Abstract
The periodic inventory routing problem (PIRP) determines the delivery routing and the inventory policies for retailers from a supplier in a periodic time based on the minimal cost criterion. Since it is a non-deterministic polynomial-time (NP)-hard problem, a heuristic method is needed for this problem. In the past, different global heuristic methods, such as tabu search (TS) and simulated annealing (SA), have been proposed; however, they seem ineffective. Particle swarm optimization (PSO) is known as resolving multidimensional combinatorial problems such as PIRP; however, it is easily trapped in local optimality. The authors of this paper propose a hybrid heuristic method for the PIRP. The hybrid method integrates a large neighborhood search (LNS) into PSO to overcome the drawbacks of PSO and LNS. The PSO is adopted first. A local search is applied to each particle in different iterations. Then, a local optimal solution (particle) for each particle is obtained. Last, the LNS is applied to the global best solution to avoid becoming trapped in local optimality. The results show that the proposed hybrid heuristic method is 10.93 % better than the existing method and 1.86 % better than the pure heuristic method in terms of average cost.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Anderson H, Hoff A, Christiansen M, Hasle G, Lokketangen A (2010) Industrial aspect and literature survey: combined inventory management and routing. Comput Oper Res 37:1515–1536
Chih M (2015) Self-adaptive check and repair operator-based particle swarm optimization for the multidimensional knapsack problem. Appl Soft Comput 26:378–389
Choong F, Phon-Amnuaisuk S, Alias MY (2011) Metaheuristic methods in hybrid flow shop scheduling problem. Expert Syst Appl 38:10787–10793
Federgruen A, Zipkin P (1984) A combined vehicle routing and inventory allocation problem. Oper Res 32:1019–1037
Fu C, Fu Z (2010) Optimization on stochastic inventory-routing problem in multi-cycle two-echelon system. Comput Eng Appl 46:198–201
Gaur V, Fisher ML (2004) A periodic inventory routing problem at a supermarket chain. Oper Res 52:813–822
Golden B, Assad A, Dahl R (1984) Analysis of a large scale vehicle routing problem with an inventory component. Large Scale Syst 7:181–190
Hansen P, Mladenović N (2001) Variable neighborhood search: principles and applications. Eur J Oper Res 130:449–467
Hansen P, Mladenović N (2010) Variable neighborhood search: methods and applications. Ann Oper Res 175:367–407
Jordehi AR, Jasni J (2013) Parameter selection in particle swarm optimisation: a survey. J Exp Theoret Artif Intell 24:527–542
Kiranyaz S, Pulkkinen J, Gabbouj M (2011) Multi-dimensional particle swarm optimization in dynamic environments. Expert Syst Appl 38:2212–2223
Küçükoğlu İ, Öztürk N (2015) An advanced hybrid meta-heuristic algorithm for the vehicle routing problem with backhauls and time windows. Comput Ind Eng 86:60–68
Lee YH, Jung JW, Lee KM (2006) Vehicle routing scheduling for cross-docking in the supply chain. Comput Ind Eng 51:247–256
Li JQ, Pan QK, Liang YC (2010) An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems. Comput Ind Eng 59:647–662
Liu S-C, Chen A-Z (2012) Variable neighborhood search for the inventory routing and scheduling problem in a supply chain. Expert Syst Appl 39:4149–4159
Liu S-C, Lin C-C (2005) A heuristic method for the combined location routing and inventory problem. Int J Adv Manuf Technol 26:372–381
Marinakis Y, Marinaki M, Dounias G (2010) A hybrid particle swarm optimization algorithm for the vehicle routing problem. Eng Appl Artif Intell 23:463–472
Mirabi M (2014) A hybrid electromagnetism algorithm for multi-depot periodic vehicle routing problem. Int J Adv Manuf Technol 71:509–518
Miyamoto Y, Kubo M (2001) Case study: the inventory routing for vending machines. J Oper Res Soc Jpn 44:378–389
Moin NH, Salhi S (2007) Inventory routing problems: a logistical overview. J Oper Res Soc 58:1185–1194
Qin L, Miao L, Qing FR, Zhang Y (2014) A local search method for periodic inventory routing problem. Expert Syst Appl 41:765–778
Shi Y, Eberhart R (1998) A modified particle swarm optimizer. IEEE World Congress on Computational Intelligence, Anchorage, AK, pp 69–73
Sindhuchao S, Romeijn HE, Akcali E, Boondiskulchok R (2005) An integrated inventory-routing system for multi-item joint replenishment with limited vehicle capacity. J Glob Optim 32:93–118
Tasgetiren MF, Liang Y-C, Sevkli M, Gencyilmaz G (2007) A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem. Eur J Oper Res 177:1930–1947
Vidal T, Crainic TG, Gendreau M, Prins C (2013) Heuristics for multi-attribute vehicle routing problems: a survey and synthesis. Eur J Oper Res 231:1–21
Wang Y, Ma X, Xu M, Liu Y, Wang Y (2015) Two-echelon logistics distribution region partitioning problem based on a hybrid particle swarm optimization–genetic algorithm. Expert Syst Appl 42:5019–5031
Wei L, Zhang Z, Zhang D, Lim A (2015) A variable neighborhood search for the capacitated vehicle routing problem with two-dimensional loading constraints. Eur J Oper Res 243:798–814
Zachariadis EE, Tarantilis CD, Kiranoudis CT (2014) An integrated local search method for inventory and routing decisions. Expert Syst Appl 36:10239–10248
Zhang Y, Qi M, Miao L, Liu E (2014) Hybrid metaheuristic solutions to inventory location routing problem. Transp Res E 70:305–323
Zhao Q, Wang SY, Lai KK (2007) A partition approach to the inventory/routing problem. Eur J Oper Res 177:786–802
Zhao Q, Chen S, Zang C (2008) Model and algorithm for inventory/routing decision in a three-echelon logistics system. Eur J Oper Res 191:623–635
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, SC., Lu, MC. & Chung, CH. A hybrid heuristic method for the periodic inventory routing problem. Int J Adv Manuf Technol 85, 2345–2352 (2016). https://doi.org/10.1007/s00170-015-8081-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-015-8081-3