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Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem.

In this paper a literature review of mathematical models for supply chain design is proposed. Based on publications of last twelve years and selected from the major international journals in operations management, logistics and operational research fields, this work gives a picture of the strategic decisions, economic parameters, constrains and model features for strategic planning and design of supply chains. After a description of the review methodology and of the comparison parameters, some guidelines are given in order to support future woks in this field

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Supply chain is a complex network which involves the products, services and information flows between suppliers and customers. A typical supply chain is composed of different levels. Hence, there is a need to optimise the supply chain by finding the optimum configuration of the network in order to get a good compromise between the multi-objectives such as cost minimisation and lead-time minimisation. There are several multi-objective optimisation methods which have been applied to find the optimum solutions set based on the Pareto frontline. In this study, a swarm based optimisation method, namely, the Bees Algorithm is proposed in dealing with multi-objective supply chain model to find the optimum configuration of given supply chain problem which minimises the total cost and the total lead-time. The supply chain problem utilised in this study is taken from literature and several experiments have been conducted in order to show the performance of the proposed model and the results have been compared to those achieved by the Ant Colony optimisation. The results show that the proposed Bees Algorithm is able to achieve better Pareto solutions for the supply chain problem