1,721,232 research outputs found
Evolutionary Computing and Swarm Intelligence algorithms for JSSP: Genetic Algorithm vs. Ant Colony Optimization techniques
No full textTo effectively manage and control the execution of production process, a correct scheduling activity must be performed. In any manufacturing environment, resources utilization, production rate, customer service level can be switched across the definition of suitable jobs’ sequence and tasks’ allocation. Being a NP complete and highly constrained problem, the resolution of the Job Shop Scheduling Problem (JSSP) is recognized as a key point to the factory optimization process. In recent years, a great number of multi-objective meta-heuristics has been proposed to evaluate the quality of a scheduling solution and obtain sets of compromising solutions. Powerful methods for running these kinds of optimization problems have been inspired by research on evolutionary theory and swarm intelligence approach. The cooperative behaviour that emerges from the organization of multi agent systems is the inspiring source of the two implemented approaches. The pursuit of optimal solution, on both benchmark and real-world job shop problem, has been successful tested for Genetic Algorithms (GA) and Ant Colony Optimization (ACO) techniques.
This work starts with analysis on optimization methods for JSSP. Across the implementation of a new Genetic Algorithm and an improved model based on ant’s way, the performance of the two meta-heuristic approaches has been evaluated and compared. Similarity/dissimilarity of evolutionary and swarm intelligent approaches has pointed out. The logic, the parameters, the representation schemes and operators used in these two approaches have been widely discussed during this paper. A guide to the implementation of GAs and ACO approach to JSSP was performed
Towards An Optimal Mix In Planning and Scheduling Of Operating Theatres
No full textThe absolute necessity to offer a constant high level of medical services to patients, notwithstanding the precariousness of resources as well as economy, is pushing healthcare managers/administrators towards increasing interest in worthy of admiration tools successfully applied into the industrial frame. It is uncovering the usefulness to apply operations management and scheduling approaches to services. Starting from a discrete event simulation model of real emergency facilities, the present paper deals with allocation scheduling in operating theatres framed to day care patient’s arrival rate and pathological circumstances. It studies the impact of forecast and resources rationalization and calls and optimal arrangement for bed assignment and operating room services. A weekly time bucket has been used in planning. A daily based server mix and patient rate, in Cork University Hospital – Ireland, with different types of patient and multiple servers, which are either specialist and/or cross trained, will be analyzed. The application of an approach for optimization was used to evaluate alternative configurations in order to anticipate favourable assessments and to prevent mislaid of time and resources and quality. Fitting system constraints, a queuing network in multi-agent approach has been formulated. Parameters such as staff assessment, rota, paths, personnel management as well as turnover, equipment and instrument configuration have been taken into consideration.
Moreover, decide on the right mix of flexible and dedicated equipments is part of designing the service in medical delivery system. Hence, strategic system and human resources flexibility in services are going to be investigated. The impact of replacing dedicated servers by flexible ones will be evaluated. The flexible capacity will be quantified and patients expectation in terms of waiting time will be shielded. The right level of flexibility of health care services, enabling decision makers to improve system performance in accordance with strategic objectives, will be evaluated. Taking into account the service delivery and medical staff availability and the patient downtime cost, the authors will show that total flexibility is not always the best choice. As final output, authors are proposing a viable tool, i.e., synoptic prospect, which can give managers and planners the opportunity to identify, measure and test, ex ante, the booked feasibility and system availability in terms of flexibility in operating care services, hence enabling them to improve system performance in accordance with strategic objectives and forecast and overcrowding
The Balancing Problem in an Emergency Room based on Ant Colony Optimization algorithm
No full textMarket place requirements are putting increasing pressures on organisations to be more competitive. The requirement for performance improvement has pushed engineers and managers towards the careful study of critical parameters and towards the “re-engineering” approach. A key problem for engineers engaged in the development of powerful analytical tools is to explain their use in concrete terms to management. This problem is worsened where the value added is difficult to measure due to the nature of the process. The Health Care system is a case in point. Emergency Department crowding and rising health-care costs are perceived as significant issues that are getting worse. In order to respond to the growing number of incoming patients, hospital departments, including emergency rooms, have to re-evaluate their current facilities, procedures and practises from an operations management perspective. In a typical Emergency Department it is important to minimise (under fixed constraints) patient waiting time; but also staff idle time while maintaining the high utilization rate of medical facilities. Traditionally, these capacity problems have been solved, mainly, by increasing the number of available resources.
This paper presents some observations arising from the development of a case study in the public health care system. In particular, we developed and tested a simulation model, but also a process scheduling model of the Emergency Department (ED) of Cork University Hospital (CUH) - Ireland. Based on the analogy of a job shop scheduling problem and known patient scheduling methodologies, we used an Ant Colony Optimization (ACO) algorithm for the balancing of the process. The algorithm is based on Swarm Intelligent (SI) meta-heuristic techniques. The problem is multi-objective in its general formulation. The proposal of the present work is to optimise patient scheduling under defined precedence, zoning and capacity constraints. In addition to this goal, the approach will be to attempt to balance the workload between and within resource types (i.e., work-centres or medical staff: doctors, nurses, administrators etc...). The proposed model will be integrated into the simulation model, resulting in minimising the number of resources and balancing the workload within each resource
Human factor and entropy evaluation in collaborative workplace environment
No full textThe integration of advanced technologies, that the Industry 4.0 framework introduced, in the workplace environment requires the human workforce to adapt to the workplace changes. A rising concern is the role of older workers and how smoothly can they integrate in workplace environments with automation and cobotics. To this extent, we introduce a statistical model based on entropy and human factor sustainability concept. The aim of the proposed model is to calculate the level of uncertainty inside a collaborative workplace by computing the probability of error of human workforce. The probability is calculated by utilising the parameters and variables that governs the interactions between human operators and robotics – cyber systems. This computational model was tested in a complex workplace framework by usage of advanced ICT technologies and tasks of increased complexity. Results show that the age factor plays a role in the uncertainty inside the workplace
An Automated Procedure for Machines Distribution among Plants based on Genetic Algorithm Approach
No full textThe present research is focused on the development of a facility layout problem (FLP) using genetic approach for robust optimization (i.e., GA) and analysis of Variance ANOVA used to identify critical parameters of the genetic algorithm. The main advantage of such type of algorithms with respect to the other artificial intelligent approaches is represented by the possibility to simulate the natural evolution of a survival fitness function and, in the specific case, the minimization of material handling costs for different located machines. Hard constraints (e.g., machine overall dimensions and production cycle) have been taken into consideration. The implemented algorithm uses a string encoding, a partially matched crossover (i.e., PMC) to generate the offspring and a repair method to modify infeasible solutions. The repair method analyzes the solution, identifies the plants in which the constraints are not observed and places a random machine into another plant. The input data are production cycles, areas of a plant, machines and storehouse. To investigate the effects of individual parameters of GAs, a design of experiments (DOE) was employed. Early analysis of factors (i.e. dimension of population, fraction of elitism and fraction of eliminated genes during generations) was conducted with a full factorial plan with two levels for each factor and four repetitions. A trade-off of the population dimension and maximum number of generations allows to achieve good results in short computation time. The results are presented in terms of reduction of external transport costs
Overall Measurement Of Process Equipment Effectiveness For Performance Monitoring and Decisions Assess
No full textOverall Equipment Effectiveness provides a consistent method to measure productivity and to perform diagnosis at the equipment level. OEE metric is based on the idea of comparing Valuable Production Time with Up Time. It distils complex data, including downtime and waste in individual machines/equipments and other unplanned losses which reduce throughput, in a simple understandable metric. However, according to experience and literature review, its excessive synoptic view manifests lack at the factory level and timing of monitoring and planning in plants. This research is going to explore the use of OEE, not only as an operational measure of equipment effectiveness, but also as an indicator of which activities have to get better in process decision making issues. Hidden capacity identification, bottleneck detection, buffer consistency and location, planned improvements within a manufacturing environment can be diagnosed according with a measure of Overall Process Equipment Effectiveness (i.e., OPEE). As reminder of this paper the theoretical concept of OEE metric is considered first and constituent parts of OEE are examined. An exhaustive literature review and a critical analysis of OEE measurement, in production systems, are drawn up. Afterwards, the new OPEE approach is proposed. The coming up of such a metric was logical achieved and finally demonstrated implementing a virtual set of predefined subsystems including series, parallel, assembly and expansions. The new metric achieves and manifests a performance monitoring perspective in identifying bottlenecks as well as putting forward to deciding. Such a vision is suggested as a standard in simulation frame
Effectiveness of Digital Factory for simple repetitive task simulation in medium-small enterprices
No full textDigital Factory (DF) is widely applying to analyze and optimize the production of large products which involve complex assembly processes (e.g. automotive, shipbuilding, aerospace fields). The possibility to preview the product evolution allows to discover potential inconsistencies in the assembly cycles, evaluate the operation last, distribute and balance tasks among the assembly stations and workers, at design stage.
Moreover, time and methods analysis with ergonomic simulation tools drives to a sound workplace optimization which consists in non value added actions and stressful work postures reduction. DF application also leads towards a more standardized task definition in terms of cycle time and repeatability. Therefore, even medium and small industries that produce non complex handworks could benefit from the employment of DF tools, especially for the correct evaluation of tasks duration and calculation of human resource requirements.
Through the analysis of three industrial cases the authors have analyzed some of potentials and possible limits of DF applications to medium and small companies
- …
