Open AccessSequential Routing-Loading Algorithm for Optimizing One-Door Container Closed-Loop Logistics Operations
Author(s) -
Paulina Kus Ariningsih,
Titi Iswari,
Kevin Djoenneady Poetra,
Y.M. Kinley Aritonang
Publication year - 2020
Publication title -
jurnal optimasi sistem industri
Language(s) - English
Resource type - Journals
eISSN - 2442-8795
pISSN - 2088-4842
DOI - 10.25077/josi.v19.n2.p122-132.2020
Subject(s) - container (type theory) , vehicle routing problem , simulated annealing , computer science , pickup , routing (electronic design automation) , genetic algorithm , truck , algorithm , process (computing) , set (abstract data type) , mathematical optimization , real time computing , engineering , automotive engineering , embedded system , mathematics , artificial intelligence , mechanical engineering , programming language , machine learning , image (mathematics) , operating system
One-door container type of vehicle is the main tool for urban logistics in Indonesia which may take the form of truck, car, or motorcycle container. The operations would be more effective when it is performed through pickup-delivery or forward-reverse at a time. However, there is difficulty to optimize the operation of routing and container loading processes in such a system. This article is proposing an improvement for algorithm for sequential routing- loading process which had been tested in the small datasets but not yet tested in the case of big data set and vehicle routing problem with time windows. The improvement algorithm is tested in big data set with the input of the vehicle routing problem with time windows (VRP-TW) using the solution optimization of the Simulated Annealing process with restart point procedure (SA-R) for the routing optimization and Genetic Algorithm (GA) to optimize the container loading algorithm. The large data sets are hypothetical generated data for 800-2500 single-sized products, 4 types of container capacity, and 100-400 consumer spots. As result, the performance of the proposed algorithm in terms of cost is influenced by the number of spots to be visited by the vehicle and the vehicle capacity. Limitations and further analysis are also described in this article.