scholarly journals Research on Optimization and Design of Sustainable Urban Underground Logistics Network Framework

2020 ◽  
Vol 12 (21) ◽  
pp. 9147
Author(s):  
Hairui Wei ◽  
Anlin Li ◽  
Nana Jia
Keyword(s):  
Hybrid Method ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Programming Model ◽  
Mixed Integer ◽  
Set Cover ◽  
Exhaust Emission ◽  
Logistics Network ◽  
Design And Optimization ◽  
Fuzzy Clustering Method

As a new mode of transportation, the underground logistics system (ULS) has become one of the solutions to the problems of environmental pollution and traffic congestion. Considering the environmental and economic factors in urban logistics, this paper conducts comprehensive design and optimization research on the network nodes and passages of urban underground logistics and proposes a relatively complete framework for a sustainable underground logistics network. A hybrid method is proposed, which includes the set cover model used to perform the first location of urban underground logistics nodes, the fuzzy clustering method applied to classify the located logistics nodes into the first-level and second-level nodes considering the congestion in different urban areas of the city and a mixed integer programming model proposed to optimize and design the underground logistics passage to find optimal passage parameters at every underground logistics node. Based on the above hybrid method, a sustainable underground logistics network framework including all-levels logistics nodes and passages is formed, with a subdistrict of Nanjing as a case study. The discussion of results shows that this underground logistics network framework proposal is very effective in reducing logistics time cost, exhaust emission and congestion cost. It provides support for decisions in the design and development of urban sustainable underground logistics networks.

scholarly journals Network Planning Method for Capacitated Metro-Based Underground Logistics System

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Jianjun Dong ◽  
Wanjie Hu ◽  
Shen Yan ◽  
Rui Ren ◽  
Xiaojing Zhao
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Programming Model ◽  
Evaluation Model ◽  
Network Planning ◽  
Exact Algorithm ◽  
Planning Method ◽  
Mixed Integer ◽  
Logistics System ◽  
Metro System

Underground logistics system (ULS) tends to alleviate traffic congestion, increase city logistics efficiency, mitigate the negative effects of traditional logistics processes, and improve the sustainability of urban areas. However, the relatively high cost and risk of underground construction are serious obstacles to implementing ULS. Integrating ULS into modern metro system (M-ULS) is considered to be feasible and efficient to solve this problem. This paper aims at developing a metro system-based ULS network planning method. First, an evaluation model of underground freight volume was proposed considering service capacity, freight flow, and regional accessibility. Second, a set of mixed integer programming model was developed to solve the problem of optimal nodes’ location-allocation (LAP) in the network. Then, a hybrid algorithm was designed with a combination of E-TOPSIS, exact algorithm, and heuristic algorithm. Finally, two lines of Nanjing Metro were selected as a case to validate the proposed planning method. The results showed that the new system can significantly reduce the construction costs of ULS and alleviate traffic congestion. Moreover, the potential of metro stations and underground tunnels can be fully exploited to achieve higher logistics benefits.

scholarly journals Drone-Based Parcel Delivery Using the Rooftops of City Buildings: Model and Solution

2020 ◽  
Vol 10 (12) ◽  
pp. 4362 ◽  
Author(s):  
Junsu Kim ◽  
Hongbin Moon ◽  
Hosang Jung
Keyword(s):  
Urban Areas ◽  
Programming Model ◽  
Computation Time ◽  
Intelligent Transportation ◽  
Mixed Integer ◽  
Operation Planning ◽  
Logistics System ◽  
Proposed Model ◽  
Optimality Gap ◽  
Good Operation

In general, the demand for delivery cannot be fulfilled efficiently due to the excessive traffic in dense urban areas. Therefore, many innovative concepts for intelligent transportation of freight have recently been developed. One of these concepts relies on drone-based parcel delivery using rooftops of city buildings. To apply drone logistics system in cities, the operation design should be adequately prepared. In this regard, a mixed integer programming model for drone operation planning and a heuristic based on block stacking are newly proposed to provide solutions. Additionally, numerical experiments with three different problem sizes are conducted to check the feasibility of the proposed model and to assess the performance of the proposed heuristic. The experimental results show that the proposed model seems to be viable and that the developed heuristic provides very good operation plans in terms of the optimality gap and the computation time.

scholarly journals Optimization of a logistics network considering allocation of facilities and taxation aspects

2020 ◽  
Vol 27 (4) ◽  
Author(s):  
Bruno Vinícius Ribeiro Furlanetto ◽  
Fernando Augusto Silva Marins ◽  
Aneirson Francisco da Silva ◽  
Cristiane Maria Defalque
Keyword(s):  
Programming Model ◽  
Animal Feed ◽  
Mixed Integer ◽  
Global Localization ◽  
Excise Tax ◽  
Logistics Network ◽  
Goods And Services ◽  
Brazilian State ◽  
Tax Benefits ◽  
Tax Changes

Abstract This article analyzes the impacts of operational and tax changes in a logistics network, considering the location of facilities and the following taxes: the Brazilian State Excise Tax on Circulation of Goods and Services, the Import Duty, the Brazilian State Excise Tax in Tax Substitution, the Social Integration Program, the Contribution for the Financing of Social Security and the Brazilian Federal Excise Tax on Industrialized Products. The influence of incorporations and outsourcing of distribution services in solving global localization issues concerning various links in a chain suplly has also been considered. The problem was modeled and solved by the GAMS modeling language using Solver CPLEX. The proposed Mixed Integer Linear Programming model minimizes operating costs taking into account tax benefits and the best use of the credits related to the Tax on Circulation of Goods and Services of a multiproduct network. A real application involving a company in the animal feed production sector was developed. The results showed that the model allowed to evaluate conveniently how the choice of the facilities and the characteristics of the product flows impacted the overall costs of the system. The results also evidenced the need to make decisions based on the existing tax structure, since the scenarios without tax optimization generated substantial losses to the companies. This information added quality to the manager of the company studied.

scholarly journals Simultaneous Optimization of Road Tolls and Tradable Credits in Public-private Mixed Networks

2017 ◽  
Vol 29 (6) ◽  
pp. 603-611 ◽  
Author(s):  
Nan Jiang ◽  
Xiaoning Zhang ◽  
Hua Wang
Keyword(s):  
Traffic Congestion ◽  
Programming Model ◽  
User Equilibrium ◽  
Simultaneous Optimization ◽  
Management Policy ◽  
Mixed Integer ◽  
Private Company ◽  
Mixed Integer Linear Program ◽  
The Government ◽  
Tradable Credits

This paper investigates a hybrid management policy of road tolls and tradable credits in mixed road networks with both public and private roads. In the public sub-network, a tradable credit scheme is applied to mitigate traffic congestion. In the private sub-network, tolls are collected by the private company, but the toll levels and toll locations are determined by the government. The purpose of toll charge is two-fold: on the one hand, the government uses it as a tool for mitigating congestion; on the other hand, a threshold of revenue should be guaranteed for the profitability of the private company. A bi-level programming model is formulated to minimize the total travel time in the network by taking into account the user equilibrium travel behaviour and the revenue requirement of private firms. To obtain a  global optimum solution, the bi-level model is transformed into an equivalent single-level mixed integer linear program that can be easily solved with commercial software. Numerical examples are provided to demonstrate the effectiveness of the developed model and the efficiency of the proposed algorithm. It is shown that the mixed management schemes can achieve favourable targets, namely, joint implementation of road tolls and tradable credits can effectively mitigate traffic congestion and meanwhile maintain reasonable revenue for the private company.

scholarly journals Reverse Logistics Network Optimizing by Genetic Algorithm: A Case Study of Automotive Wiring Harnesses

2014 ◽  
Vol 564 ◽  
pp. 740-746 ◽  
Author(s):  
Abdolhossein Sadrnia ◽  
N. Ismail ◽  
M.K.A.M. Ariffin ◽  
Zulkifli Norzima ◽  
Omid Boyer
Keyword(s):  
Genetic Algorithm ◽  
Reverse Logistics ◽  
Programming Model ◽  
Mixed Integer ◽  
Profit Model ◽  
Logistics Network ◽  
Wiring Harness ◽  
Reverse Logistics Network ◽  
Optimum Solution

The shortage of material and environmental legislations have encouraged car manufacturers to recycle used material in end of life vehicles (ELVs), reverse logistics are essential to the concerns of the automotive supply chain. In this research, a profit model multi-echelon reverse logistics network including collection center, shredder center and recycling center is developed to recycle automotive parts. The work was continued by illustrating empirical application in wiring harness manufacturer that would like to recycle wire harnesses and extract copper. With regards to the complexity of the reverse logistics network, traditional method cannot be implemented for solving them. Thus, an evolutionary algorithm based genetic algorithm (GA) is applied as a solution methodology to solve mixed integer linear programming model and find the optimum solution. The results emphasize the efficiency of the modeling and solving method so that in the case study the company gained more than 27 thousand dollars through the establishment of reverse logistics for recycling copper.

scholarly journals A Hybrid Method for Modeling and Solving Supply Chain Optimization Problems with Soft and Logical Constraints

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Paweł Sitek ◽  
Krzysztof Bzdyra ◽  
Jarosław Wikarek
Keyword(s):  
Supply Chain ◽  
Hybrid Method ◽  
Optimization Problems ◽  
Programming Model ◽  
Combinatorial Problem ◽  
Search Space ◽  
Mixed Integer ◽  
Optimization Models ◽  
Supply Chain Optimization ◽  
Logical Constraints

This paper presents a hybrid method for modeling and solving supply chain optimization problems with soft, hard, and logical constraints. Ability to implement soft and logical constraints is a very important functionality for supply chain optimization models. Such constraints are particularly useful for modeling problems resulting from commercial agreements, contracts, competition, technology, safety, and environmental conditions. Two programming and solving environments, mathematical programming (MP) and constraint logic programming (CLP), were combined in the hybrid method. This integration, hybridization, and the adequate multidimensional transformation of the problem (as a presolving method) helped to substantially reduce the search space of combinatorial models for supply chain optimization problems. The operation research MP and declarative CLP, where constraints are modeled in different ways and different solving procedures are implemented, were linked together to use the strengths of both. This approach is particularly important for the decision and combinatorial optimization models with the objective function and constraints, there are many decision variables, and these are summed (common in manufacturing, supply chain management, project management, and logistic problems). TheECLiPSesystem with Eplex library was proposed to implement a hybrid method. Additionally, the proposed hybrid transformed model is compared with the MILP-Mixed Integer Linear Programming model on the same data instances. For illustrative models, its use allowed finding optimal solutions eight to one hundred times faster and reducing the size of the combinatorial problem to a significant extent.

Research on Remanufacturing Closed-Loop Logistics Network Design under Low-Carbon Restriction

2012 ◽  
Vol 159 ◽  
pp. 224-234
Author(s):  
Ya Can Wang ◽  
Tao Lu ◽  
Chun Hua Gao ◽  
Chun Hui Zhang ◽  
Chi Chen
Keyword(s):  
Network Design ◽  
Co2 Emission ◽  
Closed Loop ◽  
Programming Model ◽  
Pareto Frontier ◽  
Mixed Integer ◽  
Low Carbon ◽  
Waste Generation ◽  
Low Carbon Economy ◽  
Logistics Network

In this paper we study how to trade off the economic and ecological effects in the remanufacturing closed-loop logistics network design in the context of low-carbon economy. We establish a multi-objective mixed integer linear programming model to find the optimal facility locations and materials flow allocation. In the objective function, we set three minimum targets: economic cost, CO2 emission and waste generation. Through an iterative algorithm, we get the Pareto Frontier of our problem. In the numeric study, we find that in order to achieve a Pareto improvement over an original system, three of the critical rates (i.e. return rate, recovery rate, and cost substitute rate) should be increased. Also, to meet the need of low-carbon dioxide, we plot an iso-CO2 emission curve in which decision makers have a series of optimal choices with the same CO2 emission but different cost and waste generation. Each choice may have different network design but all of these are Pareto optimal solutions, which provide a comprehensive evaluation of both economics and ecology for the decision making.

scholarly journals Designing a Resilient and Sustainable Logistics Network under Epidemic Disruptions and Demand Uncertainty

2021 ◽  
Vol 13 (24) ◽  
pp. 14053
Author(s):  
Aymen Aloui ◽  
Nadia Hamani ◽  
Laurent Delahoche
Keyword(s):  
Demand Uncertainty ◽  
Programming Model ◽  
Mixed Integer ◽  
Design Strategies ◽  
Logistics Network ◽  
Location Allocation ◽  
Logistics Networks ◽  
Sustainable Logistics ◽  
Stochastic Mixed Integer Programming ◽  
Demand Fluctuations

To face the new challenges caused by modern industry, logistics operations managers need to focus more on integrating sustainability goals, adapt to unexpected disruptions and find new strategies and models for logistics management. The COVID-19 pandemic has proven that unforeseen fragilities, negatively affecting the supply chain performance, can arise rapidly, and logistics systems may confront unprecedented vulnerabilities regarding network structure disruption and high demand fluctuations. The existing studies on a resilient logistics network design did not sufficiently consider sustainability aspects. In fact, they mainly addressed the independent planning of decision-making problems with economic objectives. To fill this research gap, this paper concentrates on the design of resilient and sustainable logistics networks under epidemic disruption and demand uncertainty. A two-stage stochastic mixed integer programming model is proposed to integrate key decisions of location–allocation, inventory and routing planning. Moreover, epidemic disruptions and demand uncertainty are incorporated through plausible scenarios using a Monte Carlo simulation. In addition, two resiliency strategies, namely, capacity augmentation and logistics collaboration, are included into the basic model in order to improve the resilience and the sustainability of a logistics chain network. Finally, numerical examples are presented to validate the proposed approach, evaluate the performance of the different design models and provide managerial insights. The obtained results show that the integration of two design strategies improves resilience and sustainability.

scholarly journals Vehicles Allocation for Fruit Distribution Considering CO2 Emissions and Decisions on Subcontracting

2018 ◽  
Vol 10 (7) ◽  
pp. 2449 ◽  
Author(s):  
Rafael Tordecilla-Madera ◽  
Andrés Polo ◽  
Adrián Cañón
Keyword(s):  
Co2 Emissions ◽  
Carbon Emissions ◽  
Urban Areas ◽  
Co2 Emission ◽  
Programming Model ◽  
Pareto Frontier ◽  
Operating Costs ◽  
Mixed Integer ◽  
Conflicting Objectives ◽  
Constraint Method

An important problem in rural-area supply chains is how to transport the harvested fruit to urban areas. Low- and medium-capacity vehicles are used in Colombia to carry out this activity. Operating them comes with an inherent cost and generates carbon emissions. Normally, minimizing operating costs and minimizing carbon emissions are conflicting objectives to allocate such vehicles efficiently in any of the supply chain echelons. We designed a multi-objective mixed-integer programming model to address this problem and solved it via the ε-constraint method. It includes decisions mainly about quantities of fruit to transport and store, types of vehicles to allocate according to their capacities, CO2 emission levels of these vehicles, and subcontracting on the collection process. The main results show two schedules for allocating the vehicles, showing minimum and maximum CO2 emissions. Minimum CO2 emissions scheme require subcontracting and the maximum CO2 scheme does not. Then, a Pareto frontier shows that CO2 emissions level are inversely proportional to total management cost for different scenarios in which fruit supply was modified.

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