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Applications of Optimization in Vehicle Routing Problem and Smart Homes Powered by Local Resources.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Applications of Optimization in Vehicle Routing Problem and Smart Homes Powered by Local Resources./
Author:	Ahkamiraad, Azadeh .
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	117 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-09, Section: B.
Contained By:	Dissertations Abstracts International81-09B.
Subject:	Operations research. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27666383
ISBN:	9781392758175

Applications of Optimization in Vehicle Routing Problem and Smart Homes Powered by Local Resources.
Ahkamiraad, Azadeh .

Applications of Optimization in Vehicle Routing Problem and Smart Homes Powered by Local Resources. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 117 p.

Source: Dissertations Abstracts International, Volume: 81-09, Section: B.

Thesis (Ph.D.)--State University of New York at Binghamton, 2019.

This item must not be sold to any third party vendors.

This dissertation deals with the optimization problems in two application types of settings: distribution centers for industrial applications and smart household appliances for residential applications.In the first part, a mixed-integer linear programming model of a special kind of capacitated and multiple cross-docked vehicle routing problem (VRP) with pickup, delivery, and time windows is formulated. The problem is to design a set of routes for vehicle fleets servicing pickup and delivery nodes with defined demands and time windows to achieve the minimum transportation and fixed costs. The described problem is defined as an NP-hard problem, therefore a metaheuristic method as a hybrid of the genetic algorithm and particle swarm optimization (HGP) is proposed to solve the formulated NP-hard problem. Small-size problems are solved by HGP and then compared with the exact method using CPLEX to validate the effectiveness of the proposed hybrid algorithm. The proposed HGP is capable to reach the exact solution obtained from optimization solvers in a smaller time period for small sizes of the problem. For medium and large sizes, CPLEX could not reach an exact solution, but extensive experiments have been conducted for medium and large-size problems and the results show the proposed HGP provides better solutions in the allocated time compared to CPLEX.In the second part of the dissertation, another optimization problem is considered for smart, efficient structured households using a special form of vehicles known as electric vehicles (EVs). The usage of EVs is viewed from another aspect as a source of energy combined with other local resources in a smart household. It is expected that in the near future, micro-grid solutions will provide fundamental changes in energy consumption behavior. The tendency toward intermittent energy storage systems such as renewables and electric vehicles (EVs) are growing considerably. The dissertation, therefore, introduces an intelligent home demand-side management framework for residential areas empowered by EVs, storage facilities, and renewables as local resources. With the objective of minimizing the energy cost, a mixed-integer linear program is proposed to reach the maximum use of renewables while scheduling the operation of different appliances under varied demand response strategies. The mathematical formula is carried out in Lingo and simulation results under varied scenarios demonstrate the effectiveness of the technique. The proposed real-time price-based framework can be embedded into the smart meter to automatically project the optimal schedule for residential appliances.

ISBN: 9781392758175Subjects--Topical Terms:

573517
Operations research.
Subjects--Index Terms:

Cross docking

Applications of Optimization in Vehicle Routing Problem and Smart Homes Powered by Local Resources.
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520 $a This dissertation deals with the optimization problems in two application types of settings: distribution centers for industrial applications and smart household appliances for residential applications.In the first part, a mixed-integer linear programming model of a special kind of capacitated and multiple cross-docked vehicle routing problem (VRP) with pickup, delivery, and time windows is formulated. The problem is to design a set of routes for vehicle fleets servicing pickup and delivery nodes with defined demands and time windows to achieve the minimum transportation and fixed costs. The described problem is defined as an NP-hard problem, therefore a metaheuristic method as a hybrid of the genetic algorithm and particle swarm optimization (HGP) is proposed to solve the formulated NP-hard problem. Small-size problems are solved by HGP and then compared with the exact method using CPLEX to validate the effectiveness of the proposed hybrid algorithm. The proposed HGP is capable to reach the exact solution obtained from optimization solvers in a smaller time period for small sizes of the problem. For medium and large sizes, CPLEX could not reach an exact solution, but extensive experiments have been conducted for medium and large-size problems and the results show the proposed HGP provides better solutions in the allocated time compared to CPLEX.In the second part of the dissertation, another optimization problem is considered for smart, efficient structured households using a special form of vehicles known as electric vehicles (EVs). The usage of EVs is viewed from another aspect as a source of energy combined with other local resources in a smart household. It is expected that in the near future, micro-grid solutions will provide fundamental changes in energy consumption behavior. The tendency toward intermittent energy storage systems such as renewables and electric vehicles (EVs) are growing considerably. The dissertation, therefore, introduces an intelligent home demand-side management framework for residential areas empowered by EVs, storage facilities, and renewables as local resources. With the objective of minimizing the energy cost, a mixed-integer linear program is proposed to reach the maximum use of renewables while scheduling the operation of different appliances under varied demand response strategies. The mathematical formula is carried out in Lingo and simulation results under varied scenarios demonstrate the effectiveness of the technique. The proposed real-time price-based framework can be embedded into the smart meter to automatically project the optimal schedule for residential appliances.
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