scholarly journals Optimization of Bus Bridging Service under Unexpected Metro Disruptions with Dynamic Passenger Flows

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Jiadong Wang ◽  
Zhenzhou Yuan ◽  
Yonghao Yin
Keyword(s):  
Disruption Management ◽  
Unexpected Events ◽  
Nsga Ii ◽  
Fleet Size ◽  
Proposed Model ◽  
Passenger Flows ◽  
Metro Systems ◽  
Bridging Strategy ◽  
Vehicle Capacity ◽  
Shanghai Metro

A metro disruption is a situation where metro service is suspended for some time due to unexpected events such as equipment failure and extreme weather. Metro disruptions reduce the level of service of metro systems and leave numerous passengers stranded at disrupted stations. As a means of disruption management, bus bridging has been widely used to evacuate stranded passengers. This paper focuses on the bus bridging problem under operational disruptions on a single metro line. Unlike previous studies, we consider dynamic passenger flows during the disruption. A multi-objective optimization model is established with objectives to minimize total waiting time, the number of stranded passengers and dispatched vehicles with constraints such as fleet size and vehicle capacity. The NSGA-II algorithm is used for the solution. Finally, we apply the proposed model to Shanghai Metro to access the effectiveness of our approaches in comparison with the current bridging strategy. Sensitivity analysis of the bus fleet size involved in the bus bridging problem was conducted.

scholarly journals A Mixed Decision Strategy for Freight and Passenger Transportation in Metro Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Yutao Ye ◽  
Junhua Guo ◽  
Lixin Yan
Keyword(s):  
Search Algorithm ◽  
Neighborhood Search ◽  
Decision Strategy ◽  
Model Framework ◽  
Long Distance ◽  
Support Tool ◽  
Passenger Transportation ◽  
Proposed Model ◽  
Passenger Flows ◽  
Metro Systems

This paper proposes a mixed decision strategy for freight and passenger transportation in metro systems during off-peak hours (MTS-OPH). The definition of the mixed decision strategy is proposed, and fixed and flexible loading modes are considered for different passenger flow volumes. A mathematical model of the MTS-OPH is proposed and solved using an improved variable neighborhood search algorithm. Case studies demonstrate the performance and applicability of the proposed model and algorithm, and the MTS-OPH is discussed for different delivery distances, passenger flows, and metro network types. The proposed strategy is suitable for long-distance delivery, and the proposed model framework can be applied to different types of metro networks with different levels of complexity. The mixed decision strategy provides a decision support tool for metro and freight companies and can propose corresponding solutions according to different passenger flows.

scholarly journals An Optimization Method for an Integrated Energy System Scheduling Process Based on NSGA-II Improved by Tent Mapping Chaotic Algorithms

Processes ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 426 ◽  
Author(s):  
Shengran Chen ◽  
Shengyan Wang
Keyword(s):  
Economic Dispatch ◽  
Energy System ◽  
Optimization Method ◽  
Nsga Ii ◽  
Tent Map ◽  
Chaos Optimization ◽  
Basic Model ◽  
Proposed Model ◽  
Integrated Energy System ◽  
Supply Equipment

The integrated energy system is a vital part of distributed energy industries. In addition to this, the optimal economic dispatch model, which takes into account the complementary coordination of multienergy, is an important research topic. Considering the constraints of power balance, energy supply equipment, and energy storage equipment, a basic model of optimal economic dispatch of an integrated energy system is established. On this basis, a multiobjective function solving algorithm of NSGA-II, based on tent map chaos optimization, is proposed. The proposed model and algorithm are applied. The simulation results show that the optimal economic scheduling model of the integrated energy system established in this paper can provide a more economic system operation scheme and reduce the operation cost and risks associated with an integrated energy system. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) multiobjective function solving algorithm, based on tent map chaos optimization, has better performance and efficiency.

A Bi-Objective Order Allocation Optimization Model for Horizontal Manufacturing Collaborative Alliance

2010 ◽  
Vol 102-104 ◽  
pp. 836-840 ◽  
Author(s):  
Fang Qi Cheng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Optimal Solution ◽  
Order Allocation ◽  
Nsga Ii ◽  
Manufacturing Enterprises ◽  
Proposed Model ◽  
Optimal Solution Set ◽  
Simulation Results ◽  
Enterprise Community

Horizontal manufacturing collaborative alliance is a dispersed enterprise community consisting of several enterprises which produce the same kind of products. To correctly assign order among member companies of horizontal manufacturing collaborative alliance is one of the most important ways to improve the agility and competitiveness of manufacturing enterprises. For the order allocation problem, a bi-objective optimization model is developed to minimize the comprehensive cost and balance the production loads among the selected manufacturing enterprises. Non-dominated sorting genetic algorithm (NSGA-II) is applied to solve the optimization functions. The optimal solution set of Pareto is obtained. The simulation results indicate that the proposed model and algorithm is able to obtain satisfactory solutions.

A maximal covering facility location model for emergency services within an M (t)/M/m/m queuing system

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Iman Bahrami ◽  
Roya M. Ahari ◽  
Milad Asadpour
Keyword(s):  
Facility Location ◽  
Emergency Services ◽  
Queuing System ◽  
Location Model ◽  
Computational Time ◽  
Nsga Ii ◽  
Content Type ◽  
Maximal Covering ◽  
Proposed Model

Purpose In emergency services, maximizing population coverage with the lowest cost at the peak of the demand is important. In addition, due to the nature of services in emergency centers, including hospitals, the number of servers and beds is actually considered as the capacity of the system. Hence, the purpose of this paper is to propose a multi-objective maximal covering facility location model for emergency service centers within an M (t)/M/m/m queuing system considering different levels of service and periodic demand rate. Design/methodology/approach The process of serving patients is modeled according to queuing theory and mathematical programming. To cope with multi-objectiveness of the proposed model, an augmented ε-constraint method has been used within GAMS software. Since the computational time ascends exponentially as the problem size increases, the GAMS software is not able to solve large-scale problems. Thus, a NSGA-II algorithm has been proposed to solve this category of problems and results have been compared with GAMS through random generated sample problems. In addition, the applicability of the proposed model in real situations has been examined within a case study in Iran. Findings Results obtained from the random generated sample problems illustrated while both the GAMS software and NSGA-II almost share the same quality of solution, the CPU execution time of the proposed NSGA-II algorithm is lower than GAMS significantly. Furthermore, the results of solving the model for case study approve that the model is able to determine the location of the required facilities and allocate demand areas to them appropriately. Originality/value In the most of previous works on emergency services, maximal coverage with the minimum cost were the main objectives. Hereby, it seems that minimizing the number of waiting patients for receiving services have been neglected. To the best of the authors’ knowledge, it is the first time that a maximal covering problem is formulated within an M (t)/M/m/m queuing system. This novel formulation will lead to more satisfaction for injured people by minimizing the average number of injured people who are waiting in the queue for receiving services.

A One to Multiple Order Allocation Optimization Model of Horizontal Manufacturing Collaborative Alliance

2012 ◽  
Vol 201-202 ◽  
pp. 996-999
Author(s):  
Jin Gao
Keyword(s):  
Optimization Model ◽  
Optimal Solution ◽  
Multi Objective Optimization ◽  
Order Allocation ◽  
Nsga Ii ◽  
Manufacturing Enterprises ◽  
Proposed Model ◽  
Optimal Solution Set ◽  
Simulation Results ◽  
Enterprise Community

Horizontal manufacturing collaborative alliance is a dispersed enterprise community consisting of several enterprises which produce the same kind of products. To correctly assign order among member companies of horizontal manufacturing collaborative alliance is one of the most important ways to improve the agility and competitiveness of manufacturing enterprises. For the order allocation problem, a multi-objective optimization model is developed to minimize the comprehensive cost and balance the production loads among the selected manufacturing enterprises. Non-dominated sorting genetic algorithm (NSGA-II) is applied to solve the optimization functions. The optimal solution set of Pareto is obtained. The simulation results indicate that the proposed model and algorithm is able to obtain satisfactory solutions.

scholarly journals An Integrated Energy-Efficient Operation Methodology for Metro Systems Based on a Real Case of Shanghai Metro Line One

Energies ◽  
2014 ◽  
Vol 7 (11) ◽  
pp. 7305-7329 ◽  
Author(s):  
Cheng Gong ◽  
Shiwen Zhang ◽  
Feng Zhang ◽  
Jianguo Jiang ◽  
Xinheng Wang
Keyword(s):  
Energy Efficient ◽  
Real Case ◽  
Efficient Operation ◽  
Metro Systems ◽  
Shanghai Metro

A bi-objective model for vessel emergency maintenance under a condition-based maintenance strategy

SIMULATION ◽  
2017 ◽  
Vol 94 (7) ◽  
pp. 609-624 ◽  
Author(s):  
Jinlou Zhao ◽  
Liqian Yang
Keyword(s):  
Complex Problem ◽  
Spare Parts ◽  
Condition Based Maintenance ◽  
Maintenance Strategy ◽  
Nsga Ii ◽  
Maintenance Costs ◽  
Inventory Costs ◽  
Penalty Costs ◽  
Proposed Model ◽  
Objective Model

When sailing on the open seas, far from onshore dockyards, if a crucial part of the ship’s machinery fails, the ship will experience a costly event that carries a high risk of seriously affecting ship operations. If the ship receives warning of an impending defect, then it can try to sail to a dockyard and simultaneously order the spare parts needed to fix the problem. In this paper, we define this type of maintenance situation as ‘vessel emergency maintenance’. It is a complex problem, due to uncertainties with both the machinery condition development and spare parts delivery. To solve this problem, our paper proposes a bi-objective model under a condition-based maintenance strategy, with the aim of simultaneously minimizing maintenance costs and maximizing ship reliability. Maintenance costs include four things: (1) fuel consumption costs; (2) renting extra vessels; (3) shipping delay penalty costs; and (4) spare parts inventory costs. Ship reliability is represented by the reliability of the ship’s main engine, and can be described through a stochastic process. To solve this bi-objective model, we employ a non-dominated sorting genetic algorithm II (NSGA-II) to generate the Pareto optimal front of the two objectives. A numerical experiment is presented to demonstrate the applicability of the proposed model. The results indicate that the proposed model can provide emergency maintenance decision support for ship operators while they are sailing at sea.

scholarly journals Using NSGAII to Solve Bi-objective Bed Allocation Problems

2015 ◽  
Vol 1 (3) ◽  
pp. 397
Author(s):  
Jalal A. Sultan ◽  
Ban A. Mitras ◽  
Raghad M. Jasim
Keyword(s):  
Queuing Theory ◽  
Optimization Problems ◽  
Admission Rate ◽  
Nsga Ii ◽  
Multi Objective ◽  
Decision Aiding ◽  
Proposed Model ◽  
Objective Model ◽  
Bed Allocation ◽  
Suitable Framework

The Bed Allocation Problem (BAP) is NP-complete and always high dimensional. In this paper, a bi-objective decision aiding model based on queuing theory is introduced for allocation of beds in a hospital. The problem is modeled as an M/PH/n queue. The objectives include maximizing the patient admission rate human resources, in particular, maximization of the nursing work hours. The proposed model is solved by using Non-dominated Sorting Genetic Algorithm-II (NSGA-II), which is a very effective algorithm for solving multi-objective optimization problems and finding optimal Pareto front. The paper describes an application of the model, dealing with a public hospital in Iraq. The results related that multi-objective model was presented suitable framework for bed allocation and optimum use.

scholarly journals A Real-Time Timetable Rescheduling Method for Metro System Energy Optimization under Dwell-Time Disturbances

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Guang Yang ◽  
Junjie Wang ◽  
Feng Zhang ◽  
Shiwen Zhang ◽  
Cheng Gong
Keyword(s):  
Real Time ◽  
Energy Optimization ◽  
Control Model ◽  
Random Disturbance ◽  
Chain Reactions ◽  
Metro Systems ◽  
Regenerative Energy ◽  
Shanghai Metro ◽  
Proper Solutions ◽  
Metro System

Automatic Train Systems (ATSs) have attracted much attention in recent years. A reliable ATS can reschedule timetables adaptively and rapidly whenever a possible disturbance breaks the original timetable. Most research focuses the timetable rescheduling problem on minimizing the overall delay for trains or passengers. Few have been focusing on how to minimize the energy consumption when disturbances happen. In this paper, a real-time timetable rescheduling method (RTTRM) for energy optimization of metro systems has been proposed. The proposed method takes little time to recalculate a new schedule and gives proper solutions for all trains in the network immediately after a random disturbance happens, which avoids possible chain reactions that would attenuate the reuse of regenerative energy. The real-time feature and self-adaptability of the method are attributed to the combinational use of Genetic Algorithm (GA) and Deep Neural Network (DNN). The decision system for proposing solutions, which contains multiple DNN cells with same structures, is trained by GA results. RTTRM is upon the foundation of three models for metro networks: a control model, a timetable model and an energy model. Several numerical examples tested on Shanghai Metro Line 1 (SML1) validate the energy saving effects and real-time features of the proposed method.

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