Solving the Vehicle Routing Problem with Stochastic Demands via Hybrid Genetic Algorithm-Tabu Search

Ismail, Zuhaimy; Irhamah, [No Value]

doi:10.3844/jmssp.2008.161.167

Cited by 20 publications

(15 citation statements)

References 19 publications

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“…Both algorithms are categorized as Ant Colony Optimization (ACO) algorithms. In particular, it can be observed that ACS is the most aggressive of the ACO algorithms and it returns the best solution quality for very short computation times [5,7] . ACS has an advantage over Simulated Annealing and Genetic Algorithm approaches when the graph may change dynamically where the Ant Colony algorithm can be run continuously and adapt to changes in real time.…”

Section: Discussionmentioning

confidence: 99%

“…The problem described above can be modeled as a Vehicle Routing Problem with Stochastic Demand (VRPSD) as the amount of solid waste is stochastic and may be presented in a complete graph [7] and Secomandi, [9,10] . Let the set of nodes be {0, 1,…,n}.…”

Section: Problemmentioning

confidence: 99%

“…This study shows the way of a real life problem being formulated into Vehicle Routing Problem with Stochastic Demand Model and solved by the designed algorithm. Since there is no open source data for VRPSD problem, thus this case study adopted a set of data modified from the well known 50-customer problem in Eilon et al [7] as in Table 1. The customers' locations are shown on a plane by designed program as in Fig.…”

Section: Problemmentioning

confidence: 99%

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Ant Colony Optimization for Solving Solid Waste Collection Scheduling Problems

Ismail¹,

Loh²

2009

J. of Mathematics and Statistics

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Problem statement: Southern Waste Management environment (SWM environment) is a company responsible for the collection and disposal of solid waste for the city of Johor Bahru, a city with over one million populations. The company is implementing an integrated solid waste management system where it involved in the optimization of resources to ensure the effectiveness of its services. Formulating this real life problem into vehicle routing problem with stochastic demand model and using some designed algorithms to minimize operation cost of solid waste management. Approach: The implementation of Ant Colony Optimization (ACO) for solving solid waste collection problem as a VRPSD model was described. A set of data modified from the well known 50 customers problems were used to find the route such that the expected traveling cost was minimized. The total cost was minimized by adopting a preventive restocking policy which was trading off the extra cost of returning to depot after a stock-out with the cost of returning depot for restocking before a stock-out actually occurs. For comparison purposes, Simulated Annealing (SA) was used to generate the solution under the same condition. Results: For the problem size with 12 customers with vehicle capacity 10 units, both algorithms obtained the same best cost which is 69.4358 units. But the percentage deviations of averages from the associated best cost are 0.1322 and 0.7064 for ACS and SA. The results indicated that for all demand ranges, proposed ACO algorithm showed better performance than SA algorithm. Conclusion: SA was able to obtain good solutions for small ranges especially small size of problem. For ACS, it is always provide good results for all tested ranges and problems sizes of the tested problem.

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Section: Discussionmentioning

confidence: 99%

Section: Problemmentioning

confidence: 99%

Section: Problemmentioning

confidence: 99%

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Ant Colony Optimization for Solving Solid Waste Collection Scheduling Problems

Ismail¹,

Loh²

2009

J. of Mathematics and Statistics

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“…Similar case study could be seen in Irhamah and Ismail (2009) and Ismail and Irhamah (2008). This research tries to refine CARP model with stochastic demand (CARPSD) and CARP with time windows (CARPTW) according to specific case of truck operation in waste collection by taking into account the element of rain drops.…”

Section: Problem Identificationmentioning

confidence: 99%

Implementation Weather-Type Models of Capacitated Arc Routing Problem via Heuristics

Ismail¹,

Ramli

2011

American Journal of Applied Sciences

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Problem statement:In this study, we introduced a new and real-life condition of Capacitated Arc Routing Problem (CARP), a model that represents vehicle's operation in waste collection. In general, we studied the element of rain drops that affected the collected waste weight in total by imposed a new variable namely rainy weight age. In rainy days, the household refusals did not increase in volumes, but in weights due to rain drops. Consequently, this matter thus burdened vehicle's capacity and prolonged its operation time. This dynamic variable thus changes the initial CARP model where the existing model did not consider other external elements that have effected onto the model. Approach: Then we developed and enhanced CARP by integrating stochastic demand and time windows to suit the models with our specific case. Results: Objectively, CARP with stochastic demand (CARPSD) and CARP with time windows (CARPTW) were designed to minimize the total routing cost and number of trips for a vehicle. Our approach is to design CARP models in almost likely to road layout in residential area and graphically this model is called mesh network. We also developed a constructive heuristic that is called nearest procedure based on highest demand/cost (NPHDC) and work in conjunction with switching rules to search the feasible solution. Conclusion: Our preliminary results show a higher cost and more trips are needed when the vehicle operates in rainy day compared to normal day operation.

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“…Many researches' have been done in VRP using GA (Ismail and Irhamah, 2008). proposed a new scheme based on a hybrid GA and Tabu Search heuristic for VRPSD with preventive restocking.…”

Section: Heuristic Algorithm For Vrpsdmentioning

confidence: 99%

Meta Heuristic Algorithms for Vehicle Routing Problem with Stochastic Demands

Shanmugam¹

2011

Journal of Computer Science

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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.

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Solving the Vehicle Routing Problem with Stochastic Demands via Hybrid Genetic Algorithm-Tabu Search

Cited by 20 publications

References 19 publications

Ant Colony Optimization for Solving Solid Waste Collection Scheduling Problems

Ant Colony Optimization for Solving Solid Waste Collection Scheduling Problems

Implementation Weather-Type Models of Capacitated Arc Routing Problem via Heuristics

Meta Heuristic Algorithms for Vehicle Routing Problem with Stochastic Demands

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