Research article Special Issues

Proactive dynamic vehicle routing problems considering cooperation services for the store-depot-integrated retailer


  • Distribution costs remain consistently high in crowded city road networks, posing challenges for traditional distribution methods in efficiently handling dynamic online customer orders. To address this issue, this paper introduces the Proactive Dynamic Vehicle Routing Problem considering Cooperation Service (PDVRPCS) model. Based on proactive prediction and order-matching strategies, the model aims to develop a cost-effective and responsive distribution system. A novel solution framework is proposed, incorporating a proactive prediction method, a matching algorithm and a hybrid Genetic Algorithm-Simulated Annealing (GA-SA) algorithm. To validate the effectiveness of the proposed model and algorithm, a case study is conducted. The experimental results demonstrate that the dynamic scheme can significantly reduce the number of vehicles required for distribution, leading to cost reduction and increased efficiency.

    Citation: Xianlong Ge, Yonghong Liang, Yuanzhi Jin, Chunbing Song. Proactive dynamic vehicle routing problems considering cooperation services for the store-depot-integrated retailer[J]. Mathematical Biosciences and Engineering, 2023, 20(10): 18030-18062. doi: 10.3934/mbe.2023801

    Related Papers:

    [1] Edward J. Allen . Derivation and computation of discrete-delayand continuous-delay SDEs in mathematical biology. Mathematical Biosciences and Engineering, 2014, 11(3): 403-425. doi: 10.3934/mbe.2014.11.403
    [2] Tyler Cassidy, Morgan Craig, Antony R. Humphries . Equivalences between age structured models and state dependent distributed delay differential equations. Mathematical Biosciences and Engineering, 2019, 16(5): 5419-5450. doi: 10.3934/mbe.2019270
    [3] Heping Ma, Hui Jian, Yu Shi . A sufficient maximum principle for backward stochastic systems with mixed delays. Mathematical Biosciences and Engineering, 2023, 20(12): 21211-21228. doi: 10.3934/mbe.2023938
    [4] Paul L. Salceanu . Robust uniform persistence in discrete and continuous dynamical systems using Lyapunov exponents. Mathematical Biosciences and Engineering, 2011, 8(3): 807-825. doi: 10.3934/mbe.2011.8.807
    [5] Frédéric Mazenc, Gonzalo Robledo, Daniel Sepúlveda . A stability analysis of a time-varying chemostat with pointwise delay. Mathematical Biosciences and Engineering, 2024, 21(2): 2691-2728. doi: 10.3934/mbe.2024119
    [6] A. Vinodkumar, T. Senthilkumar, S. Hariharan, J. Alzabut . Exponential stabilization of fixed and random time impulsive delay differential system with applications. Mathematical Biosciences and Engineering, 2021, 18(3): 2384-2400. doi: 10.3934/mbe.2021121
    [7] Jordi Ripoll, Jordi Font . Numerical approach to an age-structured Lotka-Volterra model. Mathematical Biosciences and Engineering, 2023, 20(9): 15603-15622. doi: 10.3934/mbe.2023696
    [8] Jin Li, Yongling Cheng, Zongcheng Li, Zhikang Tian . Linear barycentric rational collocation method for solving generalized Poisson equations. Mathematical Biosciences and Engineering, 2023, 20(3): 4782-4797. doi: 10.3934/mbe.2023221
    [9] Paolo Fergola, Marianna Cerasuolo, Edoardo Beretta . An allelopathic competition model with quorum sensing and delayed toxicant production. Mathematical Biosciences and Engineering, 2006, 3(1): 37-50. doi: 10.3934/mbe.2006.3.37
    [10] Toshikazu Kuniya, Tarik Mohammed Touaoula . Global stability for a class of functional differential equations with distributed delay and non-monotone bistable nonlinearity. Mathematical Biosciences and Engineering, 2020, 17(6): 7332-7352. doi: 10.3934/mbe.2020375
  • Distribution costs remain consistently high in crowded city road networks, posing challenges for traditional distribution methods in efficiently handling dynamic online customer orders. To address this issue, this paper introduces the Proactive Dynamic Vehicle Routing Problem considering Cooperation Service (PDVRPCS) model. Based on proactive prediction and order-matching strategies, the model aims to develop a cost-effective and responsive distribution system. A novel solution framework is proposed, incorporating a proactive prediction method, a matching algorithm and a hybrid Genetic Algorithm-Simulated Annealing (GA-SA) algorithm. To validate the effectiveness of the proposed model and algorithm, a case study is conducted. The experimental results demonstrate that the dynamic scheme can significantly reduce the number of vehicles required for distribution, leading to cost reduction and increased efficiency.



    1. Introduction

    Magnetostrictive materials have been studied due to their great potential as sensor and actuator elements in a wide variety of applications that can benefit from their remote operation, high energy density and short response time [1]. Among giant magnetostrictive materials, Terbium Dysprosium Iron alloy (Terfenol-D) is the most attractive one because of its high saturation magnetostrain (1600 ppm), good coupling coefficient (as high as 60%), and high Young’s modulus [2]. On the other hand, piezoelectric ceramics, especially lead zircornate titanate (PZT), are widely used in smart materials and structures due to their high output force, compact size, and high power density. Magnetoelectric (ME) composites require giant magnetostrictive and piezoelectric materials, with a strong coupling between them, and many applications of these composites such as magnetic field sensing devices [3,4], coil-less transformers and read/write devices [5] are currently under investigation.

    Recently, energy harvesting devices can be used as the power source for structural health monitoring sensors, tire pressure monitoring sensors, medical implants and other wireless sensors. The devices of energy harvesting from ambient sources, such as mechanical loads, provide a promising alternative to battery-powered systems. Gao et al. [6] discussed the effect of the length ratio between the substrate layer and the piezoelectric layer on the induced voltage of the PZT/stainless steel cantilevers due to a constant force. Hu et al. [7] designed and tested an optimal vibration-based energy harvesting system using magnetostrictive material. Dai et al. [8] investigated the performances of electric output due to vibration in three layered magnetostrictive/piezoelectric composite harvesters.

    Energy harvesting magnetostrictive/piezoelectric laminates are subjected to high mechanical loads, and these loads cause high response levels that increase the generated power but induce the delamination and reduce the lifetime of the laminates. In this work, we study the detection and response characteristics of clamped-free giant magnetostrictive/piezoelectric laminates under concentrated loading in a combined numerical and experimental approach. Two and three layered laminates are fabricated by bonding Terfenol-D layers on one side and both sides of PZT layer, respectively. The tip deflection, stress, induced voltage and induced magnetic field for the laminates due to concentrated loads are calculated by the finite element analysis (FEA). The tip deflection, induced voltage and induced magnetic field are also measured, and the data produced by the experiment are then compared with analytical results.

    2. Analysis

    2.1. Basic equations

    The basic equations for magnetostrictive and piezoelectric materials are outlined here. The equilibrium equations in the rectangular Cartesian coordinate system O-@x_1 x_2 x_3@ are given by

    @\sigma_{ji, j} = 0@ (1)
    @B_{i, i}=0@ (2)
    @D_{i, i}=0@ (3)
    where @\sigma_{ij}@ is the stress tensor, @B_{i}@ is the magnetic induction vector, @D_{i}@ is the electric displacement vector, a comma followed by an index denotes partial differentiation with respect to the space coordinate xi, and the summation convention for repeated tensor indices is applied. The constitutive laws are given as follows:
    @\varepsilon_{ij} = s^{H}_{ijkl}\sigma_{kl}+d'_{kij}H_{k}@ (4)
    @B_{i} = d'_{ikl}\sigma_{kl}+\mu_{ik} H_{k}@ (5)
    for the magnetostrictive material, and
    @\varepsilon_{ij} = s^{E}_{ijkl}\sigma_{kl}+d_{kij}E_{k}@ (6)
    @D_{i} = d_{ikl}\sigma_{kl}+\epsilon^{T}_{ik}E_{k}@ (7)
    for the piezoelectric material. Here, @\varepsilon_{ij}@ is the strain tensor, @H_{i}@ is the magnetic field intensity vector, @E_{i}@ is the electric field intensity vector, @s^{H}_{ijkl}, d'_{kij}, \mu_{ij}@ are the constant magnetic field elastic compliance, magnetoelastic constant and magnetic permittivity of magnetostrictive material, and @s^{E}_{ijkl}, d_{kij}, \epsilon^{T}_{ij}@ are the constant electric field elastic compliance, direct piezoelectric constant and dielectric permittivity of piezoelectric material. Valid symmetry conditions for the material constants are
    @s^{H}_{ijkl}=s^{H}_{jikl}=s^{H}_{ijlk}=s^{H}_{klij}, \ \ d'_{kij}=d'_{kji}, \ \\mu_{ij}=\mu_{ji}@ (8)
    @s^{E}_{ijkl}=s^{E}_{jikl}=s^{E}_{ijlk}=s^{E}_{klij}, \ \ d_{kij}=d_{kji}, \ \\epsilon^{T}_{ij}=\epsilon^{T}_{ji}@ (9)
    The relation between the strain tensor and the displacement vector @u_{i}@ is given by
    @\varepsilon_{ij}=\frac{1}{2}(u_{j, i}+u_{i, j})@ (10)
    The magnetic and electric field intensities are written as
    @H_{i}=\varphi_{, i}@ (11)
    @E_{i}=-\phi_{, i}@ (12)
    where @\varphi@ and @\phi@ are the magnetic and electric potentials, respectively.

    2.2. Model

    Two layered magnetostrictive/piezoelectric laminate is shown in Figure 1(a), in which a magnetostrictive layer, Terfenol-D, of length @l_{m}@ = 15 mm, width @w_{m}@ = 5 mm and thickness @h_{m}@ = 1 and 3 mm is perfectly bonded on the upper surface of a piezoelectric layer, PZT, of length @l_{p}@ = 20 mm, width @w_{p}@ = 5 mm and thickness @h_{p}@ = 0.5 mm. We will use subscripts m and p to refer to Terfenol-D and PZT layers, respectively. Dimensions @h_{m}@(@h_{p}@), @w_{m}@(@w_{p}@), @l_{m}@(@l_{p}@) are measured along the @x_1=x@, @x_2=y@ and @x_3=z@ axis, respectively. The origin of the coordinate system is located at the center of the bottom left side of upper Terfenol-D layer, and the left end @z = 0@ is clamped. Three layered laminate is also considered (see Figure 1(b)). Easy axis of the magnetization of Terfenol-D layer is the z-direction, while the polarization of PZT layer is the x-direction. The constitutive relations for Terfenol-D and PZT layers are given in Appendix A.

    Figure 1. Illustration of (a) two-layered and (b) three-layered magnetostrictive/piezoelectric laminate configurations.

    As we know, a magnetic domain switching gives rise to the changes of the magnetoelastic constants [9], and the constants @d'_{15}, d'_{31} and d'_{33}@ for Terfenol-D layer in the z-direction magnetic field are

    d15=dm15d31=dm31+m31Hzd33=dm33+m33Hz
    (13)
    where @d^{m}_{15}, d^{m}_{31}, d^{m}_{33}@ are the piezo-magnetic constants, and @m_{31}@ and @m_{33}@ are the second-order magnetoelastic constants. When the length of Terfenol-D is much longer than other two sizes (width and thickness) and a magnetic field is along the length direction (easy axis), the longitudinal (33) magnetostrictive deformation mode is dominant. So it is assumed that only the constant @d^{m}_{31}@ varies with magnetic field [10], and the constant @m_{31}@ equals to zero.

    We performed finite element calculations to obtain the tip deflection, stress, induced voltage and induced magnetic field for the magnetostrictive/piezoelectric laminates. The average induced magnetic field in the z-direction at the side surface (at @z = l_{m}@ plane) is calculated as

    @B_\textrm{in} = \frac{1}{A}\int_{A} B_{z}(x, y, l_{m}) dA@ (14)
    where the integration is over the surface area, @A = w_{m}h_{m}@, of Terfenol-D layer. The basic equations for the magnetostrictive materials are mathematically equivalent to those for piezoelectric materials. So coupled-field solid elements in ANSYS were used in the analysis. Only a half of the laminate was modeled. In total, 15500 and 27500 elements and 18931 and 32291 nodes were used for @h_{m}@ = 1 and 3 mm of two-layered model, respectively. For three-layered model, 23000 and 47000 elements and 27236 and 53812 nodes were used for @h_{m}@ = 1 and 3 mm, respectively. It should be noted that before carrying out simulations, a mesh sensitivity study was performed to ensure that the mesh was fine enough. The finite element computations were provided by modifying the program with routines developed by our previous works [11,12].

    3. Materials and Methods

    Terfenol-D (Etrema Products, Inc., USA) of @l_{m}@ = 15 mm, @w_{m}@ = 5 mm, @h_{m}@ = 1 and 3 mm and PZT C- 91 (Fuji Ceramics, Co. Ltd., Japan) of @l_{p}@ = 20 mm, @w_{p}@ = 5 mm, @h_{p}@ = 0.5 mm were used to make giant magnetostrictive/piezoelectric laminates by epoxy bonding (EP-34B; Kyowa Electronic Instruments Co. Ltd., Japan). Owing to cost and time constrains, the number of specimens (one or two at each types and thicknesses) was limited. It is noted that Terfenol-D is a rare earth iron and very expensive. Table 1 and Table 2 list the material properties of Terfenol-D [13,14] and C-91 [15], respectively. The second-order magnetoelastic constants @m_{33}@ of Terfenol-D layer with @h_{m}@ = 1 and 3 mm of two-layered laminate are 5.0 × 10-12 and 3.3 × 10-12 m2/A2 [11], and the constants @m_{33}@ of @h_{m}@ = 1 and 3 mm of three-layered laminate are 5.2 × 10-12 and 2.3 × 10-12 m2/A2 [12], respectively.

    Table 1. Material properties of Terfenol-D.
    Elastic compliance (×10-12m2/N)Piezo-magnetic constant (×10-9m/A)Magnetic permittivity (×10-6H/m)
    @s^{H}_{11}@@s^{H}_{33}@@s^{H}_{44}@@s^{H}_{12}@@s^{H}_{13}@@d^{m}_{31}@@d^{m}_{33}@@d^{m}_{15}@@\mu_{11}@@\mu_{33}@
    Terfenol-D17.917.926.3-5.88-5.88-5.311286.296.29
     | Show Table
    DownLoad: CSV
    Table 2. Material properties of C-91.
    Elastic compliance (×10-12m2/N)Direct piezoelectric constant (×10-12m/V)Dielectric permittivity (×10-10C/Vm)
    @s^{E}_{11}@@s^{E}_{33}@@s^{E}_{44}@@s^{E}_{12}@@s^{E}_{13}@@d^{m}_{31}@@d^{m}_{33}@@d^{m}_{15}@@\epsilon^{T}_{11}@@\epsilon^{T}_{33}@
    C-9117.118.641.4-6.3-7.3-340645836395490
     | Show Table
    DownLoad: CSV

    Consider magnetostrictive/piezoelectric laminates under concentrated loading. Figure 2 and 3, for example, shows the setup for the experiment of a two-layered laminate (Figure 1(a)). Concentrated load P0 was applied at x = y = 0, z = @l_{p}@ by the cantilever load cell [6], as shown in Figure 2. First, the displacement for the two-layered and three-layered laminates under concentrated loading was measured with a laser displacement meter (see Figure 3(a)). Next, the induced voltage of these laminates was measured using an oscilloscope (see Figure 3(b)). The @x = h_{p}@ plane was grounded. Then, the induced magnetic field over the total area on @z = l_{m}@ plane of Terfenol-D layer was measured using a Tesla meter (see Figure 3(c)). The hall probe was touched on the edge of Terfenol-D layer, and this set-up allowed a precision of induced magnetic field measurement of ± 0.01 mT.

    Figure 2. Diagram of the experimental setup.

    Figure 3. Experimental setup for measuring (a) the tip deflection, (b) the induced voltage and (c) the induced magnetic field.

    4. Results and Discussion

    We first present results for the two-layered magnetostirctive/piezoelectric laminates. Figure 4 shows the tip deflection @w_{\rm tip}@ versus applied concentrated load P0 at @x = h_{p}, y = 0, z = l_{p}@ for the two-layered laminates with @h_{m}@ = 1 and 3 mm. The lines and plots denote the results of FEA and test. The experimental scatter is small, and the representative plots from the tests are shown. The tip deflection increases as the thickness of the Terfenol-D layer decreases. The FEA results are good agreement with experimental measurements. Figure 5 shows the induced voltage Vin versus applied concentrated load P0 at @x = 0@ plane for the two-layered laminates with @h_{m}@ = 1 and 3 mm, obtained from the FEA and test. As the concentrated load increases, the induced voltage increases. The comparison between the numerical predictions and the experimental results for the two-layered laminate with @h_{m}@ = 3 mm yields a good agreement. For the laminate with @h_{m}@ = 1 mm, the trend is similar between the numerical predictions and the experimental results, though the experimental data are smaller than the predicted ones because of the voltage saturation under high mechanical loads. Figure 6 shows the induced magnetic field Bin versus applied concentrated load P0 for the two-layered laminates with @h_{m}@ = 1 and 3 mm, obtained from the FEA. Also shown are the measured data for @h_{m}@ = 3 mm. The comparison between the FEA and test is reasonable. As the concentrated load increases, the induced magnetic field increases. The induced magnetic field increases as the thickness of the Terfenol-D layers decreases. The variations of normal stress @\sigma_{zz}@ along the thickness direction are calculated at the clamped end (@y = 0@ mm and @z = 0@ mm) for the two-layered laminates and the results are shown in Figure 7. All calculations are done at a fixed tip deflection of @w_{\rm tip}@ = 1 @\mu@m. The applied loads of PZT layer for @w_{\rm tip}@ = 1 @\mu@m are about P0 = 27.0, 62.7 mN for @h_{m}@ = 1 and 3 mm, and the corresponding induced voltage and induced magnetic field are about Vin = 0.338, 0.229 V and Bin = 0.024, 0.032 mT, respectively. Small stress gaps at the interface between Terfenol-D and PZT layers are observed. Figure 8 also shows the variations of normal stress @\sigma_{zz}@ along the thickness direction near the free edge of Terfenol-D layer (@y = 0@ mm and @z = 14.5@ mm) for the two-layered laminates at the same condition. The normal stress in Terfenol-D layer is almost zero and the normal stress in PZT layer changes from tensile to compressive. There are some stress gaps at the interface between Terfenol-D and PZT layers. Figure 9 shows the variations of shear stress @\sigma_{zz}@ along the length direction at the interface between Terfenol-D and PZT layers (@x = 0@ mm and @y = 0@ mm) for the two-layered laminates at the same condition. The maximum shear stress is observed near the free edge of Terfenol-D layer. Low shear stress is noted for small Terfenol-D layer thickness.

    Figure 4. Tip deflection versus concentrated load at @x = h_{m}, y = 0, z = l_{p}@ for two-layerd magnetostrictive/piezoelectric laminates.
    Figure 5. Induced voltage versus concentrated load at x = 0 plane for two-layered magnetostrictive/piezoelectric laminates.
    Figure 6. Induced magnetic field versus concentrated load at @z = l_{m}@ plane for two-layerd magnetostrictive/piezoelectric laminates.
    Figure 7. Normal stress distribution along the thickness direction at y = 0 and z = 0 mm for two-layered magnetostrictive/piezoelectric laminates at a tip deflection of 1 @\mu@m.
    Figure 8. Normal stress distribution along the thickness direction at y = 0 and z = 14.5 mm for two-layered magnetostrictive/piezoelectric laminates at a tip deflection of 1 @\mu@m.
    Figure 9. Shear stress distribution along the length direction at x = 0 and y = 0 mm for two-layered magnetostrictive/piezoelectric laminates at a tip deflection of 1 @\mu@m.

    Next, the results of the three-layered magnetostrictive/piezoelectric laminates are presented. Figure 10 shows the tip deflection @w_{\rm tip}@ versus applied concentrated load P0 at @x = h_{p}, y = 0, z = l_{p}@ for the three-layered laminates with @h_{m}@ = 1 and 3 mm. The lines and plots denote the results of FEA and test. The experimental scatter is small, and the representative plots from the tests are shown. The tip deflection for @h_{m}@ = 1 mm is larger than that for @h_{m}@ = 3 mm, and both the numerical predictions and the experimental results show a same tendency. Figure 11 shows the induced voltage Vin versus applied concentrated load P0 for the three-layered laminate with @h_{m}@ = 3 mm. Also shown is the induced magnetic field Bin. Only one datum for Bin is plotted due to the accuracy limit of the Tesla meter. As the concentrated load increases, both the induced voltage and the induced magnetic field increase. The induce voltage of the two-layered laminate was much larger than that of the three-layered laminate. In addition, the induced voltage increases with decrease in the thickness of magnetostrictive layers. It stems from the facts that two-layered laminate and thin magnetostrictive layers are more easily deformed than three-layered laminate and thick magnetostrictive layers. However, if the thickness of the Terfenol-D layer is reduced to several micrometers or more less, induced voltage decreases because the volume effect on the magnetization is more dominant than the magnetic field generation by deformation. The variations of normal stress @\sigma_{zz}@ along the thickness direction are calculated near the free edge of Terfenol-D layer (@y = 0@ mm and @z = 14.5@ mm) for the three-layered laminates and the results are shown in Figure 12. All calculations are done at a fixed tip deflection of @w_{\rm tip}@ = 1 @\mu@m. The applied loads of PZT layer for @w_{\rm tip}@ = 1 @\mu@m are about P0 = 62.7, 78.3 mN for @h_{m}@ = 1 and 3 mm, and the corresponding induced voltage and induced magnetic field are about Vin = 2.64, 3.30 mV and Bin = 0.051, 0.026 mT, respectively. There are some stress gaps at the interface between Terfenol-D and PZT layers. At smaller Terfenol-D layer thickness, lower stress gap is found for the same tip deflection. The normal stress in Terfenol-D layer is almost zero and the normal stress in PZT layer changes from tensile to compressive.

    Figure 10. Tip deflection versus concentrated load at @x = h_{p}, y = 0, z = l_{p}@ for three-layerd magnetostrictive/piezoelectric laminates.
    Figure 11. Induced voltage and induced magnetic field versus concentrated load for three-layerd magnetostrictive/piezoelectric laminate with @h_{m}@ = 3 mm.
    Figure 12. Normal stress distribution along the thickness direction at y = 0 and z = 14.5 mm for three-layered magnetostrictive/piezoelectric laminates at a tip deflection of 1 @\mu@m.

    These results are helpful in considering the energy harvesting from impact. And, it is important that the internal stress is evaluated for fracture and delamination of the laminates also. Our present study offers a method for aiding the design of new energy harvesting devices, and provides a rational basis for refining the real device designing in order to reduce fracture and increase efficiency of electric power generation. Work in this area is currently being pursued.

    5. Conclusion

    A numerical and experimental investigation of the magnetostrictive/piezoelectric laminates under concentrated loading was conducted. The tip deflection, induced voltage and induced magnetic field are predicted using finite element simulations, and comparison with the measured data shows that current predictions are reasonable. It was found that smaller magnetostrictive layer thickness gives larger tip deflection, induced voltage and induced magnetic field. Also, the induce voltage of the twolayered laminate was much larger than that of the three-layered laminate. In addition, the stress gap at the interface is small when the magnetostrictive layer thickness is small. This study may be useful in designing advanced magnetostrictive/piezoelectric laminates with energy harvesting capabilities.

    Acknowledgments

    This work was supported by Grant-in-Aid for JSPS Fellows (23-3402).

    Conflict of Interest

    All authors declare no conflicts of interest in this paper.

    Appendix A

    For Terfenol-D, the constitutive relations can be written in the following form:

    {εxxεyyεzzεyzεzxεxy}=[sH11sH12sH13000sH12sH11sH13000sH13sH13sH33000000sH44/2000000sH44/2000000sH66/2]{σxxσyyσzzσyzσzxσxy}+[00d3100d3100d330d15/20d15/200000]{HxHyHz}
    (A.1)
    {BxByBz}=[0000d150000d1500d31d31d33000]{σxxσyyσzzσyzσzxσxy}+[μ11000μ11000μ33]{HxHyHz}
    (A.2)
    where
    sH11=sH1111=sH2222, sH12=sH1122, sH13=sH1133=sH2233, sH33=sH3333sH44=4sH2323=4sH3131, sH66=4sH1212=2(sH11sH12)}
    (A.3)
    d15=2d131=2d223,  d31=d311=d322,  d33=d333
    (A.4)
    The constitutive relations for PZT (hexagonal crystal of class 6mm) are
    {εxxεyyεzzεyzεzxεxy}=[sE33sE13sE13000sE13sE11sE12000sE13sE12sE11000000sE66/2000000sE44/2000000sE44/2]{σxxσyyσzzσyzσzxσxy}+[d3300d3100d310000000d15/20d15/20]{ExEyEz}
    (A.5)
    {DxDyDz}=[d33d31d3100000000d150000d150]{σxxσyyσzzσyzσzxσxy}+[ϵT33000ϵT11000ϵT11]{ExEyEz}
    (A.6)
    where
    sE11=sE2222=sE3333, sE12=sE2233, sE13=sE1122=sE1133, sE33=sE1111sE44=4sE1212=4sE1313, sE66=4sE2323=2(sE11sE12)}
    (A.7)
    d15=2d313=2d212,  d31=d122=d133,  d33=d111
    (A.8)



    [1] N. Bose, Wal-Mart's next move against Amazon: more warehouses, faster shipping, Technol. News, 2016.
    [2] A. H. Hübner, H. Kuhn, J. Wollenburg, Last mile fulfilment and distribution in omni-channel grocery retailing: a strategic planning framework, Int. J. Retail Distrib. Manage., 44 (2016). https://doi.org/10.1108/IJRDM-11-2014-0154
    [3] G. Bensinger, Amazon's next delivery drone: you, in Wall Street Journal, 108 (2015).
    [4] B. W. Thomas, Waiting strategies for anticipating service requests from known customer locations, Transp. Sci., 41 (2007), 281–429. https://doi.org/10.1287/trsc.1060.0183 doi: 10.1287/trsc.1060.0183
    [5] C. Lima, B. M. Luz, S. T. Takemoto, P. Barisson, R. Tezzin, L. Peres, et al., Strategic modeling for the characterization of the conditions that allow the anticipation of the consumer's requests, Open J. Social Sci., 3 (2015), 146–160. https://doi.org/10.4236/jss.2015.310021 doi: 10.4236/jss.2015.310021
    [6] K. S. Hartzel, C. A. Wood, Factors that affect the improvement of demand forecast accuracy through point-of-sale reporting, Eur. J. Oper. Res., 260 (2017), 171–182. https://doi.org/10.1016/j.ejor.2016.11.047 doi: 10.1016/j.ejor.2016.11.047
    [7] S. Ma, R. Fildes, A retail store SKU promotions optimization model for category multi-period profit maximization, Eur. J. Oper. Res., 260 (2017), 680–692. https://doi.org/10.1016/j.ejor.2016.12.032 doi: 10.1016/j.ejor.2016.12.032
    [8] S. Zhu, R. Dekker, W. van Jaarsveld, R. W. Renjie, A. J. Koning, An improved method for forecasting spare parts demand using extreme value theory, Eur. J. Oper. Res., 261 (2017), 169–181. https://doi.org/10.1016/j.ejor.2017.01.053 doi: 10.1016/j.ejor.2017.01.053
    [9] R. Belka, M. Godlewski, Vehicle routing optimization system with smart geopositioning updates, Appl. Sci., 11 (2021), 10933. https://doi.org/10.3390/app112210933 doi: 10.3390/app112210933
    [10] F. Ferrucci, S. Bock, Pro-active real-time routing in applications with multiple request patterns, Eur. J. Oper. Res., 253 (2016), 356–371. https://doi.org/10.1016/j.ejor.2016.02.016 doi: 10.1016/j.ejor.2016.02.016
    [11] A. M. Fathollahi-Fard, A. Ahmadi, F. Goodarzian, N. Cheikhrouhou, A bi-objective home healthcare routing and scheduling problem considering patients' satisfaction in a fuzzy environment, Appl. Soft Comput., 93 (2020), 106385. https://doi.org/10.1016/j.asoc.2020.106385 doi: 10.1016/j.asoc.2020.106385
    [12] E. Fernández, M. Roca-Riu, M. G. Speranza, The shared customer collaboration vehicle routing problem, Eur. J. Oper. Res., 265 (2018), 1078–1093. https://doi.org/10.1016/j.ejor.2017.08.051 doi: 10.1016/j.ejor.2017.08.051
    [13] J. Paul, N. Agatz, R. Spliet, R. D. Koster, Shared capacity routing problem - an omni-channel retail study, Eur. J. Oper. Res., 273 (2019), 731–739. https://doi.org/10.1016/j.ejor.2018.08.027 doi: 10.1016/j.ejor.2018.08.027
    [14] C. Archetti, M. Savelsbergh, M. G. Speranza, The vehicle routing problem with occasional drivers, Eur. J. Oper. Res., 254 (2016), 472–480. https://doi.org/10.1016/j.ejor.2016.03.049 doi: 10.1016/j.ejor.2016.03.049
    [15] G. Macrina, L. Pugliese, F. Guerriero, D. Laganà, The vehicle routing problem with occasional drivers and time windows, in ODS 2017: Optimization and Decision Science: Methodologies and Applications, (2017), 577–587. https://doi.org/10.1007/978-3-319-67308-0_58
    [16] A. M. Arslan, N. Agatz, L. Kroon, R. Zuidwijk, Crowdsourced delivery: a dynamic pickup and delivery problem with Ad-hoc drivers, Transp. Sci., 53 (2016), 1–318. https://doi.org/10.1287/trsc.2017.0803 doi: 10.1287/trsc.2017.0803
    [17] M. Mojtahedi, A. Fathollahi-Fard, R. Tavakkoli-Moghaddam, S. Newton, Sustainable vehicle routing problem for coordinated solid waste management, J. Ind. Inf. Integr., 23 (2021), 100220. https://doi.org/10.1016/j.jii.2021.100220 doi: 10.1016/j.jii.2021.100220
    [18] A. Fathollahi-Fard, M. Hajiaghaei-Keshteli, R. Tavakkoli-Moghaddam, N. Smith, Bi-level programming for home health care supply chain considering outsourcing, J. Ind. Inf. Integr., 25 (2022), 100246. https://doi.org/10.1016/j.jii.2021.100246 doi: 10.1016/j.jii.2021.100246
    [19] Y. Wang, Y. Yuan, K. Assogba, K. Gong, H. Wang, M. Xu, et al., Design and profit allocation in two-echelon heterogeneous cooperative logistics network optimization, J. Adv. Transp., 2018 (2018), 4607493. https://doi.org/10.1155/2018/4607493 doi: 10.1155/2018/4607493
    [20] I. Dayarian, M. Savelsbergh, Crowdshipping and same-day delivery: employing in-store customers to deliver online orders, Prod. Oper. Manage., 29 (2020), 2153–2174. https://doi.org/10.1111/poms.13219 doi: 10.1111/poms.13219
    [21] S. Luo, Y. Wang, J. Tang, X. Guan, M. Xu, Two-echelon multidepot logistics network design with resource sharing, J. Adv. Transp., 2021 (2021), 6619539. https://doi.org/10.1155/2021/6619539 doi: 10.1155/2021/6619539
    [22] X. Ren, X. Jiang, L. Ren, L. Meng, A multi-center joint distribution optimization model considering carbon emissions and customer satisfaction, Math. Biosci. Eng., 20 (2023), 683–706. https://doi.org/10.3934/mbe.2023031 doi: 10.3934/mbe.2023031
    [23] K. Gdowska, A. Viana, J. P. Pedroso, Stochastic last-mile delivery with crowdshipping, Transp. Res. Procedia, 30 (2018), 90–100. https://doi.org/10.1016/j.trpro.2018.09.011 doi: 10.1016/j.trpro.2018.09.011
    [24] S. Y. Tan, W. C. Yeh, The vehicle routing problem: state-of-the-art classification and review, Appl. Sci., 11 (2021), 10295. https://doi.org/10.3390/app112110295 doi: 10.3390/app112110295
    [25] R. A. Russell, W. C. Chiang, Scatter search for the vehicle routing problem with time windows, Eur. J. Oper. Res., 169 (2006), 606–622. https://doi.org/10.1016/j.ejor.2004.08.018 doi: 10.1016/j.ejor.2004.08.018
    [26] Q. Zhang, T. Zhen, Y. Zhu, W. Zhang, Z. Ma, A hybrid intelligent algorithm for the vehicle routing with time windows, in Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues, (2008), 47–54. https://doi.org/10.1007/978-3-540-87442-3_7
    [27] C. B. Cheng, K. P. Wang, Solving a vehicle routing problem with time windows by a decomposition technique and a genetic algorithm, Expert Syst. Appl., 36 (2009), 7758–7763. https://doi.org/10.1016/j.eswa.2008.09.001 doi: 10.1016/j.eswa.2008.09.001
    [28] T. Vidal, T. G. Crainic, M. Gendreau, C. Prins, A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows, Comput. Oper. Res., 40 (2013), 475–489. https://doi.org/10.1016/j.cor.2012.07.018 doi: 10.1016/j.cor.2012.07.018
    [29] S. Belhaiza, P. Hansen, G. Laporte, A hybrid variable neighborhood tabu search heuristic for the vehicle routing problem with multiple time windows, Comput. Oper. Res., 52 (2014), 269–281. https://doi.org/10.1016/j.cor.2013.08.010 doi: 10.1016/j.cor.2013.08.010
    [30] Y. Shi, L. Lv, F. Hu, Q. Han, A heuristic solution method for multi-depot vehicle routing-based waste collection problems, Appl. Sci., 10 (2020), 2403. https://doi.org/10.3390/app10072403 doi: 10.3390/app10072403
    [31] J. Pasha, A. L. Nwodu, A. Fathollahi-Fard, G. Tian, Z. Li, H. Wang, et al., Exact and metaheuristic algorithms for the vehicle routing problem with a factory-in-a-box in multi-objective settings, Adv. Eng. Inf., 52 (2022), 101623. https://doi.org/10.1016/j.aei.2022.101623 doi: 10.1016/j.aei.2022.101623
    [32] A. Fathollahi-Fard, A. Ahmadi, B. Karimi, Multi-objective optimization of home healthcare with working-time balancing and care continuity, Sustainability, 13 (2021), 12431. https://doi.org/10.3390/su132212431 doi: 10.3390/su132212431
    [33] R. Baños, J. Ortega, C. Gil, A. Márquez, F. de Toro, A hybrid meta-heuristic for multi-objective vehicle routing problems with time windows, Comput. Ind. Eng., 65 (2013), 286–296. https://doi.org/10.1016/j.cie.2013.01.007 doi: 10.1016/j.cie.2013.01.007
    [34] E. Pourrahmani, M. Jaller, Crowdshipping in last mile deliveries: operational challenges and research opportunities, Socio-Econ. Plann. Sci., 78 (2021), 101063. https://doi.org/10.1016/j.seps.2021.101063 doi: 10.1016/j.seps.2021.101063
    [35] M. Jaller, C. Otero-Palencia, A. Pahwa, Automation, electrification, and shared mobility in urban freight: opportunities and challenges, Transp. Res. Procedia, 46 (2020), 13–20. https://doi.org/10.1016/j.trpro.2020.03.158 doi: 10.1016/j.trpro.2020.03.158
    [36] W. Dai, Q. Zhong, C. Qi, Multi-stage multi-attribute decision-making method based on the prospect theory and triangular fuzzy MULTIMOORA, Soft Comput., 24 (2020), 9429–9440. https://doi.org/10.1007/s00500-018-3017-0 doi: 10.1007/s00500-018-3017-0
    [37] D. Kahneman, A. Tversky, Prospect theory: an analysis of decision under risk, in Handbook of the Fundamentals of Financial Decision Making, World Scientific, (2013), 99–127. https://doi.org/10.1142/9789814417358_0006
    [38] S. Datta, N. Sahu, S. Mahapatra, Robot selection based on grey‐MULTIMOORA approach, in Grey Systems: Theory and Application, 3 (2013). https://doi.org/10.1108/GS-05-2013-0008
    [39] X. Ge, Y. Jin, Chapter 18 - Artificial intelligence algorithms for proactive dynamic vehicle routing problem, in Applications of Artificial Intelligence in Process Systems Engineering, (2021), 497–522. https://doi.org/10.1016/B978-0-12-821092-5.00011-5
    [40] A. K. Jain, Y. Zhou, T. Mustufa, E. C. Burdette, G. S. Chirikjian, G. Fichtinger, Matching and reconstruction of brachytherapy seeds using the Hungarian algorithm (MARSHAL), Med. Phys., 32 (2005), 3475–3492. https://doi.org/10.1118/1.2104087 doi: 10.1118/1.2104087
    [41] A. Mirzaeinia, S. Bradley, M. Hassanalian, Drone-station matching in smart cities through Hungarian algorithm: power minimization and management, in AIAA Propulsion and Energy 2019 Forum, 2019 (2019), 4151. https://doi.org/10.2514/6.2019-4151
    [42] G. A. Mills-Tettey, A. Stentz, M. B. Dias, The dynamic Hungarian algorithm for the assignment problem with changing costs, 2007. Available from: https://www.ri.cmu.edu/pub_files/pub4/mills_tettey_g_ayorkor_2007_3/mills_tettey_g_ayorkor_2007_3.pdf.
    [43] M. R. Chen, Y. Z. Lu, A novel elitist multiobjective optimization algorithm: multiobjective extremal-optimization, Eur. J. Oper. Res., 188 (2008), 637–651. https://doi.org/10.1016/j.ejor.2007.05.008 doi: 10.1016/j.ejor.2007.05.008
    [44] J. Chen, J. Shi, A multi-compartment vehicle routing problem with time windows for urban distribution – a comparison study on particle swarm optimization algorithms, Comput. Ind. Eng., 133 (2019), 95–106. https://doi.org/10.1016/j.cie.2019.05.008 doi: 10.1016/j.cie.2019.05.008
  • This article has been cited by:

    1. I. P. Longo, E. Queirolo, C. Kuehn, On the transition between autonomous and nonautonomous systems: The case of FitzHugh–Nagumo’s model, 2024, 34, 1054-1500, 10.1063/5.0234833
    2. Dimitri Breda, Davide Liessi, Lyapunov exponents of renewal equations: Numerical approximation and convergence analysis, 2024, 0, 1531-3492, 0, 10.3934/dcdsb.2024152
    3. Miryam Gnazzo, Nicola Guglielmi, On the Numerical Approximation of the Distance to Singularity for Matrix-Valued Functions, 2025, 46, 0895-4798, 1484, 10.1137/23M1625299
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1766) PDF downloads(66) Cited by(0)

Article outline

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog