Estado del arte del proyecto

En un modelo de gestión de inventario para productos perecederos, el agotamiento debido a la interacción con la demanda es importante, pero también, el daño a los productos es una variable relevante. Este artículo considera que los fenómenos de demanda y ventas no siempre van de la mano. El proceso de demanda se relaciona con la voluntad de adquirir productos en buenas condiciones, dando al cliente el poder de evaluar la calidad del producto antes de que se realice una compra efectiva. También consideramos el costo de deshacerse de las unidades no vendidas, además de los costos convencionales de almacenamiento y adquisición. Luego propusimos un modelo matemático para derivar la Cantidad de orden económica (EOQ) en condiciones específicas, a fin de minimizar el costo de gestión esperado de los productos perecederos, suponiendo una demanda constante y una disminución lineal de la probabilidad de compra durante el ciclo de vida del producto. Propusimos varias instancias aleatorias y validamos el modelo matemático mediante simulación. Luego encontramos los parámetros óptimos para la política de inventario utilizando una aproximación numérica de tercer orden. Por último, desarrollamos un análisis de sensibilidad durante el ciclo de vida del producto para demostrar que el modelo propuesto se aproxima a un modelo EOQ tradicional para productos perecederos cuando el ciclo de vida es lo suficientemente grande

Sheet-Metal Production Scheduling Using AlphaGo Zero

This work investigates the applicability of a reinforcement learning (RL) approach, specifically AlphaGo Zero (AZ), for optimizing sheet-metal (SM) production schedules with respect to tardiness and material waste. SM production scheduling is a complex job shop scheduling problem (JSSP) with dynamic operation times, routing flexibility and supplementary constraints. SM production systems are capable of processing a large number of highly heterogeneous jobs simultaneously. While very large relative to the JSSP literature, the SM-JSSP instances investigated in this work are small relative to the SM production reality. Given the high dimensionality of the SM-JSSP, computation of an optimal schedule is not tractable. Simple heuristic solutions often deliver bad results. We use AZ to selectively search the solution space. To this end, a single player AZ version is pretrained using supervised learning on schedules generated by a heuristic, fine-tuned using RL and evaluated through comparison with a heuristic baseline and Monte Carlo Tree Search. It will be shown that AZ outperforms the other approaches. The work’s scientific contribution is twofold: On the one hand, a novel scheduling problem is formalized such that it can be tackled using RL approaches. On the other hand, it is proved that AZ can be successfully modified to provide a solution for the problem at hand, whereby a new line of research into real-world applications of AZ is opened

Learning Technology in Scheduling Based on the Mixed Graphs

We propose the adaptive algorithm for solving a set of similar scheduling problems using learning technology. It is devised to combine the merits of an exact algorithm based on the mixed graph model and heuristics oriented on the real-world scheduling problems. The former may ensure high quality of the solution by means of an implicit exhausting enumeration of the feasible schedules. The latter may be developed for certain type of problems using their peculiarities. The main idea of the learning technology is to produce effective (in performance measure) and efficient (in computational time) heuristics by adapting local decisions for the scheduling problems under consideration. Adaptation is realized at the stage of learning while solving a set of sample scheduling problems using a branch-and-bound algorithm and structuring knowledge using pattern recognition apparatus

Evolving Neural Networks to Solve a Two-Stage Hybrid Flow Shop Scheduling Problem with Family Setup Times

We present a novel strategy to solve a two-stage hybrid flow shop scheduling problem with family setup times. The problem is derived from an industrial case. Our strategy involves the application of NeuroEvolution of Augmenting Topologies - a genetic algorithm, which generates arbitrary neural networks being able to estimate job sequences. The algorithm is coupled with a discrete-event simulation model, which evaluates different network configurations and provides training signals. We compare the performance and computational efficiency of the proposed concept with other solution approaches. Our investigations indicate that NeuroEvolution of Augmenting Topologies can possibly compete with state-of-the-art approaches in terms of solution quality and outperform them in terms of computational efficiency

Production and inventory control in complex production systems using approximate dynamic programming.

Production systems focus not only on providing enough product to supply the market, but also on delivering the right product at the right price, while lowering the cost during the production process. The dynamics and uncertainties of modern production systems and the requirements of fast response often make its design and operation very complex. Thus, analytical models, such as those involving the use of dynamic programming, may fail to generate an optimal control policy for modern production systems. Modern production systems are often in possession of the features that allow them to produce various types of product through multiple working stations interacting with each other. The production process is usually divided into several stages, thus a number of intermediate components (WIP) are made to stock and wait to be handled by the next production stage. In particular, development of an efficient production and inventory control policy for such production systems is difficult, since the uncertain demand, system dynamics and large changeover times at the work stations cause significant problems. Also, due to the large state and action space, the controlling problems of modern production systems often suffer from the curse of dimensionality

Revenue Management for Make-to-Order and Make-to-Stock Systems

With the success of Revenue Management (RM) techniques over the past three decades in various segments of the service industry, many manufacturing firms have started exploring innovative RM technologies to improve their profits. This dissertation studies RM for make-to-order (MTO) and make-to-stock (MTS) systems. We start with a problem faced by a MTO firm that has the ability to reject or accept the order and set prices and lead-times to influence demands. The firm is confronted with the problem to decide, which orders to accept or reject and trade-off the price, lead-time and potential for increased demand against capacity constraints, in order to maximize the total profits in a finite planning horizon with deterministic demands. We develop a mathematical model for this problem. Through numerical analysis, we present insights regarding the benefits of price customization and lead-time flexibilities in various demand scenarios. However, the demands of MTO firms are always hard to be predicted in most situations. We further study the above problem under the stochastic demands, with the objective to maximize the long-run average profit. We model the problem as a Semi-Markov Decision Problem (SMDP) and develop a reinforcement learning (RL) algorithm-Q-learning algorithm (QLA), in which a decision agent is assigned to the machine and improves the accuracy of its action-selection decisions via a “learning process. Numerical experiment shows the superior performance of the QLA. Finally, we consider a problem in a MTS production system consists of a single machine in which the demands and the processing times for N types of products are random. The problem is to decide when, what, and how much to produce so that the long-run average profit. We develop a mathematical model and propose two RL algorithms for real-time decision-making. Specifically, one is a Q-learning algorithm for Semi-Markov decision process (QLS) and another is a Q-learning algorithm with a learning-improvement heuristic (QLIH) to further improve the performance of QLS. We compare the performance of QLS and QLIH with a benchmarking Brownian policy and the first-come-first-serve policy. The numerical results show that QLIH outperforms QLS and both benchmarking policies

SURROGATE SEARCH: A SIMULATION OPTIMIZATION METHODOLOGY FOR LARGE-SCALE SYSTEMS

For certain settings in which system performance cannot be evaluated by analytical methods, simulation models are widely utilized. This is especially for complex systems. To try to optimize these models, simulation optimization techniques have been developed. These attempt to identify the system designs and parameters that result in (near) optimal system performance. Although more realistic results can be provided by simulation, the computational time for simulator execution, and consequently, simulation optimization may be very long. Hence, the major challenge in determining improved system designs by incorporating simulation and search methodologies is to develop more efficient simulation optimization heuristics or algorithms. This dissertation develops a new approach, Surrogate Search, to determine near optimal system designs for large-scale simulation problems that contain combinatorial decision variables. First, surrogate objective functions are identified by analyzing simulation results to observe system behavior. Multiple linear regression is utilized to examine simulation results and construct surrogate objective functions. The identified surrogate objective functions, which can be quickly executed, are then utilized as simulator replacements in the search methodologies. For multiple problems containing different settings of the same simulation model, only one surrogate objective function needs to be identified. The development of surrogate objective functions benefits the optimization process by reducing the number of simulation iterations. Surrogate Search approaches are developed for two combinatorial problems, operator assignment and task sequencing, using a large-scale sortation system simulation model. The experimental results demonstrate that Surrogate Search can be applied to such large-scale simulation problems and outperform recognized simulation optimization methodology, Scatter Search (SS). This dissertation provides a systematic methodology to perform simulation optimization for complex operations research problems and contributes to the simulation optimization field

Optimization of stochastic-dynamic decision problems with applications in energy and production systems

Die vorliegende Arbeit beschäftigt sich mit der mathematischen Optimierung von stochastisch-dynamischen Entscheidungsproblemen. Diese Problemklasse stellt eine besondere Herausforderung für die mathematische Optimierung dar, da bislang kein Lösungsverfahren bekannt ist, das in polynomieller Zeit zu einer exakten Lösung konvergiert. Alle generischen Verfahren der dynamischen Optimierung unterliegen dem sogenannten "Fluch der Dimensionen", der dazu führt, dass die Problemkomplexität exponentiell in der Anzahl der Zustandsvariablen zunimmt. Da Entscheidungsprobleme von realistischer Größenordnung meist über eine Vielzahl von Zustandsvariablen verfügen, stoßen exakte Lösungsverfahren schnell an ihre Grenzen. Einen vielversprechenden Ausweg, um dem Fluch der Dimensionen zu entgehen, stellen Verfahren der "approximativ-dynamischen Optimierung" dar (engl.: "approximate dynamic programming"), welche versuchen eine Nährungslösung des stochastisch-dynamischen Problems zu berechnen. Diese Verfahren erzeugen eine künstliche Stichprobe des Entscheidungsprozesses mittels Monte-Carlo-Simulation und konstruieren basierend auf dieser Stichprobe eine Approximation der Wertfunktion des dynamischen Problems. Dabei wird die Stichprobe so gewählt, dass lediglich diejenigen Zustände in die Stichprobe aufgenommen werden, welche für den Entscheidungsprozess von Bedeutung sind, wodurch eine vollständige Enumeration des Zustandsraums vermieden wird. In dieser Arbeit werden Verfahren der approximativ-dynamischen Optimierung auf verschiedene Probleme der Produktions- und Energiewirtschaft angewendet und daraufhin überprüft, ob sie in der Lage sind, das zugrundeliegende mathematische Optimierungproblem nährungsweise zu lösen. Die Arbeit kommt zu dem Ergebnis, dass sich komplexe stochastisch-dynamische Bewirtschaftungsprobleme effizient lösen lassen, sofern das Optimierungsproblem konvex und der Zufallsprozess unabhängig vom Entscheidungsprozess ist. Handelt es sich hingegen um ein diskretes Optimierungsproblem, so stoßen auch Verfahren der approximativ-dynamischen Optimierung an ihre Grenzen. In diesem Fall sind gut kalibrierte, einfache Entscheidungsregeln möglicherweise die bessere Alternative.This thesis studies mathematical optimization methods for stochastic-dynamic decision problems. This problem class is particularly challenging, as there still exists no algorithm that converges to an exact solution in polynomial time. Existing generic solution methods are all subject to the "curse of dimensionality", which means that problem complexity increases exponentially in the number of state variables. Since problems of realistic size typically come with a large number of state variables, applying exact solution methods is impractical. A promising methodology to break the curse of dimensionality is "approximate dynamic programming". To avoid a complete enumeration of the state space, solution techniques based on this methodology use Monte Carlo simulation to sample states that are relevant to the decision process and then approximate the value function of the dynamic program by a function of much lower complexity. This thesis applies approximate dynamic programming techniques to different resource management problems that arise in production and energy settings and studies whether these techniques are capable of solving the underlying optimization problems. The thesis concludes that stochastic-dynamic resource management problems can be solved efficiently if the underlying optimization problem is convex and randomness independent of the resource states. If the optimization problem is discrete, however, the problem remains hard to solve, even for approximate dynamic programming techniques. In this case, simple but well-adjusted decision policies may be the better choice

Aplicación de la meta-heurística colonia de hormigas para la resolución de problemas multi-objetivo de programación de la producción en Flowshops híbridos (flexibles)

150 páginasCon el fin de mejorar los niveles de competitividad, las empresas de manufactura y de servicio están obligadas a la implementación constante de procedimientos formales que les permitan optimizar sus procesos. En ese sentido, en lo referente a las operaciones de manufactura, la logística de producción, y más específicamente la programación de operaciones, juega un papel importante en cuanto al uso eficiente de los recursos. La programación de operaciones (scheduling, en inglés) es una rama de la optimización combinatoria que consiste en la asignación de recursos para la realización de un conjunto de actividades con el fin de optimizar uno o varios objetivos. Debido a la complejidad intrínseca en la mayoría de los problemas de programación de la producción, los cuales son del tipo NP-duro (esto es, el tiempo que requieren para resolver un caso particular de un problema crece en el peor de los casos de manera exponencial con respecto al tamaño del problema), los métodos exactos convencionales de resolución tales como: programación lineal, entera y mixta, entre otros, no son eficientes en términos del tiempo de cálculo para llegar a la solución óptima. Por lo tanto, se hace necesario el uso de enfoques alternativos para resolver este tipo de problemas en un tiempo razonablemente corto para el tomador de decisiones, sobre todo aquellas que se toman diariamente. Dentro de estos enfoques se encuentran las metaheurísticas, que consisten en procedimientos formales desarrollados con el fin de superar esta dificultad que se presenta con los métodos tradicionales. Los procedimientos meta-heurísticos más comunes para la resolución de problemas combinatorios son: los algoritmos genéticos, la búsqueda tabú, la colonia de hormigas y el recocido simulado entre otros