A new bi-objective model of the urban public transportation hub network design under uncertainty

Kaveh, Firoozeh; Tavakkoli–Moghaddam, Reza; Triki, Chefi; Rahimi, Yaser; Jamili, Amin

doi:10.1007/s10479-019-03430-9

Cited by 21 publications

(16 citation statements)

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“…First, the periodic variant of the windy or rural hierarchical postman problem versions can be defined and analyzed. Another avenue of research consists in studying the fuzzy or the stochastic versions of the problem (see Kaveh et al 2021) and its dynamic aspect (see Ghiani et al 2007). Also, another line of study is decomposing large-scale instances that can arise in real-life applications into sub-problems and solving them independently, and then testing the efficiency of such decomposition approach.…”

Section: Discussionmentioning

confidence: 99%

On the periodic hierarchical Chinese postman problem

Keskin

Triki

2021

Soft Comput

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This paper presents a mathematical formulation and a heuristic approach for a new variant of the Hierarchical Chinese Postman Problem (HCPP). Indeed, we introduce the concept of periodicity, and we define and solve, for the first time, the Periodic-HCPP, denoted as P-HCPP. Given that the resulting integer programming model makes use of a big number of binary variables and given the extended time horizon considered, 30 days in our case, the problem is characterized by a high level of complexity. However, our developed heuristic is able to solve instances having up to 40 nodes, 520 arcs and 5 hierarchies, whereas a general-purpose solver like Gurobi was not able to provide solutions for instances having more than 10 nodes. While the collected results are very encouraging, we provide at the end of this paper a set of possible future extensions of this work.

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Section: Discussionmentioning

confidence: 99%

On the periodic hierarchical Chinese postman problem

Keskin

Triki

2021

Soft Comput

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“…A multi-objective mathematical programming is used to deal with the problems that have more than one objective function. This technique has been widely implemented for addressing real-life applications (Song et al, 2019;Yakavenka et al, 2019;Kaveh et al, 2019). The generic multiple objective programming model (Steuer, 1986) is formulated as follows:…”

Section: A Multi-objective Mathematical Programming Based On the -Constraint Methodsmentioning

confidence: 99%

A stochastic programming model for an energy planning problem: formulation, solution method and application

et al. 2021

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The paper investigates national/regional power generation expansion planning for medium/longterm analysis in the presence of electricity demand uncertainty. A two-stage stochastic programming is designed to determine the optimal mix of energy supply sources with the aim to minimise the expected total cost of electricity generation considering the total carbon dioxide emissions produced by the power plants.Compared to models available in the extant literature, the proposed stochastic generation expansion model is constructed based on sets of feasible slots (schedules) of existing and potential power plants. To reduce the total emissions produced, two approaches are applied where the first one is performed by introducing emission costs to penalise the total emissions produced. The second approach transforms the stochastic model into a multi-objective problem using the -constraint method for producing the Pareto optimal solutions. As the proposed stochastic energy problem is challenging to solve, a technique that decomposes the problem into a set of smaller problems is designed to obtain good solutions within an acceptable computational time. The practical use of the proposed model has been assessed through application to the regional power system in Indonesia. The computational experiments show that the proposed methodology runs well and the results of the model may also be used to provide directions/guidance for Indonesian government on which power plants/technologies are most feasible to be built in the future. Keywords Energy planning • Stochastic programming • Multi-objective optimization 1 IntroductionGlobal electricity production has grown continuously year by year since 1974. According to International Energy Agency (www.iea.org), world gross electricity production has increased from 6,299 to 26,730 TWh between 1974 and 2018 with an average annual growth rate of 3.3 %. Electricity generation can be divided into three types, namely fossil fuel-based, nuclear, and renewable energy power generation. The fossil fuelbased plants include coal, petroleum, and natural gas which produce large quantity of cheap energy with greenhouse gas (GHG) emissions. The renewable energy consists of hydro, solar, wind, geothermal and biomass which generate clean energy (without GHG emissions or potentially carbon neutral in the case of biomass). However, the cost and feasibility of renewable energy are highly dependent on the potential of regions and its weather conditions (Thangavelu et al, 2015). The strategic energy planning includes the medium-to long-term expansion of the electricity generating capacity. Cost effective and low emission energy

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“…Dukkanci and Kara [17] developed the hierarchical multimodal hub covering problem (HMHCP) over a service network configuration and proposed a MIP formulation with some valid inequalities to strengthen the proposed model. Kaveh et al [18] presented a new bi-objective multimodal HLP with multiple assignment and capacity considerations for the design of an urban public transportation network.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…According to the CCP, the chance constraints 14- (17) jointly guarantee that the delivery time should not not exceed the departure time with given confidence levels γ 1 , γ 2 and γ 3 in the lowest, middle and top layers of the network, respectively. Constraint (18) guarantees that the cargoes between any O-D pairs have arrived at their final destination within the LAT limit. Since it is assumed that travel time data is symmetric, it is multiplied by 2.…”

Section: ) Decision Variablesmentioning

confidence: 99%

Stochastic Hierarchical Multimodal Hub Location Problem for Cargo Delivery Systems: Formulation and Algorithm

et al. 2020

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This study aims at developing a stochastic hierarchical multimodal hub location modeling framework for cargo delivery systems to capture uncertainty in hub construction cost and travel time at the strategic level. From a ring-star-star type network design perspective, a stochastic model is established to formulate this problem formally via the expected value and chance-constrained programming techniques. In particular, three types of chance constraints are proposed to ensure that the on-time delivery with prespecified confidence levels in their respective layer networks. For normal distributions, the original stochastic model can be reformulated as a crisp equivalent mixed-integer linear programming (MILP) model by invoking the central limit theorem. Since the number of constraints and variables increases drastically with the size of cargo delivery distribution network, a memetic algorithm (MA) is designed. This algorithm incorporates genetic search and local intensification to obtain optimal/near-optimal solutions for realistic instance size within a reasonable time limit. For general distributions, it is difficult to convert the stochastic model into its deterministic counterpart. Hence, a hybrid methodology is further designed by combining the MA and Monte Carlo (MC) simulation to solve the proposed stochastic model. To demonstrate the properties of the proposed model and the performance of the designed algorithm, a series of numerical experiments are set up based on the Civil Aeronautics Board (CAB) and Turkish network data sets. Computational results indicate as the confidence level increases, the airport hubs are located further apart in the cargo delivery distribution network for gaining a greater time advantage. In addition, comparative results demonstrate that the MA algorithm proposed herein performs better than the genetic algorithm (GA) in terms of computing speed and quality of the solution. INDEX TERMS Hierarchical multimodal hub location, cargo delivery systems, stochastic programming, mixed-integer linear programming, memetic algorithm, Monte Carlo simulation.

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A new bi-objective model of the urban public transportation hub network design under uncertainty

Cited by 21 publications

References 49 publications

On the periodic hierarchical Chinese postman problem

On the periodic hierarchical Chinese postman problem

A stochastic programming model for an energy planning problem: formulation, solution method and application

Stochastic Hierarchical Multimodal Hub Location Problem for Cargo Delivery Systems: Formulation and Algorithm

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