Multi-Depot Capacitated Vehicle Routing Problem by Using Stable Marriage and K-Means Clustering to Minimize Number of Unserved Customers and Total Travel Distance

doi:10.22266/ijies2021.1231.54

Cited by 4 publications

(4 citation statements)

References 24 publications

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“…The multi-day travel itinerary generation problem can be assumed as a Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). Previous studies have explored various aspects of VRP, such as multi-depot capacitated VRP using stable marriage and k-means clustering [23], CVRP considering traffic density with GA [24], VRP of bulk product shipment with hybrid metaheuristics [25], VRP with soft time windows using the hybrid of improved BSO and ACO (IBSO-ACO) [26], CVRP utilizing the hybrid of Chicken Swarm Optimization (CSO) and tabu search [27], VRPTW employing the hybrid of ACS and BSO [17], and VRP with the hybrid of ACO and DEA [28]. Ant colony-based algorithms (ACO and ACS) are frequently used in VRP highlighting their efficacy [17,26,28,29].…”

Section: Related Workmentioning

confidence: 99%

Generating a Multi-Day Travel Itinerary Recommendation Using the Hybrid Ant Colony System and Brainstorm Optimization Algorithm

2024

IJIES

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Constructing a multi-day travel itinerary is a notable challenge, particularly for individuals planning extended trips. This study addresses the complexity by aiming to automatically generate an optimal multi-day travel itinerary that satisfies user interests. The problem is framed as a Capacitated Vehicle Routing Problem with Time Window (CVRPTW), and user interests are shaped by attributes such as rating and cost of points of interest (POIs), travel duration, the number of POIs in the itinerary, and penalty attributes (POI penalty and time penalty). The generated solution must adhere to constraints like daily travel duration limits and the operational hours of POIs. To ensure alignment with user interests, the Multi-Utility Attribute Theory (MAUT) is employed as the fitness function. This study proposes a VRP approach utilizing the hybrid Ant Colony System (ACS) and Brainstorm optimization (BSO) algorithm (the hybrid ACS-BSO) for multi-day travel itinerary generation, addressing the Traveling Salesman Problem (TSP) approach limitations. The hybrid ACS-BSO outperforms conventional algorithms, such as Genetic Algorithm (GA), Tabu Search (TS), and Simulated Annealing (SA), across 5 sets of random POIs with an average fitness value of 0.6704. moreover, the hybrid ACS-BSO outperforms the other conventional algorithms in optimizing each attribute. In terms of travel duration attribute, the hybrid ACS-BSO generate an itinerary requiring only 6 days to visit 40 POIs, while the other algorithms need 7 days. In terms of cost and rating attributes, the hybrid ACS-BSO achieves the best fitness values compared to the others. Furthermore, the hybrid ACS-BSO outperforms the standalone algorithms (ACS and BSO) across varying numbers of POIs, but it faces a maximum 311 seconds running time for 87 POIs, indicating a time complexity weakness. Comparatively, ACS, BSO, and the hybrid ACS-BSO in VRP approach surpass their TSP counterparts, affirming the effectiveness of the VRP approach.

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Section: Related Workmentioning

confidence: 99%

Generating a Multi-Day Travel Itinerary Recommendation Using the Hybrid Ant Colony System and Brainstorm Optimization Algorithm

2024

IJIES

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“…Then, they become the objective of any optimization work to find the most efficient way to achieve any goal using limited resources. Due to its characteristics, optimization has become a popular process in many studies in operations research, such as manufacturing [1], health care [2], transportation [3], education [4], and so on.…”

Section: Introductionmentioning

confidence: 99%

“…Maximization is related to the return that is wanted to achieve, such as revenue [5], profit, number of customers, etc. Contrary, minimization is related to resources that are utilized, such as cost [3], energy consumption [6], travel distance [7], processing time [8], delay [9], and so on. Meanwhile, the optimization process should be conducted within certain areas called boundaries, problem spaces, or constraints.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, the optimization process should be conducted within certain areas called boundaries, problem spaces, or constraints. For example, in the vehicle routing problem, vehicles should operate within a specific range of operational time [10], and goods loaded cannot surpass a particular maximum capacity [3]. The other example in the healthcare system is that all patients should be served within the given rooms [2] or nurses [2].…”

Section: Introductionmentioning

confidence: 99%

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https://inass.org/wp-content/uploads/2022/05/2022083131-2.pdf

2022

IJIES

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This work proposes a new metaheuristic algorithm: a fixed-step average and subtraction-based optimizer (FS-ASBO). This algorithm is the improved version of the average and subtraction-based optimizer (ASBO). There are several improvements related to the original ASBO. First, the proposed algorithm replaces the randomized step size in the guided movement with the fixed step size. Second, the proposed algorithm adds an exploration mechanism after the guided movement in every iteration when the new candidate fails to find a better solution. This proposed algorithm is then implemented into a simulation to evaluate its performance. Through simulation, the proposed algorithm is challenged to solve theoretical optimization problems and real-world optimization problems. The 23 well-known benchmark functions represent the theoretical optimization problem. Meanwhile, the housing optimization problem represents the real-world one. In the simulation, the proposed algorithm is compared with particle swarm optimization (PSO), marine predator algorithm (MPA), Komodo mlipir algorithm (KMA), static Komodo algorithm (SKA), and ASBO. The result shows that this proposed algorithm is competitive to solve theoretical problem and superior to solve real-world problem. The proposed algorithm outperforms all sparing algorithms in solving seven functions. In housing optimization problem, it creates 12%, 10%, 8%, 11%, and 10% better total gross profit than the ASBO, PSO, MPA, KMA, and SKA.

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The Combination Of K-Means And A* Methods For Determining The Best Route For Vegetable Sellers

Prasetyo,

Arius,

Prasetyo

et al. 2024

2024 International Electronics Symposium (IES)

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Multi-Depot Capacitated Vehicle Routing Problem by Using Stable Marriage and K-Means Clustering to Minimize Number of Unserved Customers and Total Travel Distance

Cited by 4 publications

References 24 publications

Generating a Multi-Day Travel Itinerary Recommendation Using the Hybrid Ant Colony System and Brainstorm Optimization Algorithm

Generating a Multi-Day Travel Itinerary Recommendation Using the Hybrid Ant Colony System and Brainstorm Optimization Algorithm

https://inass.org/wp-content/uploads/2022/05/2022083131-2.pdf

The Combination Of K-Means And A* Methods For Determining The Best Route For Vegetable Sellers

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