Optimization in multimodal freight transportation problems: A Survey

Archetti, Claudia; Peirano, Lorenzo; Speranza, Maria

doi:10.1016/j.ejor.2021.07.031

Cited by 90 publications

(43 citation statements)

References 107 publications

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“…Mixed-Integer Linear Programming (MILP) plays an important role across a range of scientific disciplines such as mathematics, operations research, engineering, and computer science as well as within a range of areas of strategic importance to society such as biology 1,2 , healthcare 3,4 , humanitarian applications [5][6][7][8] , manufacturing [9][10][11][12] , pharmacy [13][14][15][16] , power and energy systems [17][18][19] , transportation and logistics 20,21 and many others.…”

mentioning

confidence: 99%

Surrogate “Level-Based” Lagrangian Relaxation for mixed-integer linear programming

Bragin

Tucker

2022

Sci Rep

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Mixed-Integer Linear Programming (MILP) plays an important role across a range of scientific disciplines and within areas of strategic importance to society. The MILP problems, however, suffer from combinatorial complexity. Because of integer decision variables, as the problem size increases, the number of possible solutions increases super-linearly thereby leading to a drastic increase in the computational effort. To efficiently solve MILP problems, a “price-based” decomposition and coordination approach is developed to exploit 1. the super-linear reduction of complexity upon the decomposition and 2. the geometric convergence potential inherent to Polyak’s stepsizing formula for the fastest coordination possible to obtain near-optimal solutions in a computationally efficient manner. Unlike all previous methods to set stepsizes heuristically by adjusting hyperparameters, the key novel way to obtain stepsizes is purely decision-based: a novel “auxiliary” constraint satisfaction problem is solved, from which the appropriate stepsizes are inferred. Testing results for large-scale Generalized Assignment Problems demonstrate that for the majority of instances, certifiably optimal solutions are obtained. For stochastic job-shop scheduling as well as for pharmaceutical scheduling, computational results demonstrate the two orders of magnitude speedup as compared to Branch-and-Cut. The new method has a major impact on the efficient resolution of complex Mixed-Integer Programming problems arising within a variety of scientific fields.

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confidence: 99%

Surrogate “Level-Based” Lagrangian Relaxation for mixed-integer linear programming

Bragin

Tucker

2022

Sci Rep

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“…Ordinary Goods. Network design and service network design are the most common problems studied, and detailed information could be seen in Archetti's survey [15]. Specifically, Crainic gave the first model of multimodal transport service network design, which considered multimodal and multicommodity cargo transportation.…”

Section: Multimodal Transportation Service Network Design Ofmentioning

confidence: 99%

Optimizing the Service Network Design Problem for Railroad Intermodal Transportation of Hazardous Materials

Huang

Zhao

2022

Journal of Advanced Transportation

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The service network design problem is one of the key problems in the field of railroad intermodal transportation of hazardous materials (hazmat). It lies in determining the frequency of the trains and the allocation of both hazardous and ordinary shipments to these trains, as well as the railroad connecting plan of all shipments based on the established train formation plan. Based on a unified method, we build a multiobjective mixed-integer linear-programming model that comprehensively considers both hazardous and ordinary materials, the compatibility of goods and services, the capacity of the transfer station, and the delivery time limit of each shipment. An augmented ε-constraint algorithm is customized to solve the model. Computational results of a practical example in China show that an increase of 8.4 million yuan in the total cost could reduce nearly 24541 the total risk. Moreover, the algorithms comparison and sensitivity analysis indicate that the augmented ε-constraint algorithm is a more suitable approach to handle the model because of its advantage in the uniqueness of distinct nondominated solutions. Finally, the sensitivity analysis shows that the total cost and risk could simultaneously be reduced if the delivery time limit is relaxed or if the transfer time is shortened.

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“…An illustration of the emergency logistics planning problem is shown in Figure 1. With an increasing attention to emergency logistics, emergency logistics under multimodal transportation has also been a heated research field, extending to many real-world applications [5][6][7]. The focus of this study is emergency logistics planning for multi-commodity delivering under multimodal transportation.…”

Section: Introductionmentioning

confidence: 99%

Multitask Emergency Logistics Planning under Multimodal Transportation

2022

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Multitask emergency logistics planning is a complex optimization problem in practice. When a disaster occurs, relief materials or rescue teams should be dispatched to destinations as soon as possible. In a nutshell, the problem can be described as an optimization of multipoint-to-multipoint transportation delivery problem in a given multimodal traffic network. In this study, a multimodal traffic network is considered for emergency logistics transportation planning, and a mixed-integer programming (MIP) formulation is proposed to model the problem. In order to solve this model, we propose a two-layer solution method. The inner layer is to manage the single-task route recommendation, for which we develop a shortest-path algorithm with the multimodal traffic network. Here, the optimal substructure of the algorithm and its time complexity are presented. With the route of each task calculated by the single-task solver, a general optimization algorithm based on improved particle swarm optimization (PSO) is proposed at the outer layer to coordinate the execution of each task constrained by the limited transportation capacity, so as to derive solutions for multi-commodity emergency logistics planning. Extensive computational results show that the proposed method can find solutions of good quality in reasonable time. Meanwhile, through the sensitivity analysis of the algorithm, we find the appropriate parameters for general optimization algorithm to solve the problem proposed in this paper. The proposed approach is effective and practical for solving multitask emergency logistics planning problem under multimodal transportation, which can find a satisfactory solution in an acceptable time.

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Optimization in multimodal freight transportation problems: A Survey

Cited by 90 publications

References 107 publications

Surrogate “Level-Based” Lagrangian Relaxation for mixed-integer linear programming

Surrogate “Level-Based” Lagrangian Relaxation for mixed-integer linear programming

Optimizing the Service Network Design Problem for Railroad Intermodal Transportation of Hazardous Materials

Multitask Emergency Logistics Planning under Multimodal Transportation

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