scholarly journals An Agent-Based Model for Dispatching Real-Time Demand-Responsive Feeder Bus

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xin Li ◽  
Ming Wei ◽  
Jia Hu ◽  
Yun Yuan ◽  
Huifu Jiang
Keyword(s):  
Real Time ◽  
Optimal Solution ◽  
Computation Time ◽  
Walking Distance ◽  
Mixed Integer ◽  
Access Time ◽  
Metro Station ◽  
Mip Model ◽  
Feeder Bus ◽  
Operation Cost

This research proposed a feeder bus dispatching tool that reduces rides’ effort to reach a feeder bus. The dispatching tool takes in real-time user specific request information and optimizes total cost accordingly (passenger access time cost and transit operation cost) by choosing the best pick-up locations and feeder buses’ routes. The pick-up locations are then transmitted back to passengers along with GPS guidance. The tool fits well with the Advanced Traveler Information Services (ATIS) which is one of the six high-priority dynamic mobility application bundles currently being promoted by the United State Department of Transportation. The problem is formulated into a Mixed Integer Programming (MIP) model. For small networks, out-of-the-shelf commercial solvers could be used for finding the optimal solution. For large networks, this research developed a GA-based metaheuristic solver which generates reasonably good solutions in a much shorter time. The proposed tool is evaluated on a real-world network in the vicinity of Jiandingpo metro station in Chongqing, China. The results demonstrated that the proposed ATIS tool reduces both buses operation cost and passenger walking distance. It is also able to significantly bring down computation time from more than 1 hour to about 1 min without sacrificing too much on solution optimality.

scholarly journals Research on Planning and Model Design of Micro-cycle Bus Line Based on Metro Station Connection

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Qiushui Fang ◽  
Zhingming Li ◽  
Zhen Wang ◽  
Jincheng Wu ◽  
Hongling Yu ◽  
...  
Keyword(s):  
Public Transport ◽  
Public Transportation ◽  
Travel Demand ◽  
Urban Expansion ◽  
Optimal Solution ◽  
Walking Distance ◽  
Model Design ◽  
Metro Station ◽  
City Bus ◽  
The Cost

Public transport coverage fails to keep pace with urbanization and urban expansion, which makes the “last kilometer" problem of residents’ travel increasingly prominent”. However, the practice has proved that microcirculation public transportation plays an important role in expanding the coverage of public transportation and promoting the integration of public transportation. Therefore, this paper takes a city bus community as an example. Firstly, it analyses the bus travel demand of commuters connecting to the subway station during the early workday rush hours on basis of IC Big Data, obtains candidate stations of microcirculation bus lines through K-means clustering. Secondly, it establishes the model, the target of which is to minimize  the cost residents' travel and bus operation, under the limited condition of walking distance, passenger number, station spacing and departure frequency. Finally, the genetic algorithm is used to find the optimal solution of the model, so it’s no doubt that the most feasible circular bus route is obtained. The results have positive significance for promoting the construction and operation of public transport integration and promoting the convenience and efficiency of public transport travel. 

scholarly journals Optimal Coordinated Bidding of a Profit Maximizing, Risk-Averse EV Aggregator in Three-Settlement Markets Under Uncertainty

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1755 ◽  
Author(s):  
Yelena Vardanyan ◽  
Henrik Madsen
Keyword(s):  
Real Time ◽  
Value At Risk ◽  
Risk Measure ◽  
Convex Combination ◽  
Computation Time ◽  
Mixed Integer ◽  
Conditional Value At Risk ◽  
Two Stage ◽  
Mixed Integer Linear Program ◽  
Market Prices

This paper develops a two-stage stochastic and dynamically updated multi-period mixed integer linear program (SD-MILP) for optimal coordinated bidding of an electric vehicle (EV) aggregator to maximize its profit from participating in competitive day-ahead, intra-day and real-time markets. The hourly conditional value at risk (T-CVaR) is applied to model the risk of trading in different markets. The objective of two-stage SD-MILP is modeled as a convex combination of the expected profit and the T-CVaR hourly risk measure. When day-ahead, intra-day and real-time market prices and fleet mobility are uncertain, the proposed two-stage SD-MILP model yields optimal EV charging/discharging plans for day-ahead, intra-day and real-time markets at per device level. The degradation costs of EV batteries are precisely modeled. To reflect the continuous clearing nature of the intra-day and real-time markets, rolling planning is applied, which allows re-forecasting and re-dispatching. The proposed two-stage SD-MILP is used to derive a bidding curve of an aggregator managing 1000 EVs. Furthermore, the model statistics and computation time are recorded while simulating the developed algorithm with 5000 EVs.

scholarly journals A Linear Relaxation-Based Heuristic for Iron Ore Stockyard Energy Planning

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5232
Author(s):  
Marcos Wagner Jesus Servare Junior ◽  
Helder Roberto de Oliveira Rocha ◽  
José Leandro Félix Salles ◽  
Sylvain Perron
Keyword(s):  
Energy Cost ◽  
Average Distance ◽  
Optimal Solution ◽  
Computation Time ◽  
Electrical Energy ◽  
Linear Relaxation ◽  
Mixed Integer ◽  
Management Decision ◽  
Management Decision Making ◽  
Feasible Solutions

Planning the use of electrical energy in a bulk stockyard is a strategic issue due to its impact on efficiency and responsiveness of these systems. Empirical planning becomes more complex when the energy cost changes over time. The mathematical models currently studied in the literature consider many actors involved, such as equipment, sources, blends, and flows. Each paper presents different combinations of actors, creating their own transportation flows, thus increasing the complexity of this problem. In this work, we propose a new mixed integer linear programming (MILP) model for stockyard planning solved by a linear relaxation-based heuristic (LRBH) to minimize the plan’s energy cost. The proposed algorithm will allow the planner to find a solution that saves energy costs with an efficient process. The numerical results show a comparison between the exact and heuristic solutions for some different instances sizes. The linear relaxation approach can provide feasible solutions with a 3.99% average distance of the objective function in relation to the optimal solution (GAP) in the tested instances and with an affordable computation time in instances where the MILP was not able to provide a solution. The model is feasible for small and medium-sized instances, and the heuristic proposes a solution to larger problems to aid in management decision making.

scholarly journals Optimization of Collection and Consolidation Operations in Cross-Border Multi-modal Distribution Networks

2021 ◽  
Author(s):  
Gulcin Ermis ◽  
Francesco Alesiani ◽  
Konstantinos Gkiotsalitis
Keyword(s):  
Real Time ◽  
Bin Packing ◽  
Solution Method ◽  
Distribution Networks ◽  
Mixed Integer ◽  
Due Dates ◽  
Matching Problem ◽  
Bipartite Matching ◽  
Mip Model ◽  
Dynamic Objects

This study introduces a model to solve a dynamic network optimization model on a heterogeneous graph. We use this model to optimize the collection and consolidation operations on a cross-country multi-modal distribution network. The model's dynamic objects are trucks, trailers, orders, unvisited collection and customs check points. Information about dynamic objects is extracted from a real-time database. The model's static objects include objects that are known in advance, such as warehouses. The constraints of the problem include due dates, vehicle capacity, availability of vehicles, and precedence constraints of visiting locations. We propose a mixed-integer programming model and provide a solution using matheuristics. We decompose the master MIP model into subproblems that can be solved to optimality with LP solvers. We also reduce the graph complexity by variable fixing due to optimized subproblems or by bounding the maximum number of paths to be selected due to the solutions of priority-based bin packing algorithms. Finally, we convert the resulting problem into a bipartite matching problem by expanding the graph nodes which can then be solved in polynomial time. We implement our solution method on real-time data retrieved from the tracking system of a third-party logistics company. Experiments show that our solution method significantly outperforms other heuristics in terms of solution quality which is measured with respect to lateness, empty kilometers traveled, travel times, number of required/used vehicles, load factors, and ratio of served orders.

scholarly journals A High-speed Train Timetable Rescheduling Approach in case of Disturbances

2020 ◽  
Vol 308 ◽  
pp. 02001
Author(s):  
Xinyu Gao
Keyword(s):  
High Speed ◽  
Programming Model ◽  
Optimal Solution ◽  
Computation Time ◽  
Mixed Integer ◽  
Railway Line ◽  
Train Timetable ◽  
Train Operation ◽  
Time Required ◽  
Short Time

This paper from a macroscopic viewpoint develops a train timetable rescheduling approach on a single high-speed railway line under disturbances, i.e. inevitable train delays in the duration of the train operation. A mixed-integer linear programming model is formulated to minimize the arrival delay and the departure delay altogether. The commercial optimization software CPLEX is adopted in an effort to seek the optimal solution in an acceptably short time required in the real-time rescheduling process. The proposed approach is further tested on a real-world case study and the numerical results show that compared with the results obtain by the traditional genetic algorithm, using CPLEX to solve the model can yield better solutions and consume the desired computation time, thereby demonstrating its effectiveness and efficiency.

scholarly journals An Enhanced Approach for the Multiple Vehicle Routing Problem with Heterogeneous Vehicles and a Soft Time Window

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 650 ◽  
Author(s):  
He-Yau Kang ◽  
Amy Lee
Keyword(s):  
Computer Science ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Computational Time ◽  
Routing Problem ◽  
Mip Model ◽  
Science Theory

The vehicle routing problem (VRP) is a challenging combinatorial optimization problem. This research focuses on the problem under which a manufacturer needs to outsource materials from other suppliers and to ship the materials back to the company. Heterogeneous vehicles are available to ship the materials, and each vehicle has a limited loading capacity and a limited travelling distance. The purpose of this research is to study a multiple vehicle routing problem (MVRP) with soft time window and heterogeneous vehicles. Two models, using mixed integer programming (MIP) and genetic algorithm (GA), are developed to solve the problem. The MIP model is first constructed to minimize the total transportation cost, which includes the assignment cost, travelling cost, and the tardiness cost, for the manufacturer. The optimal solution can present multiple vehicle routing and the loading size of each vehicle in each period. The GA is next applied to solve the problem so that a near-optimal solution can be obtained when the problem is too difficult to be solved using the MIP. A case of a food manufacturing company is used to examine the practicality of the proposed MIP model and the GA model. The results show that the MIP model can obtain the optimal solution under a short computational time when the scale of the problem is small. When the problem becomes non-deterministic polynomial hard (NP-hard), the MIP model cannot find the optimal solution. On the other hand, the GA model can obtain a near-optimal solution within a reasonable amount of computational time. This paper is related to several important topics of the Symmetry journal in the areas of mathematics and computer science theory and methods. In the area of mathematics, the theories of linear and non-linear algebraic structures and information technology are adopted. In the area of computer science, theory and methods, and metaheuristics are applied.

scholarly journals Biased Random-key Genetic Algorithm for theHybrid Vehicle-drone Routing Problem for Pick-upand Delivery

2021 ◽  
Author(s):  
Anderson Zudio ◽  
Igor Machado Coelho ◽  
Luiz Satoru Ochi
Keyword(s):  
Carrying Capacity ◽  
Vehicle Routing Problem ◽  
Computation Time ◽  
Mixed Integer ◽  
Solution Quality ◽  
Test Set ◽  
Routing Problem ◽  
Mip Model ◽  
Flight Range ◽  
Feasible Solutions

The Hybrid Vehicle drone Routing Problem (HVDRP) was recently introduced as an extension of the classic Vehicle Routing Problem (VRP). In this version, one vehicle is equipped with multiple drones to serve customers with demands for pick-up and delivery. The vehicle travels between stations that serve as parking locations to dispatch drones to attend clients. The drones have limitations in their maximum flight range and carrying capacity. We propose a BRKGA algorithm to solve HVDRP with a decoder component specially tailored to find feasible solutions. The proposed method is empirically analyzed in solution quality through a test set that a mixed-integer programming (MIP) model implementation can optimally solve in reasonable computation time. The computational result shows that the best solution found by BRKGA for each instance of the test set matches the solution quality devised by the MIP implementation. The data also show that the proposed algorithm achieves the best solution consistently through many independent executions. The instance set used and its respective best solutions attained for this work are publicly available.

scholarly journals Metaheuristics for a Flow Shop Scheduling Problem with Urgent Jobs and Limited Waiting Times

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 323
Author(s):  
BongJoo Jeong ◽  
Jun-Hee Han ◽  
Ju-Yong Lee
Keyword(s):  
Manufacturing Systems ◽  
Simulated Annealing Algorithm ◽  
Flow Shop ◽  
Waiting Times ◽  
Optimal Solution ◽  
Computation Time ◽  
Metaheuristic Algorithms ◽  
Flow Shop Scheduling ◽  
Mixed Integer ◽  
Scheduling Problem

This study considers a scheduling problem for a flow shop with urgent jobs and limited waiting times. The urgent jobs and limited waiting times are major considerations for scheduling in semiconductor manufacturing systems. The objective function is to minimize a weighted sum of total tardiness of urgent jobs and the makespan of normal jobs. This problem is formulated in mixed integer programming (MIP). By using a commercial optimization solver, the MIP can be used to find an optimal solution. However, because this problem is proved to be NP-hard, solving to optimality requires a significantly long computation time for a practical size problem. Therefore, this study adopts metaheuristic algorithms to obtain a good solution quickly. To complete this, two metaheuristic algorithms (an iterated greedy algorithm and a simulated annealing algorithm) are proposed, and a series of computational experiments were performed to examine the effectiveness and efficiency of the proposed algorithms.

scholarly journals Optimization of Collection and Consolidation Operations in Cross-Border Multi-modal Distribution Networks

2021 ◽  
Author(s):  
Gulcin Ermis ◽  
Francesco Alesiani ◽  
Konstantinos Gkiotsalitis
Keyword(s):  
Real Time ◽  
Bin Packing ◽  
Solution Method ◽  
Distribution Networks ◽  
Mixed Integer ◽  
Due Dates ◽  
Matching Problem ◽  
Bipartite Matching ◽  
Mip Model ◽  
Dynamic Objects

This study introduces a model to solve a dynamic network optimization model on a heterogeneous graph. We use this model to optimize the collection and consolidation operations on a cross-country multi-modal distribution network. The model's dynamic objects are trucks, trailers, orders, unvisited collection and customs check points. Information about dynamic objects is extracted from a real-time database. The model's static objects include objects that are known in advance, such as warehouses. The constraints of the problem include due dates, vehicle capacity, availability of vehicles, and precedence constraints of visiting locations. We propose a mixed-integer programming model and provide a solution using matheuristics. We decompose the master MIP model into subproblems that can be solved to optimality with LP solvers. We also reduce the graph complexity by variable fixing due to optimized subproblems or by bounding the maximum number of paths to be selected due to the solutions of priority-based bin packing algorithms. Finally, we convert the resulting problem into a bipartite matching problem by expanding the graph nodes which can then be solved in polynomial time. We implement our solution method on real-time data retrieved from the tracking system of a third-party logistics company. Experiments show that our solution method significantly outperforms other heuristics in terms of solution quality which is measured with respect to lateness, empty kilometers traveled, travel times, number of required/used vehicles, load factors, and ratio of served orders.

scholarly journals Feasible Islanding Operation of Electric Networks with Large Penetration of Renewable Energy Sources considering Security Constraints

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 537 ◽  
Author(s):  
Seyed Arash Alavi ◽  
Valentin Ilea ◽  
Alireza Saffarian ◽  
Cristian Bovo ◽  
Alberto Berizzi ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Optimal Solution ◽  
Computation Time ◽  
Mixed Integer ◽  
Energy Sources ◽  
Distribution Grid ◽  
Electric Networks ◽  
Emergency Situations ◽  
The One

The high penetration of Renewable Energy Sources into electric networks shows new perspectives for the network’s management: among others, exploiting them as resources for network’s security in emergency situations. The paper focuses on the frequency stability of a portion of the grid when it remains islanded following a major fault. It proposes an optimization algorithm that considers the frequency reaction of the relevant components and minimizes the total costs of their shedding. The algorithm predicts the final frequency of the island and the active power profiles of the remaining generators and demands. It is formulated as a Mixed-Integer Non-Linear Programming problem and the high computation time due to a large-size problem is mitigated through a simplified linear version of the model that filters the integer variables. The algorithm is designed to operate on-line and preventively compute the optimal shedding actions to be engaged when islanding occurs. The algorithm is validated for a typical distribution grid: the minimum amount of shedding actions is obtained while the most frequency reactive resources are maintained in operation to assure a feasible frequency. Finally, time-domain simulations show that the optimal solution corresponds to the one at the end of the network’s transients following the islanding.

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