Operations Research Models Research Articles

Supply Chain Scenario Modeler: A Holistic Executive Decision Support Solution

McKesson is America’s oldest and largest healthcare services company. IBM Research developed an innovative scenario modeling and analysis tool, supply chain scenario modeler (SCSM), for McKesson to optimize its end-to-end pharmaceutical supply chain policies. Through integrated operations research (OR) models, SCSM optimizes the distribution network, supply flow, inventory, and transportation policies, and quantifies the impacts of changes on financial, operational, and environmental metrics. The modeling work spawned a roadmap of projects with quantified opportunities, including a new air freight supply chain path, and provided new insights that have been critical to improving McKesson’s performance as a pharmaceutical industry leader. A structured data model supporting the OR models has provided a basis for additional improvement projects. The model directly links OR modeling results to a detailed profit-and-loss statement by product category for the different supply chain paths that McKesson uses. Since this effort began in 2009, McKesson Pharmaceutical division has reduced its committed capital by more than $1 billion.

ALTERNATIVE ANALYSIS OF FINITE-TIME PROBABILITY DISTRIBUTIONS OF RENEWAL THEORY

We introduce a level-crossing analysis of the finite time-t probability distributions of the excess life, age, total life, and related quantities of renewal processes. The technique embeds the renewal process as one cycle of a regenerative process with a barrier at level t, whose limiting probability density function leads directly to the time-t quantities. The new method connects the analysis of renewal processes with the analysis of a large class of stochastic models of Operations Research. Examples are given.

An Integrated CP/OR Method for Optimal Control of Modular Hybrid Systems

This paper concerns the optimal control of modular hybrid systems synchronized by shared variables. Instead of synchronizing the discrete dynamics of the system into one global mode before optimization, Constraint Programming (CP) is used to model the discrete dynamics of each modular system separately. Integrated in the CP solver are also classic Operations Research (OR) models in the form of Nonlinear Programs (NLPs) approximating the continuous dynamics of the system. Using CP considerably decreases the number of NLPs which must be solved, compared to that of using a traditional mixed integer nonlinear programming approach.

Evaluating the performance of accounting education departments in technical and vocational schools of SAMA organization

The mathematical models of operations research are being used nowadays to evaluate the performance and efficiency of university systems. These models consider a series of educational, research and service activities as input and output parameters and calculate the ratio of the sum of weighted outputs to the sum of weighted inputs in proportion to the importance and effectiveness of each of those indicators as the performance of university systems. The main objective of this research was to ‘evaluate the performance of accounting education departments in technical and vocational schools of SAMA organization usingthe mathematical model of Data Envelopment Analysis (DEA). In order to implement the mathematical model of DEA in this study, the required educational, research and service quantitative data were collected from 8 accounting education departments in technical and vocational schools of SAMA organization across Tehran province. After the implementation of model, the expected performance of each department was calculated and the efficient departments, those who had an output proportionate to their input, were also identified. The efficient departments were also ranked using the Anderson-Peterson method in both input-oriented and output-oriented situations. Results indicated that some of the departments being evaluated in this study were inefficient in input-oriented as well as output-oriented situations.

Sustainable management of water resources in Prokletije region

The main goal of this paper is the upgrading of classic economic analyses of optimal concept selection of small hydro development. Techno-economic small hydro system needs to be environmentally friendly and socially acceptable solution. Environmental and social parameters are quantified by Delphi method. They are results of Environmental and Social impact assessment study of the project. Environmental and social parameters are incorporated in the techno-economic analyses of the optimal sustainable concept of small hydro development, by Electre method, as a possible multi-attributive operational research model. System of small hydro power plants optimization for Prokletije streams catchments area is case study where the developed model is tested and proved. Economic cost and total investment of fifteen possible small hydro power plants have been upgraded with quantified environmental and social parameters and analyzed in the function of sustainable economic development of Prokletije region. The main contribution of this research is quantification of social and environmental parameters and their simultaneous and equal incorporation with technical and economical parameters into mathematical model for decision making process, which enables sustainability.

A Genetic Algorithm-based Study on the Optimization of Scheduling of Project Development

The thesis is a study on the optimization of scheduling of project development, with the problem stemming from Clash of Clans (COC)– a mobile phone-oriented network game developed by Supercell. Based on COC’s game environment, the author, through the analysis and evaluation of optimization tools, proposes the parameters setting that fits the game and provides the game strategy schemes needed by players, i.e. they can define the sequence and time arrangement of performing a game task via the optimization algorithm, so as to significantly reduce “redundant game time”, speed up the progress of games and obtain better game experience. The objective of the study is to analyze the needs of different players on the basis of becoming familiar with the game environment and to put forward optimal game strategy schemes in addition to meeting corresponding required conditions according to the game stage each player is in. Apart from establishing an operations research model that meet the goal of optimization on the foundation of game environment, the author adopts the Genetic Algorithm (GA) to undertake guiding search for feasible schemes that meet the required conditions so as to achieve optimal solutions. To explain the practical effects of the method in detail, the thesis will show and use the algorithm with a game case and demonstrate the effects and efficiency of the algorithm with a large quantity of data.

Models for Effective Deployment and Redistribution of Bicycles Within Public Bicycle-Sharing Systems

We develop practical operations research models to support decision making in the design and management of public bicycle-sharing systems. We develop a network flow model with proportionality constraints to estimate the flow of bicycles within the network and the number of trips supported, given an initial allocation of bicycles at each station. We also examine the effectiveness of periodic redistribution of bicycles in the network to support greater flow, and the impact on the number of docks needed. We conduct our numerical analysis using transit data from train operators in Singapore. Given that a substantial proportion of passengers in the train system commute a short distance—more than 16% of passengers alight within two stops from the origin—this forms a latent segment of demand for a bicycle-sharing program. We argue that for a bicycle-sharing system to be most effective for this customer segment, the system must deploy the right number of bicycles at the right places, because this affects the utilization rate of the bicycles and how bicycles circulate within the system. We also identify the appropriate operational environments in which periodic redistribution of bicycles will be most effective for improving system performance.

Anthony G. Hopwood: Academic Iconoclast and Entrepreneur 1944–2010

A nthony Hopwood was born on May 18, 1944 in Stoke-on-Trent in England and died on May 8, 2010 in Marlow, England. He was a pioneer in behavioral accounting research. He contributed to that area with both his research and as the founding editor of Accounting, Organizations and Society (AOS), the leading behavioral accounting research journal, the reason for which he likely is best well known by scholars internationally, both in and outside of accounting. He held that position for 35 years. As Zeff (2008, 3) described it, in his remarks introducing Anthony at his Hall of Fame induction:

Special section on behavioral considerations in developing and applying operations research models

Special section on behavioral considerations in developing and applying operations research models

Robust Capacity Expansion Model Integrating the Perspectives of Production Economics and Capacity Regret

A typical production economics theory provides some factors which should be considered in capacity expansion; for example, a production function estimates the production possibility set (PPS) and satisfies the law of diminishing marginal returns (DMR). However, an operations research (OR) model, which usually focuses on addressing demand fluctuation for cost minimization and assumes a constant marginal productivity (MP), may overestimate the capacity level, thus leading to an infeasible capacity plan. This study proposes a novel two-stage model which integrates the perspectives of production economics and operations research (OR) to solve the capacity expansion problem (CEP). The first stage, which addresses the production economics issue, estimates the production possibility set (PPS) limiting production behavior and uses the directional distance function (DDF) to find the directional marginal productivity (DMP) towards marginal profit maximization. The second stage, which addresses demand fluctuation, employs the minimax regret strategy (MMR) balancing capacity shortage and capacity surplus to generate a robust capacity expansion. The results of a numerical illustration validating the proposed approach suggest a stochastic programming model for robust capacity planning, and justify the typical OR-type approach where the two major factors affecting capacity decisions are demand variation and cost structure rather than the DMR effect.

Assimilating AMSU-A Radiances in the TC Core Area with NOAA Operational HWRF (2011) and a Hybrid Data Assimilation System: Danielle (2010)

Abstract A regional hybrid variational–ensemble data assimilation system (HVEDAS), the maximum likelihood ensemble filter (MLEF), is applied to the 2011 version of the NOAA operational Hurricane Weather Research and Forecasting (HWRF) model to evaluate the impact of direct assimilation of cloud-affected Advanced Microwave Sounding Unit-A (AMSU-A) radiances in tropical cyclone (TC) core areas. The forward components of both the gridpoint statistical interpolation (GSI) analysis system and the Community Radiative Transfer Model (CRTM) are utilized to process and simulate satellite radiances. The central strategies to allow the use of cloud-affected radiances are (i) to augment the control variables to include clouds and (ii) to add the model cloud representations in the observation forward models to simulate the microwave radiances. The cloudy AMSU-A radiance assimilation in Hurricane Danielle's (2010) core area has produced encouraging results with respect to the operational cloud-cleared radiance preprocessing procedures used in this study. Through the use of the HVEDAS, ensemble covariance statistics for a pseudo-AMSU-A observation in Danielle's core area show physically meaningful error covariances and statistical couplings with hydrometeor variables (i.e., the total-column condensate in Ferrier microphysics). The cloudy radiance assimilation in the TC core region (i.e., ASR experiment) consistently reduced the root-mean-square errors of the background departures, and also generally improved the forecasts of Danielle's intensity as well as the quantitative cloud analysis and prediction. It is also indicated that an entropy-based information content quantification process provides a useful metric for evaluating the utility of satellite observations in hybrid data assimilation.

Evaluation of Blasting Patterns Using Operational Research Models / Ocena Planów Prac Strzałowych W Oparciu O Metody Badań Operacyjnych

Abstract Blasting is one of the most important operations, which has a great technical and economical effect on the mining projects. Criteria such as fragmentation (operation ultimate objective) and ground vibration, flyrock, airblast, etc. (operation side effects) should be considered in the assessment of blasting operation. A suitable pattern should be able to provide both reasonable (required) fragmentation and blasting side effects. In order to evaluate blasting performance, operational research models such as multi attribute decision making technique (MADM) can be applied. Technique for order preference by similarity to an ideal solution (TOPSIS), a branch of MADM, is a strong method for pattern ranking. The other quantitative method which is applied in the evaluation of systems’ efficiency is data envelopment analysis (DEA) model. In this paper, an attempt has been made to develop a new hybrid MADM model for selecting the most appropriate blasting pattern in Chadormalu iron mine, Iran. In this regard, DEA method was utilized to select the efficient blast patterns thereafter TOPSIS was used to recognize the most suitable pattern amongst the selected patterns by DEA method. It was concluded that the patterns J, G and B are the most appropriate patterns for blasting operations in the Chadormalu iron mine.

Special Issue Dedicated to the Memory of Paul A. Jensen

As theory and practice merge, we have seen dramatic advances in the design and use of analytic methods across all subdisciplines of operations research, industrial engineering and business-related fields. This has been accompanied by an explosive growth in computer power, which has allowed for ever-wider implementation and the solution of very large-scale problems. Nevertheless, much analysis in industry, government, and academia occurs with commonly available software such as spreadsheets running on personal computers with limited processing speeds. Because of the pervasiveness of spreadsheets, if we succeed in embedding our analytical tools available in them and in making our success stories known, we can reach a wider audience. For similar reasons, spreadsheet computation can strengthen the teaching of mathematical models and methods, particularly because it can provide a standard interface across topics ranging from optimization and decision analysis to queueing, simulation, and statistics. During his 39-year career at The University of Texas in the Cockrell School of Engineering, Paul Jensen was in the vanguard of developing and implementing computer-based tools to flatten the learning curve for those wishing to solve problems arising in operations research, industrial engineering, operations management, and engineering economics. His first of several co-authored textbooks, Network Flow Programming, broke new ground in its comprehensive coverage of single-commodity network models and algorithms, including all pure and generalized formulations with both convex and concave costs. What was unique about this award-winning book was its algorithmic details, computational flowcharts, and packaged software written in Basic for minicomputers. At the dawning of microcomputers in the early 1980s, Paul published Microsolve/Operations Research, the first compendium of OR computer programs specifically adapted for PCs. Soon after, the Student’s Guide to Operations Research appeared, surveying OR topics and related computational methods in an easyto-read style. For the remainder of his career, Paul designed, programmed and distributed a vast collection of Microsoft Excel add-ins for every aspect of OR, IE, MS, OM and more. These add-ins have been used by hundreds of thousands of analysts, and are freely available at http://www.me.utexas.edu/ ~jensen/ORMM/. This site was originally created to support the text Operations Research Models and Methods but has grown in content much beyond its initial scope. In early 2005, Paul was asked to undertake a full revision of the required undergraduate course, Engineering Economic Analysis, offered by the Mechanical Engineering Department at The University of Texas. Although just retired, he relished the challenge and jumped into the project with the enthusiasm of a new faculty member. Because he had taught the course in the traditional manner for 30 years, he understood what worked and what the potential was for engaging the students in a more interconnected way. Rather than digitizing his notes and problem sets, he set out to develop a comprehensive online course using a variety of media including videos, Excel addins, interactive quizzes, embedded calculators, and flash presentations. What emerged was a completely new course renamed Engineering Finance, along with the website http://www.me.utexas.edu/~me353/ to house the material. Paul was passionate about OR education, and in 2007 he received the INFORMS Prize for the Teaching of ORMS Practice for his work highlighted here. The breadth and scope of his contributions continue to resonate deeply within the INFORMS community

A Linear Programming Approach to Nonstationary Infinite-Horizon Markov Decision Processes

Nonstationary infinite-horizon Markov decision processes (MDPs) generalize the most well-studied class of sequential decision models in operations research, namely, that of stationary MDPs, by relaxing the restrictive assumption that problem data do not change over time. Linear programming (LP) has been very successful in obtaining structural insights and devising solution methods for stationary MDPs. However, an LP approach for nonstationary MDPs is currently missing. This is because the LP formulation of a nonstationary infinite-horizon MDP includes countably infinite variables and constraints, and research on such infinite-dimensional LPs has traditionally faced several hurdles. For instance, duality results may not hold; an extreme point may not be a basic feasible solution; and in the context of a simplex algorithm, a pivot operation may require infinite data and computations, and a sequence of improving extreme points need not converge in value to optimal. In this paper, we tackle these challenges and establish (1) weak and strong duality, (2) complementary slackness, (3) a basic feasible solution characterization of extreme points, (4) a one-to-one correspondence between extreme points and deterministic Markovian policies, and (5) we devise a simplex algorithm for an infinite-dimensional LP formulation of nonstationary infinite-horizon MDPs. Pivots in this simplex algorithm use finite data, perform finite computations, and generate a sequence of improving extreme points that converges in value to optimal. Moreover, this sequence of extreme points gets arbitrarily close to the set of optimal extreme points. We also prove that decisions prescribed by these extreme points are eventually exactly optimal in all states of the nonstationary infinite-horizon MDP in early periods.

A stochastic model to optimize forecast and fulfillment of green roof demand

Increasing demand for green roofs in cities has generated pressure on the landscape industry to supply materials in a timely manner. A demand-responsive optimal-inventory model (DOM) is developed to predict future demand and optimize contractor inventories of green roof components. Unlike other operations research models, no assumption is made on demand distribution. Instead, a stochastic model is constructed based on building owners’ behavior and two pertinent extrinsic factors: changing government incentive policy and increasing energy price. When actual demand exceeds predicted value, undesirable backorder and inventory shortfall emerge. The reverse scenario creates superfluous inventories and elevated holding cost. As managers have scanty market data for the relatively new products, they use current demand to predict next-period demand. The findings suggest adopting the safe lower limit of demand fluctuations to prevent overstocking. The numerical experiment finds the greatest demand fluctuations in the first year of the government reimbursement program. The sensitivity analysis indicates sensitive response of green-roof accumulated demand to extrinsic influences. Managers could regularly update demand forecast and inventory strategy to dovetail with changing market and policy environment. The stochastic model could capture most of the important real-world features of the green-roof market by relatively simple mathematical expressions.

Cut-off Grade Optimization for Maximizing the Output Rate

In the open-pit mining, one of the first decisions that must be made in production planning stage, after completing the design of final pit limits, is determining of the processing plant cut-off grade. Since this grade has an essential effect on operations, choosing the optimum cut-off grade is of considerable importance. Different goals may be used for determining optimum cut-off grade. One of these goals may be maximizing the output rate (amount of product per year), which is very important, especially from marketing and market share points of view. Objective of this research is determining the optimum cut-off grade of processing plant in order to maximize output rate. For performing this optimization, an Operations Research (OR) model has been developed. The object function of this model is output rate that must be maximized. This model has two operational constraints namely mining and processing restrictions. For solving the model a heuristic method has been developed. Results of research show that the optimum cut-off grade for satisfying pre-stated goal is the balancing grade of mining and processing operations, and maximum production rate is a function of the maximum capacity of processing plant and average grade of ore that according to the above optimum cut-off grade must be sent to the plant.

About Our Authors

About Our Authors

Examining the strategy development process through the lens of complex adaptive systems theory

The development of strategy remains a debate for academics and a concern for practitioners. Published research has focused on producing models for strategy development and on studying how strategy is developed in organisations. The Operational Research literature has highlighted the importance of considering complexity within strategic decision making; but little has been done to link strategy development with complexity theories, despite organisations and organisational environments becoming increasingly more complex. We review the dominant streams of strategy development and complexity theories. Our theoretical investigation results in the first conceptual framework which links an established Strategic Operational Research model, the Strategy Development Process model, with complexity via Complex Adaptive Systems theory. We present preliminary findings from the use of this conceptual framework applied to a longitudinal, in-depth case study, to demonstrate the advantages of using this integrated conceptual model. Our research shows that the conceptual model proposed provides rich data and allows for a more holistic examination of the strategy development process.

A theoretical framework for optimal observability and detectability of spilled oil in aquatic environment

A departure from ecosystem quality objectives can be defined as effects, which may vary in magnitude and occur over different time scales. Some effects may be reversible while others may persist for long periods. Thus, compared with other sources of pollution in the oceans, the risk of crude oil spillage to the sea presents the major threat for the marine ecology. This study considers the combination of information from diverse sources relating to a similar endpoint in formulating an operational research model to tackle the problem of false alarm from the use of remote sensing in spill detection. A common rubric for this combination is to apply a meta-analysis. The term suggests a move past an analysis of standalone data to one incorporating and synthesizing information from many associated sources.

About Our Authors

About Our Authors