Ogunbanwo, A., A. Williamson, M. Veluscek, R. Izsak, T. Kalganova and P. Broomhead. "Transportation Network Optimization." Encyclopedia of Business Analytics and Optimization. IGI Global, 2014. 2570-2583. Web. 16 Jul. 2019. doi:10.4018/978-1-4666-5202-6.ch229
Ogunbanwo, A., Williamson, A., Veluscek, M., Izsak, R., Kalganova, T., & Broomhead, P. (2014). Transportation Network Optimization. In J. Wang (Ed.), Encyclopedia of Business Analytics and Optimization (pp. 2570-2583). Hershey, PA: IGI Global. doi:10.4018/978-1-4666-5202-6.ch229
Ogunbanwo, A., A. Williamson, M. Veluscek, R. Izsak, T. Kalganova and P. Broomhead. "Transportation Network Optimization." In Encyclopedia of Business Analytics and Optimization, ed. John Wang, 2570-2583 (2014), accessed July 16, 2019. doi:10.4018/978-1-4666-5202-6.ch229
Transportation Network Optimization
A. Ogunbanwo (Brunel University, United Kingdom), A. Williamson (Brunel University, United Kingdom), M. Veluscek (Brunel University, United Kingdom), R. Izsak (Brunel University, United Kingdom), T. Kalganova (Brunel University, United Kingdom) and P. Broomhead (Brunel University, United Kingdom)
Source Title: Encyclopedia of Business Analytics and Optimization
A transportation or distribution network is a dynamic, stochastic and complex system that can be modeled as a graph where the nodes (vertices) represent entities that can in general can be categorized as representing producers, distribution centers and end customers (Ding, Benyoucef, & Xie, 2009) or in the more specialist case of manufacturing enterprises as manufacturing and distribution sites that procure raw material, process them into finished goods, and distribute the finish goods to customers (Ganeshan, 1999) (Figure 1 shows an example of a transportation network).
Example of transportation network, where are sources, destinations/dealers, and and are possible intermediate ports
Key Terms in this Chapter
Ant Colony Systems: A system that mimics the behavior of ants in their search of food. Some experiments and observations of ant colonies have shown that ants very often are able to find the shortest path between the colony and their food source. It is the characterization of this behavior in a stochastic algorithm, and its application in finding the shortest path between a source and a destination or as in the case of transportation networks, between the producer and the dealer that defines the method.
Particle Swarm Optimization: A search strategy that starts from an initial set of candidate solutions (the particles) and try to improve them by looking at their neighbors in the solution spaces. A solution, or particle is moved according to a local criteria (i.e. the particle moves to the local best), in combination with a criteria based on the situation of all other particles (i.e. the particle moves to the best known position of the other particles).
Linear Programming: A mathematical technique used to solve optimization problem. Linear programming require the problem to be define as a mathematical model consisting of an objective function relative to a set of variables, and a set of constraints over those variables. Linear programming may only be applied if all the relationships in the model are linear.
Multi-Objectives: An objective function of an optimization model is defined as multi-objectives if it models more than one entity is to be optimized; the optimization problem has to be solved in more than one dimension. For instance, an objective function that includes variables of profit and time is multi-objectives.
Transportation Network: A given set of connections between producers and dealers/customers which may be used by the producers to transport finished goods to the dealers.
Optimization Problem: The problem of finding the best value for a given max/min functions according to a set of constraints on the function variables.