Surrogate-Based Optimization for Solving a Mixed Integer Network Design Problem

2015 ◽  
Vol 2497 (1) ◽  
pp. 124-136 ◽  
Author(s):  
Xiqun (Michael) Chen ◽  
Zheng Zhu ◽  
Xiang He ◽  
Lei Zhang
Keyword(s):  
Network Design ◽  
Design Problem ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Surrogate Based Optimization

scholarly journals Multi-Period Network Design Problem in Regional Hazardous Waste Management Systems

2019 ◽  
Vol 16 (11) ◽  
pp. 2042 ◽  
Author(s):  
Jun Zhao ◽  
Lixiang Huang
Keyword(s):  
Network Design ◽  
Design Problem ◽  
Programming Model ◽  
Planning Horizon ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Hazardous Wastes ◽  
Total Risk ◽  
Total Cost ◽  
Treatment And Disposal

The management of hazardous wastes in regions is required to design a multi-echelon network with multiple facilities including recycling, treatment and disposal centers servicing the transportation, recycling, treatment and disposal procedures of hazardous wastes and waste residues. The multi-period network design problem within is to determine the location of waste facilities and allocation/transportation of wastes/residues in each period during the planning horizon, such that the total cost and total risk in the location and transportation procedures are minimized. With consideration of the life cycle capacity of disposal centers, we formulate the problem as a bi-objective mixed integer linear programming model in which a unified modeling strategy is designed to describe the closing of existing waste facilities and the opening of new waste facilities. By exploiting the characteristics of the proposed model, an augmented ε -constraint algorithm is developed to solve the model and find highly qualified representative non-dominated solutions. Finally, computational results of a realistic case demonstrate that our algorithm can identify obviously distinct and uniformly distributed representative non-dominated solutions within reasonable time, revealing the trade-off between the total cost and total risk objectives efficiently. Meanwhile, the multi-period network design optimization is superior to the single-period optimization in terms of the objective quality.

scholarly journals Some Computational Insights on the Optimal Bus Transit Route Network Design Problem

2012 ◽  
Vol 47 (3) ◽  
Author(s):  
Wei (David) Fan ◽  
Randy B. Machemehl
Keyword(s):  
Network Design ◽  
Design Problem ◽  
Large Scale ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Research Experiences ◽  
Bus Transit ◽  
Route Network ◽  
Transit Route ◽  
Demand Aggregation

The objective of this paper is to present some computational insights based on previous extensive research experiences on the optimal bus transit route network design problem (BTRNDP) with zonal demand aggregation and variable transit demand. A multi-objective, nonlinear mixed integer model is developed. A general meta-heuristics-based solution methodology is proposed. Genetic algorithms (GA), simulated annealing (SA), and a combination of the GA and SA are implemented and compared to solve the BTRNDP. Computational results show that zonal demand aggregation is necessary and combining metaheuristic algorithms to solve the large scale BTRNDP is very promising.

scholarly journals A Branch-and-Price Algorithm for the Liner Shipping Network Design Problem

2020 ◽  
Vol 1 (4) ◽  
Author(s):  
Kristian Thun ◽  
Henrik Andersson ◽  
Magnus Stålhane
Keyword(s):  
Network Design ◽  
Design Problem ◽  
Computational Study ◽  
Liner Shipping ◽  
Maritime Transportation ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Specific Class ◽  
Branch And Price ◽  
Price Algorithm

AbstractMaritime transportation is the backbone of the global economy and one of its most important segments is liner shipping. To design a liner shipping network is notoriously difficult but also very important since an efficient network can be the difference between prosperity and bankruptcy. In this paper, we propose a branch-and-price algorithm for the liner shipping network design problem, which is the problem of designing a set of cyclic services and to deploy a specific class of vessels to each service so that all demand can flow through the network at minimal cost. The proposed model can create services with a complex structure and correctly calculate the transshipment cost. The formulation of the master problem strengthens a known formulation with valid inequalities. Because of multiple dependencies between ports that are not necessarily adjacent and no defining state at any of the ports, the subproblem is formulated and solved as a mixed integer linear program. Strategies to improve the solution time of the subproblem are proposed. The computational study shows that the algorithm provides significantly tighter lower bounds in the root node than existing methods on a set of small instances.

Optimization of Biomass-to-Bioenergy Logistics Network Design Problem: A Case Study

2018 ◽  
Vol 16 (11) ◽  
Author(s):  
Meisam Shamsi ◽  
Reza Babazadeh ◽  
Maghsud Solimanpur
Keyword(s):  
Network Design ◽  
Design Problem ◽  
Fossil Fuels ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Efficient Design ◽  
Logistics Network ◽  
Sustainable Solutions ◽  
Proposed Model ◽  
And Performance

Abstract Increasing the emissions of greenhouse gases (GHG) due to fossil fuel consumption has led to problems such as global warming, climate change, loss of biodiversity, and urban pollutions. Bioethanol production especially from different biomass such as wheat straw has been specified as one of the sustainable solutions to deal with energy crisis. Bioethanol logistics network optimization will reduce total costs of supply chain management and improves its competency with fossil fuels. In this paper, a mixed-integer linear programming (MILP) model is proposed to integrate and optimize bioethanol logistics network design problem. The proposed model is a multi-period and multi-echelon including feedstock supply centers, collection centers, bio-refineries, and customer centers. The proposed model is applied in a real case in Iran. The results justify the applicability and performance of the model in efficient design of bioethanol logistics network problems.

scholarly journals The Reversible Lane Network Design Problem (RL-NDP) for Smart Cities with Automated Traffic

2020 ◽  
Vol 12 (3) ◽  
pp. 1226 ◽  
Author(s):  
Lígia Conceição ◽  
Gonçalo Homem de Almeida Correia ◽  
José Pedro Tavares
Keyword(s):  
Network Design ◽  
Design Problem ◽  
Traffic Assignment ◽  
Smart Cities ◽  
User Equilibrium ◽  
Sustainable Transportation ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Travel Times ◽  
The City

With automated vehicles (AVs), reversible lanes could be a sustainable transportation solution once there is vehicle-to-infrastructure connectivity informing AVs about the lane configuration changes. This paper introduced the reversible lane network design problem (RL-NDP), formulated in mixed-integer non-linear mathematical programming—both the traffic assignment and the reversible lane decisions were embedded. The model was applied on an hourly basis in the case study of the city of Delft, the Netherlands. Reversible lanes are examined under no traffic equilibrium (former paths are maintained); user-equilibrium (UE) assignment (AVs decide their own paths); and system-optimum (SO) traffic assignment (AVs are forced to follow SO paths). We found out that reversible lanes reduce congested roads, total travel times, and delays up to 36%, 9%, and 22%, respectively. The SO scenario was revealed to be beneficial in reducing the total travel time and congested roads in peak hours, whereas UE is equally optimal in the remaining hours. A dual-scenario mixing SO and UE throughout the day reduced congested roads, total travel times, and delay up to 40%, 8%, and 19%, respectively, yet increased 1% in travel distance. The spatial analysis suggested a substantial lane variability in the suburbs, yet a strong presence of reversible lanes in the city center.

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