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 Supply Chain Optimization by Mixed Integer Program for Manufacturer and Retailer System of Poultry firm in Bangladesh

2016 ◽  
Vol 34 ◽  
pp. 75-87
Author(s):  
Mohammad Khairul Islam ◽  
Mohammed Forhad Uddin ◽  
Md M Alam
Keyword(s):  
Large Scale ◽  
Raw Materials ◽  
Integer Program ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Selling Price ◽  
Supply Chain Optimization ◽  
Self Employment ◽  
Gdp Growth Rate ◽  
The Cost

In this study, we formulate mixed integer program for manufacturer and retailer system of poultry firm in Bangladesh that is one of the most promising sectors to increase Gross Domestic Product (GDP) growth rate plus equitable distribution through arranging food security as well as ensuring self-employment, creating purchasing power and reducing poverty at a large scale. From the survey, it has observed that the selling price of eggs and chicken fluctuate depending on the natural calamities. We have made a question survey on some poultry firm in the district of Mymensingh and Gazipur. This paper maximized the profit and minimizes the cost. The formulated mixed integer program has solved by branch and bound algorithm using A Mathematical Programming Language (AMPL). It has observed that the profit and selling price have very good relationship with production cost and raw materials cost but no significant relation with fixed cost.GANIT J. Bangladesh Math. Soc.Vol. 34 (2014) 75-87

scholarly journals Quantile Inverse Optimization: Improving Stability in Inverse Linear Programming

2021 ◽  
Author(s):  
Zahed Shahmoradi ◽  
Taewoo Lee
Keyword(s):  
Linear Programming ◽  
Large Scale ◽  
Robust Regression ◽  
Model Fitting ◽  
Exact Algorithm ◽  
Integer Program ◽  
Inverse Optimization ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
The Impact

Although inverse linear programming (LP) has received increasing attention as a technique to identify an LP that can reproduce observed decisions that are originally from a complex system, the performance of the linear objective function inferred by existing inverse LP methods is often highly sensitive to noise, errors, and uncertainty in the underlying decision data. Inspired by robust regression techniques that mitigate the impact of noisy data on the model fitting, in “Quantile Inverse Optimization: Improving Stability in Inverse Linear Programming,” Shahmoradi and Lee propose a notion of stability in inverse LP and develop an inverse optimization model that identities objective functions that are stable against data imperfection. Although such a stability consideration renders the inverse model a large-scale mixed-integer program, the authors analyze the connection between the model and well-known biclique problems and propose an efficient exact algorithm as well as heuristics.

3D deployment of UAVs in wireless networks for traffic offloading and edge computing

2019 ◽  
Author(s):  
◽  
Rania Islambouli
Keyword(s):  
Wireless Networks ◽  
Large Scale ◽  
Scale Up ◽  
Integer Program ◽  
Use Cases ◽  
Base Stations ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
5G Networks ◽  
Iot Devices

Unmanned aerial vehicles (UAVs) have recently emerged as enablers for mul- titude use cases in 5G networks leading to interesting industrial and business applications. 5G networks envision a multi-service network promoting various applications with a distinct set of performance and service demands. In this the- sis, we leverage the high exibility, low-cost, and mobility of UAVs to scale up and improve the e ciency of IoT and mobile networks. We study the utilization of UAVs to increase the capacity and coverage in wireless networks on one side and to extend low computational capabilities and mitigate battery limitations in constrained devices on another side. However, to unlock these promising use cases of UAVs, we address the challenges coupled with UAV utilization mainly 3D deployment and device association. First, we address the problem of deploying multiple UAVs to act as aerial base stations (ABS) in 3D space while autonomously adapting their positions as users move around within the network. We formulate the problem as a mixed integer program and then propose a novel autonomous positioning approach that can e ciently gear the UAV positions in a way to maintain target quality re- quirements. Next, we leverage the mobility and agility of UAVs and use them as mo- bile edge servers or cloudlets to o er computation o oading opportunities to IoT devices. This being said, computation tasks generated by IoT devices can be pro- cessed in less latency and with much lower energy consumption at the devices. To optimally deploy UAVs as mounted cloudlets, we formulate our problem as mixed integer program and then use an e cient meta-heuristic algorithm to generate optimized results for large scale IoT networks. The simulation results presented in this thesis demonstrate the e ectiveness of the proposed solutions and algo- rithms compared to the optimal solutions and related work in the literature for various network scenario

scholarly journals Inserting Extra Train Services on High-Speed Railway

2020 ◽  
Vol 49 (1) ◽  
pp. 16-24 ◽  
Author(s):  
Yuyan Tan ◽  
Wen Xu ◽  
Zhibin Jiang ◽  
Ziyulong Wang ◽  
Bo Sun
Keyword(s):  
General Model ◽  
High Speed ◽  
Time Windows ◽  
Integer Program ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
High Speed Railway ◽  
Train Services ◽  
Acceleration And Deceleration ◽  
Using Data

With the aim of supporting future traffic needs, an account of how to reconstruct an existing cyclic timetable by inserting additional train services will be given in this paper. The Timetable-based Extra Train Services Inserting (TETSI) problem is regarded as an integration of railway scheduling and rescheduling problem. The TETSI problem therefore is considered involving many constraints, such as flexible running times, dwell times, headway and time windows. Characterized based on an event-activity graph, a general Mixed Integer Program model for this problem is formulated. In addition, several extensions to the general model are further proposed. The real-world constraints that concerning the acceleration and deceleration times, priority for overtaking, allowed adjustments, periodic structure and frequency of services are incorporated into the general model. From numerical investigations using data from Shanghai-Hangzhou High-Speed Railway in China, the proposed framework and associated techniques are tested and shown to be effective.

Introduction: 2020 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research

2021 ◽  
Vol 51 (5) ◽  
pp. 329-331
Author(s):  
Mary E. Helander ◽  
Lawrence D. Stone
Keyword(s):  
Operations Research ◽  
Crop Yields ◽  
Integer Program ◽  
Research Management ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Seed Corn ◽  
Electric Bus ◽  
Advanced Analytics ◽  
Product Sets

The judges for the 2020 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research selected the five finalist papers featured in this special issue of the INFORMS Journal on Applied Analytics (IJAA). The prestigious Wagner Prize—awarded for achievement in implemented operations research, management science, and advanced analytics—emphasizes the quality and originality of mathematical models along with clarity of written and oral exposition. This year’s winning application is a system for optimally managing Dow Agrosciences’ (now Corteva) seed corn portfolio, which includes seeds for several hundred varieties of corn and is valued at more than $1 billion. The model employs Bayesian analytic methods to estimate crop yields from expert judgement. Stochastic optimization is then used to determine backup production in South America while dealing with yield uncertainty in North America. The remaining four papers include an efficient mixed-integer program used by Birchbox to determine individualized subscriber product sets; a scheduling system for Argentina’s premier soccer league; an incentive system for encouraging Lyft drivers to reposition to provide improved service; and a system for optimizing the electric bus network design in Rotterdam.

A stochastic mixed integer program to model spatial wildfire behavior and suppression placement decisions with uncertain weather

2016 ◽  
Vol 46 (2) ◽  
pp. 234-248 ◽  
Author(s):  
Erin J. Belval ◽  
Yu Wei ◽  
Michael Bevers
Keyword(s):  
Wildland Fire ◽  
Fire Behavior ◽  
Integer Program ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Weather Forecasts ◽  
Placement Decisions ◽  
Weather Events ◽  
Weather Changes ◽  
Nonanticipativity Constraints

Wildfire behavior is a complex and stochastic phenomenon that can present unique tactical management challenges. This paper investigates a multistage stochastic mixed integer program with full recourse to model spatially explicit fire behavior and to select suppression locations for a wildland fire. Simplified suppression decisions take the form of “suppression nodes”, which are placed on a raster landscape for multiple decision stages. Weather scenarios are used to represent a distribution of probable changes in fire behavior in response to random weather changes, modeled using probabilistic weather trees. Multistage suppression decisions and fire behavior respond to these weather events and to each other. Nonanticipativity constraints ensure that suppression decisions account for uncertainty in weather forecasts. Test cases for this model provide examples of fire behavior interacting with suppression to achieve a minimum expected area impacted by fire and suppression.

A Note on a Conceptual Framework for Dynamic Location—Allocation Analysis

1976 ◽  
Vol 8 (4) ◽  
pp. 443-446
Author(s):  
W G Truscott
Keyword(s):  
Conceptual Framework ◽  
Integer Program ◽  
Original Version ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Location Allocation ◽  
Dynamic Location

This note examines a previously published model for dynamic location—allocation analysis. The usefulness of this model is enhanced by reformulating the problem as an operational zero-one, mixed-integer program while retaining the intent of the original version.

Multi-Objective Territory Design for Sales Managers of a Direct Sales Company

2019 ◽  
pp. 160-178 ◽  
Author(s):  
Elias Olivares-Benitez ◽  
Pilar Novo Ibarra ◽  
Samuel Nucamendi-Guillén ◽  
Omar G. Rojas
Keyword(s):  
Integer Program ◽  
National Average ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Sales Managers ◽  
Territory Design ◽  
Direct Sales ◽  
Sales Company ◽  
A Company

This chapter presents a case study to organize the sales territories for a company with 11 sales managers to be assigned to 111 sales coverage units in Mexico. The assignment problem is modeled as a mathematical program with two objective functions. One objective minimizes the maximum distance traveled by the manager, and the other objective minimizes the variation of the sales growth goals with respect to the national average. To solve the bi-objective non-linear mixed-integer program, a weights method is selected. Some instances are solved using commercial software with long computational times. Also, a heuristic and a metaheuristic based on simulated annealing were developed. The design of the heuristic generates good solutions for the distance objective. The metaheuristic produces better results than the heuristic, with a better balance between the objectives. The heuristic and the metaheuristic are capable of providing good results with short computational times.

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