A Central Coordination System for managing a large supply base through supply chain collaboration

Competitive edge from the advanced technology is being replaced. Organisations are now situating in a rapidly dynamic world full of suppliers, whom can probably produce similar products or even better products as time goes. To secure the competitive advantages, purchasing managers have to continuously searching for new suppliers, monitoring and evaluating them. However, this may not be a realistic task because it can dramatically increase the management costs and administration time of an individual organisation. Driven by these reasons, this article proposes a new supplier management approach - a Central Coordination System (CCS). CCS implements the idea of supply chain collaboration. Through the collaboration of information sharing, this article suggests the establishment of a supplier's information database, which helps customers to manage their suppliers more efficiently, economically and reliably.Department of Industrial and Systems Engineerin

Development of a hybrid fuzzy genetic algorithm model for solving transportation scheduling problem

There has been an increasing public demand for passenger rail service in the recent times leading to a strong focus on the need for effective and efficient use of resources and managing the increasing passenger requirements, service reliability and variability by the railway management. Whilst shortening the passengers’ waiting and travelling time is important for commuter satisfaction, lowering operational costs is equally important for railway management. Hence, effective and cost optimised train scheduling based on the dynamic passenger demand is one of the main issues for passenger railway management. Although the passenger railway scheduling problem has received attention in operations research in recent years, there is limited literature investigating the adoption of practical approaches that capitalize on the merits of mathematical modeling and search algorithms for effective cost optimization. This paper develops a hybrid fuzzy logic based genetic algorithm model to solve the multi-objective passenger railway scheduling problem aiming to optimize total operational costs at a satisfactory level of customer service. This hybrid approach integrates genetic algorithm with the fuzzy logic approach which uses the fuzzy controller to determine the crossover rate and mutation rate in genetic algorithm approach in the optimization process. The numerical study demonstrates the improvement of the proposed hybrid approach, and the fuzzy genetic algorithm has demonstrated its effectiveness to generate better results than standard genetic algorithm and other traditional heuristic approaches, such as simulated annealing

Real-time process mining system for supply chain network: OLAP-based fuzzy approach

Currently, companies active in the development of high-tech products have become more and more complex in the age of mass customisation. Not only do they have to focus on improving product quality, but rather on gaining experience to modify the current processes in order to streamline the integrated workflow. A Real-time Process Mining System (R-PMS) is developed to analyse the proposed XML-based process data for discovering the hidden relationships among processes. The new feature of this system is the incorporation of the process mining engine, which is characterised by the combined capabilities of the Online Analytical Processing (OLAP) and Fuzzy Logic (FL), to form a robust framework for highlighting the undesirable process setting and parameters for further improvement in a real-time manner. The simulation results indicated that the OLAP-based fuzzy approach was generally superior to those of conventional methods which offer higher flexibility on production process management with decision support ability. In this paper, the detailed architecture and a case study have been included to demonstrate the feasibility of the proposed system.Department of Industrial and Systems Engineerin