algorithm, called “generalized genetic algorithm (GGA)”, is adopted to find the optimal placement of sensors. The dual-structure coding method instead of binary coding method is proposed to code the solution. Accordingly, the dual-structure coding-based selection scheme, crossover strategy and mutation mechanism are given in detail. The tallest building in the north of China is implemented to demonstrate the feasibility and effectiveness of the GGA. The sensor placements obtained by the GGA are compared with those by exiting genetic algorithm, which shows that the GGA can improve the convergence of the algorithm and get the better placement scheme.

Solving large scale crew scheduling problems

1997 ◽  
Vol 97 (2) ◽  
pp. 260-268 ◽  
Author(s):  
Hai D. Chu ◽  
Eric Gelman ◽  
Ellis L. Johnson
Keyword(s):  
Large Scale ◽  
Crew Scheduling ◽  
Scheduling Problems

scholarly journals Large-Scale Truss Topology and Sizing Optimization by an Improved Genetic Algorithm with Multipoint Approximation

2021 ◽  
Vol 12 (1) ◽  
pp. 407
Author(s):  
Tianshan Dong ◽  
Shenyan Chen ◽  
Hai Huang ◽  
Chao Han ◽  
Ziqi Dai ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Approximation Technique ◽  
Discrete Topology ◽  
Improved Genetic Algorithm ◽  
Approximation Accuracy ◽  
Design Variables ◽  
Metaheuristic Methods ◽  
Multipoint Approximation

Truss size and topology optimization problems have recently been solved mainly by many different metaheuristic methods, and these methods usually require a large number of structural analyses due to their mechanism of population evolution. A branched multipoint approximation technique has been introduced to decrease the number of structural analyses by establishing approximate functions instead of the structural analyses in Genetic Algorithm (GA) when GA addresses continuous size variables and discrete topology variables. For large-scale trusses with a large number of design variables, an enormous change in topology variables in the GA causes a loss of approximation accuracy and then makes optimization convergence difficult. In this paper, a technique named the label–clip–splice method is proposed to improve the above hybrid method in regard to the above problem. It reduces the current search domain of GA gradually by clipping and splicing the labeled variables from chromosomes and optimizes the mixed-variables model efficiently with an approximation technique for large-scale trusses. Structural analysis of the proposed method is extremely reduced compared with these single metaheuristic methods. Numerical examples are presented to verify the efficacy and advantages of the proposed technique.

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