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A quantitative study of data aggregation for a network design problem: a case of automotive distribution

JOURNAL OF INTELLIGENT MANUFACTURING 2024年 1-24页

作者： Le Bihan, Suzanne Alpan, Gulgun Penz, Bernard Univ Grenoble Alpes CNRS Grenoble INP G SCOP 46 Ave Felix Viallet F-38000 Grenoble France

This paper presents a framework for a systematic analysis of the impact of data aggregation on a multi-product multi-period network design problem with batch cost. The optimization objective is to design the vehicle distribution network for an automotive manufacturer. Numerical experiments are conducted with real production data. Given the problem's scale and complex constraints, data aggregation emerges as a natural strategy to help the convergence of resolution methods towards good solutions. We explore three aggregation dimensions: product type, spatial, and temporal, and for each of them, different levels. Addressing multiple aggregation dimensions is a novel approach that has not been extensively explored in current literature, especially within industrial settings. Our aggregation-disaggregation method reveals that data aggregation consistently leads to improved solutions within a constrained computation time, with temporal aggregation demonstrating the most significant reduction in problem size and solution improvement. Lastly, we give some managerial insights considering the industrial context.

关键词： Logistics Transportation network design problem Automotive industry Input aggregation

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Incorporating travel time means and standard deviations into transportation network design problem: a hybrid method based on column generation and Lagrangian relaxation

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TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH 2024年第2期16卷 131-143页

作者： Song, Maocan Cheng, Lin Jiangsu Univ Sch Automot & Traff Engn Zhenjiang Jiangsu Peoples R China Southeast Univ Sch Transportat Nanjing Jiangsu Peoples R China

Travel times in real-world transportation networks are affected by many disruptions. When we conduct the network design optimization, the traffic condition and its resulting travel time variability should be taken into account. However, most of the previous network design optimizations adopted the lengths or expected travel times of links. Based on travel time means and standard deviations, we develop an arc-based model that is a nonlinear and concave integer program. By the Dantzig-Wolfe reformulation, we transform it into an equivalent column-based model that is an integer linear program with a large number of variables. Based on the column-based model, we develop a hybrid method based on column generation and Lagrangian relaxation. The restricted master problem can be settled by the linear programming solvers. The pricing subproblems incorporate independent reliable shortest path problems and a knapsack problem. In numerical experiments, the proposed method can generate feasible solutions with good integrality gaps.

关键词： network design problem travel time variability mean-standard deviation objective column generation Lagrangian relaxation

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Global Optimum Search in the network design problem

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COMPUTATIONAL MATHEMATICS AND MATHEMATICAL PHYSICS 2024年第10期64卷 2238-2255页

作者： Krylatov, A. Yu. St Petersburg State Univ St Petersburg 199034 Russia Russian Acad Sci Solomenko Inst Transport Problems St Petersburg 199178 Russia

The global optimum search in the network design problem for the case of networks with disjoint paths is considered. In the considered formulation of the problem, the manager of a network invests in the capacities of its elements, seeking to minimize the total delay arising from the equilibrium flow assignment. It is proven that the solution to the problem under study must necessarily solve a certain minimax problem. Optimality conditions for solutions of the minimax problem are found under fairly natural assumptions. Based on the results, a new algorithm is developed for optimizing the topology of a network with disjoint paths.

关键词： network design problem equilibrium flow assignment

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Optimization strategies for the bilevel network design problem with affine cost functions

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2023年第2-3期91卷 329-347页

作者： Krylatov, Alexander Raevskaya, Anastasiya Ageev, Petr Yanan Univ Sch Math & Comp Sci Yanan Shaanxi Peoples R China St Petersburg State Univ St Petersburg Russia

Today artificial intelligence systems support efficient management in different fields of social activities. In particular, congestion control in modern networks seems to be impossible without proper mathematical models of traffic flow assignment. Thus, the network design problem can be referred to as Stackelberg game with independent lower-level drivers acting in a non-cooperative manner to minimize individual costs. In turn, upper-level decision-maker seeks to minimize overall travel time in the network by investing in its capacity. Hence, the decision-maker faces the challenge with a hierarchical structure which solution important due to its influence on the sustainable development of modern large cities. However, it is well-known that a bilevel programming problem is often strongly NP-hard, while the hierarchical optimization structure of a bilevel problem raises such difficulties as non-convexity and discontinuity. The present paper is devoted to the network design problem with affine cost functions. First of all, we obtain exact optimality conditions to the network design problem in a case of a single-commodity network with non-interfering routes. Secondly, we show that obtained conditions can be exploited as optimization strategies for solving the network design problem in the case of an arbitrary network with affine cost functions. These findings give fresh managerial insights to traffic engineers dealing with network design.

关键词： Bilevel optimization network design problem Congestion games Affine cost functions Optimization strategies

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Collaborative hospital supply chain network design problem under uncertainty

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OPERATIONAL RESEARCH 2022年第5期22卷 4607-4640页

作者： Dorgham, Khouloud Nouaouri, Issam Nicolas, Jean-Christophe Goncalves, Gilles Univ Artois UR 3926 Lab Genie Informat & Automat Artois LGI2A F-62400 Bethune France

Since 2016, hospitals in France have met to form Territorial Hospital Groups (THGs) in order to modernize their health care system. The main challenge is to allow an efficient logistics organization to adopt the new collaborative structure of the supply chain. In our work, we approach the concept of logistics pooling as a form of collaboration between hospitals in THGs. The aim is to pool and rationalize the storage of products in warehouses and optimize their distribution to care units while reducing logistics costs (transportation, storage, workforce, etc.). Besides, due to the unavailability and the incompleteness of data in real-world situations, several parameters embedded in supply chains could be imprecise or even uncertain. In this paper, a Fuzzy chance-constrained programming approach is developed based on possibility theory to solve a network design problem in a multi-supplier, multi-warehouse, and multi-commodity supply chain. The problem is designed as a minimum-cost flow graph and a linear programming optimization model is developed considering fuzzy demand. The objective is to meet the customers' demand and nd the best allocation of products to warehouses. Different instances were generated based on realistic data from an existing territorial hospital group, and several tests were developed to reveal the benefits of collaboration and uncertainty handling.

关键词： Optimization model network design problem Product allocation Collaboration Fuzzy chance-constrained programming

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PANDA: A physarum-inspired algorithm to solve the multi-objective discrete network design problem

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 246卷

作者： Heeroo, Kelvin Nigel Shinday Gukhool, Oomesh Univ Mauritius Fac Informat Commun & Digital Technol Dept Software & Informat Syst Reduit Mauritius Univ Mauritius Fac Informat Commun & Digital Technol Dept Software & Informat Syst Reduit Mauritius

The Multi -Objective Discrete network design problem (MO-DNDP) describes the optimisation of a transport network across multiple metrics. Due to the impractical processing times of exact solutions, heuristics have been harnessed to solve this problem. However, the scale of the networks, the set of candidate links and the metrics assessed have been limited. This research proposes a MO-DNDP that encapsulates 8 metrics across three real networks used as benchmarks scaling from 17 km2 to 41,850 km2 to encapsulate the complexity of optimising transport networks at different scales, traffic demands and constraint densities. To solve this more comprehensive MO-DNDP, this paper proposes the Physarum-inspired algorithm PANDA that improves upon the classical Physarum Solver algorithm by harnessing the attractant sensing and merging capabilities of the biological Physarum Polycephalum slime mould. PANDA is a first attempt at solving the complex MO-DNDP using the Physarum foraging model. PANDA consistently scored better than the classical Solver in the land use rating by circumventing landforms, built-up areas, and environmentally sensitive regions. Additionally, PANDA constructed networks that were on average 30 % better than the existing benchmark networks and 32 % better than the Physarum Solver networks. PANDA improved upon the benchmarks by constructing lower capacity roads that were optimised for the actual traffic demand between each origin-destination pair instead of shared arterials.

关键词： Physarum Polycephalum Bio-inspired algorithm network design problem

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A bi-level p-facility network design problem in the presence of congestion

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期176卷

作者： Zaferanieh, Mehdi Abareshi, Maryam Jafarzadeh, Morteza Hakim Sabzevari Univ Sabzevar Iran

In this paper, we propose a bi-level model for a p-facility network design problem in a transportation network in which the effect of congestion is also considered. In order to cope with the traffic burden imposed by the clients of demand nodes, the possibility of rerouting the previously existing origin-destination flows is allowed. In addition, the network designers consider a limited budget for constructing new facilities as well as applying some operational enhancements on network links to alleviate the increased traffic congestion caused by the clients of facilities and avoid inordinate growth in network travel time and corresponding cost. The upper level is dedicated to selecting the location of facilities and link enhancements with the purpose of minimizing the total travel cost, while at the lower level, a pseudo-traffic assignment problem is applied to determine the user-equilibrium path flows. By analyzing the characteristics of the model, the near-optimal solutions are given by an online supervised machine learning algorithm. Finally, the validity of the proposed model and used method is evaluated through some numerical examples.

关键词： network design problem Bi-level programming Traffic assignment problem Supervised machine learning

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Large-scale multimodal transportation network models and algorithms-Part II: network capacity and network design problem

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2022年 167卷

作者： Wang, Yu Liu, Haoxiang Fan, Yinchao Ding, Jianxun Long, Jiancheng Hefei Univ Technol Sch Management Hefei 230009 Peoples R China Hefei Univ Technol Sch Automot & Transportat Engn Hefei 230009 Peoples R China Anhui Prov Key Lab Ind Safety & Emergency Technol Hefei 230009 Peoples R China Engn Res Ctr Intelligent Transportat & Cooperat Ve Hefei 230009 Peoples R China

Transportation network capacity enhancement is essential in urban transportation planning. In this paper, a general multimodal network capacity problem (MNCP) is proposed, which can de-pict the transfers, mode overlap, and common line problem and congestion effect of transit. The problem is established as a bi-level model with combined mode choice and traffic assignment as the lower-level programming. Based on the MNCP, a tri-level multimodal network design problem (MNDP-MNCP) is developed to maximize the network capacity. The models are solved with an efficient Kriging-surrogate-based optimization algorithm in real-scale urban networks. Numerical results demonstrate the performances of the proposed framework.

关键词： network capacity network design problem Large-scale multimodal network Tri-level model Kriging-surrogate-based optimization

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A matheuristic for a telecommunication network design problem with traffic grooming

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2020年 90卷 102003-000页

作者： Wu, Xinyun Lu, Zhipeng Glover, Fred Huazhong Univ Sci & Technol Sch Comp Sci & Technol SMART Wuhan 430074 Hubei Peoples R China Univ Colorado Coll Engn & Appl Sci ECEE Boulder CO 80309 USA

This paper addresses a network design and traffic grooming problem arising in optical telecommunication networks that are based on wavelength division multiplexing. Given a set of nodes and a set of traffic demands between these nodes, the network design and traffic grooming problem (NDGP) consists of installing a minimum number of lightpaths between the nodes and of routing the demand on the lightpaths while respecting capacity constraints. We introduce a new mathematical formulation of the NDGP as well as a hybrid algorithm capable of finding high quality solutions in short computing times. The proposed algorithm uses linear and mixed integer programming as slave methods and embeds them within a tabu search procedure. Computational results and comparisons with an existing method from the literature show the effectiveness of the proposed algorithm. Further analyses also show the efficiency of the neighborhood structure and of its evaluation technique. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： network design problem Traffic grooming and routing Matheuristic Mixed integer programming Tabu search

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A conceptual framework to formulate transportation network design problem considering social equity criteria

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TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE 2019年第Jul.期125卷 171-183页

作者： Behbahani, Hamid Nazari, Sobhan Kang, Masood Jafari Litman, Todd Iran Univ Sci & Technol Heydarkhani St Tehran Iran Iran Univ Sci & Technol Dampezeshki St Tehran Iran Victoria Transport Policy Inst Www Vtpi Org 1250 Rudlin St Victoria BC V8V 3R7 Canada

In recent years, researchers have developed new methods to measure how transport decisions affect different groups of society. An example is the distribution of impacts (benefits and costs) from roadway investments, and the degree that the results are considered equitable (also called fair or just). Such decisions affect people's ability to access services and activities, and therefore their economic opportunities and development. This study suggests ways of incorporating social equity measures in transportation network planning. It describes various equity impacts that can result from transportation planning decisions, discusses various social equity concepts and theories, reviews previous attempts to incorporate equity considerations into transport networks modeling, and suggests a framework for simultaneously optimizing network design and achieving social equity objectives. According to this framework, network design can be formulated using bi-level integer programming models corresponding to seven major social equity approaches along with the classical approach of "Total Travel Time Minimization." An accessibility variable is used as the distributable benefit. This approach is more comprehensive and flexible than previous equity impact models. The proposed framework can be used to evaluate and optimize the equity impacts of various infrastructure investment decisions.

关键词： network design problem Sustainable transportation Social equity Justice network design Accessibility Infrastructure development

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