Xiang T.R. Kong

Cloud-enabled real-time platform for adaptive planning and control in auction logistics center

Key components and decisions of a typical auction logistics center are discussed associated with related management issues.Based on cloud computing and internet of things technologies, auction logistics reengineering is conducted.The referenced infrastructure of CALC is elaborated for planning and control services at the ALC.The implementation and application methods of CALC are described in a case study.Lessons learnt through the deployment are also presented. An auction logistics center (ALC) is the facility that is dedicated to all logistics and physical distribution, and provides auction functions for goods trading. Adaptive planning and control has been a hot research topic and discussed a lot in the field of manufacturing. Adaptive auction logistics planning and control (ALPC) is urgently required at the ALC to support large trading volumes and shorten processing time. To solve real-life industrial challenges, this paper presents a generic system architecture and its implementation along with the following dimensions. Firstly, a cloud-enabled platform for auction logistics center (CALC) is presented. It is proposed to implement efficient and effective ALPC, and to increase the flexibility in terms of execution of logistics operations and auction processes. Secondly, through the integration of IoT (Internet of Things) and cloud computing technologies, the proposed CALC creates a ubiquitous environment at the ALC, and establishes auction logistics services for different key stakeholders. The adaptive ALPC can be achieved with real-time visibility and traceability. Finally, this study presents a prototype of CALC to verify the proposed methodology. The case study in this paper also shows the potential of CALC to streamline operating processes in auction logistics environment.

Scheduling at an auction logistics centre with physical internet

Auction logistics centre (ALC) performs transshipment operation on auction products from their inbound-from-supplier transporters to their outbound-to-client transporters with goods trading functions. Major third-party trading service providers have solved technological problems of dealing with millions of simultaneous biddings. But logistics that fulfils the massive and lumpy auction demands in the centre is still challengeable. The lack of process visibility and synchronised schedule has made the congestion on material flow, especially for the trolley loading and auction trading stages. Space resource is wasted and auction products deteriorate as holding time increases. This paper aims to provide a first demonstration of scheduling for auctions of perishable goods using Physical Internet (PI). PI-enabled scheduling is vital to facilitate the decision-making process while ensuring required throughput time with large trading volumes. A PI-ALC is created to automate the flow of information and enable the flexible implementation of scheduling. Following the hybrid flowshop classification, a timely operation scheduling model is developed. A heuristic-based solution approach is proposed to minimise either makespan or value loss using a set of dispatching rules. Simulation experiments show that the dispatching–picking mechanisms have statistically significant interaction impacts on both performance criteria. Decision-makers should strike a balance between minimising makespan and value loss based upon the growth in the frozen buffer size. Finally, the sensitivity analyses justify that schedulers can flexibly select dispatching rules under various demand patterns and operation time windows, as well as system configurations and trolley sizes.

Ubiquitous auction learning system with TELD (Teaching by Examples and Learning by Doing) approach

As the most critical trading mechanism in supply chain management/operation management fields, Dutch auction theories and practices have been regarded as one of the key teaching subjects of many universities. The advancement of ubiquitous computing technologies has not only solved the technological problems of dealing with millions of simultaneous biddings in real practices, but also enabled students to learn elusive and complex knowledge in an interactive environment. However, little attention was paid to educational discussions and quantitative analyses when applying the ubiquitous learning (u-learning) system in auction classes. This quasi-experimental study was among the first to develop and evaluate a smart u-learning system that integrated Internet-of-Things (IoT) technologies to simulate real, authentic auction activities, while detecting learning behaviors of students. We also integrated and utilized the pedagogical approach of Teaching by Examples and Learning by Doing (TELD) in the presented system, which further strengthened dynamic interactions and timely teaching instructions for students. The data analysis showed that this innovative system had positive effects on students learning outcomes. The results also revealed that applying the u-learning system in teaching procedural knowledge, rather than conceptual knowledge, was more resource effective and less time consuming. Moreover, students had high perceptions of learning content when the system was designed with efficient pedagogical assistance, interaction flexibility and user-friendly features. Critical practical implications were also summarized for teachers, system designers, researchers, and policymakers. The approach led to better learning achievements than the traditional approach.The system was more resource effective in teaching procedural knowledge.Students had high perceptions of learning content when using the system.The approach could be an effective pedagogic support in SCM/OM-related subjects.

Robot-enabled execution system for perishables auction logistics

Purpose The purpose of this paper is to propose a concept of cloud auction robot (CAR) and its execution platform for transforming perishable food supply chain management. A new paradigm of goods-to-person auction execution model is proposed based on CARs. This paradigm can shift the management of traditional manual working to automated execution with great space and time saving. A scalable CAR-enabled execution system (CARES) is presented to manage logistics workflows, tasks and behavior of CAR-Agents in handling the real-time events and associated data. Design/methodology/approach An Internet of Things enabled auction environment is designed. The robot is used to pick up and deliver the auction products and commends are given to the robot in real-time. CARES architecture is proposed while integrating three core services from auction workflow management, auction task management, to auction execution control. A system prototype was developed to show its execution through physical emulations and experiments. Findings The CARES could well schedule the tasks for each robot to minimize their waiting time. The total execution time is reduced by 33 percent on average. Space utilization for each auction studio is improved by about 50 percent per day. Originality/value The CAR-enabled execution model and system is simulated and verified in a ubiquitous auction environment so as to upgrade the perishable food supply chain management into a new level which is automated and real-time. The proposed system is flexible to cope with different auction scenarios, such as different auction mechanisms and processes, with high reconfigurability and scalability.

Zone merge sequencing in an automated order picking system

This paper considers the zone automated order fulfilment systems with a number of zones linked by a main conveyor. Each zone has a number of dispensers and a buffer. Each dispenser holds one type of item and dispenses the items for an order into a buffer before the items are merged to the main conveyor. Due to variability of the mix of item types and number of items for each type in successive orders, idle time or open space on the conveyor can occur if the buffers merge the items to the conveyor sequentially. The idle time prolongs the order fulfilment duration. This paper aims to provide a solution to reduce the idle time or open space by optimising the merging sequences among zones. A model is developed to find merging sequences with minimum order fulfilment time. However, the model is strongly NP-hard. A greedy heuristic-based solution approach is thus developed. Experiment performance shows that the presented solution can reduce the order fulfilment time approximately 5% with both empirical data and simulation data. Moreover, the sensitivity analysis is conducted to measure the effect of the solution under the variation of zone numbers and order structures. Although the idle time reduction can be achieved with any number of zones and order structures, it was most significant in certain range of zone numbers and appropriate order structures.