Genetic algorithms in the design of complex distribution networks

Abstract

Reviews the value of network concepts as a means of portraying complex logistics and distribution systems. Reports on research which focuses on the broader issues of model formulation and solution techniques rather than specific applications. Addresses the issues of designing networks with a tree structure, and also more general ones in which loops are allowed and redundancy enforced. The decision variables involved are related to whether or not a link should exist between two specific pairs of nodes, and then what should be the level of traffic flow on that particular link. Describes the design problem in detail and possible models that could be used to represent it. Follows with a description of genetic algorithms to solve the synthesis problem of deciding the node‐link topology, and the use of linear and non‐linear programming to solve the problem of assigning traffic flow to a network with a given typology in a least‐cost manner. Concludes with a description of computational experience with solving such problems.

Optimal and heuristic base-stock levels and review periods in a serial Supply Chain

“…Simplicity of operation and power of effect are two of the main attractions of the GA approach (Goldberg, 1989). Berry et al (1998) described the application of GA in the design of complex distribution networks to decide the node-link topology and showed that the combined use of GA and linear or non-linear programming is an effective approach to solve network design problems. Disney et al (1997) demonstrated the use of a model of a decision support system coupled with a simulation facility; a GA optimisation procedure can yield enhanced performance to the production control function.…”

Realizing a new e‐commerce tool for formation of a virtual enterprise

“…There is little information on how these ideas can be realized into a set of tools that could help managers of SMEs overcome the complexities of dealing with collaborative planning efforts. Even the little information that is available for realizing tools for collaborative planning either use complex techniques such as genetic algorithm (Berry et al, 1998), artificial intelligence (McMullen, 2001), stochastic programming (MirHassani, 2000), statistical analysis (Reutterer and Kotzab, 1999) that require mathematicians or specialists for using it, or demands very expensive and time consuming installation of third party implementation.…”

A preliminary test of Hunt's General Theory of Competition: using artificial adaptive agents to study complex and ill-defined environments

“…In addition, increased attention has been given to evolutionary concepts in economic change (Nelson and Winter, 1982), game theory (Smith, 1982), new product growth (Tellis and Crawford, 1981), and breeding competitive strategies (Midgley et al, 1997). Furthermore, genetic algorithms (GAs) have been used to model new product development and design processes (Balakrishnan and Varghese, 1996;Natter et al, 2001), strategic groups (Lee et al, 2002), retail inventory and space allocation (Urban, 1998), retail site selection (Hurley et al, 1995), and the design of distribution networks (Berry et al, 1998). Recently, there have been attempts to breed artificial agents to model economic and marketing phenomena (Arthur, 1991;Holland and Miller, 1991;Midgley et al, 1997).…”