Vehicle routing problem with time windows considering overtime and outsourcing vehicles

Moon, Ilkyeong; Lee, Jeong-Hun; Seong, June

doi:10.1016/j.eswa.2012.05.081

Cited by 37 publications

(15 citation statements)

References 26 publications

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“…Restricting the route lengths and minimizing these is similar to the studies of Moon et al (2012) and Belhaiza et al (2014). In Moon et al (2012), the sum of the travel cost, regular driver labor cost and overtime driver labor cost is minimized, and in Belhaiza et al (2014) the sum of the travel times and the waiting times is minimized. These two objective functions are equivalent to minimizing the route lengths.…”

Section: Vehicle Routing Subproblemmentioning

confidence: 99%

Integration of order picking and vehicle routing in a B2C e-commerce context

et al. 2017

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E-commerce sales are increasing every year and customers who buy goods on the Internet have high service level expectations. In order to meet these expectations, a company's logistics operations need to be performed carefully. Optimizing only internal warehouse processes will often lead to suboptimal solutions. The interrelationship between the order picking process and the delivery process should not be ignored. Therefore, in this study, an order picking problem and a vehicle routing problem with time windows and release dates are solved simultaneously using a single optimization framework. To the best of our knowledge, it is the first time that an order picking problem and a vehicle routing problem are integrated. A mixed integer linear programming formulation for this integrated order picking-vehicle routing problem (OP-VRP) is provided. The integrated OP-VRP is solved for small instances and the results are compared to these of an uncoordinated approach. Computational experiments show that integration can lead to cost savings of 14% on average. Furthermore, higher service levels can be offered by allowing customers to request their orders later and still get delivered within the same time windows.

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Section: Vehicle Routing Subproblemmentioning

confidence: 99%

Integration of order picking and vehicle routing in a B2C e-commerce context

et al. 2017

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“…Penemuan solusi pada proses distribusi merupakan bagian dari proses pengambilan keputusan dalam manajemen rantai pasokan [1]. Berbagai masalah terkait dengan proses distribusi telah dibahas dalam beberapa penelitian, contohnya adalah distribusi makanan yang mudah rusak.…”

Section: Pendahuluanunclassified

Optimasi Pendistribusian Barang Farmasi Menggunakan Algoritma Genetika

Ramadhani

Fathurrachman

Fitriawanti

et al. 2018

klik

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Distribution is an activity of distributing goods from factory to agents. Distribution process is considered efficient if the process of distribution of goods done with a minimal distance, so that the time and cost required for the distribution process will also be smaller. Genetic algorithm is used to optimize the pharmaceutical goods distribution process by finding the order of agents that each vehicle must visit during the distribution process. The data used is the cost and distance data between factory and each agent. One-cut point method is used for crossover process, reciprocal exchange method is used for mutation process, and elitism method for selection process. Based on the test result that has been done, the optimal parameters which are used to produce the best solution, such as the population size is 45, the generation number is 70, and the combination of cr and mr is 0.8 and 0.3. By using the best parameters, the resulting fitness value is in the range 0.014909 up to 0.017642. Keywords: Genetic Algorithm, Distribution, Pharmaceutical, Optimization Distribusi merupakan kegiatan menyalurkan barang dari pabrik ke agen. Proses distribusi dianggap efisien jika proses penyaluran barang dilakukan dengan jarak yang minimal, sehingga waktu dan biaya yang dibutuhkan untuk proses distribusi juga akan semakin kecil. Algoritma genetika digunakan untuk melakukan optimasi pada proses distribusi barang farmasi dengan mencari solusi berupa urutan agen yang harus dikunjungi oleh setiap kendaraan saat proses distribusi. Data yang digunakan adalah data biaya dan jarak antara pabrik dengan masing-masing agen. Metode one-cut point digunakan untuk proses crossover, metode reciprocal exchange digunakan untuk proses mutasi, dan metode elitism untuk proses seleksi. Berdasarkan hasil pengujian yang telah dilakukan, parameter optimal yang digunakan untuk menghasilkan solusi terbaik, antara lain ukuran populasi sebanyak 45, generasi sebanyak 70, serta kombinasi cr dan mr yaitu 0.8 dan 0.3. Dengan menggunakan parameter terbaik tersebut, nilai fitness yang dihasilkan berada pada rentang 0.014909 sampai dengan 0.017642. Kata kunci: Algoritma Genetika, Distribusi, Farmasi, Optimasi

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“…This objective function has to be minimized. Constraints (2) and (3) indicate that each node has to be visited and abandoned only once, while Section (4) guarantees the absence of sub-tours and indicates that any subset of nodes S has to be abandoned at least 1 time. This restriction is vital, because it avoids the presence of cycles.…”

Section: A Formulationmentioning

confidence: 99%

“…Over the last decades GAs have been the focus of a large number of papers and books [1,2] and it has been applied in a wide range of fields, for example, transport [3], software engineering [4] or industry [5].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the suitability of using blind crossover operators in genetic algorithms for solving routing problems

Osaba

Carballedo

Díaz

et al. 2013

2013 IEEE 8th International Symposium on Applied Computational Intelligence and Informatics (SACI)

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Genetic algorithms (GA) are one of the most successful techniques in solving combinatorial optimization problems. Its general character has enabled its application to different types of problems: vehicle routing, planning, scheduling, etc... This article shows that there is controversy in the basic structure of the algorithm steps when it is applied at routing problems. Specifically in this paper we show that the crossover (CX) offers no advantage in the optimization process. To solve such problems, the most important steps are mutation and selection of individuals. These two steps are what help to analyze the solution space exhaustively and give GA optimization capability. To prove our hypothesis we will analyze the results obtained by applying different blind crossover operators to solve multiple instances of the TSP (Travelling Salesman Problem).

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Vehicle routing problem with time windows considering overtime and outsourcing vehicles

Cited by 37 publications

References 26 publications

Integration of order picking and vehicle routing in a B2C e-commerce context

Integration of order picking and vehicle routing in a B2C e-commerce context

Optimasi Pendistribusian Barang Farmasi Menggunakan Algoritma Genetika

Analysis of the suitability of using blind crossover operators in genetic algorithms for solving routing problems

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