Cathy H. Y. Lam

A hybrid case-GA-based decision support model for warehouse operation in fulfilling cross-border orders

The decision-making process is one of the complicated processes involved in warehouse operation for efficiently fulfilling various specific customer orders. This is especially true if the orders require cross-border delivery activities, such as palletization of the delivery goods according to regulation requirements. Case-based reasoning is an intelligent method for complex problem solving that uses past cases to find a solution to new problems. To achieve an appropriate solution, retrieving useful prior cases effectively for the problem is essential. However, current case retrieval methods are mainly based on a fixed set of attributes for different type of orders in which specific order features for case groups are neglected. In this paper a hybrid approach called the case-genetic algorithm-based decision support model (C-GADS), is proposed in classifying new customer orders into case groups with the highest similarity value, allowing for effectively selecting the most similar cases among the group. The proposed model also suggests the types of features considered in each case group. It helps enhance the effectiveness of formulating warehouse order operations based on grouping similar cases. To validate the feasibility of the proposed model, a case study is conducted and the results show that planning effectiveness is enhanced.

An order-picking operations system for managing the batching activities in a warehouse

Nowadays, customer orders with high product variety in small quantities are often received and requested for timely delivery. However, the order-picking process is a labour-intensive and costly activity to handle those small orders separately. In such cases, small orders are often grouped into batches so that two or more orders can be served at once to increase the picking efficiency and thus reduce the travel distance. In this paper, an order-picking operations system (OPOS) is proposed to assist the formulation of an order-picking plan and batch-handling sequence. The study integrates a mathematical model and fuzzy logic technique to divide the receiving orders into batches and prioritise the batch-handling sequence for picking, respectively. Through the proposed system, the order-picking process can be managed as batches with common picking locations to minimise the travel distance, and the batch-picking sequence can be determined as well. To demonstrate the use of the system, a case study in a third-party logistics warehouse is presented, and the result shows that both the order-picking activity and labour utilisation can be better organised.

A decision support system to facilitate warehouse order fulfillment in cross‐border supply chain

Purpose – The purpose of this paper is to provide a decision support system (DSS) to enhance the performance of cross‐border supply chain, the goal of which is to improve order planning and fulfill customer orders within the warehouse.Design/methodology/approach – An intelligent DSS, namely order picking planning system (OPPS) with the adoption of case‐based reasoning, is proposed to support managers in making appropriate order fulfilling decisions when an order involves cross‐border activities. Similar cases in the past are retrieved and adapted in reference to the new order. A case study is then conducted to illustrate the feasibility and effectiveness of the system.Findings – Recommendations are given to replace the objective decision‐making process in cross‐border supply chain with the help of the DSS. The warehouse order planning time has been reduced and useful information from past order records can be applied to solve new problems.Originality/value – With the increasing demand for material sourcin...

A slippery genetic algorithm-based process mining system for achieving better quality assurance in the garment industry

Provide knowledge support for quality assurance in the garment industry.Extract relationships among parameters and quality by fuzzy association rule mining.Design novel nature-inspired genetic algorithms with variable lengths.Optimize fuzzy association rules with the use of the slippery genetic algorithm. Due to the error-prone nature of garment manufacturing operations, it is challenging to guarantee the quality of garments. Previous research has been done to apply fuzzy association rule mining to determine process settings for improving the garment quality. The relationship between process parameters and the finished quality is represented in terms of rules. This paper enhances the application by encoding the rules into variable-length chromosomes for optimization with the use of a novel genetic algorithm (GA), namely the slippery genetic algorithm (sGA). Inspired by the biological slippage phenomenon in DNA replication, sGA allows changes to the chromosome lengths by insertion and deletion. During rule optimization, different parameters can be inserted to or removed from a rule, increasing the diversity of the solutions. In this paper, a slippery genetic algorithm-based process mining system (sGAPMS) is developed to optimize fuzzy rules with the aim of facilitating a comprehensive quality assurance scheme in the garment industry. The significance of this paper includes the development of a novel variable-length GA mechanism and the hybridization of fuzzy association rule mining and variable-length GAs. Though the capability of conventional GA in rule optimization has been proven, the diversity in the population is inherently limited by the fixed chromosome length. Motivated by this phenomenon, the sGA suggested in this paper allows various parameters to be considered in a rule, improving the diversity of the solutions. A case study is conducted in a garment manufacturing company to evaluate the sGAPMS. The results illustrate that better quality assurance can be achieved after rule optimization.

An examination of strategies under the financial tsunami

Purpose – The purpose of this paper is to identify the factors, such as the different strategies adopted and the size of the company, that have a significant determining impact on the financial performance of companies in extreme circumstances.Design/methodology/approach – The research target of this paper is the small and medium enterprises (SMEs) in Hong Kong. This is quantitative research and it is done on a survey basis, which includes hypothesis setting and statistical analysis. In addition, constructive suggestions are given to companies after analyzing the current situation.Findings – In total, ten factors from four dimensions are determined as the critical strategies for the company to adopt in an uncertain financial situation. The result shows the influence of different factors on return on investment for the companies with different backgrounds.Practical implications – The business environment today is full of turbulence and uncertainties; this, along with the fierce global competition, means th...

Framework to measure the performance of warehouse operations efficiency

Warehouse management system (WMS) has been introduced and widely used to increase the competitiveness that provides better control on the warehouse operations since the last decade. However, most of the WMS do not support planning and monitoring the use of warehouse resources but only focus on managing the warehouse operations. The management of warehouse resources planning with incoming customer orders are solely relied on the knowledge of warehouse experts. Decision making process becomes difficult to handle with complex orders. In this paper, a concept of warehouse resources management (WRM) is introduced to the typical WMS by extracting qualified order attributes from WMS for completing the operations with suitable resources. Through the use of rule-based reasoning (RBR) technique, the research objective is achieved to effectively fulfill the customer orders with appropriate resources, thereby achieving better customer satisfaction and increase the warehouse operation efficiency.

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

In todays 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.

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.

Multi-Agent Modeling in Managing Six Sigma Projects

In this paper, a multi-agent model is proposed for considering the human resources factor in decision making in relation to the six sigma project. The proposed multi-agent system is expected to increase the acccuracy of project prioritization and to stabilize the human resources service level. A simulation of the proposed multi-agent model is conducted. The results show that a multi-agent model which takes into consideration human resources when making decisions about project selection and project team formation is important in enabling efficient and effective project management. The multi-agent modeling approach provides an alternative approach for improving communication and the autonomy of six sigma projects in business organizations.

Franchising decision support system for formulating a center positioning strategy

Purpose – The purpose of this paper is to propose a franchising decision support system (FDSS) for future development planning by center positioning strategy formulation under a franchising business model. Design/methodology/approach – The system makes use of data collected from the franchising business and external environment analysis for decision making in center positioning problems. The fuzzy logic approach is integrated into the system for analyzing the geographical market dynamics including profitability and competitiveness in the district concerned. To demonstrate the application of the proposed FDSS, a case study is conducted in a Hong Kong-based franchising private education center, i.e. Dr I-Kids Education Center. Findings – The tailor made FDSS helps to facilitate the business operations of the franchising education center and develops a district positioning model for the centers located in the 18 districts of Hong Kong. The findings provide a solid foundation for marketing strategy and expans...