Pengembangan Algoritma Hybrid Restart Simulated Annealing with Variable Neighborhood Search (HRSA-VNS) Untuk Penyelesaian Kasus Vehicle Routing Problem with TIME Windows (VRPTW)

Determining the vehicle routing is one of the important components in existing logistics systems. It is because the vehicle route problem has some effect on transportation costs and time required in the logistics system. In determining the vehicle routes, there are some restrictions faced, such as the maximum capacity of the vehicle and a time limit in which depot or customer has a limited or spesific opening hours (time windows). This problem referred to Vehicle Routing Problem with Time Windows (VRPTW). To solve the VRPTW, this study developed a meta-heuristic method called Hybrid Restart Simulated Annealing with Variable Neighborhood Search (HRSA-VNS). HRSA-VNS algorithm is a modification of Simulated Annealing algorithm by adding a restart strategy and using the VNS algorithm scheme in the stage of finding neighborhood solutions (neighborhood search phase). Testing the performance of HRSA-VNS algorithm is done by comparing the results of the algorithm to the Best Known Solution (BKS) and the usual SA algorithm without modification. From the results obtained, it is known that the algorithm perform well enough in resolving the VRPTW case with the average differences are -2.0% with BKS from Solomon website, 1.83% with BKS from Alvarenga, and -2.2% with usual SA algorithm without any modifications

Pengembangan Model Blood Mobile Collection Routing Problem (BMCRP) pada Proses Pengumpulan Darah

This research develop a model of blood mobile collection using blood donor vehicle efficiently by determining the optimal route of blood collection to the points of blood collection. The model developed in the form of mixed integer nonlinear programming (MINLP) and this model is called Blood Mobile Collection Routing Problem (BMCRP). The purpose of this model is to minimize the total distance of the blood collection routing process in which each place of blood collection has the opening hours and the closing time (time windows) and the service time in each place. This study considers the blood age (spoilage time) for 6 hours to ensure blood quality. The mathematical model is then verified to determine whether the solution is in accordance with the characteristics of BMCRP. Verification is done by solving Blood Mobile Collection Routing small cases. The simulation of solving BMCRP is done by generating eight hypothetical data sets of small cases based on vehicle routing data problems with different characteristics. Verification of BMCRP uses LINGO software. From the simulation results, the BMCRP model can obtain optimal solutions with minimum total distance travelled and does not violate any constraints on BMCRP

Pengembangan Model Blood Mobile Collection Routing Problem (BMCRP) pada Proses Pengumpulan Darah

This research develop a model of blood mobile collection using blood donor vehicle efficiently by determining the optimal route of blood collection to the points of blood collection. The model developed in the form of mixed integer nonlinear programming (MINLP) and this model is called Blood Mobile Collection Routing Problem (BMCRP). The purpose of this model is to minimize the total distance of the blood collection routing process in which each place of blood collection has the opening hours and the closing time (time windows) and the service time in each place. This study considers the blood age (spoilage time) for 6 hours to ensure blood quality. The mathematical model is then verified to determine whether the solution is in accordance with the characteristics of BMCRP. Verification is done by solving Blood Mobile Collection Routing small cases. The simulation of solving BMCRP is done by generating eight hypothetical data sets of small cases based on vehicle routing data problems with different characteristics. Verification of BMCRP uses LINGO software. From the simulation results, the BMCRP model can obtain optimal solutions with minimum total distance travelled and does not violate any constraints on BMCRP

Sequential Routing-Loading Algorithm for Optimizing One-Door Container Closed-Loop Logistics Operations

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

Simulated Annealing for the Blood Pickup Routing Problem

[[abstract]]Blood service operation is a key component of healthcare systems all over the world. Transporting time-sensitive products like blood presents a specific and unique challenge, as such perishable products have a spoilage time before which they must be used to avoid deterioration. Therefore, if the spoilage time is exceeded, then the product should be discarded. The blood pickup routing problem (BPRP) focuses on the upstream activities of blood logistics, starting from collection or pickup of blood bags at fixed donation sites and then delivering the blood bags to the blood bank. BPRP is an extension of the well-known vehicle routing problem with time windows. In BPRP, the set of vehicle routes are constructed to minimize total distance while at the same time observing time window constraints of donation sites and the spoilage time constraint of the blood. More specifically, each donation site has its time window during which a vehicle can start collecting blood bags at the site. The blood bags must be transported back to the blood bank before the bank’s closing time and the blood’s spoilage time. This study develops a mathematical programming model for BPRP. Since CPLEX can only solve the model to optimality for small instances, this study proposes a simulated annealing (SA) based heuristic approach to solve medium and large BPRP instances. We test the proposed SA heuristic on BPRP instances generated by this study. Computational results show that the proposed SA heuristic performs well on solving BPRP

MENENTUKAN METODE INPUT PROBABILITY DISTRIBUTION DALAM PEMODELAN DAN SIMULASI DI ANTRIAN KASIR TOKO BUKU PT. XYZ

PT XYZ merupakan perusahaan yang memiliki banyak toko penjualan buku yang tersebar di Indonesia. PT XYZ berusaha melakukan perbaikan yang berkesinambungan dalam melakukan pelayanan terhadap pelanggan salah satunya adalah antrian di kasir. Sistem dapat diperbaiki dengan cara membuat skenario-skenario yang kemudian dilakukan pemodelan dan simulasi. Kita harus bisa memilih metode pemodelan dan simulasi yang terbaik yang memiliki dampak error terkecil. Memodelkan dan simulasi sistem nyata dapat dilakukan dengan beberapa pendekatan dalam input probability distribution antara lain metode trace driven, metode empris, dan metode teoritis. Pada penelitian ini dilakukan pemodelan dan simulasi di antrian kasir di PT XYZ dengan menggunakan ketiga metode pendekatan tersebut. Dari ketiga hasil simulasi didapatkan bahwa semua hasil waiting time dari ketiga metode tidak memiliki perbedaan yang berarti antara data sistem nyata dengan model sistem nyata. Kemudian dari selisih total waiting time, utilisasi, dan jumlah pelanggan yang terlayani metode trace driven adalah metode terbaik

Menentukan Metode Input Probability Distribution dalam Pemodelan dan Simulasi di Antrian Kasir Toko Buku PT. Xyz

PT XYZ merupakan Perusahaan yang memiliki banyak toko penjualan buku yang tersebar di Indonesia. PT XYZ berusaha melakukan perbaikan yang berkesinambungan dalam melakukan pelayanan terhadap pelanggan salah satunya adalah antrian di kasir. Sistem dapat diperbaiki dengan cara membuat skenario-skenario yang kemudian dilakukan pemodelan dan simulasi. Kita harus bisa memilih metode pemodelan dan simulasi yang terbaik yang memiliki dampak error terkecil. Memodelkan dan simulasi sistem nyata dapat dilakukan dengan beberapa pendekatan dalam input probability distribution antara lain metode trace driven, metode empris, dan metode teoritis. Pada penelitian ini dilakukan pemodelan dan simulasi di antrian kasir di PT XYZ dengan menggunakan ketiga metode pendekatan tersebut. Dari ketiga hasil simulasi didapatkan bahwa semua hasil waiting time dari ketiga metode tidak memiliki perbedaan yang berarti antara data sistem nyata dengan model sistem nyata. Kemudian dari selisih total waiting time, utilisasi, dan jumlah pelanggan yang terlayani metode trace driven adalah metode terbaik