An Integrated Approach to Product Delivery Planning and Scheduling
Product delivery planning and scheduling is a task of high priority in transport logistics. In distribution centres this task is related to deliveries of various types of goods in predefined time windows. In real-life applications the problem has different stochastic performance criteria and conditions. Optimisation of schedules itself is time consuming and requires an expert knowledge. In this paper an integrated approach to product delivery planning and scheduling is proposed. It is based on a cluster analysis of demand data of stores to identify typical dynamic demand patterns and product delivery tactical plans, and simulation optimisation to find optimal parameters of transportation or vehicle schedules. Here, a cluster analysis of the demand data by using the K-means clustering algorithm and silhouette plots mean values is performed, and an NBTree-based classification model is built. In order to find an optimal grouping of stores into regions based on their geographical locations and the total demand uniformly distributed over regions, a multiobjective optimisation problem is formulated and solved with the NSGA II algorithm.
If the inline PDF is not rendering correctly, you can download the PDF file here.
G. Merkuryeva V. Bolshakov "Simulation-based Fitness Landscape Analysis and Optimisation for Vehicle Scheduling Problem" in EUROCAST 2011 Part I LNCS 6927 pp. 280-286 2011.
G. A. F. Seber Multivariate Observations. Hoboken NJ: John Wiley & Sons Inc. 1984.
J. B. MacQueen "Some Methods for Classification and Analysis of MultiVariate Observations" in Proc. of the 5th Berkeley Symposium on Math. Statistics and Probability Vol. 1 p. 281-297 1967.
L. Kaufman and P. J. Rousseeuw Finding Groups in Data: An Introduction to Cluster Analysis. Hoboken NJ: John Wiley & Sons Inc. 1990.
K. Deb et al. "A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II" IEEE Transactions on Evolutionary Computation 6(2) pp. 182-197 2002.
S. Wagner "Heuristic Optimization Software Systems - Modeling of Heuristic Optimization Algorithms in the HeuristicLab Software Environment" PhD Thesis Institute for Formal Models and Verification Johannes Kepler University Linz Austria 2009.
T. D. Gwiazda "Crossover for single-objective numerical optimization problems" in Genetic algorithms reference Vol. I p. 17 2006.
Z. Michalewicz Genetic Algorithms + Data Structures = Evolution Programs. Third Revised and Extended Edition Spring-Verlag Berlin Heidelberg 1999.