Development of an OLAP Based Fuzzy Logic System for Supporting Put Away Decision

In today‘s rapidly changing and globally volatile world, manufacturers pay strong efforts on conducting lean production, outsourcing their components, and management on the complex supply chain. Warehouse management plays a vital role to be a successful player in the any kinds of industry which put-away process is a key activity that brings significant influence and challenges to warehouse performance. In this dynamic operating environment, minimizing the operation mistakes and providing accurate real time inventory information to stakeholder become the basic requirements to be an order qualifier. An OLAP based intelligent system called Fuzzy Storage Assignment System (FSAS) is proposed to increase availability of decision support data and convert the human knowledge into system for tackling the storage location assignment problem (SLAP). To validate the feasibility of this proposed system, a prototype will be worked out for a third party logistics company

Item-location assignment using fuzzy logic guided genetic algorithms

In today's logistics environment, large-scale combinatorial problems will inevitably be met during industrial operations. This paper deals with a novel real-world optimization problem, called the item-location assignment problem, faced by a logistics company in Shenzhen, China. The objective of the company in this particular operation is to assign items to suitable locations such that the required sum of the total traveling time of the workers to complete all orders is minimized. A stochastic search technique called fuzzy logic guided genetic algorithms (FLGA) is proposed to solve this operational problem. In GA, a specially designed crossover operation, called a shift and uniform based multi-point (SUMP) crossover, and swap mutation are adopted. The performance of this novel crossover operation is tested and is shown to be more effective by comparing it to other crossover methods. Furthermore, the role of fuzzy logic is to dynamically adjust the crossover and mutation rates after each ten consecutive generations. In order to demonstrate the effectiveness of the FLGA and make comparisons with the FLGA through simulations, various search methods such as branch and bound, standard GA (i.e., without the guide of fuzzy logic), simulated annealing, tabu search, differential evolution, and two modified versions of differential evolution are adopted. Results show that the FLGA outperforms the other search methods in all of the three considered scenarios