A simulation-optimization approach for scheduling in stochastic freight transportation

Layeb, Safa Bhar; Jaoua, Amel; Jbira, Asma; Makhlouf, Yosra

doi:10.1016/j.cie.2018.09.021

Cited by 44 publications

(17 citation statements)

References 31 publications

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“…A logistics network optimization decision-making model based on low-carbon economy perspective is typically constructed, and a variety of solutions are designed [1][2][3]. For instance, for effectively solving the stochastic optimization problem, and considering the empirical distribution factors of uncertain parameters, Layeb et al [27] proposed a simulation-based optimization model. Aiming at the characteristics of synchronous transportation, Resat and Turkay [15] proposed a mixed-factor programming problem in which cost, time and carbon emissions were considered in the optimization of network structure.…”

Section: The Solutions Of Costs and Energy Consumption Reductionmentioning

confidence: 99%

Modeling Method for Cost and Carbon Emission of Sheep Transportation Based on Path Optimization

et al. 2020

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Energy conservation, cost, and emission reduction are the research topics of most concern today. The aim of this paper is to reduce the cost and carbon emissions and improve the sustainable development of sheep transportation. Under the typical case of the “farmers–middlemen–slaughterhouses” (FMS) supply model, this paper comprehensively analyzed the factors, sources, and types of cost and carbon emissions in the process of sheep transportation, and a quantitative evaluation model was established. The genetic algorithm (GA) was proposed to search for the optimal path of sheep transportation, and then the model solving algorithm was designed based on the basic GA. The results of path optimization indicated that the optimal solution can be obtained effectively when the range of basic parameters of GA was set reasonably. The optimal solution is the optimal path and the shortest distance under the supply mode of FMS, and the route distance of the optimal path is 245.6 km less than that of random path. From the cost distribution, the fuel power cost of the vehicle, labor cost in transportation, and consumables cost account for a large proportion, while the operation and management cost of the vehicle and depreciation cost of the tires account for a small proportion. The total cost of the optimal path is 26.5% lower than that of the random path, and the total carbon emissions are 36.3% lower than that of random path. Path optimization can thus significantly reduce the cost of different types and significantly reduce the proportion of vehicle fuel power cost and consumables cost, but the degree of cost reduction of different types is different. The result of the optimal path is the key to be explored in this study, and it can be used as the best reference for sheep transportation. The quantitative evaluation model established in this paper can systematically measure the cost and carbon emissions generated in the sheep transportation, which can provide theoretical support for practical application.

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Section: The Solutions Of Costs and Energy Consumption Reductionmentioning

confidence: 99%

Modeling Method for Cost and Carbon Emission of Sheep Transportation Based on Path Optimization

et al. 2020

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“…ABSS provides a flexible and useful mechanism to capture these uncertainties (Heppenstall et al 2011;Moyaux, Chaib-Draa, and D'Amours 2006). According to Oliveira, Lima, and Montevechi (2016); Layeb et al (2018), this approach enables us to model the simulation environment incorporating uncertainties, and the real stochastic nature of the environment. In terms of system dynamics, maintenance tasks change over time, including the maintenance sequences regarding faulty aircraft and the scheduling of maintenance resources.…”

Section: Agent-based Modelling and Simulationmentioning

confidence: 99%

Simulation-based fuzzy-rough nearest neighbour fault classification and prediction for aircraft maintenance

Liu

Wang

Zhang

et al. 2019

Journal of Simulation

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Effective Fault Classification and Prediction (FCP) for aircraft maintenance helps improve the efficiency of maintenance. The traditional ways of the FCP mainly rely on the analysis of mechanical characteristics of components and the reliabilityrelated data. Obtaining the fault-related data is usually prior to performing the analysis of fault diagnoses. However, acquiring the data especially the sensitive data in terms of security and confidentiality is extremely difficult. Therefore, in this paper, we propose an approach of combining Agent-Based Simulation System (ABSS) with the Fuzzy-Rough Nearest Neighbour (FRNN) approach, in order to implement the FCP for aircraft maintenance without the sufficient data. To do so, firstly, a framework for integrating the FRNN approach into ABSS is proposed. The concept and architecture models of FRNN-ABSS are used to describe FRNN-ABSS system. In the following, random and sequence strategies are designed for the FCP of the engine and an algorithm is developed to accomplish the integration of the FRNN approach and ABSS, aiming at automatically performing fault diagnoses. Finally, the experiments analysing the impact of different strategies on maintenance costs and service level have been conducted. The results show that the approach proposed achieved success for random and sequence strategies: 9.3% and 2.5% of maintenance costs have been saved; 4.17% and 12.5% of delayed flights have been changed into on-time flights.

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“…The authors found only 2% increase in average travel time when comparing no demands to many (100) demands. Layeb et al (2018) formulated a Service Network Design optimization model in a multimodal perspective, including roadway, railway and waterway modes, using more realistic travel time inputs with skewed and multimodal distributions. In an ASO procedure, a multi-objective function considering transport costs, delay and CO2 emissions was minimized.…”

Section: Hybrid Simulation-optimization Modelsmentioning

confidence: 99%

“…This is an important part of railway modeling as a consequence of the exponential relationship between traffic volume and train delays that occurs mainly in single lines. Some models (Camargo and Barbieri, 2012;Layeb et al, 2018) use historic travel times without considering this feature and then are not appropriate to evaluate increase in train volume.…”

Section: Summary and Timeliness Of The Researchmentioning

confidence: 99%

“…As mentioned before, the approach to solve this problem is given by an alternate simulationoptimization (ASO) approach in which a search algorithm uses the results of a simulation trial in order to continually improve the solution. This model is solved using OptQuest, a commercial software provided by OptTek System Inc. OptQuest uses a combination of meta-heuristics like tabu search and scatter search along with neural networks for its search algorithm (Layeb et al, 2018). From an initial solution given by the user, it was defined a maximum number of iterations equal to 2000 to observe the evolution of the objective function.…”

Section: Optimization Modelmentioning

confidence: 99%

See 1 more Smart Citation

Improving Strategic Decision-Making in a Closed-Loop Rail Network with Hybrid Simulation-Optimization Modeling

Ju¹,

ANd²,

CHdO³

2019

Preprint

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The availability of rail infrastructure resources is a major driver of rail operations performance.  To evaluate the impact of infrastructure provision, network simulation models can be used to accurately represent train traffic behavior in a wide range of scenarios. However, performing this task can result in a problem of high combinatorial nature as the number of factors and their associated levels increase. This requires more sophisticated techniques such as experimental design formulations or optimization modeling in order to yield satisfactory results. Yet the research in network simulation models for rail systems has hitherto been limited to simple what-if analysis, made up from few factors that cannot represent the whole spectrum of interventions. This is especially critical in closed-loop rail systems where trains are subject to various interferences. Local improvements can be misleading as the queues are merely transferred within the network. Considering this, we propose a hybrid simulation-optimization model to aid the strategic decision of minimizing supplementary capital costs in a heavy-haul Brazilian railroad under construction. As soon as the railroad is completed, investments in both loading and unloading rail terminals will be necessary. First, we developed a representative and flexible model capable of dealing with complex relations between variable infrastructure provision and the resulting operational performance. Then, we simulated this system to prove that the current set of proposed infrastructure resources cannot meet the transportation demands. Afterwards, we demonstrate that local improvements can be delusive as the queues are shifted from loading to unloading operations, reciprocally. Then, we solve an optimization model to define the minimal supplementary investment in order to meet the commercial goals of mining companies. This is done by choosing the best trackage configuration, equipment quantity and capacity and fleet sizing in 3 different production scenarios. The best values of the objective function were found by improving both loading and unloading equipment and increasing the number of trains.

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A simulation-optimization approach for scheduling in stochastic freight transportation

Cited by 44 publications

References 31 publications

Modeling Method for Cost and Carbon Emission of Sheep Transportation Based on Path Optimization

Modeling Method for Cost and Carbon Emission of Sheep Transportation Based on Path Optimization

Simulation-based fuzzy-rough nearest neighbour fault classification and prediction for aircraft maintenance

Improving Strategic Decision-Making in a Closed-Loop Rail Network with Hybrid Simulation-Optimization Modeling

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