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<![CDATA[Pengembangan Algoritma Hybrid Restart Simulated Annealing with Variable Neighborhood Search (HRSA-VNS) untuk penyelesaian kasus Vehicle Routing Problem with Time Windows (VRPTW) ]]>
<![CDATA[Iswari, Titi]]>
<![CDATA[ Jurnal Rekayasa Sistem Industri Vol 6, No 1 (2017): Jurnal Rekayasa Sistem Industri ]]>
Publisher : <![CDATA[Universitas Katolik Parahyangan ]]>
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DOI: 10.26593/jrsi.v6i1.2427.49-56
<![CDATA[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.Keywords : vehicle routing problem, time windows, simulated annealing, VNS, restart]]>
<![CDATA[Pengembangan Model Blood Mobile Collection Routing Problem (BMCRP) pada Proses Pengumpulan Darah ]]>
<![CDATA[Iswari, Titi]]>;
<![CDATA[Setiawan, Fran]]>;
<![CDATA[Sitompul, Carles]]>
<![CDATA[ Jurnal Rekayasa Sistem Industri Vol 7, No 2 (2018): Jurnal Rekayasa Sistem Industri ]]>
Publisher : <![CDATA[Universitas Katolik Parahyangan ]]>
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DOI: 10.26593/jrsi.v7i2.2769.65-72
<![CDATA[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.]]>
<![CDATA[Pengembangan Algoritma Hybrid Restart Simulated Annealing with Variable Neighborhood Search (HRSA-VNS) untuk penyelesaian kasus Vehicle Routing Problem with Time Windows (VRPTW) ]]>
<![CDATA[Titi Iswari]]>
<![CDATA[ Jurnal Rekayasa Sistem Industri Vol. 6 No. 1 (2017): Jurnal Rekayasa Sistem Industri ]]>
Publisher : <![CDATA[Universitas Katolik Parahyangan ]]>
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Full PDF (467.539 KB)
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DOI: 10.26593/jrsi.v6i1.2427.49-56
<![CDATA[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.Keywords : vehicle routing problem, time windows, simulated annealing, VNS, restart]]>
<![CDATA[Pengembangan Model Blood Mobile Collection Routing Problem (BMCRP) pada Proses Pengumpulan Darah ]]>
<![CDATA[Titi Iswari]]>;
<![CDATA[Fran Setiawan]]>;
<![CDATA[Carles Sitompul]]>
<![CDATA[ Jurnal Rekayasa Sistem Industri Vol. 7 No. 2 (2018): Jurnal Rekayasa Sistem Industri ]]>
Publisher : <![CDATA[Universitas Katolik Parahyangan ]]>
Show Abstract
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Full PDF (2747.15 KB)
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DOI: 10.26593/jrsi.v7i2.2769.65-72
<![CDATA[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.]]>
<![CDATA[MENENTUKAN METODE INPUT PROBABILITY DISTRIBUTION DALAM PEMODELAN DAN SIMULASI DI ANTRIAN KASIR TOKO BUKU PT. XYZ ]]>
<![CDATA[Mega Purnamasari]]>;
<![CDATA[Titi Iswari]]>;
<![CDATA[Frylie Frescia Falen]]>
<![CDATA[ Jurnal PASTI (Penelitian dan Aplikasi Sistem dan Teknik Industri) Vol 13, No 3 (2019): Jurnal PASTI ]]>
Publisher : <![CDATA[Universitas Mercu Buana ]]>
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DOI: 10.22441/pasti.2019.v13i3.003
<![CDATA[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.]]>
<![CDATA[Sequential Routing-Loading Algorithm for Optimizing One-Door Container Closed-Loop Logistics Operations ]]>
<![CDATA[Paulina Kus Ariningsih]]>;
<![CDATA[Titi Iswari]]>;
<![CDATA[Kevin Djoenneady Poetra]]>;
<![CDATA[Yoon Mac Kinley Aritonang]]>
<![CDATA[ Jurnal Optimasi Sistem Industri Vol. 19 No. 2 (2020): Published in November 2020 ]]>
Publisher : <![CDATA[The Industrial Engineering Department of Engineering Faculty at Universitas Andalas ]]>
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DOI: 10.25077/josi.v19.n2.p122-132.2020
<![CDATA[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.]]>
<![CDATA[Proposing an Algorithm to Solve the Forward and Reverse Logistics Distribution Problem with One Door Container ]]>
<![CDATA[Stephen Sanjaya Budi]]>;
<![CDATA[Paulina Kus Ariningsih]]>;
<![CDATA[Titi Iswari]]>
<![CDATA[ Jurnal Teknik Industri Vol. 21 No. 1 (2019): June 2019 ]]>
Publisher : <![CDATA[Institute of Research and Community Outreach - Petra Christian University ]]>
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DOI: 10.9744/jti.21.1.1-14
<![CDATA[Forward and reverse logistics are two types of distribution methods that shall be synergized in practices. Two problems in synergizing the two distributions type are (1) how to route vehicles and (2) how to pack the goods inside the vehicle. A truck with only one door for loading and unloading process could create numerous problems of item packing activities. An item picked up from a customer could occasionally block other goods which need to be delivered; hence, the courier shall unload other items before the loading process. This condition will increase the probability of item damage, longer on-loading/off-loading (lo/lo) time, and higher lo/lo cost because of the rapid item movement. Therefore, this article aims to propose an algorithm to solve the problem by creating an algorithm hybrid of routing and packing to find the solution for routing and packing problem, sequentially, with a metaheuristic approach. The proposed method calculates the cost from routing procedure and sum of item movement in every loading and unloading process. Based on the trial on 25 cases, this algorithm generates 59.64% of the containers have zero goods repacking. Several potential future research avenues are also proposed in this article.]]>
<![CDATA[Pengembangan Model Sinkronisasi Distribusi Dua Eselon pada Urban Consolidation Center (UCC) di Sistem Logistik Perkotaan ]]>
<![CDATA[Iswari, Titi]]>
<![CDATA[ Jurnal PASTI (Penelitian dan Aplikasi Sistem dan Teknik Industri) Vol 19, No 2 (2025): Jurnal PASTI ]]>
Publisher : <![CDATA[Universitas Mercu Buana ]]>
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DOI: 10.22441/pasti.2025.v19i2.002
<![CDATA[Distribusi logistik dua eselon di kawasan perkotaan menghadapi tantangan kompleks, seperti keterbatasan kapasitas kendaraan, kapasitas penyimpanan, dan optimasi rute distribusi. Penelitian ini bertujuan mengembangkan model matematis untuk menyinkronkan distribusi dua eselon pada sistem Urban Consolidation Center (UCC), dengan mengintegrasikan aspek inventori dan perutean kendaraan secara simultan. Model mencakup tiga skema distribusi pada eselon pertama (Direct & No Inventory, Direct–Inventory, dan Route–Inventory). Optimasi pada eselon pertama diselesaikan menggunakan AMPL dengan solver CPLEX, sedangkan persoalan distribusi pada eselon kedua diseleaikan dengan pendekatan Economic Order Quantity (EOQ) dan algoritma Large Neighborhood Search (LNS). Eksperimen dilakukan dengan berbagai skenario, seperti jumlah retailer, supplier, dan biaya penyimpanan. Skema ketiga (Route & Inventory) terbukti menghasilkan total biaya logistik paling rendah, dengan efisiensi biaya yang konsisten di berbagai konfigurasi, mencapai penghematan sebesar 20 - 60%. Model ini berkontribusi dalam perancangan sistem logistik perkotaan yang lebih hemat biaya dan efisien.]]>
<![CDATA[Sequential Routing-Loading Algorithm for Optimizing One-Door Container Closed-Loop Logistics Operations ]]>
<![CDATA[Paulina Kus Ariningsih]]>;
<![CDATA[Titi Iswari]]>;
<![CDATA[Kevin Djoenneady Poetra]]>;
<![CDATA[Yoon Mac Kinley Aritonang]]>
<![CDATA[ Jurnal Optimasi Sistem Industri Vol. 19 No. 2 (2020): Published in October 2020 ]]>
Publisher : <![CDATA[The Industrial Engineering Department of Engineering Faculty at Universitas Andalas ]]>
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DOI: 10.25077/josi.v19.n2.p122-132.2020
<![CDATA[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.]]>
<![CDATA[Development Of A Vehicle Routing Problem Model To Reduce Service Delays In Pickup And Delivery Operations ]]>
<![CDATA[Gilbert Stefhen]]>;
<![CDATA[Dedy Suryadi]]>;
<![CDATA[Titi Iswari]]>
<![CDATA[ Journal of Social Research Vol. 4 No. 8 (2025): Journal of Social Research ]]>
Publisher : <![CDATA[International Journal Labs ]]>
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DOI: 10.55324/josr.v4i7.2683
<![CDATA[The rapid growth of the e-commerce sector in Indonesia has increased the demand for reliable and timely logistics services. However, challenges such as delayed pickups and deliveries continue to occur, leading to the accumulation of operational penalties. This study aims to develop a mathematical model based on the Vehicle Routing Problem (VRP) that addresses the complexities of modern logistics distribution. The proposed model, named HFMTVRPDPTW (Heterogeneous Fleet Multi-Trip Vehicle Routing Problem with Delivery and Pickup and Time Windows), incorporates five key elements: vehicle capacity, fleet heterogeneity, service time windows, simultaneous pickup and delivery, and multi-trip operations within a single working period. A systematic literature review of twenty-one prior studies was conducted to identify research gaps and inform the model’s development. The model is formulated using an integer linear programming (ILP) approach and aims to optimize service time accuracy while minimizing delays. The findings suggest that this model provides a more comprehensive and realistic approach to distribution challenges and offers an original contribution to the development of adaptive and efficient VRP models for logistics operations.]]>
