An artificial neural network model for optimization of finished goods inventory

In this paper, an artificial neural network (ANN) model is developed to determine the optimum level of finished goods inventory as a function of product demand, setup, holding, and material costs. The model selects a feed-forward back-propagation ANN with four inputs, ten hidden neurons and one output as the optimum network. The model is tested with a manufacturing industry data and the results indicate that the model can be used to forecast finished goods inventory level in response to the model parameters. Overall, the model can be applied for optimization of finished goods inventory for any manufacturing enterprise in a competitive business environment. © 2011Growing Science Ltd. All rights reserved

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Artificial intelligence and policy making:can small municipalities enable digital transformation?

This study investigates digital transformation and the usability of emerging technologies in policymaking. Prior studies categorised digital transformation into three distinct phases of digitisation, digitalisation, and digital transformation. They mainly focus on the operational or functional levels, however, this study considers digital transformation at the strategic level. Previous studies confirmed that using new emerging AI-based technologies will enable organisations to use digital transformation to achieve higher efficiency. A novel methodological AI-based approach for policymaking was constructed into three phases through the lens of organisational learning theory. The proposed framework was validated using a case study in the transportation industry of a small municipality. In the selected case study, a confirmatory model was developed and tested utilising the Structural Equation Modelling with data collected from a survey of 494 local stakeholders. Artificial Neural Network was utilised to predict and then to identify the most appropriate policy according to cost, feasibility, and impact criteria amongst six policies extracted from the literature. The results from this research confirm that utilisation of the AI-based strategic decision-making through the proposed generative AI platform at strategic level outperforms human decision-making in terms of applicability, efficiency, and accuracy.<br/

Dynamic scheduling in a multi-product manufacturing system

To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize waiting time and maximize utilization of equipment and machineries. The dynamics of real manufacturing system are very complex in nature. Schedules developed based on deterministic algorithms are unable to effectively deal with uncertainties in demand and capacity. Significant differences can be found between planned schedules and actual schedule implementation. This study attempted to develop a scheduling system that is able to react quickly and reliably for accommodating changes in product demand and manufacturing capacity. A case study, 6 by 6 job shop scheduling problem was adapted with uncertainty elements added to the data sets. A simulation model was designed and implemented using ARENA simulation package to generate various job shop scheduling scenarios. Their performances were evaluated using scheduling rules, namely, first-in-first-out (FIFO), earliest due date (EDD), and shortest processing time (SPT). An artificial neural network (ANN) model was developed and trained using various scheduling scenarios generated by ARENA simulation. The experimental results suggest that the ANN scheduling model can provided moderately reliable prediction results for limited scenarios when predicting the number completed jobs, maximum flowtime, average machine utilization, and average length of queue. This study has provided better understanding on the effects of changes in demand and capacity on the job shop schedules. Areas for further study includes: (i) Fine tune the proposed ANN scheduling model (ii) Consider more variety of job shop environment (iii) Incorporate an expert system for interpretation of results. The theoretical framework proposed in this study can be used as a basis for further investigation

A critical analysis of job shop scheduling in context of industry 4.0

Scheduling plays a pivotal role in the competitiveness of a job shop facility. The traditional job shop scheduling problem (JSSP) is centralized or semi-distributed. With the advent of Industry 4.0, there has been a paradigm shift in the manufacturing industry from traditional scheduling to smart distributed scheduling (SDS). The implementation of Industry 4.0 results in increased flexibility, high product quality, short lead times, and customized production. Smart/intelligent manufacturing is an integral part of Industry 4.0. The intelligent manufacturing approach converts renewable and nonrenewable resources into intelligent objects capable of sensing, working, and acting in a smart environment to achieve effective scheduling. This paper aims to provide a comprehensive review of centralized and decentralized/distributed JSSP techniques in the context of the Industry 4.0 environment. Firstly, centralized JSSP models and problem-solving methods along with their advantages and limitations are discussed. Secondly, an overview of associated techniques used in the Industry 4.0 environment is presented. The third phase of this paper discusses the transition from traditional job shop scheduling to decentralized JSSP with the aid of the latest research trends in this domain. Finally, this paper highlights futuristic approaches in the JSSP research and application in light of the robustness of JSSP and the current pandemic situation

Hardware Accelerating the Optimization of Transaction Schedules via Quantum Annealing by Avoiding Blocking

The isolation property of database theory guarantees to avoid problems of not synchronized parallel execution of several transactions. In this paper we propose an algorithm for an optimal transaction schedule for the different cores of a multi-core CPU with minimal execution time ensuring the isolation property. Optimizing the transaction schedule is a combinatorial problem, which is ideal to be solved by quantum annealers as special form of quantum computers. In our contribution we show how to transform an instance of the transaction schedule problem into a formula that is accepted by quantum annealers including a proof of validity and optimality of the obtained result. Furthermore, we analyze the number of required qubits and the preprocessing time, and introduce an approach for caching formulas as result of preprocessing for the purpose of reducing the preprocessing time. In an experimental evaluation, the runtime on a quantum annealer outperforms the runtime of traditional algorithms to solve combinatorial problems like simulated annealing already for small problem sizes

Artificial intelligence and policy making; can small municipalities enable digital transformation?

This study investigates digital transformation and the usability of emerging technologies in policymaking. Prior studies categorised digital transformation into three distinct phases of digitisation, digitalisation, and digital transformation. They mainly focus on the operational or functional levels, however, this study considers digital transformation at the strategic level. Previous studies confirmed that using new emerging AI-based technologies will enable organisations to use digital transformation to achieve higher efficiency. A novel methodological AI-based approach for policymaking was constructed into three phases through the lens of organisational learning theory. The proposed framework was validated using a case study in the transportation industry of a small municipality. In the selected case study, a confirmatory model was developed and tested utilising the Structural Equation Modelling with data collected from a survey of 494 local stakeholders. Artificial Neural Network was utilised to predict and then to identify the most appropriate policy according to cost, feasibility, and impact criteria amongst six policies extracted from the literature. The results from this research confirm that utilisation of the AI-based strategic decision-making through the proposed generative AI platform at strategic level outperforms human decision-making in terms of applicability, efficiency, and accuracy

Deep Reinforcement Learning Management System For Secure Intralogistics

O crescimento e desenvolvimento da Indústria tem vindo a ser notório ao longo dos últimos anos devido, em boa parte, às revoluções industriais que existiram. Estas fizeram com que a competitividade na Indústria aumentasse e, com isso, fosse necessário ter em conta fatores potenciadores de sucesso perante as outras ofertas existentes no mercado. O planeamento da produção é crucial e, quando realizado com qualidade, traz vários benefícios. Um bom planeamento permite ampliar a capacidade de produção, minimi- zando os desperdícios e, consequentemente, aumentar a competitividade da empresa. Nesse sentido, a presente dissertação tem por objetivo criar um sistema apto a obter um planeamento capaz de trazer benefícios à produção num caso real. De forma a caracterizar o problema, foi realizado um estudo que permitiu identificar o mesmo como um Flow-Shop Scheduling Problem (FSSP) assim como as soluções existentes para o mesmo. Destas soluções, optou-se por recorrer ao algoritmo Q-learning de forma a obter um sistema capaz de otimizar a produção. De modo a visualizar e a tornar este sistema mais fidedigno, recorreu-se ao software SIMIO para se realizarem comparações por forma a aferir os resultados. Quanto aos resultados obtidos, estes mostraram-se positivos para os diferentes testes realizados. Primeiramente, com o intuito de validar o algoritmo a utilizar recorreu-se à otimização de casos conhecidos, nos quais se obtiveram resultados próximos dos ótimos garantindo o bom funcionamento do mesmo. De seguida, modelou-se o sistema para que o mesmo representasse, de forma assertiva, o sistema real em estudo. Por último, realizaram-se otimizações no planeamento do caso real que resultaram em melhorias significativas do mesmo. Em suma, o sistema mostrou-se capaz de obter soluções que otimizam e beneficiam o sistema real. Contudo, o algoritmo demostrou algumas limitações e identificaram-se pontos do sistema passiveis de serem otimizados para que seja possível obter melhores resultados.The growth and development of Industry has been notorious over the last few years due, largely, to the industrial revolutions that have taken place. These revolutions in- creased industry’s competitiveness and, consequently, it is necessary to consider aspects that enhance success compared to other competitors on the market. Production planning is crucial, and, when done properly it brings several benefits. Good planning allows expanding production’s capacity, minimize losses and, conse- quently, increase company’s competitiveness. In that regard, this dissertation aims to create a system capable of achieve a planning capable of bringing benefits to a real pro- duction case. To characterize the problem, a study was carried out to identify the problem as a Flow-Shop Scheduling Problem (FSSP) as well as the existing solutions for it. From these solutions, we chose to use the Q-learning algorithm to obtain a system capable of optimiz- ing production. To visualize and to make this system more reliable, the software SIMIO was used to carry out comparisons to assess the results. As for the obtained results, they were positive for the different tests performed. First, to validate the used algorithm, we resorted to the optimization of known cases, in which the obtained results were close to the optimal, ensuring its proper functioning. The, the system was modelled to represent, in an assertive way, the real system under study. Finally, optimizations were carried out in the planification of the real case, resulting in significant improvements in the case. To finalize, the system proved capable of obtaining solutions that optimize and im- prove the real system. However, the algorithm revealed some limitations and some points that could be optimize so that is possible to obtain better results

Análisis de la Relajación Lagrangiana como método de programación de talleres flexibles en un entorno multiagente

Esta tesis está relacionada con la programación de operaciones de tipo distribuido y analiza el método de Relajación Lagrangiana para su aplicación como mecanismo de generación de precios en el contexto de las subastas combinatorias iterativas. El desarrollo de los sistemas multiagente ha permitido la implementación de sistemas distribuidos de programación y control de la producción. En ellos, los mecanismos de coordinación utilizados son un importante campo de investigación. En este trabajo se estudia la resolución del problema de programación de talleres flexibles mediante el método de Relajación Lagrangiana, que permite descomponerlo para ser implementado en un sistema multiagente. El sistema resultante puede ser entendido como una subasta combinatoria utilizada como mecanismo de negociación. Se han analizado las características y limitaciones de las distintas alternativas para ser implementadas de forma asíncrona y descentralizada, en base a la calidad de la solución propuesta y velocidad de convergencia. ______________________________ This thesis is related to operations scheduling in distributed systems and analyzes the application of the Lagrangian Relaxation method as price mechanism in the context of iterative combinatorial auctions. The development of multiagent systems has enabled the implementation of distributed production scheduling systems. The coordination mechanism is crucial in these systems and its design is an important field of research. In this work we study the resolution of the flexible job shop scheduling problem by the Lagrangian relaxation method. It decomposes the problem to be implemented in a multiagent system. The resulting system can be understood as a combinatorial auction that is used as a negotiation mechanism among the agents. The aim of this work is to study the characteristics and limitations of the different alternatives to be implemented asynchronously and in a decentralized structure, based on the quality and convergence of the solution