Distribution Management-Mathematical Modelling and Practical Analysis

Eilon, Samuel; Watson-Gandy, C. D. T.; Christofides, Nicos; Neufville, Richard de

doi:10.1109/tsmc.1974.4309370

Cited by 74 publications

(76 citation statements)

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“…Results using SA are from (Lin, Kao and Hsu, 1993). Eil50 and Eil75 are from (Eilon, Watson-Gandy and Christofides, 1969), and are included in TSPLIB with an additional city as Eil51.tsp and Eil76.tsp. KroA100 is also in TSPLIB.…”

Section: Ant-q: a Summary Of Resultsmentioning

confidence: 99%

A study of some properties of Ant-Q

Dorigo

Gambardella

1996

Lecture Notes in Computer Science

125

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Abstract. Ant-Q is an algorithm belonging to the class of ant colony based methods, that is, of combinatorial optimization methods in which a set of simple agents, called ants, cooperate to find good solutions to combinatorial optimiza tion problems. The main focus of this article is on the experimental study of the sensitivity of the Ant-Q algorithm to its parameters and on the investigation of synergistic effects when using more than a single ant. We conclude comparing Ant-Q with its ancestor Ant System, and with other heuristic algorithms.

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Section: Ant-q: a Summary Of Resultsmentioning

confidence: 99%

A study of some properties of Ant-Q

Dorigo

Gambardella

1996

Lecture Notes in Computer Science

125

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“…Results using SA and AG are from Lin et al (1993). Oliver30 is from Oliver et al (1987), Eil50, Eil75 are from Eilon et al (1969) and are included in TSPLIB with an additional city as Eil51.tsp and Eil76.tsp. KroA100 is also in TSPLIB.…”

Section: Artificial Antsmentioning

confidence: 99%

Ant colonies for the travelling salesman problem

1997

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“…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 [6] as in Table 1.…”

Section: Methodsmentioning

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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Distribution Management-Mathematical Modelling and Practical Analysis

Cited by 74 publications

References 0 publications

A study of some properties of Ant-Q

A study of some properties of Ant-Q

Ant colonies for the travelling salesman problem

Ant Colony Optimization for Solving Solid Waste Collection Scheduling Problems

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