A comparison of two lot sizing-sequencing heuristics for the process industry

Production Models;produktie

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

Simulation modeling of tool delivery system in a machining line

This paper describes an industrial project aiming to enhance the existing simulation modeling suites used at a car engine factory in the UK. The company continues to enhance its simulation modeling capabilities towards so called the `total plant modeling' which not only covers the production facilities but also key ancillary facilities. Tool delivery is one such ancillary process. The existing modeling practices at the company are limited to modeling tool changes and assume that tools meet their expected life and the replacement is always available. In reality, the tools are not always reaching the expected life, the facilities in the tool crib are a limiting resource and the tool inventory has to be minimized. The tool delivery system developed in this project has specific features that model how the tool crib operates, how tools are supplied to the machining lines and various operating strategie

Enhancing Manufacturing Planning and Control Systems Through Artificial Intelligence Techniques

Manufacturing planning and control systems are currently dominated by systems based upon Material Requirements Planning (MRP). MRP systems have a number of fundamental flaws. A potential alternative to MRP systems is suggested after research into the economic batch scheduling problem. Based on the ideas of economic batch scheduling, and enhanced through artificial intelligence techniques, an alternative approach to manufacturing planning and control is developed. A framework for future research on this alternative to MRP is presented

The ConWip Production Control System: a Literature Review

International audienceA growing body of literature dealing with ConWip has been observedduring the past decade. Considering the current industrial challengescharacterized by adaptability, product customization, shortened lead times andcustomer satisfaction, ConWip appears to be an effective and adaptedproduction control system for manufacturers. Given this context, this paper aimsto update the previous literature review about ConWip that was made in 2003and to provide an understanding key through an original classification method.This method allows the reader to distinguish papers that concentrate on ConWipsizing, ConWip performance, ConWip environment or on the comparison ofConWip with other PCS. It also provides a reading key about the researchapproach. Taking these criteria into account, this paper helps to answer thefollowing questions: how can ConWip be implemented? How can ConWip beoptimized? Why and when should ConWip be used? The paper then concludeswith some research avenues

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

Electricity and Fuel Consumption in a Lean Energy Supply Chain

Human activities are the main sources of environmental pollution. Awareness about this fact, motivated us to make changes in different paradigms of our lives including industrial or personal activities. Environmental activities assumed to have conflict with financial objectives, in this study we try to align business requirements with environmental concerns. Among all human activities, generating energy has the most negative impact on the environment. The major part of the generated energy will be consumed in transportation and industrial demand which makes them the most effective targets for the reduction of greenhouse gas emission. In a lean environment, small batch sizes increase the number of set-ups and consequently, energy consumption in manufacturing. On the other hand, small batch sizes increase the delivery rates and complexity of transportation. Therefore, the focus of this study will be on reducing the environmental impact of human activities in transportation and industrial loads as a part of lean supply chain. The focus in transportation will be on trucking with gasoline or diesel as the source of energy. In industrial loads, the emerging opportunities after deregulation of the electricity market and incentive programs toward cleaner productions encouraged us to focus on electrical demand in the industry. Despite motivations for reducing emissions in supply chain management, lack of knowledge and expertise in measuring, modeling and optimizing energy consumption is a barrier in production section. In this dissertation, a framework of a power measurement and simulation will be introduced. In the next section, a production planning model incorporating energy will be developed considering different states of electricity consumption (idle, startup, etc.). As the next segment of the supply chain, a method for optimal carrier selection and routing will be developed and tested based on real world data. This model can use the advantage of geographically distributed carriers while utilizing private fleet at an acceptable level. Based on the insight developed in transportation and industrial loads, an experience based performance measure will be developed to quantify the performance and associated energy consumption in the supply chain

Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation

An economic development model analyzes the adoption of alternative strategy capable of leveraging the economy, based essentially on RES. The combination of wind turbine, PV installation with new technology battery energy storage, DSM network and RES forecasting algorithms maximizes RES integration in isolated islands. An innovative model of power system (PS) imbalances is presented, which aims to capture various features of the stochastic behavior of imbalances and to reduce in average reserve requirements and PS risk. Deep learning techniques for medium-term wind speed and solar irradiance forecasting are presented, using for first time a specific cloud index. Scalability-replicability of the FLEXITRANSTORE technology innovations integrates hardware-software solutions in all areas of the transmission system and the wholesale markets, promoting increased RES. A deep learning and GIS approach are combined for the optimal positioning of wave energy converters. An innovative methodology to hybridize battery-based energy storage using supercapacitors for smoother power profile, a new control scheme and battery degradation mechanism and their economic viability are presented. An innovative module-level photovoltaic (PV) architecture in parallel configuration is introduced maximizing power extraction under partial shading. A new method for detecting demagnetization faults in axial flux permanent magnet synchronous wind generators is presented. The stochastic operating temperature (OT) optimization integrated with Markov Chain simulation ascertains a more accurate OT for guiding the coal gasification practice