Optimum Allocation of Inspection Stations in Multistage Manufacturing Processes by Using Max-Min Ant System

In multistage manufacturing processes it is common to locate inspection stations after some or all of the processing workstations. The purpose of the inspection is to reduce the total manufacturing cost, resulted from unidentified defective items being processed unnecessarily through subsequent manufacturing operations. This total cost is the sum of the costs of production, inspection and failures (during production and after shipment). Introducing inspection stations into a serial multistage manufacturing process, although constituting an additional cost, is expected to be a profitable course of action. Specifically, at some positions the associated inspection costs will be recovered from the benefits realised through the detection of defective items, before wasting additional cost by continuing to process them. In this research, a novel general cost modelling for allocating a limited number of inspection stations in serial multistage manufacturing processes is formulated. In allocation of inspection station (AOIS) problem, as the number of workstations increases, the number of inspection station allocation possibilities increases exponentially. To identify the appropriate approach for the AOIS problem, different optimisation methods are investigated. The MAX-MIN Ant System (MMAS) algorithm is proposed as a novel approach to explore AOIS in serial multistage manufacturing processes. MMAS is an ant colony optimisation algorithm that was designed originally to begin an explorative search phase and, subsequently, to make a slow transition to the intensive exploitation of the best solutions found during the search, by allowing only one ant to update the pheromone trails. Two novel heuristics information for the MMAS algorithm are created. The heuristic information for the MMAS algorithm is exploited as a novel means to guide ants to build reasonably good solutions from the very beginning of the search. To improve the performance of the MMAS algorithm, six local search methods which are well-known and suitable for the AOIS problem are used. Selecting relevant parameter values for the MMAS algorithm can have a great impact on the algorithm’s performance. As a result, a method for tuning the most influential parameter values for the MMAS algorithm is developed. The contribution of this research is, for the first time, a methodology using MMAS to solve the AOIS problem in serial multistage manufacturing processes has been developed. The methodology takes into account the constraints on inspection resources, in terms of a limited number of inspection stations. As a result, the total manufacturing cost of a product can be reduced, while maintaining the quality of the product. Four numerical experiments are conducted to assess the MMAS algorithm for the AOIS problem. The performance of the MMAS algorithm is compared with a number of other methods this includes the complete enumeration method (CEM), rule of thumb, a pure random search algorithm, particle swarm optimisation, simulated annealing and genetic algorithm. The experimental results show that the effectiveness of the MMAS algorithm lies in its considerably shorter execution time and robustness. Further, in certain conditions results obtained by the MMAS algorithm are identical to the CEM. In addition, the results show that applying local search to the MMAS algorithm has significantly improved the performance of the algorithm. Also the results demonstrate that it is essential to use heuristic information with the MMAS algorithm for the AOIS problem, in order to obtain a high quality solution. It was found that the main parameters of MMAS include the pheromone trail intensity, heuristic information and evaporation of pheromone are less sensitive within the specified range as the number of workstations is significantly increased

Using Multiple Linear Regression and Artificial Neural Network to Predict Surface Roughness in Turning Operations

Quality of surface roughness has a great impact on machine parts during their useful life. The machining process is more complex, and therefore, it is very hard to develop a comprehensive model involving all cutting parameters. In this paper, the surface roughness is measured during turning operation at different cutting parameters such as speed, feed rate, and depth of cut. Two mathematical models are developed to predict the surface roughness and to select the required surface roughness by using the Multi-regression model and Artificial Neural Networks (ANN). To test the developed models, 27 pieces of steel alloy HRC15 were operated and the roughness of their surfaces measured. The results showed that the ANN model estimates the surface roughness with high accuracy compared to the multiple regression model with the average deviation from the real values of about 1%

The Impact of Residential Optimally Designed Rooftop PV System on Libya Power Shortage Case

The average yearly hours of sunshine in Libya reaches 3200 hours and solar irradiance rate approximately ranges from 6 to 7kWh/m2/day. However, small solar parks projects are now undergoing and some are lately under cadastral and field survey. In meanwhile, $922.7M is the average annual government fund paid for electricity generation sector. It thus results in Tariff of 0.082$/kWh. This paper studies the potential of hybrid rooftop PV solar systems to supply household appliances and then proposes a 5.65 kWp PV solar system appropriate for Libyan home’s rooftop to mitigate the consequences of load shedding due to electric power shortage. Accordingly, oil uses in electricity generation will be gradually reduced as a result to rooftop PV systems widely spread. Finally, the overall benefits, simulation summery and implementation approach are provided

SITE SELECTION OF DESALINATION PLANT IN LIBYA BY USING COMBINATIVE DISTANCE-BASED ASSESSMENT (CODAS) METHOD

Libya is one of the arid regions of the world, and it is facing a serious water supply shortage due to the increase in both population and water consumption in various sectors. Ground water is the main source of water in Libya, but it is limited and over exploited. Desalination of sea water is one of the possibilities for Libyan government to meet the problem of water shortage. Selecting the best location of desalination plant is important and a complex process because it is related to a variety of criteria. The aim of this paper is to select the best location of desalination plant in the northwestern coast of Libya. The selection of the best location was done by two main steps. The first step based on the criterion of minimizing water transportation cost, and the second step considered the influence of the external criteria on the location selection. The results of the case study show that the best location is the capital city (Tripoli) with respect to the assessment of Combinative Distance-based Assessment (CODAS) method. The sensitivity analysis was conducted to evaluate the robustness of the selected locations and it reveals that the CODAS method is stable and efficient to deal with multi-criteria decision-making problems. This study provides a suitable and useful tool for the decision makers concerning the optimum location of desalination facilities

A dynamic programming model for designing a quality control plan in a manufacturing process

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Process quality planning should establish the quality control plan to achieve the desired quality level with the minimum quality cost (appraisal and failure costs) for the final product. This plan sets out the critical quality variables, the control stations in the process, and the control method at each control station. The quality costs associated with quality control and defective products can be greater than or less than ideal regarding the required quality level. The purpose of this paper is to provide a stochastic dynamic programming model for designing the quality control plan in a manufacturing process, which allows obtaining the desired level of control with the lowest cost. Inputs to the model are, in particular, control stations in the process, levels of quality, control methodologies (no control, statistical process control, 100% inspection), probabilities of changing the quality level and quality costs. The output of this model is the quality control plan that satisfies the desired level of quality at the lowest cost. This plan establishes the control stations, the methodology used in each control station, the desired quality level for the final product, and the estimated quality costs. Finally, an illustrative example based on a manufacturing process demonstrates the applicability of this approach and several considerations are reported about future research directions.FCT - Fundação para a Ciência e a Tecnologia(UID/CEC/00319/2019

Integrating quality costs and real time data to define quality control

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) The control of critical to quality (CTQ) parameters can be done in a given process or in a downstream process. Companies must decide which CTQ parameters will be controlled, in which process, and define the control method: statistical process control (SPC) or 100% inspection. However, operational constraints can influence its definition. Overall, the control for a given process can be excessive or insufficient, resulting in a non-optimal quality cost. This paper discusses the relevance of different factors that can influence the selection of a quality control method. Then, it assesses the likelihood of companies having reliable data on such factors and it is proposed a model to minimize the total quality costs of a given process. The model uses information like SPC efficiency in detecting potential process variations, false alarms, measurement system error, inspection cost, repair cost and the cost of passing defective units to the next process. The quality control method can be updated whenever recent data on the 18 parameters are available. Through an application example, quality control mechanisms are selected to minimize quality costs.FCT - Fundação para a Ciência e a Tecnologia(UID/CEC/00319/2019

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Allocation of quality control stations in multistage manufacturing systems

The allocation of quality control stations (AQCS) in multistage manufacturing systems has been studied extensively over the decades. This paper reviews the existing approaches, models comparison and solution techniques applied in AQCS. The relevance of the models and the effectiveness of the inspection strategies are examined by developing a generalised model. The conducting simulation experiments show that as the number of workstation increases the processing tine to solve the problem increases significantly. This led to the development of a heuristic algorithm with local search. The performance the heuristic was compared with the optimization method based on complete enumeration method (CEM). It was found that the heuristic method can derive an acceptable solution significantly faster than the CEM. The review has shown that the most common techniques used are dynamic programming and non-linear programming. The paper suggests some biologically inspired optimisation algorithms can be of interest for further study. (C) 2011 Elsevier Ltd. All rights reserved

A case study of supplier selection for a steelmaking company in libya by using the combinative distance-based assessment (CODAS) model

Multi-Criteria Decision Making (MCDM) problems have received considerable attention from various researchers over the past decades. A great variety of methods and approaches has been developed in this field. The aim of this paper is to use a new COmbinative Distance-based ASsessment (CODAS) method to handle MCDM problems for a steelmaking company in Libya. So far no literature dealing with supplier selection using the (CODAS) method in the steelmaking company in Libya has been found. The concept of this method is based on computing the Euclidean distance and the Taxicab distance in order to determine the desirability of an alternative. The Euclidean distance is used as a primary measure, while the Taxicab distance as a secondary one. The developed method was applied to a real-world case study for ranking the suppliers in the Libyan Iron and Steel Company (LISCO). An attempt in this regard could enhance a decision-making technique for selecting the best suppliers for the selected case company. The results showed that the proposed method was effectively able to select the best supplier among six alternative ones

EVALUATION OF PRODUCTION PRODUCTIVITY USING OVERALL EQUIPMENT EFFECTIVENESS

The purpose of this paper is to evaluate production productivity by the use of the Overall Equipment Effectiveness (OEE) indicator. The OEE measures how effectively an equipment is utilized. A case study at Libyan Iron and Steel Company was conducted. The results show that the OEE for production line 2 is much better than production line1. However, the average OEE for both production lines are 67.62% and 72.79% respectively. These low values of OEE are a result mainly of the reduction in the quality rate. The average quality rate for both production lines 1 and 2 are 95.47% and 92.94% respectively