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The timeless search for optimizing the demand and supply of any resource is one of the main issues for humanity nearly from the beginning of time. The relevant cost of adding an extra resource reacts by means of more energy requirement, more emissions, interaction with policies and market status makes is even more complicated. Optimization of demand and supply is the key to successfully solve the problem. There are various optimization algorithms in the literature and most of them uses various algorithms of iteration and some degree of randomness to find the optimum solution. Most of the metaheuristic and artificial intelligence algorithms require the randomness where to make a new decision to go forward. So this chapter is about the possible use of chaotic random numbers in the metaheuristic and artificial intelligence algorithms that requires random numbers. The authors only provide the necessary information about the algorithms instead of providing full detailed explanation of the subjects assuming the readers already have theoretical basic information.

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Dealing with short term and long term production planning and scheduling has already been solved with different optimization and artificial methods and approaches. Under normal manufacturing conditions supply and demand progress controlled and supported by decision support systems, ERP and MRP software packages aiming maximum utilization of resources and minimizing the stocks aiming for JIT. These software packages becoming even more intelligent and proactive based on the data in the database systems. However all these systems start with initial assumption of under normal conditions of flow time ordering to delivering. In case of unplanned stops, failures, malfunctions, shortages of unplanned inventory levels alter these initial conditions and progress to a diverging outcomes and consequences. This paper aiming a dynamic allocation and routing of mobile resources in a manufacturing plant by reallocating them by using a modified Particle Swarm Optimization using Chaotic Randomness.