1,216 research outputs found
๊ฐ๋ฏธ์๊ณ ๋ฆฌ์ฆ์ ์ด์ฉํ ๋๋ก ์ ์ ์ค ๊ฒฝ๋ก ์ต์ ํ
ํ์๋
ผ๋ฌธ(์์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ๊ฑด์คํ๊ฒฝ๊ณตํ๋ถ, 2022.2. ๊น๋๊ท.Drones can overcome the limitation of ground vehicles by replacing the congestion time and allowing rapid service. For sudden snowfall with climate change, a quickly deployed drone can be a flexible alternative considering the deadhead route and the labor costs. The goal of this study is to optimize a drone arc routing problem (D-ARP), servicing the required roads for snow removal. A D-ARP creates computational burden especially in large network. The D-ARP has a large search space due to its exponentially increased candidate route, arc direction decision, and continuous arc space. To reduce the search space, we developed the auxiliary transformation method in ACO algorithm and adopted the random walk method. The contribution of the work is introducing a new problem and optimization approach of D-ARP in snow removal operation and reduce its search space. The optimization results confirmed that the drone travels shorter distance compared to the truck with a reduction of 5% to 22%. Furthermore, even under the length constraint model, the drone shows 4% reduction compared to the truck. The result of the test sets demonstrated that the adopted heuristic algorithm performs well in the large size networks in reasonable time. Based on the results, introducing a drone in snow removal is expected to save the operation cost in practical terms.๋๋ก ์ ํผ์ก์๊ฐ๋๋ฅผ ๋์ฒดํ๊ณ ๋น ๋ฅธ ์๋น์ค๋ฅผ ๊ฐ๋ฅํ๊ฒ ํจ์ผ๋ก์จ ์ง์์ฐจ๋์ ํ๊ณ๋ฅผ ๊ทน๋ณตํ ์ ์๋ค. ์ต๊ทผ ๊ธฐํ๋ณํ์ ๋ฐ๋ฅธ ๊ฐ์์ค๋ฐ ๊ฐ์ค์ ๊ฒฝ์ฐ์, ๋๋ก ๊ณผ ๊ฐ์ด ๋น ๋ฅด๊ฒ ํฌ์
ํ ์ ์๋ ์๋น์ค๋ ์ดํ ๊ฒฝ๋ก์ ๋
ธ๋๋น์ฉ์ ๊ณ ๋ คํ์ ๋๋ ์ ์ฐํ ์ด์ ์ต์
์ด ๋ ์ ์๋ค. ๋ณธ ์ฐ๊ตฌ์ ๋ชฉ์ ์ ๋๋ก ์ํฌ ๋ผ์ฐํ
(D-ARP)์ ์ต์ ํํ๋ ๊ฒ์ด๋ฉฐ, ์ด๋ ์ ์ค์ ํ์ํ ๋๋ก๋ฅผ ์๋น์คํ๋ ๊ฒฝ๋ก๋ฅผ ํ์ํ๋ ๊ฒ์ด๋ค. ๋๋ก ์ํฌ ๋ผ์ฐํ
์ ํนํ ํฐ ๋คํธ์ํฌ์์ ์ปดํจํฐ ๋ถํ๋ฅผ ์์ฑํ๋ค. ๋ค์ ๋งํดD-ARP๋ ํฐ ๊ฒ์๊ณต๊ฐ์ ํ์๋ก ํ๋ฉฐ, ์ด๋ ๊ธฐํ๊ธ์์ ์ผ๋ก ์ฆ๊ฐํ๋ ํ๋ณด ๊ฒฝ๋ก ๋ฐ ํธ์ ๋ฐฉํฅ ๊ฒฐ์ ๊ทธ๋ฆฌ๊ณ ์ฐ์์ ์ธ ํธ์ ๊ณต๊ฐ์ผ๋ก๋ถํฐ ๊ธฐ์ธํ๋ค. ๊ฒ์๊ณต๊ฐ์ ์ค์ด๊ธฐ ์ํด, ์ฐ๋ฆฌ๋ ๊ฐ๋ฏธ์๊ณ ๋ฆฌ์ฆ์ ๋ณด์กฐ๋ณํ๋ฐฉ๋ฒ์ ์ ์ฉํ๋ ๋ฐฉ์์ ๋์
ํ์์ผ๋ฉฐ ๋ํ ๋๋ค์ํฌ ๊ธฐ๋ฒ์ ์ฑํํ์๋ค. ๋ณธ ์ฐ๊ตฌ์ ๊ธฐ์ฌ๋ ์ ์ค ์ด์์ ์์ด D-ARP๋ผ๋ ์๋ก์ด ๋ฌธ์ ๋ฅผ ์ค์ ํ๊ณ ์ต์ ํ ์ ๊ทผ๋ฒ์ ๋์
ํ์์ผ๋ฉฐ ๊ฒ์๊ณต๊ฐ์ ์ต์ํํ ๊ฒ์ด๋ค. ์ต์ ํ ๊ฒฐ๊ณผ, ๋๋ก ์ ์ง์ํธ๋ญ์ ๋นํด ์ฝ 5% ~ 22%์ ๊ฒฝ๋ก ๋น์ฉ ๊ฐ์๋ฅผ ๋ณด์๋ค. ๋์๊ฐ ๊ธธ์ด ์ ์ฝ ๋ชจ๋ธ์์๋ ๋๋ก ์ 4%์ ๋น์ฉ ๊ฐ์๋ฅผ ๋ณด์๋ค. ๋ํ ์คํ๊ฒฐ๊ณผ๋ ์ ์ฉํ ํด๋ฆฌ์คํฑ ์๊ณ ๋ฆฌ์ฆ์ด ํฐ ๋คํธ์ํฌ์์๋ ํฉ๋ฆฌ์ ์๊ฐ ๋ด์ ์ต์ ํด๋ฅผ ์ฐพ์์ ์
์ฆํ์๋ค. ์ด๋ฌํ ๊ฒฐ๊ณผ๋ฅผ ๋ฐํ์ผ๋ก, ๋๋ก ์ ์ ์ค์ ๋์
ํ๋ ๊ฒ์ ๋ฏธ๋์ ์ ์ค ์ด์ ๋น์ฉ์ ์ค์ง์ ์ผ๋ก ๊ฐ์์ํฌ ๊ฒ์ผ๋ก ๊ธฐ๋๋๋ค.Chapter 1. Introduction 4
1.1. Study Background 4
1.2. Purpose of Research 6
Chapter 2. Literature Review 7
2.1. Drone Arc Routing problem 7
2.2. Snow Removal Routing Problem 8
2.3. The Classic ARPs and Algorithms 9
2.4. Large Search Space and Arc direction 11
Chapter 3. Method 13
3.1. Problem Statement 13
3.2. Formulation 16
Chapter 4. Algorithm 17
4.1. Overview 17
4.2. Auxilary Transformation Method 18
4.3. Ant Colony Optimization (ACO) 20
4.4. Post Process for Arc Direction Decision 23
4.5. Length Constraint and Random Walk 24
Chapter 5. Results 27
5.1. Application in Toy Network 27
5.2. Application in Real-world Networks 29
5.3. Application of the Refill Constraint in Seoul 31
Chapter 6. Conclusion 34
References 35
Acknowledgment 40์
Two-Echelon Vehicle and UAV Routing for Post-Disaster Humanitarian Operations with Uncertain Demand
Humanitarian logistics service providers have two major responsibilities
immediately after a disaster: locating trapped people and routing aid to them.
These difficult operations are further hindered by failures in the
transportation and telecommunications networks, which are often rendered
unusable by the disaster at hand. In this work, we propose two-echelon vehicle
routing frameworks for performing these operations using aerial uncrewed
autonomous vehicles (UAVs or drones) to address the issues associated with
these failures. In our proposed frameworks, we assume that ground vehicles
cannot reach the trapped population directly, but they can only transport
drones from a depot to some intermediate locations. The drones launched from
these locations serve to both identify demands for medical and other aids
(e.g., epi-pens, medical supplies, dry food, water) and make deliveries to
satisfy them. Specifically, we present two decision frameworks, in which the
resulting optimization problem is formulated as a two-echelon vehicle routing
problem. The first framework addresses the problem in two stages: providing
telecommunications capabilities in the first stage and satisfying the resulting
demands in the second. To that end, two types of drones are considered. Hotspot
drones have the capability of providing cell phone and internet reception, and
hence are used to capture demands. Delivery drones are subsequently employed to
satisfy the observed demand. The second framework, on the other hand, addresses
the problem as a stochastic emergency aid delivery problem, which uses a
two-stage robust optimization model to handle demand uncertainty. To solve the
resulting models, we propose efficient and novel solution approaches
An ACO-Inspired, Probabilistic, Greedy Approach to the Drone Traveling Salesman Problem
In recent years, major companies have done research on using drones for parcel delivery. Research has shown that this can result in significant savings, which has led to the formulation of various truck and drone routing and scheduling optimization problems. This paper explains and analyzes a new approach to the Drone Traveling Salesman Problem (DTSP) based on ant colony optimization (ACO).
The ACO-based approach has an acceptance policy that maximizes the usage of the drone. The results reveal that the pheromone causes the algorithm to converge quickly to the best solution. The algorithm performs comparably to the MIP model, CP model, and EA of Rich & Ham (2018), especially in instances with a larger number of stops
ํธ๋ญ์ ์ด๋ํ ๋๋ก ๊ธฐ์ง๋ก ์ฌ์ฉํ๋ ํ์ ์ฉ๋ ํธ๋ญ-๋๋ก ๊ฒฝ๋ก ๋ฐฐ์ ๋ฌธ์
ํ์๋
ผ๋ฌธ(์์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ๊ฑด์คํ๊ฒฝ๊ณตํ๋ถ, 2022.2. ๊น๋๊ท.Drones initially received attention for military purposes as a collective term for unmanned aerial vehicles (UAVs), but recently, efforts to use them in logistics have been actively underway. If drones are put into places where low-weight and high-value items are currently difficult to deliver by existing delivery means, it will have the effect of greatly reducing costs. However, the disadvantages of drones in delivery are also clear. In order to improve the delivery capacity of drones, the size of drones must increase when drones are equipped with large-capacity batteries.
This thesis introduced two methods and presented algorithms for each method among VRP-D. First of all, CVP-D is a method in which carriers such as trucks and ships with large capacity and slow speed carry robots and drones with small capacity. Next, in the CVRP-D, the vehicle and the drone move different paths simultaneously, and the drone can visit multiple nodes during one sortie.
The two problems are problems in which restrictions are added to the vehicle route problem (VRP), known as the NP-hard problem. The algorithm presented in this study derived drone-truck routes for two problems within a reasonable time. In addition, sensitivity analysis was conducted to observe changes in the appropriate network structure for the introduction of drone delivery and the main parameters of the drone. In addition, the validity of the proposed algorithm was verified through comparison with the data used as a benchmark in previous studies. These research results will contribute to the creation of delivery routes quickly, considering the specification of a drone.๋๋ก ์ ๋ฌด์ธํญ๊ณต๊ธฐ(UAV)์ ํต์นญ์ผ๋ก ์ด๊ธฐ์๋ ๊ตฐ์ฌ์ ๋ชฉ์ ์ผ๋ก ์ฃผ๋ชฉ์ ๋ฐ์์ผ๋ ์ต๊ทผ ๋ฌผ๋ฅ์์ ์ฌ์ฉํ๋ ค๋ ๋
ธ๋ ฅ์ด ์ ๊ทน์ ์ผ๋ก ์งํ๋๊ณ ์๋ค. ๋๋ก ์ด ์ ์ค๋-๊ณ ๊ฐ์น ๋ฌผํ์ ๋ฐฐ์ก์์ ํ์ฌ ๊ธฐ์กด ๋ฐฐ์ก์๋จ์ ์ํด ๋ฐฐ์ก์ด ์ด๋ ค์ด ๊ณณ์ ํฌ์
์ด ๋๋ค๋ฉด ํฐ ๋น์ฉ์ ๊ฐ์ ํจ๊ณผ๊ฐ ์์ ๊ฒ์ด๋ค. ํ์ง๋ง ๋ฐฐ์ก์ ์์ด์ ๋๋ก ์ ๋จ์ ๋ ๋ช
ํํ๋ค. ๋๋ก ์ ๋ฐฐ์ก๋ฅ๋ ฅ์ ํฅ์์ํค๊ธฐ ์ํด์๋ ๋๋ก ์ด ๋์ฉ๋ ๋ฐฐํฐ๋ฆฌ๋ฅผ ํ์ฌํ๋ฉด ๋๋ก ํฌ๊ธฐ๊ฐ ์ฆ๊ฐํ์ฌ์ผ ํ๋ค. ์ด๋ฌํ ๋จ์ ์ ๊ทน๋ณตํ๊ธฐ ์ํด์ ๋๋ก ๊ณผ ํธ๋ญ์ ๊ฒฐํฉํ์ฌ ์ด์ํ๋ ๋ฐฉ์์ด ์ฐ๊ตฌ๋์ด์๋ค.
์ด๋ฌํ ๋ฐฉ์ ์ค ๋ณธ ์ฐ๊ตฌ์์๋ ๋ ๊ฐ์ง ๋ฐฉ์์ ์๊ฐํ๊ณ , ๊ฐ๊ฐ์ ๋ฐฉ์์ ๋ํ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ์๋ค. ๋จผ์ , CVP-D๋ ์ฉ๋์ด ํฌ๊ณ ์๋๊ฐ ๋๋ฆฐ ํธ๋ญ์ด๋ ๋ฐฐ ๋ฑ์ ์บ๋ฆฌ์ด๊ฐ ์ฉ๋์ด ์์ ๋ก๋ด, ๋๋ก ๋ฑ์ ์ฃ๊ณ ๋ค๋๋ฉด์ ๋ฐฐ์ก์ ํ๋ ๋ฐฉ์์ด๋ค. ๋ค์์ผ๋ก, CVRP-D๋ ์ฐจ๋๊ณผ ๋๋ก ์ด ๋์์ ๊ฐ๊ธฐ ๋ค๋ฅธ ๊ฒฝ๋ก๋ฅผ ์ด๋ํ๋ฉฐ, ๋๋ก ์ 1ํ ๋นํ(sortie)์ ๋ค์์ ๋
ธ๋๋ฅผ ๋ฐฉ๋ฌธํ๋ ๊ฒ์ด ๊ฐ๋ฅํ๋ค.
๋ ๋ฌธ์ ๋ ์ฐจ๋๊ฒฝ๋ก๋ฌธ์ (VRP)์ ์ ์ฝ์ด ๋ํด์ง ๋ฌธ์ ์ด๋ค. VRP๋ ๋ํ์ ์ธ NP-hard ๋ฌธ์ ๋ก ํด๋ฅผ ๊ตฌํ๊ธฐ ์ํด์ ํด๋ฆฌ์คํฑ ์๊ณ ๋ฆฌ์ฆ์ด ์๊ตฌ๋๋ค. ๋ณธ ์ฐ๊ตฌ์์ ์ ์ํ๋ ์๊ณ ๋ฆฌ์ฆ์ ํฉ๋ฆฌ์ ์ธ ์๊ฐ ๋ด ๋๋ฌธ์ ์ ๋๋ก -ํธ๋ญ ๊ฒฝ๋ก๋ฅผ ๋์ถํ์๋ค. ๋ํ ๋ฏผ๊ฐ๋ ๋ถ์์ ์ค์ํ์ฌ ๋๋ก ๋ฐฐ์ก ๋์
์ ์ํ ์ ์ ํ ๋คํธ์ํฌ ๊ตฌ์กฐ ๋ฐ ๋๋ก ์ ์ฃผ์ ํ๋ผ๋ฏธํฐ์ ๋ณํ์ ๋ฐ๋ฅธ ๋ณํ๋ฅผ ๊ด์ฐฐํ์๋ค. ์ด๋ ์ฐจํ ๋๋ก ์ ์ฑ๋ฅ์ ๊ดํ ์์ฌ๊ฒฐ์ ์ ๊ณ ๋ คํด์ผ ํ ์์๋ค์ ๋ํ ๊ธฐ์ค์ด ๋ ์ ์์ ๊ฒ์ผ๋ก ๊ธฐ๋๋๋ค.
๋ํ ์ ํ์ฐ๊ตฌ์์ ๋ฒค์น๋งํฌ๋ก ์ฌ์ฉ๋๋ ๋ฐ์ดํฐ์์ ๋น๊ต๋ฅผ ํตํด ์ ์ํ๋ ์๊ณ ๋ฆฌ์ฆ์ ํ๋น์ฑ์ ๊ฒ์ฆํ์๋ค. ๋ณธ ์ฐ๊ตฌ๋ ๋๋ก ๋์
์ด ๋ฐฐ์ก์๊ฐ์ ๊ฐ์์ํค๋ฉฐ, ์ด์๋ฐฉ๋ฒ์ ๋ฐ๋ผ์ ๋ฐฐ์ก์๊ฐ์ ์ฐจ์ด๊ฐ ๋ฐ์ํจ์ ๋ณด์๋ค. ์ด๋ฌํ ์ฐ๊ตฌ ์ฑ๊ณผ๋ ๋๋ก ๋ฐฐ์ก ์ ํ๊ฒฝ๊ณผ ๊ธฐ๊ณ์ ์ฑ๋ฅ์ ๊ณ ๋ คํ ๋ฐฐ์ก ๊ฒฝ๋ก๋ฅผ ๋จ์๊ฐ๋ด ์์ฑํ์ฌ ์์
์ ์ผ๋ก ์ด์ฉ๊ฐ๋ฅ ํ ๊ฒ์ด๋ค.Chapter 1. Introduction 1
1.1 Research Background 1
1.2 Research Purpose 3
1.3 Contribution of Research 4
Chapter 2. Literature review 5
2.1 Vehicle Routing Problems with Drone 5
2.2 Carrier Vehicle Problem with Drone(CVP-D) 10
2.3 Capacitated VRP with Drone(CVRP-D) 12
Chapter 3. Mathematical Formulation 14
3.1 Terminology 14
3.2 CVP-D Formulation 15
3.3 CVRP-D Formulation 19
Chapter 4. Proposed Algorithms 23
4.1 Heuristic Algorithm 23
4.1.1 Knapsack Problem 23
4.1.2 Parallel Machine Scheduling (PMS) 25
4.1.3 Set Covering Location Problem (SCLP) 27
4.1.4 Guided Local Search (GLS) Algorithm 28
4.1.5 Genetic Algorithm (GA) 29
4.2 Proposed Heuristic Algorithm : GA-CVPD 30
4.3 Proposed Heuristic Algorithm : GA-CVRPD 33
Chapter 5. Numerical Analysis 36
5.1 Data Description 36
5.2 Numerical experiment 37
5.3 Sensitivity analysis 39
5.3.1 Analysis on GA-CVPD 39
5.3.2 Analysis on GA-CVRPD 42
5.3.3 Result on different Instances 45
Chapter 6. Conclusion 48
Bibliography 50
Abstract in Korean 53
4.1.5 Genetic Algorithm (GA) 29
4.2 Proposed Heuristic Algorithm : GA-CVPD 30
4.3 Proposed Heuristic Algorithm : GA-CVRPD 33
Chapter 5. Numerical Analysis 36
5.1 Data Description 36
5.2 Numerical experiment 37
5.3 Sensitivity analysis 42
5.3.1 Analysis on GA-CVPD 39
5.3.2 Analysis on GA-CVRPD 42
5.3.3 Result on different Instances 45
Chapter 6. Conclusion 48
Bibliography 50
Abstract in Korean 53์
Arc routing problems: A review of the past, present, and future
[EN] Arc routing problems (ARPs) are defined and introduced. Following a brief history of developments in this area of research, different types of ARPs are described that are currently relevant for study. In addition, particular features of ARPs that are important from a theoretical or practical point of view are discussed. A section on applications describes some of the changes that have occurred from early applications of ARP models to the present day and points the way to emerging topics for study. A final section provides information on libraries and instance repositories for ARPs. The review concludes with some perspectives on future research developments and opportunities for emerging applicationsThis research was supported by the Ministerio de Economia y Competitividad and Fondo Europeo de Desarrollo Regional, Grant/Award Number: PGC2018-099428-B-I00. The Research Council of Norway, Grant/Award Numbers: 246825/O70 (DynamITe), 263031/O70 (AXIOM).Corberรกn, ร.; Eglese, R.; Hasle, G.; Plana, I.; Sanchรญs Llopis, JM. (2021). Arc routing problems: A review of the past, present, and future. Networks. 77(1):88-115. https://doi.org/10.1002/net.21965S8811577
Delivery by Drones with Arbitrary Energy Consumption Models: A New Formulation Approach
This paper presents a new approach for formulating the delivery problem by
drones with general energy consumption models where the drones visit a set of
places to deliver parcels to customers. Drones can perform multiple trips that
start and end at a central depot while visiting several customers along their
paths. The problem determines the routing and scheduling decisions of the
drones in order to minimize the total transportation cost of serving customers.
For the first time, the new formulation approach enables us to use the best
available energy consumption model without the need of any extra
approximations. Though the approach works in a very general setting including
non-convex energy consumption models, it is also computationally efficient as
the resulting optimization model has a linear relaxation. A numerical study on
255 benchmark instances with up to 50 customers and a specific energy function
indicate that all the instances can be solved 20 times faster on average using
the new formulation when compared to the best existing branch-and-cut
algorithm. All the 15 benchmark instances with 50 customers are solved exactly,
whereas none of them has been solved optimally before. Moreover, new instances
with up to 150 customers are solved with small error bounds within a few hours.
The new approach can be simply applied to consider the extra energy required
when a drone needs to continue hovering until opening the delivery time window.
It can also be applied to the case where the flight time is dependent on the
drone's payload weight. Owing to the flexibility of the new approach, these
challenging extensions are formulated as linear optimization models for the
first time
Optimizing Vaccine Supply Chains with Drones in Less-Developed Regions: Multimodal Vaccine Distribution in Vanuatu
In recent years, many less-developed countries (LDCs) have been exploring new opportunities provided by drones, such as the capability to deliver items with minimal infrastructure, fast speed, and relatively low cost, especially for high value-added products such as lifesaving medical products and vaccines. This dissertation optimizes the delivery network and operations for routine childhood vaccines in LDCs. It analyzes two important problems using mathematical programming, with an application in the South Pacific nation of Vanuatu. The first problem is to optimize the nation-wide multi-modal vaccine supply chain with drones to deliver vaccines from the national depot to all health zones in an LDC. The second problem is to optimize vaccine delivery using drones within a single health zone while considering the synchronization of drone deliveries with health worker outreach trips to remote clinics. Both problems consider a cold chain time limit to ensure vaccine viability. The two research problems together provide a holistic solution at the strategic and operational levels for the vaccine supply chain network in LDCs. Results from the first problem show that drones can reduce cost and delivery time simultaneously by replacing expensive and/or slow modes. The use of large drones is shown to save up to 60% of the delivery cost and the use of small drones is shown to save up to 43% of the delivery cost. The research highlights the tradeoff between delivery cost and service, with tighter cold chain limits providing faster delivery to health zones at the expense of added cost. Results from the second problem show that adding drones to delivery plans can save up to 40% of the delivery cost and improve the service time simultaneously by resupplying vaccines when the cold chain and payload limit of health workers are reached. This research contributes to both literature and practice. It develops innovative methodologies to model drone paths with relay stations and to optimize synchronized multi-stop drone trips with health worker trips. The models are tested with real-world data for an island nation (Vanuatu), which provides data for a geographic setting new to the literature on drone delivery and vaccine distribution
Exact methods for the traveling salesman problem with multiple drones
Drone delivery is drawing increasing attention in last-mile delivery. Effective solution methods to solve decision-making problems arising in drone delivery allow to run and assess drone delivery systems. In this paper, we focus on delivery systems with a single traditional vehicle and multiple drones working in tandem to fulfill customer requests. We address the Traveling Salesman Problem with Multiple Drones (TSP-MD) and investigate the modeling challenges posed by the presence of multiple drones, which have proven to be hard to handle in the literature. We propose a compact Mixed-Integer Linear Programming (MILP) model to formulate the TSP-MD and several families of valid inequalities. Moreover, we illustrate an exact decomposition approach based on the compact MILP and a branch-and-cut algorithm. We show that this exact approach can solve instances with up to 24 customers to proven optimality, improving upon existing exact methods that can solve similar problems with up to ten customers only
Adaptive large neighborhood search algorithm โ performance evaluation under parallel schemes & applications
Adaptive Large Neighborhood Search (ALNS) is a fairly recent yet popular single-solution heuristic for solving discrete optimization problems. Even though the heuristic has been a popular choice for researchers in recent times, the parallelization of this algorithm is not widely studied in the literature compared to the other classical metaheuristics. To extend the existing literature, this study proposes several different parallel schemes to parallelize the basic/sequential ALNS algorithm. More specifically, seven different parallel schemes are employed to target different characteristics of the ALNS algorithm and the capability of the local computers. The schemes of this study are implemented in a master-slave architecture to manage and assign loads in processors of the local computers. The overall goal is to simultaneously explore different areas of the search space in an attempt to escape the local minima, taking effective steps toward the optimal solution and, to the end, accelerating the convergence of the ALNS algorithm. The performance of the schemes is tested by solving a capacitated vehicle routing problem (CVRP) with available wellknown test instances. Our computational results indicate that all the parallel schemes are capable of providing a competitive optimality gap in solving CVRP within our investigated test instances. However, the parallel scheme (scheme 1), which runs the ALNS algorithm independently within different slave processors (e.g., without sharing any information with other slave processors) until the synchronization occurs only when one of the processors meets its predefined termination criteria and reports the solution to the master processor, provides the best running time with solving the instances approximately 10.5 times faster than the basic/sequential ALNS algorithm. These findings are applied in a real-life fulfillment process using mixed-mode delivery with trucks and drones. Complex but optimized routes are generated in a short time that is applicable to perform last-mile delivery to customers
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