TY - JOUR
AU - Shanmugam, Geetha
AU - Ganesan, Poonthalir
AU - Vanathi, Dr. P.T.
PY - 2011
TI - Meta Heuristic Algorithms for Vehicle Routing Problem with Stochastic Demands
JF - Journal of Computer Science
VL - 7
IS - 4
DO - 10.3844/jcssp.2011.533.542
UR - https://thescipub.com/abstract/jcssp.2011.533.542
AB - Problem statement: The shipment of goods from manufacturer to the consumer is a focal
point of distribution logistics. In reality, the demand of consumers is not known a priori. This kind of
distribution is dealt by Stochastic Vehicle Routing Problem (SVRP) which is a NP-hard problem. In
this proposed work, VRP with stochastic demand is considered. A probability distribution is considered
as a random variable for stochastic demand of a customer. Approach: In this study, VRPSD is
resolved using Meta heuristic algorithms such as Genetic Algorithm (GA), Particle Swarm
Optimization (PSO) and Hybrid PSO (HPSO). Dynamic Programming (DP) is used to find the
expected cost of each route generated by GA, PSO and HPSO. Results: The objective is to minimize
the total expected cost of a priori route. The fitness value of a priori route is calculated using DP. In
proposed HPSO, the initial particles are generated based Nearest Neighbor Heuristic (NNH). Elitism is
used in HPSO for updating the particles. The algorithm is implemented using MATLAB7.0 and tested
with problems having different number of customers. The results obtained are competitive in terms of
execution time and memory usage. Conclusion: The computational time is reduced as polynomial time
as O(nKQ) time and the memory required is O(nQ). The ANOVA test is performed to compare the
proposed HPSO with other heuristic algorithms.