Train Scheduling Method to Reduce Substation Energy Consumption and Peak Power of Metro Transit Systems

2021 ◽  
pp. 036119812097467
Author(s):  
Bo Jin ◽  
Xiaoyun Feng ◽  
Qingyuan Wang ◽  
Pengfei Sun ◽  
Qian Fang
Keyword(s):  
Energy Consumption ◽  
Optimization Model ◽  
Peak Power ◽  
Programming Model ◽  
Rapid Development ◽  
Mixed Integer ◽  
Power Requirement ◽  
Transit Systems ◽  
Train Scheduling ◽  
Timetable Optimization

The rapid development of metro transit systems brings very significant energy consumption, and the high service frequency of metro trains increases the peak power requirement, which affects the operation of systems. Train scheduling optimization is an effective method to reduce energy consumption and substation peak power by adjusting timetable parameters. This paper proposes a train timetable optimization model to coordinate the operation of trains. The overlap time between accelerating and braking phases is maximized to improve the utilization of regenerative braking energy (RBE). Meanwhile, the overlap time between accelerating phases is minimized to reduce the substation peak power. In addition, the timetable optimization model is rebuilt into a mixed integer linear programming model by introducing logical and auxiliary variables, which can be solved by related solvers effectively. Case studies based on one of Guangzhou Metro Lines indicate that, for all-day operation, the utilization of RBE would likely be improved on the order of 23%, the substation energy consumption would likely be reduced on the order of 14%, and the duration of substation peak power would likely be reduced on the order of 66%.

scholarly journals A Green Transportation Planning Approach for Coal Heavy-Haul Railway System by Simultaneously Optimizing Energy Consumption and Capacity Utilization

2021 ◽  
Vol 13 (8) ◽  
pp. 4173
Author(s):  
Jianjun Fu ◽  
Junhua Chen
Keyword(s):  
Energy Consumption ◽  
Optimization Model ◽  
Programming Model ◽  
Short Form ◽  
Capacity Utilization ◽  
Sensitivity Analyses ◽  
Mixed Integer ◽  
Trajectory Data ◽  
Heavy Haul ◽  
Heavy Haul Railway

Coal heavy-haul railway has been aiming at maximizing capacity utilization, but ignoring energy consumption for a long time. With the focus on green production, heavy-haul railways need transportation organization plans that can balance energy consumption and capacity utilization. Based on this, this paper proposes a data mining + optimization framework that uses train trajectory data to estimate train energy consumption and then uses a mixed integer programming model to simultaneously optimize plans from energy and capacity aspects. We use Gaussian distribution to describe features of energy consumption under different situations, and build a multi-dimensional cube to store these features to connect with the optimization model. In addition, a branch-and-bound algorithm is design to solve the optimization model. From the sensitivity analyses we can conclude that (1) shortening the departure interval from 13 min to 9 min will generate more energy consumption, about 3.6%; (2) combining short-form trains (50 units) with long-form trains (100 units) while increasing the carrying capacity will generate more energy consumption, about 5~14%; and (3) by controlling weights of the optimization model, capacity–energy-balanced plans can be obtained. The results can contribute to improving the sustainability of railways.

scholarly journals Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption

2019 ◽  
Vol 11 (11) ◽  
pp. 3127 ◽  
Author(s):  
Tarik Chargui ◽  
Abdelghani Bekrar ◽  
Mohamed Reghioui ◽  
Damien Trentesaux
Keyword(s):  
Energy Consumption ◽  
Programming Model ◽  
Mixed Integer ◽  
Computational Time ◽  
Neighborhood Search ◽  
Optimal Solutions ◽  
Multi Objective ◽  
Sustainable Logistics ◽  
Cross Docking ◽  
Physical Internet

In the context of supply chain sustainability, Physical Internet (PI or π ) was presented as an innovative concept to create a global sustainable logistics system. One of the main components of the Physical Internet paradigm consists in encapsulating products in modular and standardized PI-containers able to move via PI-nodes (such as PI-hubs) using collaborative routing protocols. This study focuses on optimizing operations occurring in a Rail–Road PI-Hub cross-docking terminal. The problem consists of scheduling outbound trucks at the docks and the routing of PI-containers in the PI-sorter zone of the Rail–Road PI-Hub cross-docking terminal. The first objective is to minimize the energy consumption of the PI-conveyors used to transfer PI-containers from the train to the outbound trucks. The second objective is to minimize the cost of using outbound trucks for different destinations. The problem is formulated as a Multi-Objective Mixed-Integer Programming model (MO-MIP) and solved with CPLEX solver using Lexicographic Goal Programming. Then, two multi-objective hybrid meta-heuristics are proposed to enhance the computational time as CPLEX was time consuming, especially for large size instances: Multi-Objective Variable Neighborhood Search hybridized with Simulated Annealing (MO-VNSSA) and with a Tabu Search (MO-VNSTS). The two meta-heuristics are tested on 32 instances (27 small instances and 5 large instances). CPLEX found the optimal solutions for only 23 instances. Results show that the proposed MO-VNSSA and MO-VNSTS are able to find optimal and near optimal solutions within a reasonable computational time. The two meta-heuristics found optimal solutions for the first objective in all the instances. For the second objective, MO-VNSSA and MO-VNSTS found optimal solutions for 7 instances. In order to evaluate the results for the second objective, a one way analysis of variance ANOVA was performed.

Route selection for best distances in road databases based on drivers’ and customers’ preferences

2012 ◽  
Vol 42 (6) ◽  
pp. 1126-1140 ◽  
Author(s):  
P. Flisberg ◽  
B. Lidén ◽  
M. Rönnqvist ◽  
J. Selander
Keyword(s):  
Integer Programming ◽  
Shortest Path ◽  
Optimization Model ◽  
Programming Model ◽  
Mixed Integer ◽  
Route Selection ◽  
Speed Limits ◽  
Online System ◽  
Selection For ◽  
National Road

The importance of road databases for distance calculations and route selection is increasing. One reason is that payments and invoicing are often based on the distance driven. However, it can be hard to agree on a “best” distance because of drivers’ preferences. These preferences can be described by road features such as road length, quality, width, speed limits, etc. Moreover, a pure standard “shortest path”, which is often used in road databases, can result in a route that is considerably shorter than a preferred and agreed distance. Consequently, there is a need to find suitable weights for the features of the roads that provide fair and agreed distances at the same time for all users. We propose an approach to find values of such weights for the features. The optimization model to find weights is an inverse shortest path problem formulated in a mixed integer programming model. The approach is tested for the Swedish Forestry National Road database. Since 2010, it has been in daily use to establish distances and is available for all forestry companies and haulers in Sweden through an online system.

scholarly journals Five-Echelon Multiobjective Health Services Supply Chain Modeling under Disruption

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Farnaz Javadi Gargari ◽  
Mahjoube Sayad ◽  
Seyed Ali Posht Mashhadi ◽  
Abdolhossein Sadrnia ◽  
Arman Nedjati ◽  
...  
Keyword(s):  
Supply Chain ◽  
Optimization Model ◽  
Optimization Problem ◽  
Programming Model ◽  
Transportation Cost ◽  
Real Data ◽  
Mixed Integer ◽  
Fixed Cost ◽  
Hub Location ◽  
Goal Programming Model

Medicine unreliability problem is taken into consideration as one of the most important issues in health supply chain management. This research is associated with the development of a multiobjective optimization problem for the selection of suppliers and distributors. To achieve the purposes, the optimal quota allocation is determined with respect to disruption of suppliers in a five-echelon supply chain network and consideration of the distributor centers as a hub location-allocation mode. The objective of the optimization model is involved in simultaneous minimization of transactions costs dealing with suppliers, expected purchasing costs from suppliers, expected percentages of delayed and returned products in each distributor, as well as transportation cost in each echelon and fixed cost for distributor centers, and finally maximization of the expected scores for suppliers and high priority of product customers. The optimization problem is formulated as a mixed-integer nonlinear programming model. The proposed optimization model is utilized to investigate a numerical case study for asthma-specific medicines. The analyzing procedure is conducted based on the collected real data from Cobel Darou pharmaceutical company in 2019. Furthermore, a fuzzy multichoice goal programming model is considered to solve the proposed optimization model by R optimization solver. The numerical results confirmed the authenticity of the model.

Regional Power Planning Optimization Model and Scenario Analysis Considering the Environmental Cost

2013 ◽  
Vol 805-806 ◽  
pp. 1122-1128
Author(s):  
Zong Wu Wang ◽  
Guo He Huang ◽  
Xiao Kun Li
Keyword(s):  
Linear Programming ◽  
Scenario Analysis ◽  
Pollution Control ◽  
Optimization Model ◽  
Programming Model ◽  
Environmental Cost ◽  
Mixed Integer ◽  
Regional Power ◽  
Planning Optimization ◽  
Decision Alternatives

In this study, a regional power planning optimization model (RPPOM) is developed considering the environmental cost and the restriction of resource and environment, based on interval linear programming and mixed integer linear programming. Model is applied to a case study on the power planning in Henan province, and scenario analysis is conducted. Interval solutions associated with scenario of pollution control have been obtained. They can be used for generating decision alternatives and helping decision makers identify desired power policies for power planning to meet the growth in electricity demand considering the constraints of resources and environment with a minimized system cost. Scenario analysis of environmental pollution control at different levels can also be tackled.

scholarly journals An Optimization Model for Energy Community Costs Minimization Considering a Local Electricity Market between Prosumers and Electric Vehicles

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 129
Author(s):  
Ricardo Faia ◽  
João Soares ◽  
Zita Vale ◽  
Juan Manuel Corchado
Keyword(s):  
Electric Vehicles ◽  
Electricity Market ◽  
Optimization Model ◽  
Local Community ◽  
Programming Model ◽  
Mixed Integer ◽  
Energy Costs ◽  
Local Energy ◽  
Distribution Grid ◽  
Energy Community

Electric vehicles have emerged as one of the most promising technologies, and their mass introduction may pose threats to the electricity grid. Several solutions have been proposed in an attempt to overcome this challenge in order to ease the integration of electric vehicles. A promising concept that can contribute to the proliferation of electric vehicles is the local electricity market. In this way, consumers and prosumers may transact electricity between peers at the local community level, reducing congestion, energy costs and the necessity of intermediary players such as retailers. Thus, this paper proposes an optimization model that simulates an electric energy market between prosumers and electric vehicles. An energy community with different types of prosumers is considered (household, commercial and industrial), and each of them is equipped with a photovoltaic panel and a battery system. This market is considered local because it takes place within a distribution grid and a local energy community. A mixed-integer linear programming model is proposed to solve the local energy transaction problem. The results suggest that our approach can provide a reduction between 1.6% to 3.5% in community energy costs.

scholarly journals Designing a clothing supply chain network considering pricing and demand sensitivity to discounts and advertisement

2020 ◽  
Author(s):  
Mohammad Mahdi Paydar ◽  
Marjan Olfati ◽  
chefi Triki
Keyword(s):  
Supply Chain ◽  
Programming Model ◽  
Rapid Development ◽  
Supply And Demand ◽  
Clothing Industry ◽  
Sensitivity Analyses ◽  
Mixed Integer ◽  
Supply Chain Network ◽  
Total Profit ◽  
Demand Sensitivity

These days, clothing companies are becoming more and more developed around the world. Due to the rapid development of these companies, designing an efficient clothing supply chain network can be highly beneficial, especially with the remarkable increase in demand and uncertainties in both supply and demand. In this study, a bi-objective stochastic mixed-integer linear programming model is proposed for designing the supply chain of the clothing industry. The first objective function maximizes total profit and the second one minimizes downside risk. In the presented network, the initial demand and price are uncertain and are incorporated into the model through a set of scenarios. To solve the bi-objective model, weighted normalized goal programming is applied. Besides, a real case study for the clothing industry in Iran is proposed to validate the presented model and developed method. The obtained results showed the validity and efficiency of the current study. Also, sensitivity analyses are conducted to evaluate the effect of several important parameters, such as discount and advertisement, on the supply chain .  The results indicate that considering the optimal amount for discount parameter can conceivably enhance total profit by about 20% compared to the time without this discount scheme. When we take the optimized parameter into account for advertisement, 12% is obtained for the total profit. Based on our findings, the more the expected profit value, the higher the total amount of total profit and risk.  The results of this research also provide some interesting managerial insights for managers.

scholarly journals Day-Ahead and Intra-Day Collaborative Optimized Operation among Multiple Energy Stations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 936
Author(s):  
Jingjing Zhai ◽  
Xiaobei Wu ◽  
Zihao Li ◽  
Shaojie Zhu ◽  
Bo Yang ◽  
...  
Keyword(s):  
Demand Response ◽  
Optimization Model ◽  
Programming Model ◽  
Energy System ◽  
Linearization Method ◽  
Mixed Integer ◽  
Operation Model ◽  
Piecewise Linearization ◽  
Constraints Network ◽  
Operation Scheduling

An integrated energy system (IES) shows great potential in reducing the terminal energy supply cost and improving energy efficiency, but the operation scheduling of an IES, especially integrated with inter-connected multiple energy stations, is rather complex since it is affected by various factors. Toward a comprehensive operation scheduling of multiple energy stations, in this paper, a day-ahead and intra-day collaborative operation model is proposed. The targeted IES consists of electricity, gas, and thermal systems. First, the energy flow and equipment composition of the IES are analyzed, and a detailed operation model of combined equipment and networks is established. Then, with the objective of minimizing the total expected operation cost, a robust optimization of day-ahead and intra-day scheduling for energy stations is constructed subject to equipment operation constraints, network constraints, and so on. The day-ahead operation provides start-up and shut-down scheduling of units, and in the operating day, the intra-day rolling operation optimizes the power output of equipment and demand response with newly evolved forecasting information. The photovoltaic (PV) uncertainty and electric load demand response are also incorporated into the optimization model. Eventually, with the piecewise linearization method, the formulated optimization model is converted to a mixed-integer linear programming model, which can be solved using off-the-shelf solvers. A case study on an IES with five energy stations verifies the effectiveness of the proposed day-ahead and intra-day collaborative robust operation strategy.

scholarly journals Analysis of Drone Fleet Type and Quantity for Medical Package Distribution in Emergency System

2020 ◽  
Vol 4 (1) ◽  
pp. 12
Author(s):  
Yujie Jiang ◽  
Shizhong Ma ◽  
Shilong Zhu
Keyword(s):  
Puerto Rico ◽  
Integer Programming ◽  
Mixed Integer Programming ◽  
Optimization Model ◽  
Programming Model ◽  
Mixed Integer ◽  
Iso Standard ◽  
Multi Objective ◽  
Emergency Medical ◽  
Emergency System

In order to cope with the disasters caused by the worst hurricane in Puerto Rico in 2017, it is necessary to build an emergency system to reduce the losses. An emergency system should include the location of ISO standard dry cargo containers and the distribution of emergency medical packages. This paper discusses the distribution of emergency medical package. Based on the above location results of ISO standard dry cargo container, taking the demand of disaster areas not exceed its supply into consideration and considering the timeliness and weak economy, a multi-objective mixed integer programming model is constructed on the premise of minimum transportation time and cost. It is determined that the drone fleet consists of four B, one C and one F drones. Through the optimization model, the distribution plan of emergency medical packages is formulated.

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