Application of Learning Curves in Operations Management Decisions

In the time of industry 4.0 and big data, methods which are based on the collection and the processing of a large amount of data in order to support managerial decisions have outstanding significance. The learning curve theory pertains to these methods. The purpose of this paper is to explore some application possibilities of the classical learning curve in manufacturing and service operations. The learning effect assumes that as the quantity of units manufactured increases, the time needed to produce an individual unit decreases. The function describing this phenomenon is the learning curve. Various learning curves have been developed and applied in the area of production economics and much research studies the significance of the learning effect in management decisions. This study summarizes the main learning curve models and demonstrates how learning can be considered in three classical areas of operations management. First, the calculation of economic manufacturing quantity in the presence of learning is studied. Next, the effect of learning in break-even analysis and assembly line balancing is explored. The results show that with the consideration of the learning effect, calculations become more complex and require greater efforts, but the application of the learning curve concept can provide valuable insight both at operational and strategic levels

Economic Design of X-bar control chart using particle swarm optimization

Control chart is the most widely used tools for statistical process control. For detecting shift in process mean, chart is the simplest and most commonly used. Control chart should be designed economically in order to achieve minimum quality control costs. The major function of control chart is to detect the occurrence of assignable causes so that the necessary corrective action can be taken before a large quantity of nonconforming product is manufactured. The control chart dominates the use of any other control chart technique if quality is measured on a continuous scale. The design of a control chart refers to the selection of three parameters i.e., sample size, width of control limit, and interval between samples. Economic design of control chart has gained considerable importance in providing better quality of end products to customer at less cost. In the present work, a computer programme in C language based on a non-traditional optimization technique namely particle swarm optimization has been developed for the economic design of the control chart giving the optimum values of the sample size, sampling interval and width of control limits such that the expected total cost per hour is minimized. The results obtained are found to be better compared to that reported in the literature

Contrôle de la production et de la qualité des systèmes manufacturiers non-fiables

RÉSUMÉ Dans les trois dernières décennies, plusieurs politiques de commande optimale stochastique ont été développées pour contrôler les systèmes de production à flux continu sujets aux phénomènes aléatoires. Les opérations après production tel que le transport et l’inspection de la qualité ont toutefois été peu considérées dans ces politiques. Ce mémoire de maîtrise s’intéresse plus particulièrement au problème de commande optimale stochastique des systèmes de production par lots dans un contexte de transport et de contrôle de la qualité par échantillonnage. Ces systèmes sont caractérisés par une dynamique complexe vu les multiples décisions de production et de qualité considérées et par un niveau stochastique élevé où les pannes, les réparations et la qualité effective du processus sont aléatoires. Les systèmes de production par lot dans tels contextes ne peuvent pas être représentés par les modèles de flux continu classiques. Dans la première phase de ce mémoire, nous avons étudié le cas des systèmes de production par lots, non-fiables et parfaits, avec un délai de transport. Le problème est formulé sous forme d’un modèle de programmation dynamique stochastique. Les conditions optimales décrites par les équations Hamilton-Jacobi-Bellman sont résolus numériquement. Ensuite, une loi de commande stochastique sous-optimale basée sur une combinaison de la politique de contrôle à seuil critique modifiée et une politique du lot économique de production est ainsi obtenue. Une approche expérimentale basée sur la simulation est appliquée pour déterminer les valeurs optimales des paramètres de la loi de commande quelque soit la distribution des temps de pannes et de réparation. Dans la deuxième phase de travail, nous avons intégré le contrôle de la qualité en supposant que le système produit un pourcentage aléatoire d’items défectueux. Le problème est décrit par un modèle de programmation dynamique stochastique. Une heuristique de commande est proposée par extension de la politique de commande obtenue dans la première phase en prenant en compte les effets de l’imperfection de la production sur l’inventaire et sur la satisfaction de la demande. Des analyses de sensibilité approfondies permettent d’observer les impacts des différents paramètres de coût et de qualité sur les paramètres optimaux de la politique de commande de la production.----------ABSTRACT In the past three decades, many stochastic optimal control policies have been developed to control the continuous-flow production systems to meet stochastic phenomena. However, operations such as transportation and quality inspection had been little studied in these policies. This master's thesis focuses on the stochastic optimal control problem of batch production systems in the context of transportation and quality control by sampling. These systems are characterized by a complex dynamic due to the many considered decisions of production and quality and by a high stochastic level where all breakdowns, repairs and process imperfection are random. The batch production systems in such contexts cannot be represented by the classical continuous-flow models. In the first part of the master's project, we studied the case of unreliable and perfect batch production systems with a transportation delay. The problem is formulated as a stochastic dynamic programming model. The optimality conditions described by Hamilton-Jacobi-Bellman equations are solved numerically. Then, a suboptimal stochastic control policy based on a combination of a modified hedging point policy and a state dependent economic manufacturing quantity policy is obtained. A simulation-based experimental approach is used to determine the optimal values of the control policy parameters when the failure and repair times follow general distributions. In the second part of the project, we integrated the quality control issue assuming that the system generates a random proportion of defective items. The problem is described by a stochastic dynamic programming model. A heuristic control policy is proposed by extending the control policy obtained in the first part, taking into account the effects of imperfect quality items on the inventory and demand satisfaction. A thorough sensitivity analysis shows interesting behaviours about the impact of various cost and quality parameters on the optimal parameters of the production control policy. Finally, some extensions of the two obtained control policies are proposed by integrating the concept of dynamic lot sizing and a control policy for inspection personnel management. The experiments have shown that these both extensions lead always to economic gains. Other extensions and further research are also discussed

IoT-MQTT based denial of service attack modelling and detection

Internet of Things (IoT) is poised to transform the quality of life and provide new business opportunities with its wide range of applications. However, the bene_ts of this emerging paradigm are coupled with serious cyber security issues. The lack of strong cyber security measures in protecting IoT systems can result in cyber attacks targeting all the layers of IoT architecture which includes the IoT devices, the IoT communication protocols and the services accessing the IoT data. Various IoT malware such as Mirai, BASHLITE and BrickBot show an already rising IoT device based attacks as well as the usage of infected IoT devices to launch other cyber attacks. However, as sustained IoT deployment and functionality are heavily reliant on the use of e_ective data communication protocols, the attacks on other layers of IoT architecture are anticipated to increase. In the IoT landscape, the publish/- subscribe based Message Queuing Telemetry Transport (MQTT) protocol is widely popular. Hence, cyber security threats against the MQTT protocol are projected to rise at par with its increasing use by IoT manufacturers. In particular, the Internet exposed MQTT brokers are vulnerable to protocolbased Application Layer Denial of Service (DoS) attacks, which have been known to cause wide spread service disruptions in legacy systems. In this thesis, we propose Application Layer based DoS attacks that target the authentication and authorisation mechanism of the the MQTT protocol. In addition, we also propose an MQTT protocol attack detection framework based on machine learning. Through extensive experiments, we demonstrate the impact of authentication and authorisation DoS attacks on three opensource MQTT brokers. Based on the proposed DoS attack scenarios, an IoT-MQTT attack dataset was generated to evaluate the e_ectiveness of the proposed framework to detect these malicious attacks. The DoS attack evaluation results obtained indicate that such attacks can overwhelm the MQTT brokers resources even when legitimate access to it was denied and resources were restricted. The evaluations also indicate that the proposed DoS attack scenarios can signi_cantly increase the MQTT message delay, especially in QoS2 messages causing heavy tail latencies. In addition, the proposed MQTT features showed high attack detection accuracy compared to simply using TCP based features to detect MQTT based attacks. It was also observed that the protocol _eld size and length based features drastically reduced the false positive rates and hence, are suitable for detecting IoT based attacks

The multi-products EMQ model

Since the introduction of the classical economic manufacturing quantities (EMQ) concept early in the twentieth century, many variants of the single-product EMQ model have been solved. These single-product EMQ models usually suppose the product is produced cyclically every T time units. This dissertation examines the general cyclical model (GCM), a generalization of the single-product EMQ. In the GCM n≥2 products are produced on a single facility according to cyclical schedules. The GCM, unlike some variants of the multiproducts, single-facility problem, permits each product i to be produced every Ti time units where it is not necessary that Ti = Tj if i ≠ j. However, a schedule of n products must be feasible; that is, only one product may occupy the facility at a time. Thus, the objective of the GCM is to find cycle times {Ti} that minimize the inventory and production costs subject to the restriction that the schedule is feasible. To mathematically address this feasibility problem, delay times {di} are introduced where di is the time at which the first use period of product i begins. Then conditions are given that are both necessary and sufficient to assure that a specified schedule {Ti, di} of n products is feasible. These feasibility conditions remove a major handicap suffered by previous researcher. Then delay-independent necessary and sufficient feasibility conditions are derived for the two-products and the three-products case of the GCM. Also, delay-independent necessary feasibility conditions are derived for the four-products case. Finally, an efficient algorithm is developed that finds feasible optimal schedules for the n-products model

Extensions of semiparametric expectile regression

Expectile regression can be seen as an extension of available (mean) regression models as it describes more general properties of the response distribution. This thesis introduces to expectile regression and presents new extensions of existing semiparametric regression models. The dissertation consists of four central parts. First, the one-to-one-connection between expectiles, the cumulative distribution function (cdf) and quantiles is used to calculate the cdf and quantiles from a fine grid of expectiles. Quantiles-from-expectiles-estimates are introduced and compared with direct quantile estimates regarding e�ciency. Second, a method to estimate non-crossing expectile curves based on splines is developed. Also, the case of clustered or longitudinal observations is handled by introducing random individual components which leads to an extension of mixed models to mixed expectile models. Third, quantiles-from-expectiles-estimates in the framework of unequal probability sampling are proposed. All methods are implemented and available within the package expectreg via the open source software R. As fourth part, a description of the package expectreg is given at the end of this thesis

Dynamic Lot Sizing and Scheduling in a Multi-Item Production System

In this research, algorithms are developed to address the problem of dynamic lot sizing and scheduling in a single level (or single operation) production system. This research deviates from previous research in this area in that it does not have the kind of assumptions regarding the real world production system that normally were made to reduce the complexity of the problem. Specifically, this research explicitly considers finite capacity, multiple items, known deterministic dynamic demand, sequence dependent setup times and setup costs, setup carryover and variable backlogging. The objective is to simultaneously determine the lot size and the sequence of production runs in each period to minimize the sum of setup, inventory, and backlogging costs. The research here is motivated by observations of a real world production system that has a highly automated operation with sequence dependent setup times. For problems of this kind, optimal solution algorithms do not yet exist and, therefore, heuristic solution algorithms are of interest. Two distinct approaches are proposed to address the problem. The first is a greedy approach that eliminates setups while potential savings are greater than the increase in inventory or backlogging costs incurred. The second approach solves the much easier single item problem optimally for each item and then adapts the solution to account for capacity constraints. An intelligent modification to the second approach is also tried where a overload penalty is used between successive runs of the single product optimization algorithms A common component of each approach is a dynamic programming algorithm implemented to determine the optimal sequence of production within each period and across the scheduling horizon. The addition of sequence dependent considerations introduces a traveling salesman type problem to the lot sizing and sequencing decisions. The algorithms have been tested over several combinations of demand and inventory related cost factors. Specifically the following factors at two levels each have been used: problem size, demand type, utilization, setup cost, backlogging cost, and backlogging limit. The test results indicate that, while the performance of the proposed algorithms appear to be affected by all the factors listed above, overall the regeneration algorithm with overload penalty outperforms all of the other algorithms at all factor level combinations. In summary, the contribution of this research has been the development of three new algorithms for dynamic lot sizing and scheduling of multiple items in a single level production system. Through extensive statistical analysis, it has been shown that these algorithms, in particular the regeneration algorithm with overload penalty , outperform the conventional scheduling techniques such as no lot sizing and economic manufacturing quantity

Manufacturing lot size and product distribution problem with rework, outsourcing and discontinuous inventory distribution policy

Product quality, timely delivery, and lower cost are critical operational goals to nowadays manufacturers, and company managements constantly seek different approaches to achieve these goals in order to stay competitive in turbulent global markets. This study investigates a practical manufacturing lot size and distribution problem with rework, outsourcing, and discontinuous inventory distribution policy. In real manufacturing environments, due to different controllable and/or uncontrollable factors, production of the nonconforming products is inevitable. Careful inspection into identifying nonconforming items and instant correction of the defects are considered in the proposed study. In additions, due to the limited production capacity in real manufacturing environments, sometimes, outsourcing can be used to cope with occasional unsteady demands, or running short of in-house capacity, to allow the management to maintain a smooth operation and/or shorten the production cycle length. Furthermore, in vendor-buyer integrated supply chains, multi-delivery policy is often considered for distributing finished products to customers. Motivated by the aforementioned practical situations, this study develops a mathematical model to explicitly investigate such a manufacturing lot-size and product distribution problem. Optimization techniques are employed to solve the problem and a numerical example is provided to show the applicability of our research results

Determining replenishment lot size and shipment policy for an extended EPQ model with delivery and quality assurance issues

AbstractThis paper derives the optimal replenishment lot size and shipment policy for an Economic Production Quantity (EPQ) model with multiple deliveries and rework of random defective items. The classic EPQ model assumes a continuous inventory issuing policy for satisfying demand and perfect quality for all items produced. However, in a real life vendor–buyer integrated system, multi-shipment policy is practically used in lieu of continuous issuing policy and generation of defective items is inevitable. It is assumed that the imperfect quality items fall into two groups: the scrap and the rework-able items. Failure in repair exists, hence additional scrap items generated. The finished items can only be delivered to customers if the whole lot is quality assured at the end of rework. Mathematical modeling is used in this study and the long-run average production–inventory-delivery cost function is derived. Convexity of the cost function is proved by using the Hessian matrix equations. The closed-form optimal replenishment lot size and optimal number of shipments that minimize the long-run average costs for such an EPQ model are derived. Special case is examined, and a numerical example is provided to show its practical usage