Stephen W.Y. Cheng

The impact of textile industry on China's environment

Similar to other industrial activities, the textiles and clothing sector plays a significant role in Chinas escalating economy. Although the industry brings economic benefits to the country, the processes involved in textile production result in unprecedented environmental problems. It is well known that the textile industry has a negative impact on the environment. However, it remains unclear how this impact is generated. This article evaluates the relationship between Chinas textile industry and the environment using a quantitative framework. The rationale behind the negative impact of the expanding textile industry on Chinas environment is discussed. Furthermore, suggestions will be given to address the problems associated with the textile industry in China.

An intelligent fuzzy-based storage assignment system for packaged food warehousing

In the packaged food industry, fast cargo receiving, reliable storage and accurate order picking in warehouses within short period of time are critical for achieving customer satisfaction. Food easily deteriorates when unloaded packaged food is exposed in an open area, waiting for inbound and packing operations, according to customer orders. In addition, the risk of damaging the packaging of food is higher when the food is frequently transported by forklift trucks during order picking. This highlights the need to provide decision support in warehouse zoning and storage assignment for preventing the above risks occurring. This paper proposes a tri-modular intelligent fuzzy-based storage assignment system, integrating fuzzy logic and association rules mining techniques, to reduce the order-picking and cargo exposure time, as well as the transport frequency and distance. The fuzzy zoning module is used to allocate different types of packaged food to various warehouse zones based on their particular characteristics. The location assignment module reveals hidden relationships in the sales of products, in turns suggesting which products should be placed together in the same zone. A case study is carried out to examine the intelligent system.

Design of a case-based multi-agent wave picking decision support system for handling e-commerce shipments

The emerging trend of e-commerce business poses serious challenges in the field of logistics. To handle e-commerce shipments, warehouses must be able to efficiently handle a large number of stock-keeping units (SKUs), pick and pack small volume orders, and deliver them on time in small parcel shipments to consumers. In this sense, traditional order fulfillment, which encompasses receiving, put-away, picking, and transport through the warehouse, might not be able to fully fulfill the requirements of e-commerce. Considering the fact that order picking in warehouses is one of the most costly activities amongst the logistics operating categories, there is a crucial need to adopt a wave picking strategy to handle e-commerce shipments, an order picking approach that groups the orders for picking at the same time to minimize repeated visits to nearby storage locations. To apply the wave picking strategy properly, decision support for establishing the timing of each wave and the quantity of items to be picked is essential. Therefore, in this paper, a case-based multi-agent wave picking decision support system is proposed to help decision-makers in generating wave picking sequences in order to handle e-commerce shipments, through the integration of case-based reasoning and multi-agent technique. After a pilot study of the proposed system in a third-party logistics service provider, the order-processing efficiency was greatly enhanced.

An intelligent medical Replenishment System for managing the medical resources in the healthcare industry

Due to rapidly ageing population, the need for care and attention homes for the elderly and patient with chronic illnesses has increased significantly in recent years. However, the continuous increase in operation and medical costs and the problem of drugs shortages bring increasing pressure to care and attention homes in regard to medical resource allocation. In such situations, patients may not receive appropriate treatment and hence dissatisfaction with the quality of service may result. Therefore, it is essential to have a decision support system to ensure that an optimal amount of medical resources are stored so as to maintain a sustainable healthcare service. In this paper, an intelligent medical replenishment system (IMRS) is proposed to assist healthcare workers in arranging the appropriate type and quantity of drugs, based on the needs of patients. In IMRS, artificial intelligent techniques, i.e. fuzzy association rules mining and fuzzy logic, are applied to evaluate the historical diagnosis records of patients and determine the amount and frequency of medical resources for replenishment. To validate the feasibility of the proposed system, a pilot study is conducted in a care and attention home located in Hong Kong. The result shows that the IMRS is effective in improving the healthcare service quality for the elderly in terms of the elderly satisfaction and medical resources fulfillment.

An intelligent performance assessment system for enhancing the service quality of home care nursing staff in the healthcare industry

Due to the aging population in Hong Kong, the need for home care service is growing rapidly and requires nursing staff to frequently visit the homes of the elderly for service. For years, a shortage of qualified nursing staff and the tight service schedule has brought increasing pressure to the existing home care service, sometimes leading to high complaint rates by the elderly and their family members. In order to maintain the home care service quality, it is critical to have an evaluation approach by assessing the workload and characteristics of the home care nursing staff. In this paper, an intelligent performance assessment system (IPAS) is designed to evaluate the performance of home care nursing staff in the healthcare industry. IPAS integrates Online Analytical Processing (OLAP) for the collecting and storing of data on the elderly patient, nursing staff and healthcare agency when providing home care services, and fuzzy logic for evaluating the service quality of the nursing staff. The healthcare agency can then formulate a follow up plan based on the assessment results. By conducting a pilot study in a local healthcare agency, the nursing staff loyalty can be increased while the quality of home care service can be enhanced.

A hybrid RFID case-based system for handling air cargo storage location assignment operations in distribution centers

Logistics service providers (LSPs) often face the challenge of uncertainty in air import operations. Due to the limited capacity of aircraft, the problem of cargo offload occurs frequently. Without prior notice, cargoes are delivered in separate flights, which increase handling complexity. On the other hand, due to the short transit time between flights, the shipper at the origin does not have sufficient time to inform the LSPs the details of cargo being loaded before its arrival. This results in a lack of time for LSPs to assign storage location for the arrived cargo at the distribution center (DC). Hence, the order picking process becomes inefficient and time-consuming, which leads to trucks idling in the docking area waiting for the consolidated goods. The problems become more obvious when handling electronic components and high tech products. In this study, a hybrid RFID case-based decision support system, integrating RFID and case-based reasoning technologies, is proposed for handling air cargo storage location assignment operations in distribution centers. After pilot study of the proposed system in a third-party LSP, the order-picking efficiency is improved while the truck idling time at the docking area is reduced significantly.