Multi-Product Multi-Period Inventory Routing Optimization with Time Window Constrains

Xiao, N.; Rao, Y. L.

doi:10.2507/ijsimm15(2)co8

Cited by 11 publications

(5 citation statements)

References 35 publications

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“…Niknejad and Petrovic studied VRP considering customer preference in dynamic environment and gave a solution idea [18]. Xiao and Rao believe that there are many uncertain factors in the real environment, which brings great difficulties to the location selection of logistics delivery [19]. The traditional twolayer objective planning cannot meet the requirements of uncertain conditions.…”

Section: Related Workmentioning

confidence: 99%

Application of Swarm Intelligence Optimization Algorithm in Logistics Delivery Path Optimization under the Background of Big Data

Zhao

2023

Journal of Function Spaces

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Traditional logistics delivery route optimization algorithm has some problems such as long time to find the optimal route. Based on this, this paper discusses swarm intelligence optimization algorithm and logistics delivery route optimization. To solve the logistics vehicle routing problem, considering that the basic ACO (ant colony optimization) has the disadvantages of slow convergence speed and easy to fall into local optimum, this paper proposes a new hybrid population optimization algorithm and applies it to VRPTW (vehicle routing problem with time windows). In addition, the concept of crowding degree in AFSA (artificial fish swarm algorithm) is introduced into ACO. In the early stage of the optimization process, a strong crowding degree limit is set to ensure that most ants are not affected by pheromone concentration to conduct random optimization. The simulation results show that the AC (accuracy rate) of this algorithm is 95.08%, which is higher than the traditional PSO (particle swarm optimization) algorithm and general heuristic algorithm. The hybrid algorithm can effectively improve the optimization efficiency of VRPTW, lay a foundation for solving large-scale VRPTW, and provide new research ideas and methods. At the same time, the results fully show that the algorithm in this paper has certain advantages in performance, and it can be applied to logistics delivery route optimization.

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Section: Related Workmentioning

confidence: 99%

Application of Swarm Intelligence Optimization Algorithm in Logistics Delivery Path Optimization under the Background of Big Data

Zhao

2023

Journal of Function Spaces

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“…We optimized the monitoring time with time window constrains, which have been widely used in the vehicle routing problems (Chen et al 1998;Ibaraki et al 2005;Xiao and Rao 2016). We optimized the monitoring time with time window constrains, which have been widely used in the vehicle routing problems (Chen et al 1998;Ibaraki et al 2005;Xiao and Rao 2016).…”

Section: Spatiotemporal Monitoring Network Designmentioning

confidence: 99%

“…In each design step, one optimal monitoring location and time were chosen from the candidate monitoring locations and times, respectively, through the DW analysis. We optimized the monitoring time with time window constrains, which have been widely used in the vehicle routing problems (Chen et al 1998;Ibaraki et al 2005;Xiao and Rao 2016). A time window was essentially a time interval with a length of L tw , which contained N tw candidate monitoring intervals (L s ), that is, L tw = N tw × L s , and it remained unchanged during the design process.…”

Section: Spatiotemporal Monitoring Network Designmentioning

confidence: 99%

Bayesian Monitoring Design for Streambed Heat Tracing: Numerical Simulation and Sandbox Experiments

Zhang

et al. 2018

Groundwater

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Heat tracing methods have been widely employed for subsurface characterization. Nevertheless, there were very few studies regarding the optimal monitoring design for heat tracing in heterogeneous streambeds. In this study, we addressed this issue by proposing an efficient optimal design framework to collect the most informative diurnal temperature signal for Bayesian estimation of streambed hydraulic conductivities. The data worth (DW) was measured by the expected relative entropy between the prior and posterior distributions of the conductivity field. An adaptively refined Gaussian process surrogate was employed to alleviate the computational burden, resulting in at least three orders of magnitude of speed-up. The applicability of the optimal experimental design framework was evaluated by both numerical and sandbox experimental cases. Results showed that the most informative locations centered in the transition zones among the main patterns of the hydraulic conductivity field, while the most informative times centered in a short period after the minimum/maximum temperature appeared. With the fixed number of measurements, extending the calibration period was more beneficial than increasing the monitoring frequency in improving the estimation results. To our best knowledge, this work is the first study on Bayesian monitoring design for streambed characterization with the heat tracing method. The method and results can provide guidance on selecting monitoring strategies under budget-limited conditions.

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“…Cornillier et al [13] propose a heuristic for the multiperiod petrol station replenishment problem, while Benantar et al [14] formulate the problem taking into account multi-compartment trucks and time windows. Other noteworthy contributions in this area include the ones by Xiao and Rao [15] and Cornillier et al [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation of Tank Truck Loading Operations in a Fuel Distribution Terminal

Reis¹,

Pitombeira-Neto²,

Rolim³

2017

Int. j. simul. model.

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Tank truck loading operations in fuel distribution terminals have particular characteristics which are challenging to simulate. Demands for different fuel types are correlated rather than independent. In addition, queuing policies which do not address the current state of the terminal may increase equipment idleness and trucks' lead time. We propose a decomposition of the joint probability distribution of fuel types and a procedure to simulate it. We also propose a new queuing policy aimed at reducing equipment idleness and trucks' lead time. The procedures are tested and validated through a discrete-event simulation model of a real fuel distribution terminal. We simulate alternative scenarios in which we increase fuel flow rate as well as the number of loading arms and test a new queuing policy. Results show the effectiveness of the simulation procedure in replicating real performance indicators. Moreover, the proposed idleness-based queuing policy resulted in lower idleness and lead time compared with a first-come-first-served policy.

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Multi-Product Multi-Period Inventory Routing Optimization with Time Window Constrains

Cited by 11 publications

References 35 publications

Application of Swarm Intelligence Optimization Algorithm in Logistics Delivery Path Optimization under the Background of Big Data

Application of Swarm Intelligence Optimization Algorithm in Logistics Delivery Path Optimization under the Background of Big Data

Bayesian Monitoring Design for Streambed Heat Tracing: Numerical Simulation and Sandbox Experiments

Simulation of Tank Truck Loading Operations in a Fuel Distribution Terminal

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