A new interval-valued fuzzy optimization model for truck scheduling in a multi-door cross-docking system by considering transshipment and flexible dock doors extra cost

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran

2 Head of Department of Industrial Engineering, Shahed University Tehran, Iran

Abstract

In a cross-docking system, doors can be used exclusively for receiving or sending operations, or they can be flexible enough to be used for both. Flexible doors have recently attracted a lot of attention as a way to improve cross-docking systems' performance. Despite the advantages of flexible doors, applying them is usually associated with additional costs due to the need for dual-function equipment to receive and send. Moreover, the distance that goods move within cross-docking facilities, from receiving to sending dock doors, significantly affects how well these facilities work. This paper presents a new bi-objective mixed-integer linear programming model for scheduling the inbound and outbound trucks in a cross-docking facility. The model objectives are to minimize total system costs, including additional costs of applying flexible doors and transshipment (maximize efficiency) and to minimize outbound trucks' tardiness from predetermined due dates (maximize responsiveness). As a result of the uncertainties in cross-dock truck scheduling problems, the parameters are considered triangular interval-valued fuzzy (IVF) numbers. Moreover, a new IVF-uncertain solution approach based on max-min operator and compromise programming concepts is proposed to solve multi-objective mathematical programming problems with triangular IVF numbers. The proposed model and IVF-solution approach are used for cross-docking operations planning at a well-known food manufacturing group. The results are also analyzed according to their sensitivity to changes in key parameters of the cross-docking problem. The comparison of the proposed approach and some fuzzy known methods for solving multi-objective models demonstrates the superior performance of the proposed approach in the real case study.

Keywords

Main Subjects


[1] M. H. Alavidoost, H. Babazadeh, S. T. Sayyari, An interactive fuzzy programming approach for bi-objective straight
and U-shaped assembly line balancing problem, Applied Soft Computing, 40 (2016), 221-235.
[2] G. Alpan, A. L. Ladier, R. Larbi, B. Penz, Heuristic solutions for transshipment problems in a multiple door cross
docking warehouse, Computers and Industrial Engineering, 61(2) (2011), 402-408.
[3] J. J. Bartholdi, K. R. Gue, The best shape for a crossdock, Transportation Science, 38(2) (2004), 235-244.
[4] A. Bellanger, S. Hana , C. Wil, Three-stage hybrid- owshop model for cross-docking, Computers and Operations
Research, 40(4) (2013), 1109-1121.
[5] L. Berghman, C. Briand, R. Leus, P. Lopez, The truck scheduling problem at crossdocking terminals - exclusive
versus mixed mode, In Proceedings of 4th International Conference on Operations Research and Enterprise Systems
(ICORES), 2015.
[6] P. Bodnar, R. de Koster, K. Azadeh, Scheduling trucks in a cross-dock with mixed service mode dock doors, Transportation Science, 51(1) (2017), 112-131.
[7] N. Boysen, M. Fliedner, Cross dock scheduling: Classi cation, literature review and research agenda, Omega, 38(6)
(2010), 413-422.
[8] N. Boysen, M. Fliedner, A. Scholl, Scheduling inbound and outbound trucks at cross docking terminals, OR Spectrum,
32(1) (2010), 135-161.
[9] F. Chen, C. Y. Lee, Minimizing the makespan in a two-machine cross-docking  ow shop problem, European Journal
of Operational Research, 193(1) (2009), 59-72.
[10] F. Chen, K. Song, Minimizing makespan in two-stage hybrid cross docking scheduling problem, Computers and
Operations Research, 36(6) (2009), 2066-2073.
[11] C. Cornelis, G. Deschrijver, E. E. Kerre, Advances and challenges in interval-valued fuzzy logic, Fuzzy Sets and
Systems, 157(5) (2006), 622-627.
[12] G. Correa Issi, R. Linfati, J. W. Escobar, Mathematical optimization model for truck scheduling in a distribution
center with a mixed service-mode dock area, Journal of Advanced Transportation, (2020), 1-13.
[13] F. Dalouchei, S. M. Mousavi, J. Antucheviciene, A. Minaei, A bi-objective model for scheduling construction projects
using critical chain method and interval-valued fuzzy sets, Buildings, 12(7) (2022), 904.
[14] G. Ertek, Managing supply chains on the Silk Road: Strategy, performance, and risk, CRC Press, 2012.
[15] F. Essghaier, H. Allaoui, G. Goncalves, Truck to door assignment in a shared cross-dock under uncertainty, Expert
Systems with Applications, (2021), 114889.
[16] S. Gelareh, F. Glover, O. Guemri, S. Hana , P. Nduwayo, R. Todosijevic, A comparative study of formulations for
a cross-dock door assignment problem, Omega, 91 (2020), 102015.
[17] M. B. Gorza lczany, A method of inference in approximate reasoning based on interval-valued fuzzy sets, Fuzzy Sets
and Systems, 21(1) (1987), 1-17.
[18] M. Guignard, P. M. Hahn, A. A. Pessoa, D. C. da Silva, Algorithms for the cross-dock door assignment problem,
In Proceedings of the Fourth International Workshop on Model-Based Metaheuristics, (2012), 145-162.
[19] M. H. Haghighi, S. M. Mousavi, A mathematical model and two fuzzy approaches based on credibility and expected
interval for project cost-quality-risk trade-o  problem in time-constrained conditions, Algorithms, 15 (2022), 226.
[20] M. Jimenez, M. Arenas, A. Bilbao, M. V. Rodr, Linear programming with fuzzy parameters: An interactive method
resolution, European Journal of Operational Research, 177(3) (2007), 1599-609.
[21] D. Konur, M. M. Golias, Analysis of di erent approaches to cross-dock truck scheduling with truck arrival time
uncertainty, Computers and Industrial Engineering, 65(4) (2013), 663-672.
[22] Y. J. Lai, C. L. Hwang, Fuzzy mathematical programming: Methods and applications, Springer-Verlag, 1992.
[23] Y. J. Lai, C. L. Hwang, Possibilistic linear programming for managing interest rate risk, Fuzzy Sets and Systems,
54(2) (1993), 135-146.
[24] Y. J. Lai, T. Y. Liu, C. L. Hwang, Topsis for MODM, European Journal of Operational Research, 76(3) (1994),
486-500.
[25] X. Q. Li, B. Zhang, H. Li, Computing ecient solutions to fuzzy multiple objective linear programming problems,
Fuzzy Sets and Systems, 157(10) (2006), 1328-1332.
[26] T. W. Liao, P. J. Egbelu, P. C. Chang, Two hybrid di erential evolution algorithms for optimal inbound and
outbound truck sequencing in cross docking operations, Applied Soft Computing, 12(11) (2012), 3683-3697.
[27] M. Y. Maknoon, P. Baptiste, Moving freight inside cross docking terminals, In 2010 8th International Conference
on Supply Chain Management and Information, (2010), 1-6.
[28] V. Mohagheghi, S. M. Mousavi, B. Vahdani, An assessment method for project cash  ow under interval-valued
fuzzy environment, Journal of Optimization in Industrial Engineering, 10(22) (2017), 73-80.
[29] S. M. Mousavi, A new interval-valued hesitant fuzzy pairwise comparison-compromise solution methodology: An
application to cross-docking location planning, Neural Computing and Applications, 31(9) (2019), 5159-5173.
[30] S. M. Mousavi, A bi-objective mathematical programming model for truck scheduling problem in a cross-dock system considering the sequencing and outsourcing of products under interval-valued fuzzy uncertainty conditions, Journal of Industrial Engineering Research In Production Systems, 8(16) (2020), 137-157.
[31] S. M. Mousavi, B. Vahdani, R. Tavakkoli-Moghaddam, H. Hashemi, Location of cross-docking centers and vehicle
routing scheduling under uncertainty: A fuzzy possibilistic-stochastic programming model, Applied Mathematical
Modelling, 38(7-8) (2014), 2249-2264.
[32] D. Ozgen, B. Gulsun, Combining possibilistic linear programming and fuzzy AHP for solving the multi-objective
capacitated multi-facility location problem, Information Sciences, 268 (2014), 185-201.
[33] M. Rajabzadeh, S. M. Mousavi, Allocation of products to a heterogeneous  eet of trucks in a cross-docking center
based on carbon emissions and costs in food and beverage industry: Novel uncertain solution approaches, Journal of
Environmental Management, 332 (2023), 117071.
[34] A. Rijal, M. Bijvank, R. de Koster, Integrated scheduling and assignment of trucks at unit-load cross-dock terminals
with mixed service mode dock doors, European Journal of Operational Research, 278(3) (2019), 752-771.
[35] H. Savoji, S. M. Mousavi, J. Antucheviciene, M. Pavlovskis, A robust possibilistic bi-objective mixed integer model
for green biofuel supply chain design under uncertain conditions, Sustainability, 14(20) (2022), 13675.
[36] S. I. Sayed, I. Contreras, J. A. Diaz, D. E. Luna, Integrated cross-dock door assignment and truck scheduling with
handling times, TOP, 28(3) (2020), 705-727.
[37] R. Shahabi-Shahmiri, S. Asian, R. Tavakkoli-Moghaddam, S. M. Mousavi, M. Rajabzadeh, A routing and scheduling
problem for cross-docking networks with perishable products, heterogeneous vehicles and split delivery, Computers
and Industrial Engineering, 157 (2021), 107299.
[38] A. Shahmardan, M. S. Sajadieh, Truck scheduling in a multi-door cross-docking center with partial unloading-
Reinforcement learning-based simulated annealing approaches, Computers and Industrial Engineering, 139 (2020),
106134.
[39] G. Stalk, P. Evans, L. E. Shulman, Competing on capabilities: The new rules of corporate strategy, Harvard Business
Review, 63(2) (1992), 57-69.
[40] B. Vahdani, M. Zandieh, Scheduling trucks in cross-docking systems: Robust meta-heuristics, Computers and
Industrial Engineering, 58(1) (2010), 12-24.
[41] J. Van Belle, P. Valckenaers, G. Vanden Berghe, D. Cattrysse, A tabu search approach to the truck scheduling
problem with multiple docks and time windows, Computers and Industrial Engineering, 66(4) (2013), 818-826.
[42] J. S. Yao, F. T. Lin, Constructing a fuzzy  ow-shop sequencing model based on statistical data, International Journal
of Approximate Reasoning, 29(3) (2002), 215-234.
[43] W. Yu, P. J. Egbelu, Scheduling of inbound and outbound trucks in cross docking systems with temporary storage,
European Journal of Operational Research, 184(1) (2008), 377-396.
[44] H. J. Zimmermann, Fuzzy programming and linear programming with several objective functions, Fuzzy Sets and
Systems, 1(1) (1978), 45-55.