Market-Based Models for Digital Signage Network Promotion Management

Digital signage network (DSN) is capable of delivering customized content to designated screens in a real-time or near real-time manner, which provides tremendous potential for building dynamic demand stimulation tools. However current DSN media buying process is mainly carried out through manually conducted negotiation between the DSN operator and the advertisers. This practice does not capitalize the unique technology advantage offered by the newly emerged advertising medium. We propose automated DSN media buying models which allow advertisers to customize their promotion schedules in a highly responsive manner. Specifically, we design a direct revelation mechanism and an iterative bidding model for DSN promotion scheduling. We show that the direct revelation mechanism computes optimal solutions. We evaluate the revenue performance of the iterative bidding model through a computational study. The implementation of the iterative bidding mechanism is also described

Application of Reinforcement Learning to Multi-Agent Production Scheduling

Reinforcement learning (RL) has received attention in recent years from agent-based researchers because it can be applied to problems where autonomous agents learn to select proper actions for achieving their goals based on interactions with their environment. Each time an agent performs an action, the environment¡Šs response, as indicated by its new state, is used by the agent to reward or penalize its action. The agent¡Šs goal is to maximize the total amount of reward it receives over the long run. Although there have been several successful examples demonstrating the usefulness of RL, its application to manufacturing systems has not been fully explored. The objective of this research is to develop a set of guidelines for applying the Q-learning algorithm to enable an individual agent to develop a decision making policy for use in agent-based production scheduling applications such as dispatching rule selection and job routing. For the dispatching rule selection problem, a single machine agent employs the Q-learning algorithm to develop a decision-making policy on selecting the appropriate dispatching rule from among three given dispatching rules. In the job routing problem, a simulated job shop system is used for examining the implementation of the Q-learning algorithm for use by job agents when making routing decisions in such an environment. Two factorial experiment designs for studying the settings used to apply Q-learning to the single machine dispatching rule selection problem and the job routing problem are carried out. This study not only investigates the main effects of this Q-learning application but also provides recommendations for factor settings and useful guidelines for future applications of Q-learning to agent-based production scheduling

Autonomous Finite Capacity Scheduling using Biological Control Principles

The vast majority of the research efforts in finite capacity scheduling over the past several years has focused on the generation of precise and almost exact measures for the working schedule presupposing complete information and a deterministic environment. During execution, however, production may be the subject of considerable variability, which may lead to frequent schedule interruptions. Production scheduling mechanisms are developed based on centralised control architecture in which all of the knowledge base and databases are modelled at the same location. This control architecture has difficulty in handling complex manufacturing systems that require knowledge and data at different locations. Adopting biological control principles refers to the process where a schedule is developed prior to the start of the processing after considering all the parameters involved at a resource involved and updated accordingly as the process executes. This research reviews the best practices in gene transcription and translation control methods and adopts these principles in the development of an autonomous finite capacity scheduling control logic aimed at reducing excessive use of manual input in planning tasks. With autonomous decision-making functionality, finite capacity scheduling will as much as practicably possible be able to respond autonomously to schedule disruptions by deployment of proactive scheduling procedures that may be used to revise or re-optimize the schedule when unexpected events occur. The novelty of this work is the ability of production resources to autonomously take decisions and the same way decisions are taken by autonomous entities in the process of gene transcription and translation. The idea has been implemented by the integration of simulation and modelling techniques with Taguchi analysis to investigate the contributions of finite capacity scheduling factors, and determination of the ‘what if’ scenarios encountered due to the existence of variability in production processes. The control logic adopts the induction rules as used in gene expression control mechanisms, studied in biological systems. Scheduling factors are identified to that effect and are investigated to find their effects on selected performance measurements for each resource in used. How they are used to deal with variability in the process is one major objective for this research as it is because of the variability that autonomous decision making becomes of interest. Although different scheduling techniques have been applied and are successful in production planning and control, the results obtained from the inclusion of the autonomous finite capacity scheduling control logic has proved that significant improvement can still be achieved

Quality embedded intelligent remanufacturing

This thesis is motivated from the four keywords: remanufacturing, quality, multi-agent and intelligence. Recent years' environmental problems caused tightening the regulations and legislations for used products. Therefore remanufacturing is getting more attention. The quality of used products is uncertain and even dynamically changes during the remanufacturing process, and each used product should be individually handled in a different way depending on its quality. Fortunately recent developing wireless technologies like radio frequency identification (RFID) may enable remanufacturing control systems to identify, track, and control each used product and disassembled subassembly/part (PDSP) automatically. The multi-agent approach can be a good solution for the individual control of each PDSP, because a centralized control system is not eligible to managing so many elements in the remanufacturing system. The objective of this thesis is to propose a quality embedded remanufacturing system (QRS) which comprises a multi-agent framework and a scheduling mechanism. First, this thesis discusses the fundamental concepts for the proposed modeling tools and scheduling mechanism: the QRS quality characteristics and the multi-agent framework. As the second step, this thesis proposes QRS modeling tools which support the PDSP/resource quality representation and comprise: intuitive remanufacturing system representation (IRSR) and dynamic token two-level colored Petri-nets (DTPN). The former is designed from the user-side perspective and the latter is from the system-side perspective. The multi-agent framework is constructed based on the model represented with the proposed tools. Last, this thesis proposes a real-time scheduling mechanism for the QRS which enables the constructed framework to execute. The scheduling mechanism embeds a communication protocol among agents and dispatching rules formulated depending on the PDSP/resource quality. A knowledge-based approach is adopted to increase efficiency of the scheduling mechanism, where the knowledge is learned by simulations. A heuristic method is also proposed to reduce the simulation time

Programación de la producción en un taller de flujo híbrido sujeto a incertidumbre: arquitectura y algoritmos. Aplicación a la industria cerámica

En un marco de competencia global en el cual los tiempos de respuesta son cada vez más relevantes como elemento competitivo y donde, en no pocas ocasiones, las empresas tiende a ofrecer un catálogo de productos amplio y diferenciado de la competencia, existen múltiples retos que las Organizaciones deben afrontar. Dentro de éstas la Dirección de Operaciones tiene el reto de adaptar los procesos de Gestión de los Sistemas Productivos y Logísticos a las actuales necesidades. En este proceso de cambio es habitual partir de Sistemas Productivos poco flexibles y orientados a la producción en masa en los que es fundamental emplear el mejor "saber-hacer" para procurar obtener el rendimiento más adecuado de los recursos disponibles. El despliegue de unas buenas prácticas en el ámbito de la Programación de la Producción puede ayudar en buena medida a mejorar la eficiencia de los recursos. Tradicionalmente se ha venido considerando a la Programación de la Producción con una visión bastante cuantitativa en la que su misión consistía en asignar, secuenciar y temporizar los diferentes trabajos del periodo en base a los recursos disponibles. No obstante, sin dejar de ser válido este planteamiento, en esta tesis se desea enfatizar como en realidad el fin último de las técnicas y métodos desarrollados durante años en el ámbito de la Programación de la Producción no es otro que el de ser empleados dentro de un Sistemas de Ayuda a la Toma de Decisiones. Y en este sentido, las decisiones operativas que se toman en el área del Programador de la Producción deben estar conectadas en todos los casos, al menos, con su entorno decisional más directo como es el de la Planificación de la Producción. Una revisión literaria en profundidad al extenso trabajo realizado en más de 50 años de existencia de lo que se ha denominado, empleando la terminología en lengua inglesa, como "Scheduling" pone de manifiesto la existencia una necesidad que debe ser cubierta.Gómez Gasquet, P. (2010). Programación de la producción en un taller de flujo híbrido sujeto a incertidumbre: arquitectura y algoritmos. Aplicación a la industria cerámica [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7728Palanci