Genetic Algorithm in Vehicle Routing Problem

Zhang, Yueqin; Liu, Jinfeng; Fu, Duan; Ren, Jing

doi:10.1109/iihmsp.2007.4457776

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…This study aim some numerical test on a set of benchmark problems and indicate that tabu search out performs the best existing heuristics in VRP. The other metaheuristic algorithm was developed by Zhang, Liu, Duan, and Ren in 2007 [6]. They stated that genetic algorithm is an excellent approach to solving complex problem in optimization with difficult constraints.…”

Section: Stochastic Vehicle Routing Problem (Svrp)mentioning

confidence: 99%

Route Determination Method to Minimize Distribution Cost and Total Time Balance by Using Multi-Objective Genetic Algorithm

Krismodianto¹,

Santoso²

2019

Proceedings of the 2019 1st International Conference on Engineering and Management in Industrial System (ICOEMIS 2019)

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The subject of this research is a distribution company engaged in the distribution of eggs to many customers in Bandung. Distribution is carried out every day with 3 types of vehicles which are one way pick up, three ways pick up, and engkel truck. Based on preliminary research, the current route determination method is based on experience so the distance and travel time are thought could be improved. This affects the distribution costs that must be spent to distribute eggs to customers. Distributor also experience workload imbalance of vehicles that be used for the distribution process regarding to the travel time charged to each vehicle. The proposal in this study is used the metaheuristic method with the multi-objective genetic algorithm concerning to the multi-objective vehicle routing problem. This research is conducted during the fourth week of October 2018 and the chosen method is the proposed route determination method. The results of the comparison between the current system and the proposed system from this study obtained savings in terms of distribution costs of 18.03%, while in terms of total time balance, the distribution time range can be reduced to 95.52% so the workload of the vehicles are more balanced.

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Section: Stochastic Vehicle Routing Problem (Svrp)mentioning

confidence: 99%

Route Determination Method to Minimize Distribution Cost and Total Time Balance by Using Multi-Objective Genetic Algorithm

Krismodianto¹,

Santoso²

2019

Proceedings of the 2019 1st International Conference on Engineering and Management in Industrial System (ICOEMIS 2019)

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“…Algoritma genetik adalah pendekatan yang sangat baik untuk memecahkan masalah yang kompleks dalam optimasi dengan kendala yang sulit terutama pada Vehicle Routing Problem (VRP). Hasil percobaan menunjukkan bahwa algoritma genetika dengan menerapkan Finite Automaton (FA) dalam menghasilkan populasi individu untuk menyelesaikan permasalahan pada Vehicle Routing Problem (VRP) [9]. Menurut penelitian sebelumnya yang memberikan hasil efisien dalam implementasi algoritma genetika.…”

Section: Pendahuluanunclassified

Optimasi Algoritma Genetika Dalam Menentukan Rute Optimal Topologi Cincin Pada Wide Area Network

Muftikhali

Yudhistira

Kusumawati

et al. 2018

JIM

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ABSTRAKImplementasi jaringan komputer memiliki banyak masalah, terutama pada Wide Area Network (WAN), salah satunya adalah masalah routing. Routing masalah adalah bagaimana melakukan transmisi data antara node dengan jalur tercepat, terutama dalam topologi ring. Dalam topologi ring, setiap simpul hanya bisa dilalui oleh sebanyak satu. Untuk mengatasi masalah ini, kami menggunakan algoritma genetika. Kami telah diuji pada arsitektur wan dari PT. XYZ terletak di Kotawaringin Barat, Kalimantan Tengah. Dengan ukuran populasi 100, metode seleksi elitisme, dan tingkat mutasi 0,08, hasil yang optimal diperoleh dengan jarak akhir adalah 44. Penggunaan metode seleksi elitisme dan nilai rate mutasi sangat berpengaruh untuk mendapatkan rute yang optimal.

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“…The computation data was cited from [22]. It was shown as follows: a distribution center delivers products to eight customers by two vehicles; each customer's demand and the distance matrix were listed in Table IV, in which the value 0 indicates the distribution center.…”

Section: B Experiments and Resusltsmentioning

confidence: 99%

Lifecycle-based Swarm Optimization Method for Constrained Optimization

Shen¹,

Zhu²,

Jin³

et al. 2011

JCP

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Abstract-Each biologic must go through a process from birth, growth, reproduction until death, this process known as life cycle. This paper borrows the biologic life cycle theory to propose a Lifecycle-based Swarm Optimization (LSO) algorithm. Based on some features of life cycle, LSO designs six optimization operators: chemotactic, assimilation, transposition, crossover, selection and mutation. In this paper, the capability of the LSO to address constrained optimization problem was investigated. Firstly, the proposed method was test on some well-known and widely used benchmark problems. When compared with PSO, we can see that LSO can obtain the better solution and lower standard deviation than PSO on many different types of constrained optimization problems. Finally, LSO was also used for seeking the optimal route for vehicle route problem in logistics system. The result of LSO is the best when comparing with PSO and GA. The results of above two types of experiments, which include not only the ordinary benchmark problem but also the practical problems in engineering, demonstrate that LSO is a competitive and effective approach for solving constrained problems.

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Genetic Algorithm in Vehicle Routing Problem

Cited by 7 publications

References 3 publications

Route Determination Method to Minimize Distribution Cost and Total Time Balance by Using Multi-Objective Genetic Algorithm

Route Determination Method to Minimize Distribution Cost and Total Time Balance by Using Multi-Objective Genetic Algorithm

Optimasi Algoritma Genetika Dalam Menentukan Rute Optimal Topologi Cincin Pada Wide Area Network

Lifecycle-based Swarm Optimization Method for Constrained Optimization

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