A multiple channel queueing model under an uncertain environment with multiclass arrivals for supplying demands in a cement industry

In recent years, cement consumption has increased in most Asian countries, including Malaysia. There are many factors which affect the supply of the increasing order demands in the cement industry, such as traffic congestion, logistics, weather and machine breakdowns. These factors hinder smooth and efficient supply, especially during periods of peak congestion at the main gate of the industry where queues occur as a result of inability to keep to the order deadlines. Basic elements, such as arrival and service rates, that cannot be predetermined must be considered under an uncertain environment. Solution approaches including conventional queueing techniques, scheduling models and simulations were unable to formulate the performance measures of the cement queueing system. Hence, a new procedure of fuzzy subset intervals is designed and embedded in a queuing model with the consideration of arrival and service rates. As a result, a multiple channel queueing model with multiclass arrivals, (M1, M2)/G/C/2Pr, under an uncertain environment is developed. The model is able to estimate the performance measures of arrival rates of bulk products for Class One and bag products for Class Two in the cement manufacturing queueing system. For the (M1, M2)/G/C/2Pr fuzzy queueing model, two defuzzification techniques, namely the Parametric Nonlinear Programming and Robust Ranking are used to convert fuzzy queues into crisp queues. This led to three proposed sub-models, which are sub-model 1, MCFQ-2Pr, sub-model 2, MCCQESR-2Pr and sub-model 3, MCCQ-GSR-2Pr. These models provide optimal crisp values for the performance measures. To estimate the performance of the whole system, an additional step is introduced through the TrMF-UF model utilizing a utility factor based on fuzzy subset intervals and the α-cut approach. Consequently, these models help decision-makers deal with order demands under an uncertain environment for the cement manufacturing industry and address the increasing quantities needed in future

Review on the Research for Separated Continuous Linear Programming: With Applications on Service Operations

We give a review on the research for a class of optimization model—separated continuous linear programming (SCLP). SCLP takes several similar forms and can be used to find the dynamic control of a multiclass fluid network. We review the duality theory and solution methods for it. We also present application examples of SCLP on service operations

Prediction of job completion times and optimal overtime allocation for satisfying production due dates

One of the important aspects contributing to the competitiveness and success of a manufacturer is the efficient management for timely order delivery. After production orders are scheduled, there arises the need of a support tool to aid in the analysis with the available information, and to support managerial decision making which ultimately aims at on-time delivery. One way in which companies can meet due-dates of orders that are in jeopardy of being late, is to schedule overtime. This research presents a method used for 1) predict the completion times of scheduled jobs; and 2) optimizing overtime allocation when delays are foreseen. Mathematical mixed-integer linear program models are developed to represent the above problems for a tandem production line with single machine work stages. Non-operational downtime occurrences are considered in the production horizons which can be varied by work stage. Buffer areas (queues) are also included in the production system. These MILP models are solved using commercial optimizer ILOG-OPL studio. Using VBA script with OPL, a friendly interface is built in MS Excel for ease in user manipulation. The interface can also be used in production test to hypothetical “what if” questions. The models are verified using simulation. Runtime evaluation is also preformed to determine the capabilities and limitations of the models

Enabling flexibility through strategic management of complex engineering systems

”Flexibility is a highly desired attribute of many systems operating in changing or uncertain conditions. It is a common theme in complex systems to identify where flexibility is generated within a system and how to model the processes needed to maintain and sustain flexibility. The key research question that is addressed is: how do we create a new definition of workforce flexibility within a human-technology-artificial intelligence environment? Workforce flexibility is the management of organizational labor capacities and capabilities in operational environments using a broad and diffuse set of tools and approaches to mitigate system imbalances caused by uncertainties or changes. We establish a baseline reference for managers to use in choosing flexibility methods for specific applications and we determine the scope and effectiveness of these traditional flexibility methods. The unique contributions of this research are: a) a new definition of workforce flexibility for a human-technology work environment versus traditional definitions; b) using a system of systems (SoS) approach to create and sustain that flexibility; and c) applying a coordinating strategy for optimal workforce flexibility within the human- technology framework. This dissertation research fills the gap of how we can model flexibility using SoS engineering to show where flexibility emerges and what strategies a manager can use to manage flexibility within this technology construct”--Abstract, page iii

Elastic-PPQ: A two-level autonomic system for spatial preference query processing over dynamic data streams

Paradigms like Internet of Things and the most recent Internet of Everything are shifting the attention towards systems able to process unbounded sequences of items in the form of data streams. In the real world, data streams may be highly variable, exhibiting burstiness in the arrival rate and non-stationarities such as trends and cyclic behaviors. Furthermore, input items may be not ordered according to timestamps. This raises the complexity of stream processing systems, which must support elastic resource management and autonomic QoS control through sophisticated strategies and run-time mechanisms. In this paper we present Elastic-PPQ, a system for processing spatial preference queries over dynamic data streams. The key aspect of the system design is the existence of two adaptation levels handling workload variations at different time-scales. To address fast time-scale variations we design a fine regulatory mechanism of load balancing supported by a control-theoretic approach. The logic of the second adaptation level, targeting slower time-scale variations, is incorporated in a Fuzzy Logic Controller that makes scale in/out decisions of the system parallelism degree. The approach has been successfully evaluated under synthetic and real-world datasets

Optimal admission control in tandem and parallel queueing systems with applications to computer networks

Modern computer networks require advanced, efficient algorithms to control several aspects of their operations, including routing data packets, access to secure systems and data, capacity and resource allocation, task scheduling, etc. A particular class of problems that arises frequently in computer networks is that of admission and routing control. Two areas where admission control problems are common are traffic control and authentication procedures. This thesis focuses on developing tools to solve problems in these areas. We begin the thesis with a brief introductory chapter describing the problems we will be addressing. Then, we follow this with a review of the relevant literature on the problems we study and the methodologies we use. Then, we have the main body of the dissertation, which is divided into three parts, described below. In the first part, we analyze a problem related to data routing in a network. Specifically, we study the problem of admission control to a system of two stations in tandem with finite buffers, Poisson arrivals to the first station, and exponentially distributed service times at both stations. We assume costs are incurred either when a customer is rejected at the time of arrival to the first station or when the second station is full at the time of service completion at the first station. We propose a Markov decision process formulation for this problem. Then, we use this model to show that, when one of the buffers has size one, the structure of the optimal policy is threshold and that only two particular policies can be optimal. We provide the exact optimality thresholds for small systems. For larger systems, we formulate heuristic policies and use numerical experiments to show that these policies achieve near-optimal performance. For the second part of this thesis, we investigate the system described above in a more general case, where the capacity of the buffers at both station is equal, finite and arbitrary. We focus on two specific, extremal policies, which we call the Prudent and Greedy policies. We derive a closed-form expression for the long-run average reward under the Prudent policy and provide a necessary and sufficient threshold condition for it to be optimal. For the Greedy policy, we give a matrix-analytic solution for the long-run average reward and provide a sufficient condition for it to be optimal. We also prove that it is always optimal to admit customers in the states where the Prudent policy admits customers. Next, we use an example to illustrate that the optimal policy can have a complicated form. Finally, we propose two heuristic policies and use numerical experiments to show that they perform much better than the Prudent and Greedy policies, and in fact, achieve near-optimal performance. In the third and final part of this dissertation, we shift our attention to a different admission and routing control problem. We study a centralized system where requests for authentication arrive from different users. The system has multiple authentication methods available and a controller must decide how to assign a method to each request. We consider three different performance measures: usability, operating cost, and security. First, we model the problem using a cost-based approach, which assigns a cost to each measure of performance. Under this approach, we find that if each authentication method has infinitely many servers the optimal policy is static and deterministic. On the other hand, if there is one method that has finite capacity and the rest have infinitely many servers, we show that the optimal policy is of trunk reservation form. Then, we model the problem using a constraint-based approach, which assumes hard constraints on some of the measures of performance. We show that if each method has infinitely many servers, the optimal policy is static and randomized. While, if one method has finite capacity and the rest have infinitely many servers, we show that the optimal policy has a 2-randomized trunk reservation form. Finally, we illustrate how to use our results to construct an efficient frontier of non-dominated solutions. We end this dissertation with a short recapitulation of our main contributions and a discussion on potential avenues for future research.Ph.D

Classification of the Existing Knowledge Base of OR/MS Research and Practice (1990-2019) using a Proposed Classification Scheme

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordOperations Research/Management Science (OR/MS) has traditionally been defined as the discipline that applies advanced analytical methods to help make better and more informed decisions. The purpose of this paper is to present an analysis of the existing knowledge base of OR/MS research and practice using a proposed keywords-based approach. A conceptual structure is necessary in order to place in context the findings of our keyword analysis. Towards this we first present a classification scheme that relies on keywords that appeared in articles published in important OR/MS journals from 1990-2019 (over 82,000 articles). Our classification scheme applies a methodological approach towards keyword selection and its systematic classification, wherein approximately 1300 most frequently used keywords (in terms of cumulative percentage, these keywords and their derivations account for more than 45% of the approx. 290,000 keyword occurrences used by the authors to represent the content of their articles) were selected and organised in a classification scheme with seven top-level categories and multiple levels of sub-categories. The scheme identified the most commonly used keywords relating to OR/MS problems, modeling techniques and applications. Next, we use this proposed scheme to present an analysis of the last 30 years, in three distinct time periods, to show the changes in OR/MS literature. The contribution of the paper is thus twofold, (a) the development of a proposed discipline-based classification of keywords (like the ACM Computer Classification System and the AMS Mathematics Subject Classification), and (b) an analysis of OR/MS research and practice using the proposed classification

Throughput and Yield Improvement for a Continuous Discrete-Product Manufacturing System

A seam-welded steel pipe manufacturing process has mainly four distinct major design and/or operational problems dealing with buffer inventory, cutting tools, pipe sizing and inspection-rework facility. The general objective of this research is to optimally solve these four important problems to improve the throughput and yield of the system at a minimum cost. The first problem of this research finds the optimal buffer capacity of steel strip coils to minimize the maintenance and downtime related costs. The total cost function for this coil feeding system is formulated as a constrained non-linear programming (NLP) problem which is solved with a search algorithm. The second problem aims at finding the optimal tool magazine reload timing, magazine size and the order quantity for the cutting tools. This tool magazine system is formulated as a mixed-integer NLP problem which is solved for minimizing the total cost. The third problem deals with different type of manufacturing defects. The profit function of this problem forms a binary integer NLP problem which involves multiple integrals with several exponential and discrete functions. An exhaustive search method is employed to find the optimum strategy for dealing with the defects and pipe sizing. The fourth problem pertains to the number of servers and floor space allocations for the off-line inspection-rework facility. The total cost function forms an integer NLP structure, which is minimized with a customized search algorithm. In order to judge the impact of the above-mentioned problems, an overall equipment effectiveness (OEE) measure, coined as monetary loss based regression (MLBR) method, is also developed as the fifth problem to assess the performance of the entire manufacturing system. Finally, a numerical simulation of the entire process is conducted to illustrate the applications of the optimum parameters setting and to evaluate the overall effectiveness of the simulated system. The successful improvement of the simulated system supports this research to be implemented in a real manufacturing setup. Different pathways shown here for improving the throughput and yield of industrial systems reflect not only to the improvement of methodologies and techniques but also to the advancement of new technology and national economy

Revenue Management, Operations Research and Artificial Intelligence: Tools Application as Support to Decision Makers in Parking Industry

Abstract. The successful application of Revenue Management (RM) Resumen. Revenue Management, Investigación de operaciones e inteligencia artificial: herramientas para la toma de decisiones en la industria de estacionamiento. El uso acertado del Revenue Management (RM) al problema del control de inventario del asiento (SIC) de líneas aéreas ha motivado interés en considerar incorporar las herramientas de RM en la industria de estacionamiento. El objetivo de este trabajo, es motivar estudios orientados al desarrollo de las metodologías que permitan optimimizar el beneficio en las instalaciones del estacionamiento mediante el uso de técnicas de revenue (RM), investigación de operaciones (O) e inteligencia artificial (AI). Para lograr este objetivo, primero, se desarrolla un breve resumen sobre todas esas técnicas y algunas aplicaciones en la industria del estacionamiento, seguido por algunas ideas orientadas al desarrollo de metodologías para alcanzar máximo beneficio, considerando como relevante la inclusión las técnicas de AI como herramienta cualitativa en el pronóstico de la demanda y la optimización de tarifas. Palabras clave: revenue management, investigación de operaciones, inteligencia artificial, pronóstico de demanda, optimización de tarifas, industria de estacionamient