Comparing neuro-dynamic programming algorithms for the vehicle routing problem with stochastic demands

Secomandi, Nicola

doi:10.1016/s0305-0548(99)00146-x

Cited by 183 publications

(105 citation statements)

References 35 publications

(69 reference statements)

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“…Yang et al [22] proposed a strategy for splitting the a priori tour allowing the restocking before a stockout, when this is profitable. Secomandi [23,24,25] analyzed different possibilities for applying dynamic programming to this problem. Teodorović and Pavković [26] and Gendreau et al [27] tackled the VRPSD using metaheuristic approaches.…”

Section: Literature Overviewmentioning

confidence: 99%

Out-of-the-Box and Custom Implementation of Metaheuristics. A Case Study: The Vehicle Routing Problem with Stochastic Demand

Pellegrini

Birattari

2011

Intelligent Computational Optimization in Engineering

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Abstract. Metaheuristics are a class of effective algorithms for optimization problems. A basic implementation of a metaheuristic typically requires rather little development effort. With a significantly larger investment in the design, implementation, and fine-tuning, metaheuristics can often produce state-of-the-art results. According to the amount of development effort, we say that an implementation of a metaheuristic is either an out-of-the-box version or a custom one. The possibility of implementing metaheuristics in such a flexible way is one of the major strengths of these algorithms. Nonetheless, it also hides some possible catches. In particular, it should be noticed that results obtained with out-of-the-box implementations cannot be always generalized to custom ones, and vice versa. The goal of this analysis is to stress that these two ways of using metaheuristics are different. As a case study, we focus on the vehicle routing problem with stochastic demand and on five among the most successful metaheuristics-namely, tabu search, simulated annealing, genetic algorithms, iterated local search, and ant colony optimization. We show that the relative performance of these algorithms strongly varies whether one considers out-of-the-box implementations or custom ones, in which the parameters are accurately fine-tuned. Moreover, we underline the relevance of clearly stating the framework in which the results reported in the literature have been obtained. To this aim, we consider also an implementation of the same algorithms as described in the literature.

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Section: Literature Overviewmentioning

confidence: 99%

Out-of-the-Box and Custom Implementation of Metaheuristics. A Case Study: The Vehicle Routing Problem with Stochastic Demand

Pellegrini

Birattari

2011

Intelligent Computational Optimization in Engineering

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“…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%

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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“…At one extreme is the use of a dynamic approach where one can re-optimize at any point, using the newly obtained data about customer demands, or to re-optimize after failure. Neuro Dynamic Programming has been used to implement techniques based on re-optimization; see, e.g., Secomandi (2000Secomandi ( , 2001.…”

Section: Stochastic Vehicle Routingmentioning

confidence: 99%

Solving the Vehicle Routing Problem with Stochastic Demands using the Cross-Entropy Method

Chepuri¹,

Homem-de-Mello²

2005

Ann Oper Res

130

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Abstract. An alternate formulation of the classical vehicle routing problem with stochastic demands (VRPSD) is considered. We propose a new heuristic method to solve the problem, based on the Cross-Entropy method. In order to better estimate the objective function at each point in the domain, we incorporate Monte Carlo sampling. This creates many practical issues, especially the decision as to when to draw new samples and how many samples to use. We also develop a framework for obtaining exact solutions and tight lower bounds for the problem under various conditions, which include specific families of demand distributions. This is used to assess the performance of the algorithm. Finally, numerical results are presented for various problem instances to illustrate the ideas.

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Comparing neuro-dynamic programming algorithms for the vehicle routing problem with stochastic demands

Cited by 183 publications

References 35 publications

Out-of-the-Box and Custom Implementation of Metaheuristics. A Case Study: The Vehicle Routing Problem with Stochastic Demand

Out-of-the-Box and Custom Implementation of Metaheuristics. A Case Study: The Vehicle Routing Problem with Stochastic Demand

Ant Colony Optimization for Solving Solid Waste Collection Scheduling Problems

Solving the Vehicle Routing Problem with Stochastic Demands using the Cross-Entropy Method

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