T. C. Poon

A RFID case-based logistics resource management system for managing order-picking operations in warehouses

In the supply chain, a warehouse is an essential component for linking the chain partners. It is necessary to allocate warehouse resources efficiently and effectively to enhance the productivity and reduce the operation costs of the warehouse. Therefore, warehouse management systems (WMSs) have been developed for handling warehouse resources and monitoring warehouse operations. However, it is difficult to update daily operations of inventory level, locations of forklifts and stock keeping units (SKUs) in real-time by using the bar-code-based or manual-based warehouse management systems. In this paper, RFID technology is adopted to facilitate the collection and sharing of data in a warehouse. Tests are performed for evaluating the reading performance of both the active and passive RFID apparatus. With the help of the testing results, the efficient radio frequency cover ranges of the readers are examined for formulating a radio frequency identification case-based logistics resource management system (R-LRMS). The capabilities of R-LRMS are demonstrated in GSL Limited. Three objectives are achieved: (i) a simplification of RFID adoption procedure, (ii) an improvement in the visibility of warehouse operations and (iii) an enhancement of the productivity of the warehouse. The successful case example proved the feasibility of R-LRMS in real working practice.

A real-time food safety management system for receiving operations in distribution centers

Highlights? Help reduce the difficulties in safety plan development using knowledge-based expert system. ? Help achieve improvement in operation management. ? Help achieve improvement in timeframe for resource assignment. ? Help achieve improvement in customer satisfaction and quality. Food safety plan is being promoted in the food industry by the Hong Kong Government as a preliminary quality control tool. However, it appears to be a challenging task for Distribution Centers (DC) that handles food inventory since most of them are lack of knowledge and know how technology to manage information in a real time base. This paper proposes a Radio Frequency Identification based Food Operations Assignment System (RFID-FOAS) to help DC facilitates the food safety control activities in receiving areas by generating a proper safety plan. The system has adopted the Radio Frequency Identification (RFID) technology and the Case-Based Reasoning (CBR) technique to facilitate the inventory data-capturing process and assist in formulating decisions, respectively. The developed system aims to help reduce the difficulties in safety plan development using a knowledge-based expert system. The significance and contribution of RFID-FOAS in the context of managing the inventory quality in DC for safety plan development is demonstrated through the adoption of the system in a Hong Kong-based logistics company. The generated results show that the decision-making process of the safety plan development is facilitated. Moreover, the real-time data capturing nature of RFID technology has further improved the efficiency and timeframe requested for the actions. With the support of RFID-FOAS, the data capture system and the decision-making time is minimized. As a result, inventory quality and customer satisfaction level are significantly improved.

A real-time production operations decision support system for solving stochastic production material demand problems

Research highlights? RFID technology identifies the stochastic production demand orders in a real time manner. ? GA provides feasible solutions for the stochastic orders within a short period of time. ? The proposed system reduces the effect of stochastic production demand problems and enhances productivity both on the shop floor and in the warehouse. Nowadays, shop floor managers are facing numerous unpredictable risks in the actual manufacturing environment. These unpredictable risks not only involve stringent requirements regarding the replenishment of materials but also increase the difficulty in preparing material stock. In this paper, a real-time production operations decision support system (RPODS) is proposed for solving stochastic production material demand problems. Based on Poon et al. (2009), three additional tests are proposed to evaluate RFID reading performance. Besides, by using RPODS, the real-time status of production and warehouse operations are monitored by Radio Frequency Identification (RFID) technology, and a genetic algorithm (GA) technique is applied to formulate feasible solutions for tackling these stochastic production demand problems. The capability of the RPODS is demonstrated in a mould manufacturing company. Through the case study, the objectives of reducing the effect of stochastic production demand problems and enhancing productivity both on the shop floor and in the warehouse are achieved.

A real-time warehouse operations planning system for small batch replenishment problems in production environment

A factory consists of numerous production workstations, multiple production lines and many production floors. Due to the characteristics of just-in-time and make-to-order mode manufacturing, small batches of production materials are required for production lines within a short period of time in order to facilitate daily production operations. In this paper, a real-time warehouse operation planning system (R-WOPS) for solving small batch replenishment problems is described. Through using R-WOPS, real-time production and warehouse operations are monitored by radio frequency identification (RFID) technology, and a genetic algorithm (GA) technique is applied to formulate feasible small batch replenishment solutions. Simulation tests show that R-WOPS generates pick-up and delivery route plans for small batch replenishment orders very efficiently.

Achieving quality assurance functionality in the food industry using a hybrid case-based reasoning and fuzzy logic approach

Quality control of food inventories in the warehouse is complex as well as challenging due to the fact that food can easily deteriorate. Currently, this difficult storage problem is managed mostly by using a human dependent quality assurance and decision making process. This has however, occasionally led to unimaginative, arduous and inconsistent decisions due to the injection of subjective human intervention into the process. Therefore, it could be said that current practice is not powerful enough to support high-quality inventory management. In this paper, the development of an integrative prototype decision support system, namely, Intelligent Food Quality Assurance System (IFQAS) is described which will assist the process by automating the human based decision making process in the quality control of food storage. The system, which is composed of a Case-based Reasoning (CBR) engine and a Fuzzy rule-based Reasoning (FBR) engine, starts with the receipt of incoming food inventory. With the CBR engine, certain quality assurance operations can be suggested based on the attributes of the food received. Further of this, the FBR engine can make suggestions on the optimal storage conditions of inventory by systematically evaluating the food conditions when the food is receiving. With the assistance of the system, a holistic monitoring in quality control of the receiving operations and the storage conditions of the food in the warehouse can be performed. It provides consistent and systematic Quality Assurance Guidelines for quality control which leads to improvement in the level of customer satisfaction and minimization of the defective rate.

Cross-dock job assignment problem in space-constrained industrial logistics distribution hubs with a single docking zone

This paper addresses a cross-dock operations problem in space-constrained industrial logistics distribution hubs. In these hubs, the number of incoming trucks exceeds the number of docks available, and inbound trucks and orders arrive at random. The solution lies in minimising the waiting time of trucks by coordinating the pick up/delivery sequences of inbound and outbound orders in the storage zones. A mathematical model and a meta-heuristics algorithm, which is based on a genetic algorithm, are developed to address the problem. This research is innovative because the proposed algorithm allows the insertion of inbound orders that arrive at random into the schedule, without causing any significant disturbance to the original outbound order schedule. Computational experiments are conducted to examine the performance of the algorithm under heavy and normal cross-dock conditions. Results show that the algorithm reduces the total makespan of storage operations by 10% to 20% under heavy and normal conditions. The research study benefits manufacturers by increasing cross-docking efficiency in industrial logistics systems characterised by limited temporary storage capacity and the random arrival of inbound trucks.

A hybrid scheduling decision support model for minimizing job tardiness in a make-to-order based mould manufacturing environment

In the make-to-order (MTO) mode of manufacturing, the specification of each product is unique such that production processes vary from one product to another making the production schedule complex. In order to achieve high level productivity, the production flow is not arranged in sequence; instead, the job schedule of different production jobs is adjusted to fit in with the multiple-job shop environment. A poor scheduling of jobs leads to high production cost, long production time and tardiness in job performance. The existing of tardiness in the production schedule significantly affects the harmony among the multiple jobs on the shops floor. In order to provide a complete solution for solving MTO scheduling problems with job shifting and minimizing job tardiness, a hybrid scheduling decision support model (SDSM) is introduced. The model is combined by a Genetic Algorithm (GA) and an optimisation module. GA is adopted to solve the complex scheduling problem taking into consideration of the wide variety of processes while the optimisation module is suggested for tackling tardiness in doing the jobs in a cost effective way. The simulation results reveal that the model shortens the generation time of production schedules and reduces the production cost in MTO-based production projects.

A real-time replenishment system for vending machine industry

This paper outlines the needs of the vendor-machines information network for automating the selling operations in a low operating cost. Besides, the architecture framework of real-time transmission of the selling information is determined for analyzing and providing decision support effectively

An efficient production material demand order management system for a mould manufacturing company

In a real-life dynamic production environment, problems such as workers’ absenteeism, machine breakdowns and loss of materials frequently occur. Small batches of production materials have to be delivered from a warehouse to the production shop floor often within a short period of time. In order to handle such material demand orders so as to maintain the productivity of the shop floor, it is essential to allocate warehouse resources effectively. In this article, an efficient production material demand order management system (PMDOMS) is proposed to cope with such issues. An automated data capturing technology – radio frequency identification and an advanced problem solving technique – genetic algorithms, are adopted in the PMDOMS. The integration of these technologies helps enterprises improve their operational efficiency on the production floor. Through application in a case study in the ABC Company, it is proved that PMDOMS significantly improves the productivity in both the production and the warehouse environment.

A real-time business process decisions support planning system for mould industry: a case study

Mould manufacturing is an important part of the product development process. Some products require several thousands of pressed moulds and fitting moulds to manufacture different parts of finished goods. Hence, moulds significantly affect the quality of finished goods. The process of manufacturing moulds is complicated and time-consuming and requires the investment of capital in items such as specific machines, staff training, time, etc. Due to the shorter lead times that are being demanded by customers, mould manufacturers are finding ways to reduce their production time. In the past decade, much research has focused on the improvement of the business process of mould manufacturing. In this paper, a Real-time Business Process Decisions Support (RBPDS) system in the mould industry is proposed. The system incorporates Radio Frequency Identification (RFID) technology and Case-based Reasoning (CBR). The system is used to track shop floor production information in real time and provide decision support for planning. It provides instant decision support for production planning, cost estimation and production precautions. The system is applied to a company to enhance its decision support process. The case study shows that there is a significant improvement in various business processes when they are supported by the system.