Baç, Uğur

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Profile URL
https://hdl.handle.net/20.500.14411/6820
Name Variants
Baç, Uğur
Baç,U.
B., Uğur
B.,Uğur
U., Bac
U.,Bac
Uğur, Baç
B., Ugur
Bac,Ugur
U., Baç
Uğur Baç
U.,Baç
Bac U.
Bac,U.
B.,Ugur
Ugur, Bac
Bac, Ugur
Job Title
Profesör Doktor
Email Address
ugur.bac@atilim.edu.tr
Main Affiliation
Industrial Engineering
Status
Website
ORCID ID
0000-0003-3195-0829
Scopus Author ID
56521521100
Turkish CoHE Profile ID
111745
Google Scholar ID
0arD5fsAAAAJ
WoS Researcher ID
AAB-3960-2020 (37444033)

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
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ZERO HUNGER2
ZERO HUNGER
0
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
0
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QUALITY EDUCATION4
QUALITY EDUCATION
0
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GENDER EQUALITY5
GENDER EQUALITY
0
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CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
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AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
3
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DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
2
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
3
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REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
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SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
5
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
1
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CLIMATE ACTION13
CLIMATE ACTION
3
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LIFE BELOW WATER14
LIFE BELOW WATER
0
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LIFE ON LAND15
LIFE ON LAND
0
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
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Documents

7

Citations

212

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6

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Scholarly Output

18

Articles

8

Views / Downloads

129/1421

Supervised MSc Theses

5

Supervised PhD Theses

2

WoS Citation Count

199

Scopus Citation Count

212

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0

Projects

0

WoS Citations per Publication

11.06

Scopus Citations per Publication

11.78

Open Access Source

4

Supervised Theses

7

JournalCount
Mathematics2
4th European Conference on Intellectual Capital (ECIC) -- APR 23-24, 2012 -- Arcada Univ Appl Sci, Helsinki, FINLAND1
Journal of Building Engineering1
Polish Journal of Environmental Studies1
Proceedings of the International Conference on Industrial Engineering and Operations Management -- 2nd European International Conference on Industrial Engineering and Operations Management.IEOM 2018 -- 26 July 2018 through 27 July 2018 -- 1405381
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Start date: 2020Subject: Electric vehicle recharge scheduling

Scholarly Output Search Results

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    Article
    Citation - WoS: 92
    Citation - Scopus: 103
    Optimization of Electric Vehicle Recharge Schedule and Routing Problem With Time Windows and Partial Recharge: a Comparative Study for an Urban Logistics Fleet
    (Elsevier, 2021) Bac, Ugur; Baç, Uğur; Erdem, Mehmet; Erdem, Mehmet; Baç, Uğur; Erdem, Mehmet; Industrial Engineering; Industrial Engineering
    The use of electric vehicles (EVs) is becoming more and more widespread and the interest in these vehicles is increasing each day. EVs promise to emit less air pollution and greenhouse gas (GHG) emissions with lower operational costs when compared to fossil fuel-powered vehicles. However, many factors such as the limited mileage of these vehicles, long recharging times, and the sparseness of available recharging stations adversely affect the preferability of EVs in industrial and commercial logistics. Effective planning of EV routes and recharge schedules is vital for the future of the logistics sector. This paper proposes an electric vehicle routing problem with the time windows (EVRPTW) framework, which is an extension of the well-known vehicle routing problem (VRP). In the proposed model, partial recharging is considered for the EVRPTW with the multiple depots and heterogeneous EV fleet and multiple visits to customers. While routing a set of heterogeneous EVs, their limited ranges, interdependent on the battery capacity, should be taken into consideration and all the customers' deliveries should be completed within the predetermined time windows. To deal with this problem, a series of neighbourhood operators are developed for the local search process in the variable neighbourhood search (VNS) and variable neighbourhood descent (VND) heuristics. The proposed solution algorithms are tested in large-scale instances. Results indicate that the proposed heuristics perform well as to this problem in terms of optimizing recharging times, idle waiting times, overtime of operators, compliance with time windows, number of vehicles, depots, and charging stations used.