Embed Size (px)
Citation preview
1/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
General Variable Neighborhood Search applied tothe picking process in a warehouse
Borja Menendez 1, Eduardo G. Pardo 2, Abraham Duarte 3,Antonio Alonso-Ayuso 4, Elisenda Molina 5
Universidad Rey Juan Carlos 1 2 3 4, Universidad Carlos III 5
[email protected] 1, [email protected] 2, [email protected] 3,[email protected] 4, [email protected] 5
October 8, 2014
Borja Menendez GVNS applied to warehouses
2/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
Outline
1 Introduction
2 Order Batching Problem
3 General Variable Neighborhood Search
4 Computational experiments
5 Conclusions
Borja Menendez GVNS applied to warehouses
3/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
Outline
1 Introduction
2 Order Batching Problem
3 General Variable Neighborhood Search
4 Computational experiments
5 Conclusions
Borja Menendez GVNS applied to warehouses
4/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
Introduction
A very important issue in the last few years
Two different problems:
Batching
Routing
Borja Menendez GVNS applied to warehouses
5/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
Outline
1 Introduction
2 Order Batching Problem
3 General Variable Neighborhood Search
4 Computational experiments
5 Conclusions
Borja Menendez GVNS applied to warehouses
6/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
Order Batching Problem
Two basic order-picking strategies:
Strict-order pickingOrder batching
Borja Menendez GVNS applied to warehouses
7/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
Order Batching Problem
Two basic order-picking strategies:
Strict-order pickingOrder batching
Order batching: several orders into batches
Weight constraint
Batches assigned to pickers
Objective: to minimize total time to collect batches
Borja Menendez GVNS applied to warehouses
8/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
Warehouse considerations
Parallel aisles (equal lenght). Two crossing aisles.
Borja Menendez GVNS applied to warehouses
9/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
Routing strategies (S-Shape)
Borja Menendez GVNS applied to warehouses
10/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
Routing strategies (Largest gap)
Borja Menendez GVNS applied to warehouses
11/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
Routing strategies (Combined)
Borja Menendez GVNS applied to warehouses
12/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
State of the art
Proved to be NP-hard (number of orders per batch > 2)
Three heuristic categories (De Koster et al., 1999):
Basic methods as FCFSSeed methods generates batches sequentiallySaving methods as Clarke and Wright
Albareda-Sambola et al. (2009) → VND (Exchange)
Exchange is, as far as we know, the best method
Borja Menendez GVNS applied to warehouses
13/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
Outline
1 Introduction
2 Order Batching Problem
3 General Variable Neighborhood Search
4 Computational experiments
5 Conclusions
Borja Menendez GVNS applied to warehouses
14/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
General Variable Neighborhood Search
VNS: based on systematic changes of neighborhood
Three straightforward facts:
A local minimum in a neighborhood structure is not necessarilya local minimum in another oneA global minimum is local minimum with respect to allpossible neighborhood structuresFor many problems, local minima with the same or a differentneighborhood structure are relatively close
Different schemes → General VNS (GVNS)
Borja Menendez GVNS applied to warehouses
15/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
Pseudocode GVNS
Algorithm 1 GVNS(f , maxiter , kmax)
1: for i ← 1 to maxiter do2: k ← 13: repeat4: f ′ ← Shake(f , k)5: f ′′ ← VND(f ′)6: NeighborhoodChange(f , f ′′, k)7: until k = kmax
8: end for9: return f
Borja Menendez GVNS applied to warehouses
16/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
Outline
1 Introduction
2 Order Batching Problem
3 General Variable Neighborhood Search
4 Computational experiments
5 Conclusions
Borja Menendez GVNS applied to warehouses
17/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
Computational experiments
Test cases:
600 (selected 20 to adjust parameters), fromAlbareda-Sambola et al.Instances divided into five groups (number of orders)Order size: 1-7 items, 1 kg per item, capacity: 12 kg
Parameters: kmax and maxiter set to 5
Execution: Java 6, Intel Core i7 (3.4 GHz), 4 GB RAM
Borja Menendez GVNS applied to warehouses
18/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
Comparison with Exchange algorithm
Dev. (%) CPUt (ms) #Best
GlobalGVNS 0.55 248.55 335
Exchange 1.12 421.12 134
Table : Comparison of GVNS and Exchange algorithms
Wilcoxon test: statistically significant differences
Borja Menendez GVNS applied to warehouses
19/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
Outline
1 Introduction
2 Order Batching Problem
3 General Variable Neighborhood Search
4 Computational experiments
5 Conclusions
Borja Menendez GVNS applied to warehouses
20/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
Conclusions
Order Batching Problem in a parallel aisle warehouse
Proposed a General VNS
GVNS compared with best previous algorithm
GVNS outperforms it, differences statistically significant
Borja Menendez GVNS applied to warehouses
21/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
References
Albareda-Sambola, M, A Alonso-Ayuso, E Molina and CSimon de Blas (2009), “Variable neighborhood search fororder batching in a warehouse”, Asia-Pacific Journal ofOperational Research, 1, 655–683.
De Koster, R, E van der Poort and M Wolters (1999),“Efficient orderbatching methods in warehouses”, Int J ProdRes, 37 (7), 1479–1504
Borja Menendez GVNS applied to warehouses
22/22
IntroductionOrder Batching Problem
General Variable Neighborhood SearchComputational experiments
Conclusions
General Variable Neighborhood Search
applied to the picking process in a warehouse
Borja Menendez 1, Eduardo G. Pardo 2, Abraham Duarte 3,Antonio Alonso-Ayuso 4, Elisenda Molina 5
Universidad Rey Juan Carlos 1 2 3 4, Universidad Carlos III 5
[email protected] 1, [email protected] 2, [email protected] 3,[email protected] 4, [email protected] 5
Borja Menendez GVNS applied to warehouses
