George T. S. Ho

A hybrid genetic algorithm for the multi-depot vehicle routing problem

The distribution of finished products from depots to customers is a practical and challenging problem in logistics management. Better routing and scheduling decisions can result in higher level of customer satisfaction because more customers can be served in a shorter time. The distribution problem is generally formulated as the vehicle routing problem (VRP). Nevertheless, there is a rigid assumption that there is only one depot. In cases, for instance, where a logistics company has more than one depot, the VRP is not suitable. To resolve this limitation, this paper focuses on the VRP with multiple depots, or multi-depot VRP (MDVRP). The MDVRP is NP-hard, which means that an efficient algorithm for solving the problem to optimality is unavailable. To deal with the problem efficiently, two hybrid genetic algorithms (HGAs) are developed in this paper. The major difference between the HGAs is that the initial solutions are generated randomly in HGA1. The Clarke and Wright saving method and the nearest neighbor heuristic are incorporated into HGA2 for the initialization procedure. A computational study is carried out to compare the algorithms with different problem sizes. It is proved that the performance of HGA2 is superior to that of HGA1 in terms of the total delivery time.

The Study on Using Passive RFID Tags for Indoor Positioning

Radio frequency identification (RFID) is the technology that put an RFID tag on objects or people, so that they can be identified, tracked, and managed automatically. With its wide application in the automobile assembly industry, warehouse management and the supply chain network, RFID has been recognized as the next promising technology in serving the positioning purpose. Existing positioning technologies such as GPS are not available indoors as the terminal cannot get the signal from satellites. To enhance the availability of the positioning systems for indoors, the development of RFID positioning system for locating objects or people have became a hot topic in recent research. Compared with conventional active and high-cost solutions, this paper studied the feasibility of using passive RFID tags for indoor positioning and object location detection to provide real time information for tracking movement. Results of experiment show that readability of the passive RFID positioning system is satisfactory, and it is a more cost effective solution when compared with other positioning technologies.

A fuzzy logic approach to forecast energy consumption change in a manufacturing system

This paper proposes an energy consumption change forecasting system using fuzzy logic to reduce the uncertainty, inconvenience and inefficiency resulting from variations in the production factors. The proposed fuzzy logic approach helps the manufacturer forecast the energy consumption change in the plant when certain production input factors are varied. Predictions given by the proposed system adopts the fuzzy rule reasoning mechanism so that any changes in the overall energy consumption will neither violate the stable power supply and production schedules nor result in energy wastage. To demonstrate how the fuzzy logic approach is applied to a manufacturing system, a case study of the energy consumption forecast in a clothing manufacturing plant has been conducted in an emulated environment. The result of the case indicates a percentage change in the plants energy consumption after analyzing three input parameters. This finding is able to provide a solid foundation on which decision makers and systems analysts can base suitable strategies for ensuring the efficiency and stability of a manufacturing system.

A fuzzy guided multi-objective evolutionary algorithm model for solving transportation problem

In the field of supply chain management and logistics, using vehicles to deliver products from suppliers to customers is one of the major operations. Before transporting products, optimizing the routing of vehicles is required so as to provide a low-cost and efficient service for customers. This paper deals with the problem of optimization of vehicle routing in which multiple depots, multiple customers, and multiple products are considered. Since the total traveling time is not always restrictive as a time constraint, the objective considered in this paper comprises not only the total traveling distance, but also the total traveling time. We propose using a multi-objective evolutionary algorithm called the fuzzy logic guided non-dominated sorting genetic algorithm 2 (FL-NSGA2) to solve this multi-objective optimization problem. The role of fuzzy logic is to dynamically adjust the crossover rate and mutation rate after ten consecutive generations. In order to demonstrate the effectiveness of FL-NSGA2, we compared it with the following: non-dominated sorting genetic algorithms 2 (NSGA2) (without the guide of fuzzy logic), strength Pareto evolutionary algorithm 2 (SPEA2) (with and without the guide of fuzzy logic), and micro-genetic algorithm (MICROGA) (with and without the guide of fuzzy logic). Simulation results showed that FL-NSGA2 outperformed other search methods in all of three various scenarios.

Design and development of agent-based procurement system to enhance business intelligence

The purpose of this research is to propose a procurement system across other disciplines and retrieved information with relevant parties so as to have a better co-ordination between supply and demand sides. This paper demonstrates how to analyze the data with an agent-based procurement system (APS) to re-engineer and improve the existing procurement process. The intelligence agents take the responsibility of searching the potential suppliers, negotiation with the short-listed suppliers and evaluating the performance of suppliers based on the selection criteria with mathematical model. Manufacturing firms and trading companies spend more than half of their sales dollar in the purchase of raw material and components. Efficient data collection with high accuracy is one of the key success factors to generate quality procurement which is to purchasing right material at right quality from right suppliers. In general, the enterprises spend a significant amount of resources on data collection and storage, but too little on facilitating data analysis and sharing. To validate the feasibility of the approach, a case study on a manufacturing small and medium-sized enterprise (SME) has been conducted. APS supports the data and information analyzing technique to facilitate the decision making such that the agent can enhance the negotiation and suppler evaluation efficiency by saving time and cost.

Design and development of logistics workflow systems for demand management with RFID

Research highlights? In the responsive logistics information system, radio frequency identification can provide visibility of product flow and capture the real time inventory data. ? The captured data are analysed by online analytical process to identify the market segment. ? With advert of artificial neural network, the demand pattern is recognized and the corresponding replenishment strategy can be formulated. This paper discusses demand and supply chain management and examines how artificial intelligence techniques and RFID technology can enhance the responsiveness of the logistics workflow. This proposed system is expected to have a significant impact on the performance of logistics networks by virtue of its capabilities to adapt unexpected supply and demand changes in the volatile marketplace with the unique feature of responsiveness with the advanced technology, Radio Frequency Identification (RFID). Recent studies have found that RFID and artificial intelligence techniques drive the development of total solution in logistics industry. Apart from tracking the movement of the goods, RFID is able to play an important role to reflect the inventory level of various distribution areas. In todays globalized industrial environment, the physical logistics operations and the associated flow of information are the essential elements for companies to realize an efficient logistics workflow scenario. Basically, a flexible logistics workflow, which is characterized by its fast responsiveness in dealing with customer requirements through the integration of various value chain activities, is fundamental to leverage business performance of enterprises. The significance of this research is the demonstration of the synergy of using a combination of advanced technologies to form an integrated system that helps achieve lean and agile logistics workflow.

A RFID-case-based sample management system for fashion product development

This paper presents a RFID-case-based sample management system (R-SMS) to manage an iterative process for evaluating fabric swatches in the development of new fashion products. R-SMS supports fashion designers and clothing merchandisers in working out the best way to fabricate their new collections and to expedite the product development process. This study attempts to use a case-based reasoning (CBR) technique to solve problems of fabric selection. In addition, radio frequency identification (RFID) technology is incorporated in the system to provide real-time swatches tracking and accurate fabric status updates within a fabric sample storeroom. The outcomes of this study provide fashion designers and merchandisers with appropriate fabric knowledge, enabling them to select the most appropriate fabric or fabric combination effectively and efficiently for new products; in turn, to enhance a garment enterprises overall competency in managing its fabric resources.

A Genetic Algorithm-based optimization model for supporting green transportation operations

To propose a GA-based optimization model for designing green transportation schemes.To examine the economic and environmental value based on a cost analysis in depth.To provide a guidance of implementing green transportation for the logistics service providers. Green Logistics (GL) has emerged as a trend in the management of the distribution of goods and the collection of end-of-life products. With its focus on maximizing the economic and environmental value by means of recycling and emission control, GL contributes to the sustainable development of industry but also requires a more comprehensive transportation scheme when conducting logistics services. This study is motivated by the practice of delivering and collecting water carboys. In this paper, a Genetic Algorithm-based optimization model (GOM) is proposed for designing a green transportation scheme of economic and environmental cost efficiency in forward and reverse logistics. Two vehicle routing models with simultaneous delivery and pickup (full or partial pickup) are formulated and solved by a Genetic Algorithm. A cost generation engine is designed to perform a comprehensive cost comparison and analysis based on a set of economic and environmental cost factors, so as to examine the impact of the two models and to suggest optimal transportation schemes. The computational experiments show that the overall cost is evidently lower in the full pickup model. Notably, the impact of product cost after recycling and reusing empty carboys on total cost is more significant than the impact of transportation cost and CO2 emission cost. In summary, the proposed GOM is capable of suggesting a guidance for the logistics service providers, who deal with green operations, to adopt a beneficial transportation scheme so as to eventually achieve a low economic and environmental cost.

A hybrid OLAP-association rule mining based quality management system for extracting defect patterns in the garment industry

In todays garment industry, garment defects have to be minimized so as to fulfill the expectations of demanding customers who seek products of high quality but low cost. However, without any data mining tools to manage massive data related to quality, it is difficult to investigate the hidden patterns among defects which are important information for improving the quality of garments. This paper presents a hybrid OLAP-association rule mining based quality management system (HQMS) to extract defect patterns in the garment industry. The mined results indicate the relationship between defects which serves as a reference for defect prediction, root cause identification and the formulation of proactive measures for quality improvement. Because real-time access to desirable information is crucial for survival under the severe competition, the system is equipped with Online Analytical Processing (OLAP) features so that manufacturers are able to explore the required data in a timely manner. The integration of OLAP and association rule mining allows data mining to be applied on a multidimensional basis. A pilot run of the HQMS is undertaken in a garment manufacturing company to demonstrate how OLAP and association rule mining are effective in discovering patterns among product defects. The results indicate that the HQMS contributes significantly to the formulation of quality improvement in the industry.

Optimization of system reliability in multi-factory production networks by maintenance approach

Maintaining the reliability of a system is one of the most critical and challenging tasks for factories during production. A reliable system is not only significant for improving a systems productivity and products quality, but worth even more in multi-factory production because the failure of one entity may induce a vigorous chain reaction to the others. Maintaining the reliability in an acceptable level requires an optimal maintenance strategy and planning for each entity in the network. The objective of this paper is to propose a double tier genetic algorithm approach for multi-factory production networks, aiming to keep the systems reliability in a defined acceptable level, and minimize the makespan of the jobs. The optimization algorithm simultaneously schedules perfect and imperfect maintenance during the process of distributed scheduling. The optimization reliability of the proposed algorithm is demonstrated through three numerical examples, including its ability to maintain the systems reliability in a defined acceptable level, the relationship of the acceptance level to production scheduling, and that of the machine age reduction factors to production scheduling.