scholarly journals An Optimization to Schedule Train Operations with Phase-Regular Framework for Intercity Rail Lines

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Huimin Niu ◽  
Minghui Zhang
Keyword(s):  
Programming Model ◽  
Hybrid Genetic Algorithm ◽  
Minimum Cost ◽  
Proposed Model ◽  
Rail Line ◽  
Service Oriented ◽  
Scheduling Method ◽  
Train Length ◽  
Crossover And Mutation ◽  
Dependent Demand

The most important operating problem for intercity rail lines, which are characterized with the train operations at rapid speed and high frequency, is to design a service-oriented schedule with the minimum cost. This paper proposes a phase-regular scheduling method which divides a day equally into several time blocks and applies a regular train-departing interval and the same train length for each period under the period-dependent demand conditions. A nonlinear mixed zero-one programming model, which could accurately calculate the passenger waiting time and the in-train crowded cost, is developed in this study. A hybrid genetic algorithm associated with the layered crossover and mutation operation is carefully designed to solve the proposed model. Finally, the effectiveness of the proposed model and algorithm is illustrated through the application to Hefei-Wuhan intercity rail line in China.

scholarly journals Stop Plan of Express and Local Train for Regional Rail Transit Line

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qin Luo ◽  
Yufei Hou ◽  
Wei Li ◽  
Xiongfei Zhang
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Programming Model ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Long Distance ◽  
Passenger Travel ◽  
Urban Rail ◽  
Proposed Model ◽  
Rail Line

The urban rail transit line operating in the express and local train mode can solve the problem of disequilibrium passenger flow and space and meet the rapid arrival demand of long-distance passengers. In this paper, the Logit model is used to analyze the behavior of passengers choosing trains by considering the sensitivity of travel time and travel distance. Then, based on the composition of passenger travel time, an integer programming model for train stop scheme, aimed at minimizing the total passenger travel time, is proposed. Finally, combined with a certain regional rail line in Shenzhen, the plan is solved by genetic algorithm and evaluated through the time benefit, carrying capacity, and energy consumption efficiency. The simulation result shows that although the capacity is reduced by 6 trains, the optimized travel time per person is 10.34 min, and the energy consumption is saved by about 16%, which proves that the proposed model is efficient and feasible.

scholarly journals Minimum Cost Multiobjective Programming Model for Target Efficiency in Sample Selection

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yousaf Shad Muhammad ◽  
Saima Khan ◽  
Ijaz Hussain ◽  
Alaa Mohamd Shoukry ◽  
Sadaf Shamsuddin ◽  
...  
Keyword(s):  
Multiobjective Programming ◽  
Programming Model ◽  
Sample Selection ◽  
Minimum Cost ◽  
Fixed Cost ◽  
Optimal Sample ◽  
Proposed Model ◽  
Compromise Allocation ◽  
Goal Programming Model ◽  
The Cost

In this study, we developed a model which elaborates relationship among efficiency of an estimator and survey cost. This model is based on a multiobjective optimization programming structure. Survey cost and efficiency of related estimator(s) lie in different directions, i.e., if one increases, the other decreases. The model presented in this study computes cost for a desired level of efficiency on various characteristics (goals). The calibrated model minimizes the cost for the compromise optimal sample selection from different strata when characteristic j is subject to achieve at least 1−αj level of efficiency of its estimator. In the first step, the proposed model minimizes the variance for a fixed cost, and it then finds the rise in cost for an αj percent rise in efficiency of any characteristic j. The resultant model is a multiobjective compromise allocation goal programming model.

Emergency Logistics System Based on Bilevel Optimization Model

2013 ◽  
Vol 694-697 ◽  
pp. 3605-3609
Author(s):  
Bo Liu ◽  
Bo Li ◽  
Yan Li
Keyword(s):  
Genetic Algorithm ◽  
Bilevel Programming ◽  
Programming Model ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Bilevel Optimization ◽  
Logistics System ◽  
Emergency Logistics ◽  
Proposed Model ◽  
Bilevel Programming Model

A bilevel programming model is established to determine the emergency storage centers location and the resource supply plan of the provincial and municipal levels by the collaborative mode of the vertical supply and lateral transfer for the emergency logistics system in the unusual emergencies. And the optimal solution is obtained by the hybrid genetic algorithm. Finally, the case shows the effectiveness of the proposed model and its algorithm.

scholarly journals Optimization of Multimodal Discrete Network Design Problems Based on Super Networks

2021 ◽  
Vol 11 (21) ◽  
pp. 10143
Author(s):  
Yaling Zhou ◽  
Chengxuan Cao ◽  
Ziyan Feng
Keyword(s):  
Network Design ◽  
Simulated Annealing Algorithm ◽  
Programming Model ◽  
Minimum Cost ◽  
Network Design Problem ◽  
Equilibrium Constraints ◽  
Modal Interactions ◽  
Proposed Model ◽  
Flow Equilibrium ◽  
Discrete Network Design Problem

In this paper, we investigate the multimodal discrete network design problem that simultaneously optimizes the car, bus, and rail transit network, in which inter-modal transfers are achieved by slow traffic modes including walking and bike-sharing. Specifically, a super network topology is presented to signify the modal interactions. Then, the generalized cost formulas of each type of links in the super network are defined. And based on the above formulas a bi-objective programming model is proposed to minimize the network operation cost and construction cost with traffic flow equilibrium constraints, investment constraints and expansion constraints. Moreover, a hybrid heuristic algorithm that combines the minimum cost flow algorithm and simulated annealing algorithm is presented to solve the proposed model. Finally, the effectiveness of the proposed model and algorithm is evaluated through two numerical tests: a simple test network and an actual multimodal transport network.

scholarly journals Robust sustainable multi-period hub location considering uncertain time-dependent demand

2021 ◽  
Author(s):  
amir khaleghi ◽  
Alireza Eydi
Keyword(s):  
Demand Function ◽  
Programming Model ◽  
Valid Inequalities ◽  
Interval Uncertainty ◽  
Time Dependent ◽  
Hub Location ◽  
Multi Objective ◽  
First Case ◽  
Proposed Model ◽  
Dependent Demand

This paper presents a mathematical programming model for designing a sustainable continuous-time multi-period hub network considering time-dependent demand. The present model can be used in situations where the distribution of parameters related to the demand function is unknown, and we only can determine the range of changes of these parameters. To model these conditions, we consider interval uncertainty for the demand function parameters. The proposed model is a nonlinear multi-objective model. The objectives of the model cover economic, environmental, and social aspects of sustainability. These objectives include minimizing total costs, minimizing emissions, and maximizing fixed and variable job opportunities. We linearize the model by using some linearization techniques, and then, with the help of Bertsimas and Sim’s method, we construct a robust counterpart of the model. We also present some valid inequalities to strengthen the formulation. To solve the proposed model, we use Torabi and Hassini method. From solving the proposed model, network design decisions and the best time to implement decisions during the planning horizon are determined. To validate the model, we solve a sample problem based on the Turkish dataset and compare the designed network in two cases: in the first case, the demand function parameters take nominal values, and in the second case, the value of these parameters can change up to 20% of their nominal values. The results show that in the second case, the total capacity selected for hubs and hub links is greater than the first case. To investigate changes in objective functions to parameters level of conservatism and probability of constraints violation, we perform sensitivity analysis on these parameters in both single-objective and multi-objective optimization cases and report the results.

scholarly journals Wind Farm Cable Connection Layout Optimization with Several Substations

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3615
Author(s):  
Adelaide Cerveira ◽  
Eduardo J. Solteiro Pires ◽  
José Baptista
Keyword(s):  
Energy Production ◽  
Wind Farm ◽  
Programming Model ◽  
Optimal Solution ◽  
Wind Farms ◽  
Green Energy ◽  
Fossil Energy ◽  
Proposed Model ◽  
Short Time ◽  
Cable Connection

Green energy has become a media issue due to climate changes, and consequently, the population has become more aware of pollution. Wind farms are an essential energy production alternative to fossil energy. The incentive to produce wind energy was a government policy some decades ago to decrease carbon emissions. In recent decades, wind farms were formed by a substation and a couple of turbines. Nowadays, wind farms are designed with hundreds of turbines requiring more than one substation. This paper formulates an integer linear programming model to design wind farms’ cable layout with several turbines. The proposed model obtains the optimal solution considering different cable types, infrastructure costs, and energy losses. An additional constraint was considered to limit the number of cables that cross a walkway, i.e., the number of connections between a set of wind turbines and the remaining wind farm. Furthermore, considering a discrete set of possible turbine locations, the model allows identifying those that should be present in the optimal solution, thereby addressing the optimal location of the substation(s) in the wind farm. The paper illustrates solutions and the associated costs of two wind farms, with up to 102 turbines and three substations in the optimal solution, selected among sixteen possible places. The optimal solutions are obtained in a short time.

scholarly journals Allocation and Scheduling of Handling Resources in the Railway Container Terminal Based on Crossing Crane Area

2021 ◽  
Vol 13 (3) ◽  
pp. 1190
Author(s):  
Gang Ren ◽  
Xiaohan Wang ◽  
Jiaxin Cai ◽  
Shujuan Guo
Keyword(s):  
Programming Model ◽  
Container Terminal ◽  
Simultaneous Optimization ◽  
Mixed Integer ◽  
Gantry Crane ◽  
Optimization Scheme ◽  
Long Distance ◽  
Proposed Model ◽  
Hybrid Heuristic Algorithm ◽  
Searching Ability

The integrated allocation and scheduling of handling resources are crucial problems in the railway container terminal (RCT). We investigate the integrated optimization problem for handling resources of the crane area, dual-gantry crane (GC), and internal trucks (ITs). A creative handling scheme is proposed to reduce the long-distance, full-loaded movement of GCs by making use of the advantages of ITs. Based on this scheme, we propose a flexible crossing crane area to balance the workload of dual-GC. Decomposing the integrated problem into four sub-problems, a multi-objective mixed-integer programming model (MIP) is developed. By analyzing the characteristic of the integrated problem, a three-layer hybrid heuristic algorithm (TLHHA) incorporating heuristic rule (HR), elite co-evolution genetic algorithm (ECEGA), greedy rule (GR), and simulated annealing (SA) is designed for solving the problem. Numerical experiments were conducted to verify the effectiveness of the proposed model and algorithm. The results show that the proposed algorithm has excellent searching ability, and the simultaneous optimization scheme could ensure the requirements for efficiency, effectiveness, and energy-saving, as well as the balance rate of dual-GC.

scholarly journals A Generalized Minimum Cost Flow Model for Multiple Emergency Flow Routing

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jianxun Cui ◽  
Shi An ◽  
Meng Zhao
Keyword(s):  
Flow Model ◽  
Time Window ◽  
Programming Model ◽  
Real Life ◽  
Minimum Cost ◽  
Emergency Evacuation ◽  
Mixed Integer ◽  
Fixed Cost ◽  
Minimum Cost Flow ◽  
Cost Flow

During real-life disasters, that is, earthquakes, floods, terrorist attacks, and other unexpected events, emergency evacuation and rescue are two primary operations that can save the lives and property of the affected population. It is unavoidable that evacuation flow and rescue flow will conflict with each other on the same spatial road network and within the same time window. Therefore, we propose a novel generalized minimum cost flow model to optimize the distribution pattern of these two types of flow on the same network by introducing the conflict cost. The travel time on each link is assumed to be subject to a bureau of public road (BPR) function rather than a fixed cost. Additionally, we integrate contraflow operations into this model to redesign the network shared by those two types of flow. A nonconvex mixed-integer nonlinear programming model with bilinear, fractional, and power components is constructed, and GAMS/BARON is used to solve this programming model. A case study is conducted in the downtown area of Harbin city in China to verify the efficiency of proposed model, and several helpful findings and managerial insights are also presented.

Cuckoo optimization algorithm in reverse logistics: A network design for COVID-19 waste management

2021 ◽  
pp. 0734242X2110039
Author(s):  
Elham Shadkam
Keyword(s):  
Waste Management ◽  
Network Design ◽  
Optimization Algorithm ◽  
Reverse Logistics ◽  
Heuristic Algorithms ◽  
Programming Model ◽  
Fixed Costs ◽  
Logistics System ◽  
Cuckoo Optimization Algorithm ◽  
Proposed Model

Today, reverse logistics (RL) is one of the main activities of supply chain management that covers all physical activities associated with return products (such as collection, recovery, recycling and destruction). In this regard, the designing and proper implementation of RL, in addition to increasing the level of customer satisfaction, reduces inventory and transportation costs. In this paper, in order to minimize the costs associated with fixed costs, material flow costs, and the costs of building potential centres, a complex integer linear programming model for an integrated direct logistics and RL network design is presented. Due to the outbreak of the ongoing global coronavirus pandemic (COVID-19) at the beginning of 2020 and the consequent increase in medical waste, the need for an inverse logistics system to manage waste is strongly felt. Also, due to the worldwide vaccination in the near future, this waste will increase even more and careful management must be done in this regard. For this purpose, the proposed RL model in the field of COVID-19 waste management and especially vaccine waste has been designed. The network consists of three parts – factory, consumers’ and recycling centres – each of which has different sub-parts. Finally, the proposed model is solved using the cuckoo optimization algorithm, which is one of the newest and most powerful meta-heuristic algorithms, and the computational results are presented along with its sensitivity analysis.

A Multi-Period Evacuation Vehicle Routing Problem Model

2014 ◽  
Vol 931-932 ◽  
pp. 578-582
Author(s):  
Sunarin Chanta ◽  
Ornurai Sangsawang
Keyword(s):  
Vehicle Routing ◽  
Public Transportation ◽  
Vehicle Routing Problem ◽  
Programming Model ◽  
High Water ◽  
Mixed Integer ◽  
Routing Problem ◽  
Problem Model ◽  
Proposed Model ◽  
Evacuation Routing

In this paper, we proposed an optimization model that addresses the evacuation routing problem for flood disaster when evacuees trying to move from affected areas to safe places using public transportation. A focus is on the situation of evacuating during high water level when special high vehicles are needed. The objective is to minimize the total traveled distance through evacuation periods where a limited number of vehicles is given. We formulated the problem as a mixed integer programming model based on the capacitated vehicle routing problem with multiple evcuation periods where demand changing by the time. The proposed model has been tested on a real-world case study affected by the severe flooding in Thailand, 2011.

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