OD OD OD O' Kelly 3 Weiszfeld OD 6 4 OD Campbell - OD Campbell P- P 2 7 OD OD OD OD 8-10 Alumur OD OD OD 11 Campbell 1 OD 12 Con

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1 Journal of Highway and Transportation Research and Development Vol. 30 No. 4 Apr doi /j. issn OD OD OD OD OD OD U A Resource Balanced Usage Problem for Bi-level Hub-and-spoke LTL Logistics Network Design HU Qingmi 1 HU Zhihua 1 2 TAO Sha 1 1. Logistics Research CenterShanghai Maritime UniversityShanghai China 2. School of Economics and ManagementTongji UniversityShanghai China Abstract To cope with the problem that the less-than-truckload LTL logistics network design for the certain Origin-Destination OD flow demand often leads to an unbalanced use of network resourcesthe characteristics of the uncertainty of OD flow among LTL logistics nodes and the network design difficulties brought about by the uncertain OD demand flow in LTL logistics system are analyzedand a mixed integer programming model is established to adapt to the uncertain OD demand flow based on inventory theory. The examples demonstrate that 1 the design of logistics network with uncertain OD demand flow by optimizing the cost of transport and construction of the network as well as the utilization rate of network sources comprehensively has an obvious advantage in a balanced usage of network resources over the one only considering minimizing the cost of transport and construction of the network 2 the result is valuable to adjust the structure of logistics network to achieve a balanced use of network resources in the case of uncertainty OD demand flow. Key words transport economics inventory theory balanced utilization less-than-truckload network uncertainty of demand mixed integer programming &ZD M YJC huqingmi321@ sina. com

2 OD OD OD O' Kelly 3 Weiszfeld OD 6 4 OD Campbell - OD Campbell P- P 2 7 OD OD OD OD 8-10 Alumur OD OD OD 11 Campbell 1 OD 12 Contreras 13 OD OD OD 0-1 OD OD node 2 link 2 hub spoke OD OD 1

3 OD OD OD OD OD N = { n } I N J N K L N K * L * K L s w 3 m i n i i h i k i 12 n k h i k = 槡 m i - m k 2 + n i - n k 2 z i i * i k h i t i i s y z i j i j OD ( P k) k E t i j i j OD ( C y) y c i i * k k c k = i z i + t i x i k OD z i j = t i j z i = j z i j t i = 2 1 E c y = E c i * = s i* h n* k = i * +1 k = 0 y - k p k + k - y p k 1 2 ~ 5 y y * 5 f k k O i s s i d i s s 34 5 y * 2 E c i * E c i * i* -1 k = E c i * 0 y p k h k = 0 h + s E c i * 0 p ( k) h h + s y p k k = j t i j 2 s w s 1 s 4 p i s j s i j OD ij N s p i s j = 1 i 6 w s i j s i j OD 7 a b b < a v e q r θ λ 1 λ y i s k l j s i k l j OD 2 x i k 01 1 i k 0 OD OD OD OD OD i j OD i OD j OD w s i j

4 4 127 OD OD 1 min ζ = k f k k x k k + i s k a O i s + d i s h i k x i k + 1 i s k l bp i s j y i s k l jh k l + 2 k i s v O i s + d i s x i k 6 s. t. i N k N x i k = 1 7 i k N x i k x k k i k N x i k = k N k x k k = r 10 i Ns s w O i s = j N w s i i Ns s w d i s = j N w s j jp i s ip j s j 11 i 12 i N f i i = F s O i s + d i s 13 0 O s i s + d i i k l j Ns s w k l j = w s i jx i k x j l 14 i z i + t i x i k * i k l j Ns s w k l j 0 15 k * k l Nk 1 i j l i j s yi s k l j p i s s k l j p i s j - i j + i O s i s i k N λ 1 i z i j s l k j p i s j j s l k j p i s j θ + d i s x i k λ 2 + t i x i k i s v O i s 6 7 ~ OD z i t i 2. 2 OD 1 k * 2. 2 OD 9 x 0 ~ i s s i 3. 1 j k l y 1 a = v = 1b = 0. 6s = 3h = 5θ = 0. 1 s i j OD 16 λ 1 = 0. 5λ 2 = 1. 1 λ 2 = ~ 15 1 OD r = 3f i i = F [ x ] = 150x i N 2 OD OD 3 4 OD OD OD 18 i k * K * X = k * k * s x i k * < λ 1 18 minζ = k f k k x k k + i s k a O i s i s k l bp i s j y i s k l jh k k + d i s x i l + + d i k s h i k x i k + 19 k * K * K N X 18 k * k * 2. 3 OD OD OD

5 OD 1 5 OD 1 OD OD OD OD OD OD OD OD 6912 OD OD OD OD A A1 = A2A3 Tab. 1 1 OD Predictable OD flow values OD OD A A K * = OD 2 18 k * 6 4 OD OD OD 3 18 k * A OD OD OD Tab. 2 2 OD Unpredictable OD flow values change in real time OD OD 0 2 OD Fig. 1 OD λ 2 = Flow chart of model solution 2 3 OD 4 OD λ 2 = OD

6 Fig. 5 OD λ 2 = Relation of OD demand flow distribution with unknown nodal assignment λ 2 = Tab. 3 Result summary 1 /% OD OD OD λ 2 = 1. 1 OD λ 2 = OD OD OD i z i 3 4 % = = 21 ~ i j s p i s j w s i j 22 k * i s O i s + d i s x i k * + t i x i k *

7 α = 56. 5% k * l * i j s 1 - yi s k * l * jp i s j - i j s l * k * jp i s j 3 [ i j s k * l * jp i s j + i j s l * k * jp i s j ] k * l * k * < l * 4 2 Tab. 4 Result summary 2 /% OD OD OD λ 2 = OD λ 2 = OD λ 2 = λ 2 = λ 1 OD % OD θ λ 1 λ OD r OD OD OD 2 OD OD OD 4 OD 26. 6% 2

8 HANCZAR P. An Inventory-distribution System with LTL 3 OD OD Deliveries-mixed Integer Approach J. Procedia-Social and Behavioral Sciences D - 1 LFLP J LI LihuaFU ZhuoZHOU Heping. Model and Algorithm 1 for Discrete Logistics Facility Location Problem under 2 Interval Uncertainty Demand J. Computer Engineering and Applications References Algorithm of Discrete Network Design Problem under OD 1. J ZHAO JinHUO Jiazhen. Review on Axis-spoke Service Network Design J. Shanghai Management Science CAMPBELL A MLOWE T JZHANG L. The p-hub Center Allocation Problem J. European Journal of Operational Research CUNHA C BSILVA M R. A Genetic Algorithm for the Problem of Configuring a Hub-and-spoke Network for a LTL Trucking Company in Brazil J. European Journal of Operational Research MEYER TERNST A TKRISHNAMOORTHY MA 2- Phase Algorithm for Solving the Single Allocation p-hub Center Problem J. Computers & Operations Research ERNST A TKRISHNAMOORTHY M. Efficient Algorithms for the Uncapacitated Single Allocation p-hub Median Problem J. Location Science O'KELLY M E. A Quadratic Integer Program for the Location of Interacting Hub Facilities J. European Journal of Operational Research CAMPBELL J F. Hub Location and the p-hub Median Problem J. Operations Research OD J LU HuapuYU XinxinBIAN Changzhi. Model and Demand Uncertainty J. Journal of Highway and Transportation Research and Development ALUMUR S ANICKEL SSALDANHA-DA-GAMA F. Hub Location under Uncertainty J. Transportation Research Part B Methodological CAMPBELL J F. Designing Hub Networks with Connected and Isolated Hubs C Proceedings of the 43rd Hawaii International Conference on System Sciences. Hawaii IEEE Computer Society CONTRERAS ICORDEAU J FLAPORTE G. Stochastic Uncapacitated Hub Location J. European Journal of Operational Research CONTRERAS ICORDEAU J FLAPORTE G. The Dynamic Uncapacitated Hub Location Problem J. Transportation Science SIM TLOWE T JTHOMAS B W. The Stochastic p-hub Center Problem with Service-level Constraints J. Computers & Operations Research YANG Tahui. Stochastic Air Freight Hub Location and Flight Routes Planning J. Applied Mathematical Modelling 檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸檸 BILBRO G LSNYDER W E. Optimization of Functions with Many Minima J. IEEE Transactions on Systems Manand Cybernetics XIE DanHU WeimingTAN Tieniuet al. A Multiobject Tracking System for Surveillance Viedo Analysis C Proceedings of IEEE International Conference on Pattern Recognition. CambridgeUK IEEE CHEN H TLIN H HLIU T L. Multi-object Tracking Using Dynamical Graph Matching C Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. KauaiHI IEEE

Vol. 22 No. 4 JOURNAL OF HARBIN UNIVERSITY OF SCIENCE AND TECHNOLOGY Aug GPS,,, : km, 2. 51, , ; ; ; ; DOI: 10.

22 4 2017 8 Vol. 22 No. 4 JOURNAL OF HARBIN UNIVERSITY OF SCIENCE AND TECHNOLOGY Aug. 2017 150080 GPS,,, : 27. 36 km, 2. 51, 110. 43, ; ; ; ; DOI: 10. 15938 /j. jhust. 2017. 04. 015 U469. 13 A 1007-2683