Improving Dynamic Decision-Making Through RFID: A Partially Observable Markov Decision Process (POMDP) for RFID-Enhanced Warehouse Search Operations

Misplaced items contribute significantly (2-10%) to the operational expense of a typical warehouse. In this work we develop a Partially Observable Markov Decision Process (POMDP) model for RFID directed search to detect misplaced items within a storage environment. A forklift operator (FLO) equipped with an RFID reader is assigned to search for a misplaced item in a warehouse. A FLO does not know the location of the tagged misplaced item and is guided by the imperfect variations in the strength of the signal received from the RFID tag (active or passive). The model considers five actions, five observations in scenarios with different RFID signal strength distributions namely, excellent, good and poor. An extensive simulation study has been conducted to evaluate the performance of RFID-driven POMDP search method. Specifically, the effects of signal strength distributions, initial beliefs at the start of the search and, the discount factor have been studied.Industrial Engineering & Managemen

RFID in the warehouse:a literature analysis (1995-2010) of its applications, benefits, challenges and future trends

Radio Frequency Identification (RFID) has been identified as a crucial technology for the modern 21st century knowledge-based economy. Some businesses have realised benefits of RFID adoption through improvements in operational efficiency, additional cost savings, and opportunities for higher revenues. RFID research in warehousing operations has been less prominent than in other application domains. To investigate how RFID technology has had an impact in warehousing, a comprehensive analysis of research findings available from articles through leading scientific article databases has been conducted. Articles from years 1995 to 2010 have been reviewed and analysed with respect to warehouse operations, RFID application domains, benefits achieved and obstacles encountered. Four discussion topics are presented covering RFID in warehousing focusing on its applications, perceived benefits, obstacles to its adoption and future trends. This is aimed at elucidating the current state of RFID in the warehouse and providing insights for researchers to establish new research agendas and for practitioners to consider and assess the adoption of RFID in warehousing functions

Modeling Dense Storage Systems With Location Uncertainty

This dissertation focuses on developing models to study the problem of searching and retrieving items in a dense storage environment. We consider a special storage configuration called an inverted T configuration, which has one horizontal and one vertical aisle. Inverted T configurations have fewer aisles than a traditional aisle-based storage environment. This increases the storage density; however, requires that some items to be moved out of the way to gain access to other more deeply stored items. Such movement can result in item location uncertainty. When items are requested for retrieval in a dense storage environment with item location uncertainty, searching is required. Dense storage has a practical importance as it allows for the use of available space efficiently, which is especially important with the scarce and expensive space onboard of US Navy\u27s ships that form a sea base. A sea base acts as a floating distribution center that provides ready issue material to forces ashore participating in various types of missions. The sea basing concept and the importance of a sea base\u27s responsiveness is our main motivation to conduct this research. In chapter 2, we review three major bodies of literature: 1) sea based logistics, 2) dense storage and 3) search theory. Sea based logistics literature mostly focuses on the concept and the architecture of a sea base, with few papers developing mathematical models to solve operational problems of a sea base, including papers handling the logistical and sustainment aspects. Literature related to dense storage can be broken down into work dealing with a dense storage environment with an inverted T configuration and other papers dealing with other dense storage configurations. It was found that some of the dense storage literature was motivated by the same application, i.e. sea based logistics. Finally, we surveyed the vast search theory literature and classification of search environments. This research contributes to the intersection of these three bodies of literature. Specifically, this research, motivated by the application of sea basing, develops search heuristics for dense storage environments that require moving items out of the way during searching. In chapter 3, we present the problem statements. We study two single-searcher search problems. The first problem is searching for a single item in an inverted T dense storage environment. The second one is searching for one or more items in an inverted T storage environment with items stacked over each other in the vertical direction. In chapter 4, we present our first contribution. In this contribution we propose a search plan heuristic to search for a single item in an inverted T, k-deep dense storage system with the objective of decreasing the expected search time in such an environment. In this contribution, we define each storage environment entirely by the accessibility constant and the storeroom length. In addition, equations are derived to calculate each component of the search time equation that we propose: travel, put-back and repositioning. Two repositioning policies are studied. We find that a repositioning policy that uses the open aisle locations as temporary storage locations and requires put-back of these items while searching is recommended. This recommendation is because such a policy results in lower expected search time and lower variability than a policy that uses available space outside the storage area and handles put-back independently of the search process. Statistical analysis is used to analyze the numerical results of the first contribution and to analyze the performances of both repositioning polices. We derive the probability distribution of search times in a storeroom with small configurations in terms of the accessibility constant and length. It was found that this distribution can be approximated using a lognormal probability distribution with a certain mean and standard deviation. Knowing the probability distribution provides the decision makers with the full range of all possible probabilities of search times, which is useful for downstream planning operations. In chapter 5, we present the second contribution, in which we propose a search plan heuristic but for multiple items in an inverted T, k-deep storage system. Additionally, we consider stacking multiple items over each other. Stacking items over each other, increases the number of stored items and allows for the utilization of the vertical space. In this second contribution, we are using the repositioning policy that proved its superiority in the first contribution. This contribution investigates a more general and a much more challenging environment than the one studied in the first contribution. In the second environment, to gain access to some items, not only may other items need to be moved out of the way, but also the overall number of movements for items within the system will be highly affected by the number of items stacked over each other. In addition, the searcher is given a task that includes searching and retrieving a set of items, rather than just one item. For the second contribution, the performance of the search heuristic is analyzed through a Statistical Design of Experiments, and it was found that searching and retrieving multiple items instead of just a single item, would decrease the variability in search times for each storeroom configuration. Finally, in chapter 6, conclusions of this research and suggestions for future research directions are presented

Ambulatory Monitoring Using Passive RFID Technology

Human activity recognition using wearable sensors is a growing field of study in pervasive computing that forms the basis for ubiquitous applications in areas like health care, manufacturing, human computer interaction and sports. A new generation of passive (batteryless) sensors such as sensor enabled RFID (Radio Frequency Identification) tags are creating new prospects for wearable sensor based applications. As passive sensors are lightweight and small, they can be used for unobtrusive monitoring. Furthermore, these sensors are maintenance free as they require no battery. However, recognising activities from passive sensor enabled RFID tags is challenging due to the sparse and noisy nature of the data streams from these sensors because they need to harvest adequate energy for successful operation. Therefore, within this thesis, we propose methods to recognise activities in real time using passive RFID technology by alleviating the adverse effects of sparsity and noise. We mainly consider ambulatory monitoring to facilitate mitigating falls in hospitals and older care settings as our application context. Specifically, three aspects are considered: i) data acquisition from sensor enabled RFID tags; ii) monitoring ambulatory movements using passive sensor enabled RFID tags to recognise activities leading to falls; and iii) detecting falls using a dense deployment of passive RFID tags. A generic middleware architecture and a generic tag ID format to embed sensor data and uniquely identify tag capabilities are proposed to acquire sensor data from passive sensor enabled RFID tags. The characteristics of this middleware are established using experiments with RFID readers and an example application scenario. In the context of ambulatory monitoring using passive sensor enabled RFID tags, first, an algorithm to facilitate the online interpolation of sparse accelerometer data from passive sensor enabled RFID tags is proposed followed by an investigation of features for activity recognition. Secondly, two data stream segmentation methods are proposed that can segment the data stream on possible activity boundaries to mitigate the adverse effects posed by data stream sparsity on segmentation. Thirdly, an algorithm to model the sequential nature considering previous sensor observations for a given time and their class labels to classify a sparse data stream in real time is proposed. Finally, a classification algorithm based on structured prediction is proposed to both segment and classify the sensor data stream simultaneously. The proposed methods are evaluated using four datasets that have been collected from a passive sensor enabled RFID tag with an accelerometer and successful monitoring of ambulatory movements is demonstrated to be possible by employing innovative data stream processing methods, based on machine learning. In order to detect falls, particularly long lie situation, using a dense deployment of passive RFID tags embedded in a carpet, an efficient and scalable machine learning based algorithm is proposed. This algorithm relies only on binary tag observation information. First, it identifies possible fall locations using heuristics and then the falls are identified using machine learning from features extracted considering possible fall locations alone. From an evaluation, it is demonstrated that the proposed algorithm could successfully identify falls in real time.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

XXIII Congreso Argentino de Ciencias de la Computación - CACIC 2017 : Libro de actas

Trabajos presentados en el XXIII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de La Plata los días 9 al 13 de octubre de 2017, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Facultad de Informática de la Universidad Nacional de La Plata (UNLP).Red de Universidades con Carreras en Informática (RedUNCI