Ant Colony Algorithm for Single Stage Supply Chain

Ant Colony Algorithm for Single Stage Supply Chain

R. Sridharan (National Institute of Technology Calicut, India) and Vinay V. Panicker (National Institute of Technology Calicut, India)
Copyright: © 2014 |Pages: 12
DOI: 10.4018/978-1-4666-5202-6.ch014
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Abstract

Swarm intelligence has emerged as an approach for developing meta-heuristics to solve combinatorial optimization problems. Ant Colony Optimization (ACO) is an example for a swarm-intelligence based meta-heuristic inspired by the social behavior of colonies of ants. In this chapter, an ACO-based heuristic is proposed for solving a distribution-allocation problem in a single-stage of a supply chain. Thus, this work aims at modeling and analysis of the distribution-allocation problem in a single-stage supply chain with a fixed cost for a transportation route. In addition, it provides an insight for researchers in developing heuristics based on ant colony optimization for supply chain related problems.
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Background

A distribution-allocation problem is one of the most comprehensive decision issues that need to be solved for a long term efficient operation of the whole supply chain. The problem involves determining the best way to transport goods and services from the supply point to the demand point minimizing the overall cost of the supply chain operation.

Key Terms in this Chapter

Supply Chain Management: A network of facilities, involved directly or indirectly, to fulfill a customer request.

Transportation: A supply chain driver that deals with the transfer of goods/services from the point of supply to the point of demand.

Greedy Search: Early generations of heuristics which improves the objective function value with each search move.

Ant Colony Optimization: A non-traditional optimization technique for solving computational problems that searches for an optimal path in a graph, based on the social behaviour of ants seeking a path between their colony and food source.

Monte-Carlo Simulation: A problem solving technique that estimates stochastic or deterministic parameters using the probability of certain outcomes by running multiple trial runs.

Heuristics: Rules of thumb which are derived by experience, intuition and logic.

Meta-Heuristics: A general algorithmic framework which guide the search process in order to find optimal solutions.

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