scholarly journals Optimization of Carsharing Fleet Placement in Round-Trip Carsharing Service

2021 ◽  
Vol 11 (23) ◽  
pp. 11393
Author(s):  
Boonyarit Changaival ◽  
Kittichai Lavangnananda ◽  
Grégoire Danoy ◽  
Dzmitry Kliazovich ◽  
Frédéric Guinand ◽  
...  
Keyword(s):  
Optimization Model ◽  
Optimization Problem ◽  
Exact Method ◽  
Walking Distance ◽  
Np Hard ◽  
Round Trip ◽  
Placement Problem ◽  
Nsga Ii ◽  
User Coverage ◽  
The City

In a round-trip carsharing system, stations must be located in such a way that allow for maximum user coverage with the least walking distance as well as offer certain degrees of flexibility for returning. Therefore, a balance must be stricken between these factors. Providing a satisfactory system can be translated into an optimization problem and belongs to an NP-hard class. In this article, a novel optimization model for the round-trip carsharing fleet placement problem, called Fleet Placement Problem (FPP), is proposed. The optimization in this work is multiobjective and its NP-hard nature is proven. Three different optimization algorithms: PolySCIP (exact method), heuristics, and NSGA-II (metaheuristic) are investigated. This work adopts three real instances for the study, instead of their abstracts where they are most commonly used. They are two instance:, in the city of Luxembourg (smaller and larger) and a much larger instance in the city of Munich. Results from each algorithm are validated and compared with solution from human experts. Superiority of the proposed FPP model over the traditional methods is also demonstrated.

Research on the Equipment Maintenance Tasks Scheduling Optimization Problem Based on GA

2012 ◽  
Vol 452-453 ◽  
pp. 750-754
Author(s):  
Yi Ma ◽  
Yu Lu ◽  
Li Yun Chen ◽  
Ping Gu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Optimization Problem ◽  
New Method ◽  
Equipment Maintenance ◽  
Np Hard ◽  
Scheduling Optimization ◽  
Tasks Scheduling ◽  
Problem Design ◽  
The Military

Scientific maintenance tasks scheduling can improve maintenance effectiveness greatly. Aiming for the shortage of the research on the equipment maintenance tasks scheduling optimization (EMTSO) problem, this paper proposes a new method based on GA. The detail is as follows: Make the optimization model of the EMTSO problem by analyzing the characteristics of maintenance tasks scheduling systemically. Aiming for the NP hard characteristic of the problem, design the genetic algorithm to solve it. Finally, use the instance to validate the method. The result reflects that the method proposed by this paper can solve the equipment maintenance tasks scheduling optimization problem, and it has good applicable value in the military domain.

scholarly journals The Value of Simple Heuristics for Virtualized Network Function Placement

2020 ◽  
Vol 12 (10) ◽  
pp. 161
Author(s):  
Zahra Jahedi ◽  
Thomas Kunz
Keyword(s):  
Mathematical Models ◽  
Optimization Model ◽  
Service Providers ◽  
Network Function Virtualization ◽  
Np Hard ◽  
Placement Problem ◽  
Network Function ◽  
Main Challenge ◽  
Wide Range ◽  
Network Functions

Network Function Virtualization (NFV) can lower the CAPEX and/or OPEX for service providers and allow for quick deployment of services. Along with the advantages come some challenges. The main challenge in the use of Virtualized Network Functions (VNF) is the VNFs’ placement in the network. There is a wide range of mathematical models proposed to place the Network Functions (NF) optimally. However, the critical problem of mathematical models is that they are NP-hard, and consequently not applicable to larger networks. In wireless networks, we are considering the scarcity of Bandwidth (BW) as another constraint that is due to the presence of interference. While there exist many efforts in designing a heuristic model that can provide solutions in a timely manner, the primary focus with such heuristics was almost always whether they provide results almost as good as optimal solution. Consequently, the heuristics themselves become quite non-trivial, and solving the placement problem for larger networks still takes a significant amount of time. In this paper, in contrast, we focus on designing a simple and scalable heuristic. We propose four heuristics, which are gradually becoming more complex. We compare their performance with each other, a related heuristic proposed in the literature, and a mathematical optimization model. Our results demonstrate that while more complex placement heuristics do not improve the performance of the algorithm in terms of the number of accepted placement requests, they take longer to solve and therefore are not applicable to larger networks.In contrast, a very simple heuristic can find near-optimal solutions much faster than the other more complicated heuristics while keeping the number of accepted requests close to the results achieved with an NP-hard optimization model.

A Simplified Method for Joint Optimization of Production Schedule and Preventive Maintenance in Tobacco Flow Shop

2013 ◽  
Vol 328 ◽  
pp. 17-21
Author(s):  
Jia Min Da ◽  
Li Ya Wang ◽  
Shi Xiong Zhao
Keyword(s):  
Production Process ◽  
Optimization Model ◽  
Preventive Maintenance ◽  
Optimization Problem ◽  
Flow Shop ◽  
Joint Optimization ◽  
Np Hard ◽  
Production Schedule ◽  
Tobacco Production ◽  
Simplified Method

The joint optimization problem of product schedule and preventive maintenace in the flow shop is NP hard. In this paper, a joint optimization model was firstly established by abstracting the tobacco production process into a equivalent two-machine flow shop. And then a simplified method was proposed to solve the model according to the characteristic of the problem. Finally, a case was used to verify the validity of the model and the effectiveness of the method.

scholarly journals Efficient Placement of Service Function Chains in Cloud Computing Environments

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 323
Author(s):  
Marwa A. Abdelaal ◽  
Gamal A. Ebrahim ◽  
Wagdy R. Anis
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Response Time ◽  
Optimization Problem ◽  
Service Providers ◽  
Virtual Network ◽  
Placement Problem ◽  
Service Function ◽  
Network Functions ◽  
Bandwidth Consumption

The widespread adoption of network function virtualization (NFV) leads to providing network services through a chain of virtual network functions (VNFs). This architecture is called service function chain (SFC), which can be hosted on top of commodity servers and switches located at the cloud. Meanwhile, software-defined networking (SDN) can be utilized to manage VNFs to handle traffic flows through SFC. One of the most critical issues that needs to be addressed in NFV is VNF placement that optimizes physical link bandwidth consumption. Moreover, deploying SFCs enables service providers to consider different goals, such as minimizing the overall cost and service response time. In this paper, a novel approach for the VNF placement problem for SFCs, called virtual network functions and their replica placement (VNFRP), is introduced. It tries to achieve load balancing over the core links while considering multiple resource constraints. Hence, the VNF placement problem is first formulated as an integer linear programming (ILP) optimization problem, aiming to minimize link bandwidth consumption, energy consumption, and SFC placement cost. Then, a heuristic algorithm is proposed to find a near-optimal solution for this optimization problem. Simulation studies are conducted to evaluate the performance of the proposed approach. The simulation results show that VNFRP can significantly improve load balancing by 80% when the number of replicas is increased. Additionally, VNFRP provides more than a 54% reduction in network energy consumption. Furthermore, it can efficiently reduce the SFC placement cost by more than 67%. Moreover, with the advantages of a fast response time and rapid convergence, VNFRP can be considered as a scalable solution for large networking environments.

scholarly journals Modeling and Optimizing the Multi-Objective Portfolio Optimization Problem with Trapezoidal Fuzzy Parameters

2021 ◽  
Vol 26 (2) ◽  
pp. 36
Author(s):  
Alejandro Estrada-Padilla ◽  
Daniela Lopez-Garcia ◽  
Claudia Gómez-Santillán ◽  
Héctor Joaquín Fraire-Huacuja ◽  
Laura Cruz-Reyes ◽  
...  
Keyword(s):  
Steady State ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Solution Process ◽  
Density Estimator ◽  
Nsga Ii ◽  
Spatial Spread ◽  
Multi Objective ◽  
Significant Difference ◽  
Portfolio Optimization Problem

A common issue in the Multi-Objective Portfolio Optimization Problem (MOPOP) is the presence of uncertainty that affects individual decisions, e.g., variations on resources or benefits of projects. Fuzzy numbers are successful in dealing with imprecise numerical quantities, and they found numerous applications in optimization. However, so far, they have not been used to tackle uncertainty in MOPOP. Hence, this work proposes to tackle MOPOP’s uncertainty with a new optimization model based on fuzzy trapezoidal parameters. Additionally, it proposes three novel steady-state algorithms as the model’s solution process. One approach integrates the Fuzzy Adaptive Multi-objective Evolutionary (FAME) methodology; the other two apply the Non-Dominated Genetic Algorithm (NSGA-II) methodology. One steady-state algorithm uses the Spatial Spread Deviation as a density estimator to improve the Pareto fronts’ distribution. This research work’s final contribution is developing a new defuzzification mapping that allows measuring algorithms’ performance using widely known metrics. The results show a significant difference in performance favoring the proposed steady-state algorithm based on the FAME methodology.

Trajectory Optimization Applied to Space Rendezvous

2013 ◽  
Vol 756-759 ◽  
pp. 3466-3470
Author(s):  
Xu Min Song ◽  
Qi Lin
Keyword(s):  
Simulated Annealing ◽  
Optimization Model ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Optimization Method ◽  
Nonlinear Constraints ◽  
Design Variables ◽  
Adaptive Simulated Annealing ◽  
Simulation Results

The trajcetory plan problem of spece reandezvous mission was studied in this paper using nolinear optimization method. The optimization model was built based on the Hills equations. And by analysis property of the design variables, a transform was put forward , which eliminated the equation and nonlinear constraints as well as decreaseing the problem dimensions. The optimization problem was solved using Adaptive Simulated Annealing (ASA) method, and the rendezvous trajectory was designed.The method was validated by simulation results.

Walking Distances to and from Light-Rail Transit Stations

1996 ◽  
Vol 1538 (1) ◽  
pp. 19-26 ◽  
Author(s):  
Sean O'Sullivan ◽  
John Morrall
Keyword(s):  
Central Business District ◽  
Design Guidelines ◽  
Walking Distance ◽  
Light Rail ◽  
Rail Transit ◽  
Light Rail Transit ◽  
Bus Stop ◽  
Business District ◽  
The Relationship ◽  
The City

A quantifiable basis for developing design guidelines for pedestrian access to light-rail transit (LRT) stations is provided for planners based on observations in Calgary, Canada. Calgary's LRT system, which began operations in 1981, has been operating for long enough for walking patterns to and from its stations to become established. Interviews were conducted with 1,800 peak-hour LRT users about the origins and destinations of their LRT trips. Those who walked to or from a station were asked to point out on a map their approximate origins or destinations. The distances were then measured off the maps. Walking distance guidelines were developed for central business district (CBD), transfer and local stations. Catchment area maps were produced, and the relationship between reported walking time and measured walking distance was calculated. Also compared are the walking distances at LRT stations and the walking distances at bus stops. The research strongly indicates that people walk farther to reach an LRT station than a bus stop. Using bus walking standards would result in an underestimate of LRT walking distances by about half. For the city of Calgary the average walking distance to suburban stations is 649 m with a 75th-percentile distance of 840 m. At CBD stations the average walking distance is 326 m and the 75th-percentile distance is 419 m.

scholarly journals An evaluation of three DoE-guided meta-heuristic-based solution methods for a three-echelon sustainable distribution network

2020 ◽  
Vol 296 (1-2) ◽  
pp. 421-469
Author(s):  
Sahar Validi ◽  
Arijit Bhattacharya ◽  
P. J. Byrne
Keyword(s):  
Distribution Network ◽  
Np Hard ◽  
Nsga Ii ◽  
Solution Approach ◽  
Logistics Network ◽  
Location Routing ◽  
Solution Methods ◽  
Order Preference ◽  
Operational Constraints ◽  
Transportation Routes

AbstractThis article evaluates the efficiency of three meta-heuristic optimiser (viz. MOGA-II, MOPSO and NSGA-II)-based solution methods for designing a sustainable three-echelon distribution network. The distribution network employs a bi-objective location-routing model. Due to the mathematically NP-hard nature of the model a multi-disciplinary optimisation commercial platform, modeFRONTIER®, is adopted to utilise the solution methods. The proposed Design of Experiment (DoE)-guided solution methods are of two phased that solve the NP-hard model to attain minimal total costs and total CO2 emission from transportation. Convergence of the optimisers are tested and compared. Ranking of the realistic results are examined using Pareto frontiers and the Technique for Order Preference by Similarity to Ideal Solution approach, followed by determination of the optimal transportation routes. A case of an Irish dairy processing industry’s three-echelon logistics network is considered to validate the solution methods. The results obtained through the proposed methods provide information on open/closed distribution centres (DCs), vehicle routing patterns connecting plants to DCs, open DCs to retailers and retailers to retailers, and number of trucks required in each route to transport the products. It is found that the DoE-guided NSGA-II optimiser based solution is more efficient when compared with the DoE-guided MOGA-II and MOPSO optimiser based solution methods in solving the bi-objective NP-hard three-echelon sustainable model. This efficient solution method enable managers to structure the physical distribution network on the demand side of a logistics network, minimising total cost and total CO2 emission from transportation while satisfying all operational constraints.

Optimization design of drive system for industrial robots based on dynamic performance

2017 ◽  
Vol 44 (6) ◽  
pp. 765-775 ◽  
Author(s):  
LianZheng Ge ◽  
Jian Chen ◽  
Ruifeng Li ◽  
Peidong Liang
Keyword(s):  
Optimization Model ◽  
Optimization Problem ◽  
Industrial Robot ◽  
Dynamic Performance ◽  
System Optimization ◽  
Drive System ◽  
Industrial Robots ◽  
Mixed Integer ◽  
Light Weight ◽  
Content Type

Purpose The global performance of industrial robots partly depends on the properties of drive system consisting of motor inertia, gearbox inertia, etc. This paper aims to deal with the problem of optimization of global dynamic performance for robotic drive system selected from available components. Design/methodology/approach Considering the performance specifications of drive system, an optimization model whose objective function is composed of working efficiency and natural frequency of robots is proposed. Meanwhile, constraints including the rated and peak torque of motor, lifetime of gearbox and light-weight were taken into account. Furthermore, the mapping relationship between discrete optimal design variables and component properties of drive system were presented. The optimization problem with mixed integer variables was solved by a mixed integer-laplace crossover power mutation algorithm. Findings The optimization results show that our optimization model and methods are applicable, and the performances are also greatly promoted without sacrificing any constraints of drive system. Besides, the model fits the overall performance well with respect to light-weight ratio, safety, cost reduction and others. Practical implications The proposed drive system optimization method has been used for a 4-DOF palletizing robot, which has been largely manufactured in a factory. Originality/value This paper focuses on how the simulation-based optimization can be used for the purpose of generating trade-offs between cost, performance and lifetime when designing robotic drive system. An applicable optimization model and method are proposed to handle the dynamic performance optimization problem of a drive system for industrial robot.

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