Designing a multi-objective mathematical model of routing-locating in multiple cross-dock systems using soft time window

Document Type : Scientific - Research

Authors

1 Department of industrial management, Qazvin Branch, Islamic Azad University, Qazvin, Iran

2 Ph.d student In Industrial Management, Department of Industrial management, Qazvin Branch, Islamic Azad University, Qazvin, Iran

3 Department of Industrial management of Allameh Tabataba’I University,Tehran,Iran

Abstract

Cross docking is an appropriate method to reduce the warehouse space requirements, inventory management costs, and turnaround times for customer orders .This paper focuses on the optimization of the transportation cost and the planning of the movement of inbound and outbound trucks with multi cross dock and two different types of objective functions of minimizing total cost, minimize the number of transportation into the network inside the supply chain.. Since the paper model is a linear programming integer of zero and since these models belong to the NP-hard class, their solving time severely increases with increasing the problem dimensions. In this paper, to solve the model meta-heuristic algorithms have been used. The algorithms used in solving the model are Multiple Objective Simulated Annealing (MOSA) and Multiple Objective Ant Colony (MOACO) Algorithm. Finally, the model has been solved using two algorithms and computational experiments reported carefully to illustrate and compare designing and computational.

Keywords

References

ملانوری، حسنی، توکلی مقدم، رضا، صبوحی ،فاطمه وحاج آقایی کشتلی، مصطفی (1397). "ارائه مدل حمل و نقل هزینه ثابت پله‌ای، چند محصولی، دوسطحی و حل آن با الگوریتم شبیه‌سازی تبرید"، فصلنامه مهندسی حمل و نقل، دوره دهم، شماره دوم، ص. 413-399.
 
Ahmadizar, F., Zeynivand, M. and Arkat, J. (2015)" Two-level vehicle routing with cross-docking in a three-echelon supply chain: A genetic algorithm approach", Applied Mathematical Modelling, Vol.39, pp.7065-7081.
 
Brimi, s. (2016)"Vehicle routing problem with crossdocking: Asimulated annealing approach", Procedia -Social and Behavioral Sciences, Vol.235, pp.149 – 158.
 

-                    Borumand, A. and Beheshtinia, M. (2018), "A developed genetic algorithm for solving the multi-objective supply chain scheduling problem", Kybernetes, Vol. 47 No. 7, pp. 1401-1419.

 

Beheshtinia, M., Ghasemi, A. (2018)" A multi-objective and integrated model for supply chain scheduling optimization in a multi-site manufacturing system", Engineering Optimization, Vol.50.No.9, pp.1415-1433.
 
Baniamerian, A., Bashiri, M.and Tavakkoli-Moghaddam, R. (2019)"Modified variable neighborhood search and genetic algorithm for profitable heterogeneous vehicle routing problem with cross-docking", Applied Soft Computing, Vol.75, pp.441-460.
 
Dondo, R. and Cerd, J. (2014)"A monolithic approach to vehicle routing and operations scheduling of a cross-dock system with multiple dock doors", Computers &Chemical Engineering, Vol.63, pp.184-205.
 
Dorigo, M., Maniezzo, V.and Colorni, A. (1996) "The Ant System: Optimization by a Colony of Cooperating Agent, IEEE Transactions on Systems, Man and Cybernetics", Part B Vol.26, No1, pp. 29-41.
 
HasaniGoodarzi, A.and Zegordi, S. (2016)"A Location- Routing Problem for Cross-docking Networks: A Biogeographybased Optimization Algorithm",Computers&IndustrialEngineering, Vol.102, pp.132-146.
 
Ghatreh Samani, M and Hosseini Motlagh, S. (2017) "A hybrid algorithm for a two-echelon location-routing problem with simultaneous pickup and delivery under fuzzy demand", International Journal ofTransportation Engineering, Vol.5, No.1, PP.59-85.
 
Kirkpatrick, S.and Gelatt, C.D.)1983(“Vecchi, M.P. Optimization by Simulated Annealing. Science”, Vol. 220, No. 4598, pp.671-680.
 
Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H.andTeller, E. (1953)"Equation of State Calculation by Fast Computing Machines", J. of Chem. Phys, Vol.21, pp.1087-1091.
 
Mohtashami, A. (2015)"A Novel Dynamic Genetic Algorithm-Based Method for Vehicle Schadulingin Cross Docking Systems with Frequent Unloading Operation", Computers & Industrial Engineering, Vol.90, pp.221-24.
 
Maknoon, Y.and Laporte, G. (2017)"Vehicle routing with cross-dock selection”, Computers & Operations Research, Vol.77, pp.254-266.
 
Ponboon, S., Qureshi, A. and Taniguchi, A. (2016) "Evaluation of cost structure and impact of parameters in locationrouting problem with time windows", Transportation Research Procedia, Vol.12, pp.213 – 226.
 
Rezaei, S.and kheirkhah, A. (2017) A"comprehensive approach in designing a sustainable closed-loop supply chain network using cross-docking operations, Computational and Mathematical Organization Theory,vol.24, pp.51-98.
 
Taguchi, G. (1986)"Introduction to quality engineering": designing quality into products and processes.
Taheri, S, and Beheshtinia, M. (2019)"A Genetic Algorithm Developed for a Supply Chain Scheduling Problem", Iranian Journal of Management Studies (IJMS) Vol. 12, No. 2, pp. 281-306.
 
Wisittipanich,W.and Hengmeechai,P.(2017) "Truck Scheduling in Multi-Door Cross Docking Terminal by Modified ParticleSwarm Optimization",Computers&Industrial Engineering, Vol.113,pp. 793-802.
 
Yeng, P.and chuang, Y. (2016)"Adaptive Memory ArtiÞcial Bee Colony Algorithm for Green Vehicle Routing with Cross-Docking", AppliedMathematicalModelling,Vol.40, pp.9302-9315.
 
Zuluaga, J., Thiell, M. and Perales, R. (2017) "Reverse cross-docking", Omega, Vol.66, pp.48-57.
 
Zitzler, E., Deb, K.andThiele, L. (2000)"Comparison of multiobjective evolutionary algorithms: Emprical results", Evolutionary Computation journal, Vol. 8, No. pp­125-14

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History

  • Receive Date: 01 July 2019
  • Revise Date: 29 December 2019
  • Accept Date: 31 December 2019

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