A bi-level school bus routing problem with bus stops selection and possibility of demand outsourcing

Parvasi, Seyed Parsa; Mahmoodjanloo, Mehdi; Setak, Mostafa

doi:10.1016/j.asoc.2017.08.018

Cited by 55 publications

(19 citation statements)

References 48 publications

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“…Constraints (25) illustrates that every Pioneer vessel can only stop by at the destination port for one time in one round voyage. Constraints (26) meter) causes the Pioneer vessel to not being able to stop by, therefore Sea Tollway vessel can stop by at port -r if the depth of port -r pool is larger than the draft of Sea Tollway vessel. Constraints (39) when there is wave problem (≥ 3 meter) in port -r, therefore Pioneer vessel is rerouted to the next port -r. Time Windows constraints is shown on equation (40)-(48).…”

Section: (23)mentioning

confidence: 99%

“…Saranwong and Likasiri [25] also developed upper level model to determine the distribution central location and lower level to minimalize the total customer cost. Essentially, bilevel model is very suitable for decision making system, namely on case of Parvasi et al [26], Bingfeng et al [27] dan Liu and Ceder [28] who modeled bus route and found the best route.…”

Section: Introductionmentioning

confidence: 99%

“…The origin of distribution destination and demand estimation ), J (40), M(18), N (37), O (50), P(30), Q(23), R (36), S (48), T(26), U(39), V(27),W (35), X (15), Y (20), Z (12), AA (15), AB (5), AC (16), AD (47) 565 Ton E G (25), H (40), AE (20), AF (37), AG (31), AH (20), AI (37), AJ (32), AK (35), AL (22), AM (28), AN (26), AO (35), AP (49), AQ (30), AR (22), AS (21), AT (30), AU (27), AV (35), AW (37), AX (15), AY (20), AZ (13), BA (38), BB (30), 755 Ton F K (15), L (24), BC (12), BE (31), BF (29), BG (31), BH (32), BI (30), BJ (34), BK (35), BL (34), BM (30), BN (30), BO (9), BP (22), BQ (15), BR (27), BS (27), BT (25), BU (15), BV (20), BW (12), BX (15), BY (15), BZ (20), CA (20), CB (24), CC (25), CD (4),CE (10), Information: A = Tanjung Priok; B = Tanjung Perak; C = Makassar; D = Ambon; E = Tual; F = Saumlaki; G = Banda Neira; H = Dobo; I = Namlea; J = Namrole; K = Moa; L = Kisar; M = Ambalau; N = Wamsisi; O = Leksula; P = Tifu; Q = Waemulang; R = Fogi; S = Manipa; T = Kelang; U = Buano; V = Taniwel; W = Wahai; X = Kobisadar; Y = Bula; Z = Kelimoi; AA = P. Geser; AB = Kelimuri; AC = Werinama; AD = Amahai; AE = P. Manawoka; AF = Kailakat; AG = P. Kesui; AH = P. Tior; AI = Banda Eli; AJ = Benjina; AK = Tabarfane; AL = Jerol; AM = Meror; AN = Longgar; AO = Gorom; AP = Kaisui; AQ = Kaimer; AR = Mangur; AS = Fadol; AT = P. Kur; AU = Toyando; AV = Tam; AW= Holat; AX = Weduar; AY = Elat; AZ = Mun; BA = Marlasi; BB = Lelam Kojabi; BC = Wunlah; BE = Rumean; BF = Lewa/Dai; BG = Serua; BH = Nila; BI = Teon; BJ = Wulur; BK = Lakor; BL = P. Luang; BM = Lelang; BN = Tepa; BO = Larat; BP = Sofyanin/Rumayaan; BQ = Seira; BR = Nurkat; BS = Molu; BT = Tutu Kembong; BU = Adault/Lingat; BV = Marsela; BW= Dawera/Dawelor; BX = Kroing; BY = Bebar; BZ = Ilwaki; CA = Arwala/ Sutilirang; CB = P. Leti; CC = Eray; CD = Romang; CE = Kisar1…”

mentioning

confidence: 99%

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Bi-Level Model on Freight Distribution Network Integration in Archipelagic Region with Milk Run Time Windows and Uncertainty

Humang

Hadiwardoyo²,

Nahry³

2020

International Journal of Engineering Research and Technology

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As an archipelagic country, Indonesian government gives subsidy and public service obligation (PSO) to Sea Tollway, Pelni and Pioneer vessels to distribute freights to small islands. These three types of vessel are independently managing their own time and network. As the result, the distribution network becomes unintegrated and not optimal in suppressing transport cost and subsidy-PSO. The aim of this paper is to develop and implement bi-level model in sea transport network integration on freight distribution system on archipelagic territory. Upper level model is intended to minimalize the number of operated vessels, while lower level model is intended to determine the route that maximize the profit in freight distribution that involves Sea Tollway, Pelni and Pioneer vessels. The application of genetic algorithm (GA) in problem solving on milk run time windows (MRTW) network can accommodate the uncertainty variable, namely load and wave height. The result of model validation with empirical test on the case in Indonesia shows that the model can gives optimal value in solving the network integration problem. The sensitivity analysis shows that the number of operating vessels and the uncertainty variable intervention on the determination of vessel route affect the clustering. The management of vessel route should be integrated in real time by factoring the wave height variable, with consequence that the received gross profit is decreasing by 11,8% when compared to the condition without the wave height consideration.

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Section: (23)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Bi-Level Model on Freight Distribution Network Integration in Archipelagic Region with Milk Run Time Windows and Uncertainty

Humang

Hadiwardoyo²,

Nahry³

2020

International Journal of Engineering Research and Technology

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“…Terdapat beberapa peneliti yang telah melakukan penelitian terkait dengan SBRP [4,5,6]. Li dan Fu [7] membahas terkait SBRP dengan meggunakan multi-objective combinatorial optimization.…”

Section: Pendahuluanunclassified

Penentuan Rute Dan Jumlah Kendaraan Pada Kasus School Bus Routing Problem: Penerapan Algoritma Record-to-record Travel

Ramadhan

Imran

2018

JRSI

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Program bus sekolah gratis merupakan salah satu program yang sedang dikembangkan untuk menunjang kemajuan pendidikan. Penentuan rute dan jumlah bus sekolah yang dapat meminimumkan biaya perjalanan dan pengadaan kendaran merupakan aspek penting dalam menerapkan program bus sekolah tersebut. Penelitian ini membangun algoritma dan program perangkat lunak untuk model school bus routing problem (SBRP) yang dapat meminimisasi biaya perjalanan dan biaya investasi jumlah kendaraan bus. Algoritma record-to-record travel digunakan untuk mendapatkan solusi yang baik dengan waktu komputasi yang relatif cepat pada program perangkat lunak yang dibangun. Karakteristik model SBRP pada penelitian ini, bus memiliki kapasitas terbatas dan homogen, titik awal bus berada di depot, lalu mendatangi beberapa halte dimana setiap halte memiliki jumlah penumpang yang deterministik. Setelah mendatangi halte terakhir, bus akan mendatangi halte dengan arah kebalikannya yang diakhiri di titik awal (depot). Program bus sekolah gratis di Kota Bandung dijadikan objek penelitian mengingat program tersebut sedang diterapkan dan dievaluasi untuk meningkatkan efektivitas penggunaannya. Hasil yang didapatkan pada penelitian ini, model SBRP dan algoritma record-to-record travel yang dibangun dan diujikan pada kasus bus sekolah di Kota Bandung memperlihatkan solusi yang layak dan baik. Proses pengelompokkan bus per wilayah Kota Bandung dapat dihasilkan dan menunjukkan biaya perjalanan dan biaya investasi pengadaan kendaraan bus yang minimum. Selain itu, algoritma ini terbukti dapat menyelesaikan permasalahan SBRP dengan waktu komputasi yang cepat, yaitu dibawah 10 detik.

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“…Miranda et al [7] expand the concept of mixed-load to multi-load, which means that the students can be picked up and delivered simultaneously. Taking into account that the use of private cars depends on how well public transportation systems operate, Parvasi et al [8] formulate a bilevel mathematical model, which considers the possibility of estimating the number of students who choose the public transport system. Mokhtari and Ghezavati [9] also consider a mixed load school bus routing problem and propose a hybrid multiobjective ant colony optimization algorithm to solve it, which involves two objectives: minimizing the number of the buses and minimizing the average riding time of the students.…”

Section: Introductionmentioning

confidence: 99%

A Partial Allocation Local Search Matheuristic for Solving the School Bus Routing Problem with Bus Stop Selection

et al. 2020

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This paper addresses the school bus routing problem with bus stop selection, which jointly handles the problems of determining the set of bus stops to visit, allocating each student to one of these bus stops and computing the routes that visit the selected bus stops, so that the total routing cost is minimized and the walking distance of the students is limited by a given value. A fast and efficient matheuristic is developed based on an innovative approach that first partially allocates the students to a set of active stops that they can reach, and computes a set of routes that minimizes the routing cost. Then, a refining process is performed to complete the allocation and to adapt the routes until a feasible solution is obtained. The algorithm is tested on a set of benchmark instances. The computational results show the efficiency of the algorithm in terms of the quality of the solutions yielded and the computing time.

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A bi-level school bus routing problem with bus stops selection and possibility of demand outsourcing

Cited by 55 publications

References 48 publications

Bi-Level Model on Freight Distribution Network Integration in Archipelagic Region with Milk Run Time Windows and Uncertainty

Bi-Level Model on Freight Distribution Network Integration in Archipelagic Region with Milk Run Time Windows and Uncertainty

Penentuan Rute Dan Jumlah Kendaraan Pada Kasus School Bus Routing Problem: Penerapan Algoritma Record-to-record Travel

A Partial Allocation Local Search Matheuristic for Solving the School Bus Routing Problem with Bus Stop Selection

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