Bilevel Optimization for the Hazmat Transportation Problem with Lane Reservation

Abstract

<jats:p>In this study, we investigate a bilevel optimization model for the hazmat transportation problem with lane reservation. The problem lies in selecting lanes to be reserved in the network and planning paths for hazmat transportation tasks. The trade-off among transportation cost, risk, and impact on the normal traffic is considered. By using the traffic flow theory, we quantify the impact on the normal traffic and modify the traditional risk measurement model. The problem is formulated as a multiobjective bilevel programming model involving the selection of reserved lanes for government and planning paths for hazmat carriers. Two hybrid metaheuristic algorithms based on the particle swarm optimization algorithm and the genetic algorithm, respectively, are proposed to solve the bilevel model. Their performance on small-scale instances is compared with exact solutions based on the enumeration method. Finally, the computational results on large-scale instances are compared and sensitivity analysis on the key parameters is presented. The results indicate the following: (1) Both algorithms are effective methods for solving this problem, and the method based on the particle swarm optimization algorithm requires a shorter computation time, whereas the method based on the genetic algorithm shows more advantages in optimality. (2) The bilevel model can effectively reduce the total risk of the hazmat transportation while considering the interests of hazmat carriers and ordinary travellers. (3) The utilization rate of reserved lanes increases with an increasing number of tasks. Nevertheless, once the proportion of hazmat vehicles becomes excessive, the advantage of reducing the risk of the reserved lanes gradually decreases.</jats:p>

Desain Rute dan Optimasi Biaya Transportasi Pengisian Tabung Oksigen menggunakan Metode Complete Enumeration pada CV. Tasman Gases

“…Tabung jenis 2 m 3 dimensi 120 cm x 14 cm dalam satu kali pendistribusian sejumlah 30 tabung hingga 45 tabung ke lokasi mitra menggunakan transportasi Mitsubishi L300. Permintaan pengisian tabung oksigen yang meningkat, tetapi tidak diimbangi dengan menentukan rute distribusi ke mitra tujuan akan meningkatkan biaya transportasi [1], [2], [3].Semakin meningkat biaya transportasi akan berbanding lurus dengan biaya produk dan layanan. Kondisi 6 bulan ini, CV.…”

“…Tasman Gases dapat menerapkan dan mengidentifikasi kendala transportasi dimasa mendatang. biaya bahan bakar pada transportasi yang dipakai dan memberikan kapasitas yang berlebih untuk meminimasi terjadinya arus bolak -balek distribusi [3], [27], [28], [12] Penelitian yang dilakukan oleh [29], menentukan kapasitas produk yang harus dijadwalkan untuk distribusi. Hal inilah yang cenderung membuat pihak transporter memerlukan sumber daya yang berkompeten dalam mengestimasi waktu distribusi secara cepat dan akurat.…”

State-of-the-Art Review on Traffic Control Strategies for Emergency Vehicles

“…Response time + Robustness of emergency routes [32], [33], [38] \ \ [97] Others [16], [21]- [23], [25], [41], [42] [59], [63], [65]- [67], [69], [73], [76] [87]- [89] [93]…”

“…Wu et al seek to minimize the disturbances on normal vehicles and the proposed upper-level task attempts to minimize the interference on the EV [86]. Zhang et al establish a two-level optimization model that aims to reduce the risk of dangerous goods transportation and greatly reduce the solution time [89].…”

Cost Function Determination for Human Lifting Motion via the Bilevel Optimization Technology

“…Classical methods, such as single-level reduction using Karush–Kuhn–Tucker (KKT) conditions ( Dempe et al, 2012 ) and gradient-based methods ( Sun et al, 2020 ), cannot guarantee their performances over complex and large-scale bilevel optimization problems due to their lack of well-established solution procedures ( Sinha et al, 2013 ). As alternatives to classical methods, evolutionary approaches, such as genetic algorithm (GA) ( Zhang et al, 2020 ; Liu et al, 2022 ) and covariance matrix adaptation evolution strategy ( He et al, 2019 ), have gained increasing popularity in this area, thanks to their swarm-based nature and promising abilities in handling parallel computation.…”