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Full-Text Articles in Business Administration, Management, and Operations
A Review Of Artificial Intelligence, Algorithms, And Robots Through The Lens Of Stakeholder Theory, Michael J. Matthews, Su Runkun, Lindsey Yonish, Shawn Mcclean, Joel Koopman, Kai Chi Yam
A Review Of Artificial Intelligence, Algorithms, And Robots Through The Lens Of Stakeholder Theory, Michael J. Matthews, Su Runkun, Lindsey Yonish, Shawn Mcclean, Joel Koopman, Kai Chi Yam
Management Faculty Publications
With the arrival of the Fourth Industrial Revolution, intelligent machines are affecting the daily lives of multiple organizational stakeholders. However, despite the continued expansion of intelligent machines in society, management scholarship has generally lagged, and current frameworks are under-equipped to offer meaningful guidance regarding the intersection of intelligent machines and organizations. We address this issue via a multidisciplinary review and a novel framework of intelligent machines and value creation. First, we discuss the characteristics of intelligent machines (i.e., autonomy, learning, inscrutability, and materiality) and how variation in these characteristics impacts their affordances and, subsequently, the value offered to stakeholders. We …
A Horizon Decomposition Approach For The Capacitated Lot-Sizing Problem With Setup Times, Ioannis Fragkos, Zeger Degraeve, Bert De Reyck
A Horizon Decomposition Approach For The Capacitated Lot-Sizing Problem With Setup Times, Ioannis Fragkos, Zeger Degraeve, Bert De Reyck
Research Collection Lee Kong Chian School Of Business
We introduce horizon decomposition in the context of Dantzig-Wolfe decomposition, and apply it to the capacitated lot-sizing problem with setup times. We partition the problem horizon in contiguous overlapping intervals and create subproblems identical to the original problem, but of smaller size. The user has the flexibility to regulate the size of the master problem and the subproblem via two scalar parameters. We investigate empirically which parameter configurations are efficient, and assess their robustness at different problem classes. Our branch-and-price algorithm outperforms state-of-the-art branch-and-cut solvers when tested to a new data set of challenging instances that we generated. Our methodology …
A Methodology For Configuring Cellular Manufacturing Systems, Henry C. Co, A. Abdelaziz
A Methodology For Configuring Cellular Manufacturing Systems, Henry C. Co, A. Abdelaziz
Research Reports from the Department of Operations
This paper presents a three-step procedure for configuring machines into manufacturing cells, assigning the cells to process specific sets of jobs. First, operations are assigned, with the objective of balancing the workload. Then, we extended King's algorithm for cluster analysis. Finally a direct-search algorithm for defining the composition of manufacturing cells, the method of Partition Formation (PF), is presented. A comprehensive example is also included. [Likely published circa 1984-1988.]
Two Algorithms For Inventory Decisions Under Inflationary Conditions, N.G. Duraiswamy, Arnold Reisman
Two Algorithms For Inventory Decisions Under Inflationary Conditions, N.G. Duraiswamy, Arnold Reisman
Research Reports from the Department of Operations
This paper reports two algorithms for inventory decisions under inflationary conditions. The first, a heuristic algorithm, was designed to provide efficient purchasing decisions when the demand is deterministic and constant overtime and no inventory shortage is allowed at anytime. It is assumed that price increases due to inflation occurs in a discrete fashion; that is, prices go up not continually but at discrete time points. The second, a dynamic programming algorithm was derived under the same price increase assumption, but applies to the deterministic, dynamic demand and no shortage problem. [Published circa 1979-1980.]
A Throughput-Maximizing Facility Planning And Layout Model, Henry C. Co, Albert Wu, Arnold Reisman
A Throughput-Maximizing Facility Planning And Layout Model, Henry C. Co, Albert Wu, Arnold Reisman
Research Reports from the Department of Operations
This paper presents a throughput-maximizing algorithm for facility planning and layout of flexible manufacturing systems. It uses a computationally efficient mean-value analysis model to analyze system configurations, and extends the methodology of CRAFT in layout planning. [Published circa 1986-1989.]
A Systematic Approach To Countertrade Decision Making Analysis, Kil Ju Park
A Systematic Approach To Countertrade Decision Making Analysis, Kil Ju Park
Research Reports from the Department of Operations
Countertrade literature to date has concentrated ondiscussing the motivations, reasons, and advantages of specificcountertrades and the negotiation contract requirements fortransactions between countries. However, developing ascience-based decision model for countertrade has drawn littleattention in the literature. Increasing importance ofcountertrade in the world market requires a systematic decision-making process in order to reduce the transaction negotiationtime and cost, and increase profitability of a country or aninternational company.This thesis attempts to improve the countertradenegotiation process by recognizing a hierarchy of objectives tobe met in 1) Picking the proper negotiation partner. 2)Selecting the proper quantities of the different goods to betraded. The first objective …
Optimal Decision And Detection In The Decentralized Case, Moula Cherikh
Optimal Decision And Detection In The Decentralized Case, Moula Cherikh
Research Reports from the Department of Operations
This dissertation addresses the problem of optimal decentralized detection or decision-making about some binary states of the world. In the centralized case, the solutions are well known and easily obtained using the classical theory of statistical hypothesis testing. The decentralized case has an additional degree of complexity due to the interrelationships and the dependence between the decision-makers. As formulated here, the model has applications to any problem in which stochastically independent observations are abstracted to inform a central decision-maker, or are used to determine a team action. Examples include: a strategic defense system, a battery of medical tests, or an …
Off-Day Scheduling With Hierarchical Worker Categories, Hamilton Emmons, Richard N. Burns
Off-Day Scheduling With Hierarchical Worker Categories, Hamilton Emmons, Richard N. Burns
Research Reports from the Department of Operations
Workers of m types are available with type k (k=1,...,m-1) more highly qualified and able to substitute for types k+1,...,m. For seven-day-a-week operation, daily requirements are for at least dk workers of type k or better, of which at least dk must be precisely type k. Formulas are given to find the smallest number and most economical mix of workers, assuming each worker must have two off-days per week and a given fraction of weekends off. Algorithms are presented which generate a feasible schedule, and provide workstretches between two and five days, and consecutive weekdays off when on duty for …
An Improved Counterexample To The Rudimentary Primal Algorithm, Robert Haas, Kamlesh Mathur, Harvey M. Salkin
An Improved Counterexample To The Rudimentary Primal Algorithm, Robert Haas, Kamlesh Mathur, Harvey M. Salkin
Research Reports from the Department of Operations
The rudimentary primal algorithm (RPA) is a simple and convenient algorithm to solve all-integer integer programs; its considerably more complex alternative is the "simplified primal algorithm" (SPA) of Young [3] and Glover [1]. The RPA has not been demonstrated to converge, however; Mathis [3] and Salkin [4] have in fact presented counterexamples for which it cycles indefinitely with period six. The counterexamples depend critically, though, on a rigid choice of the "optimal" pivot columns; Mathis' example converges if alternative columns are selected, and we observe here that Salkin's converges quickly too. The issue thus remained open whether the RPA might …
An Efficient Resource Directive Algorithm For Multicommodity Network Flow Problems, Chandrashekhar Madhukar Khot
An Efficient Resource Directive Algorithm For Multicommodity Network Flow Problems, Chandrashekhar Madhukar Khot
Research Reports from the Department of Operations
Resource directive algorithms for multicommodity network flow problems are decomposition schemes that work by transforming the problem into one of finding the best possible allocation of capacities to the competing commodities. The transformed problem has a piecewise linear convex master program, with single commodity network flow subproblems, which can be solved fast. Resource directive algorithms presented in the literature have attempted to exploit the convexity of the objective. However, the best resource directive algorithm available, the subgradient algorithm, remains heuristic in nature. On the other hand, optimizing algorithms are usually computationally effective variants of the revised simplex algorithm; are roughly …
Unification Of Single Objective Mathematical Programming Problems, Gregory Pollock, Arnold Reisman
Unification Of Single Objective Mathematical Programming Problems, Gregory Pollock, Arnold Reisman
Research Reports from the Department of Operations
This paper attempts to unify a broad, though not exhaustive, arena of mathematical programming. The approach involves the development of a general mathematical problem formulation, which is shown to reduce, in a deductive manner, to each of the major subfields of mathematical programming. Furthermore, a taxonomy is provided for classifying each of the special cases. Lastly, the premier algorithms for solving each of the cases are indicated and referenced.
A Fast Algorithm For The Transporter Routing Problem In A Bay Type As/R System, Ahmed Malki, Henry C. Co
A Fast Algorithm For The Transporter Routing Problem In A Bay Type As/R System, Ahmed Malki, Henry C. Co
Research Reports from the Department of Operations
We present a fast algorithm for finding the routing sequence of the transporter in a bay type AS/R system. The methodology requires minimal computer hardware and CPU time. It is based on the analysis of the tree date structure abstracted from the problem. Some test cases are evaluated on a 100-aisle by 50-items-per-aisle AS/R system configuration.
Minimization Of Total Tardiness In Many-To-Many Pickup And Delivery Systems, Carolyn Kidder Cuff
Minimization Of Total Tardiness In Many-To-Many Pickup And Delivery Systems, Carolyn Kidder Cuff
Research Reports from the Department of Operations
In the problem considered, the objective is to minimize the total tardiness in a schedule which includes many pickup and many delivery locations. Due to the large number of items and fixed number of vehicles, a feasible schedule picking all items up after their known ready time and delivering them before their due times generally does not exist. Algorithms for single and multivehicle instances are developed. A branch and bound algorithm developed for the single vehicle finds an optimal solution. However, its use is limited to comparatively small problems. The alternative heuristic consists of 2 phases. First, the vehicle is …
Resource Requirements For Scheduling With Different Processor Sizes - Part Ii, Hamilton Emmons, Venkateswara Reddy Dondeti
Resource Requirements For Scheduling With Different Processor Sizes - Part Ii, Hamilton Emmons, Venkateswara Reddy Dondeti
Research Reports from the Department of Operations
Given a start time s, duration p, and the minimal processor capacity required Rj, for each of n jobs (j=1,...,n), we wish to find the optimal mix of processors, available in r≥2 different sizes, with capacities C1
Resource Requirements For Scheduling With Different Processor Sizes - Part I, Venkateswara Reddy Dondeti, Hamilton Emmons
Resource Requirements For Scheduling With Different Processor Sizes - Part I, Venkateswara Reddy Dondeti, Hamilton Emmons
Research Reports from the Department of Operations
Given a start time sj, duration pj, and the minimal processor capacity required rj, for each of n jobs (j=1,...,n), we wish to find the optimal mix of processors, available in two different sizes, required to complete all jobs on schedule. A job with a smaller size requirement can be done by a bigger processor, but not vice versa. We present algorithms for solving this problem under two different objectives: (a) Minimize the total number of processors, and, given that, minimize the number of bigger processors: (b) Minimize the total costs of the processors.
Minimal Resources For Fixed Job Schedules With Defferent Processor Size Requirements And A Hierarchical Structure, Venkateswara Reddy Dondeti
Minimal Resources For Fixed Job Schedules With Defferent Processor Size Requirements And A Hierarchical Structure, Venkateswara Reddy Dondeti
Research Reports from the Department of Operations
In resource allocation and scheduling problems, we frequently come across situations wherein the tasks, although similar in nature, require resources of different capacities. An example is the assignment of airplanes to different flights. In this case, the basic task is the same, but depending on the expected load of a flight, we would assign a plane of larger or smaller capacity to that flight. The essential feature of this type of problem is that not only the tasks require resources of different capacities, but also a resource with a higher capacity can, if necessary, undertake a task which requires a …
A Sequential Linear Programming Approach For Solving The Linear Complementarity Problem, Syamal Roy
A Sequential Linear Programming Approach For Solving The Linear Complementarity Problem, Syamal Roy
Research Reports from the Department of Operations
This research is concerned with the linear complementarity problem (LCP). In the first part of this research, we develop an algorithm which finds a solution to the LCP or detects that none exists when the given matrix M is either a P-matrix, or M is nonsingular and its inverse is nonpositive. Our approach is to solve an equivalent constrained optimization problem, the feasible region of which is the same as that defined by the linearity constraints of the LCP. The algorithm starts with a solution to the linearity constraints, if one exists, and solves a sequence of linear programming subproblems. …
Decision Support System For Capacity Planning And Operational Design, Mahesh Chandra Pati
Decision Support System For Capacity Planning And Operational Design, Mahesh Chandra Pati
Research Reports from the Department of Operations
The purpose of this dissertation was to provide a decision support system to address the capacity planning and operational design issues of a multi-product, multi-family, multi-stage serial flow production line. The existing algorithms find production schedules either for a single-product case or for a multi-product, single-family manufacturing system of only one stage. The latter problem is an NP-complete problem. The model developed in this thesis is called the Manufacturing Analysis System (MAS). MAS determines a feasible schedule that will minimize the total setup and inventory holding costs, which should be close to the optimal schedule. The Extended Basic Period (EBP) …
A Set Partitioning Approach To The Vehicle Routing Problem, Yogesh Kumar Agarwal
A Set Partitioning Approach To The Vehicle Routing Problem, Yogesh Kumar Agarwal
Research Reports from the Department of Operations
This dissertation presents a new algorithm for the Vehicle Routing Problem (VRP) based on the set partitioning approach. Given a depot, a number of delivery points (stops) with known demands, and the vehicles with given capacities, the problem is to find the set of most economical vehicle routes satisfying all demand. Each route originates and terminates at the depot and must be feasible with respect to the truck capacity. In the set partitioning approach, each route is represented by a binary column with elements 1 for stops visited on the routes and 0 for stops not visited. The obvious difficulty …
A Stochastic Dominance Algorithm Using Piecewise Linear Approximations, Peter H. Ritchken, Yogesh Kumar Agarwal, Alok K. Gupta
A Stochastic Dominance Algorithm Using Piecewise Linear Approximations, Peter H. Ritchken, Yogesh Kumar Agarwal, Alok K. Gupta
Research Reports from the Department of Operations
Current stochastic dominance algorithms use step functions to approximate the cumulative distributions of the alternatives even when the underlying random variables are known to be continuous. Since stochastic dominance tests require repeated integration of the cumulative distribution functions, a compounding of errors may result from this approximation. This article introduces a new stochastic dominance algorithm that approximates the cumulative distribution function by piecewise linear approximations. Comparisons between the new and old algorithms are performed for normally distributed alternatives.
An Information Theoretic Forecasting Methodology, Keith Kerwin Mclain
An Information Theoretic Forecasting Methodology, Keith Kerwin Mclain
Research Reports from the Department of Operations
A forecasting methodology has been developed which is based on information theory. This methodology uses the idea that autoregressive (AR) representation of a stationary, Normally distributed time series is equivalent to a model of such a time series which has maximum information content. To apply this idea, two programs were developed. The first program utilizes an Adaptive Sequential Segmentation Algorithm, which is based on an information theoretic measure, to detect subseries in a piecewise stationary time series. This is done to accommodate abrupt or discontinuous changes in the underlying process parameters. A second program utilizes the Akaike Information Criterion to …
A New Approach For Determining When The Linear Complementarity Problem Has No Solution, Venky Venkateswaran
A New Approach For Determining When The Linear Complementarity Problem Has No Solution, Venky Venkateswaran
Research Reports from the Department of Operations
For the Linear Complementarity Problem (LCP) we have developed a finite descent algorithm that is capable of obtaining an optimal solution when the given matrix is a P-matrix. Conventional finite descent algorithms for solving geometric optimization problems move only from the current point to a geometrically adjacent one while traversing the feasible region. Here, we develop an algorithm that may 'jump' over infeasible regions during its operation. In a sense analogous to the methods of combinatorial optimization, the proposed algorithm exploits a 'nearness' structure that is defined not with respect to physical adjacency but with respect to the objective function …
Duality, Finite Improvement And Efficiently Solved Problems, J. Franco, Daniel Solow, Hamilton Emmons
Duality, Finite Improvement And Efficiently Solved Problems, J. Franco, Daniel Solow, Hamilton Emmons
Research Reports from the Department of Operations
We generalize the concept of duality, known for Linear Programming, to other optimization problems and introduce finite improvement algorithms as a class of algorithms which includes the simplex method. The concept of finite improvement is interesting because such algorithms can work on non-convex problems such as the Linear Complementarity problem. Our goal is to find the relationship between finite improvement, duality and optimization problems which can be considered tractable. The study of these concepts naturally includes consideration of complementary problems (CO-NP) and data-independent and data-dependent neighborhoods. We find that the class of optimization problems with duals is the same as …
Scheduling Stochastic Jobs With Due Dates On Parallel Machines, Hamilton Emmons, Michael Pinedo
Scheduling Stochastic Jobs With Due Dates On Parallel Machines, Hamilton Emmons, Michael Pinedo
Research Reports from the Department of Operations
Jobs with random processing times, random due dates, and weights are to be scheduled on parallel machines so as to minimize the expected total weight of tardy jobs. Under various assumptions, optimal policies are presented both for static lists and dynamic schedules, preemptive and nonpremptive. The effect of processing time variability is also investigated.
Functional Approximation Approach To Multistate Control Problems, Prasert Shusang
Functional Approximation Approach To Multistate Control Problems, Prasert Shusang
Research Reports from the Department of Operations
An iterative method for solving multistate dynamic control problems is presented. The algorithm differs from most decomposition methods currently used in the deterministic optimization of control systems. The method is described as a functional approximation of the recursive equation in dynamic programming. The basic concept is directly dependent on the Sensitivity Theorem in nonlinear programming. The method, referred to as Functional Approximation approach to multistate Control Problem (FACP) is shown to be efficient in case study for operation of multireservoir system. It results in a marked reduction in computer storage and thus directly increases the power of multistate dynamic programming …
A Finite Improvement Algorithm For The Linear Complementarity Problem, Konstantinos Paparrizos
A Finite Improvement Algorithm For The Linear Complementarity Problem, Konstantinos Paparrizos
Research Reports from the Department of Operations
This research is concerned with the development of a computationally efficient improvement algorithm for the linear complementarity problem (LCP). Our approach to finding a solution to the LCP is to solve the equivalent constrained optimization problem (COP) of maximizing the sum of the minimum of each complementary pair of variables subject to the constraints that each such minimum is nonpositive. An optimal solution with objective function value of zero yields a solution of the LCP. The algorithm, descent in nature, is similar to the simplex method in the sense that it moves between basic points of an associated system of …
Computational Study Of A Network Algorithm Which Can Start With A Non-Zero Flow, Gitta Javaheri-Khoei
Computational Study Of A Network Algorithm Which Can Start With A Non-Zero Flow, Gitta Javaheri-Khoei
Research Reports from the Department of Operations
This thesis proposes an approach for improving the efficiency of the Out-of-Kilter algorithm for solving minimum-cost circulation problems which arise in network flow theory. Two alternative schemes are presented for solving the problem by starting with infeasible initial flows. Various heuristics for choosing the initial flows are considered, and their impact on the computational performance of the proposed alternatives is investigated. Based on computational experimentation with random minimum-cost circulation problems, there is reason to suspect that for certain types of circulation problems, significant computational savings can be achieved by judiciously choosing initial flows. The method also performed well when tested …
An Efficient Algorithm For The General Multiple-Choice Knapsack Problem (Gmkp)-- Computational Results, Kamlesh Mathur, Harvey M. Salkin, Susumu Morito
An Efficient Algorithm For The General Multiple-Choice Knapsack Problem (Gmkp)-- Computational Results, Kamlesh Mathur, Harvey M. Salkin, Susumu Morito
Research Reports from the Department of Operations
In [2] a branch-and search algorithm for the General Multiple-Choice Knapsack Problem [GMKP] was developed. This paper presents extensive computational results with this algorithm. The study illustrates that the implicit enumeration criteria developed in [2] are exceptionally strong, and the algorithm is most efficient when applied to the GMKP and also when compared to existing codes for 0-1 multiple-Choice Knapsack problem.
An Efficient Algorithm For The General Multiple-Choice Knapsak Problem (Gmkp)-- Algorithm Development, Kamlesh Mathur, Susumu Morito
An Efficient Algorithm For The General Multiple-Choice Knapsak Problem (Gmkp)-- Algorithm Development, Kamlesh Mathur, Susumu Morito
Research Reports from the Department of Operations
A common problem frequently faced by business firms and individual investors is to select a few investment opportunities from many available possibilities. This problem, in its simplest form, can be modeled as a 0-1 knapsack problem. In a more general investment scenario, however, we obtain a model which is a general knapsack problem with a multiple-choice constraint. To solve this problem, an efficient enumerative algorithm is developed. The algorithm includes an efficient procedure to solve the LP-relaxed problem, a reduction algorithm which may allow the initial fixing of some of the variables, and various other implicit enumeration criteria derived from …
A Scheduling Problem In Retired Payroll Processing At The Navy Finance Center, Michele Diane Foster
A Scheduling Problem In Retired Payroll Processing At The Navy Finance Center, Michele Diane Foster
Research Reports from the Department of Operations
This study presents a better scheduling policy for processing payroll adjustments to meet specified due dates at the Navy Finance Center in the Retired Pay Department. A better schedule is found by examining alternative scheduling algorithms and simulating the workflow under different operating rules. The objective is to minimize the maximum lateness of cases. Due dates, mean processing times, and interarrival times are known and specified. Cases are independent, priority-weighted, and arrive intermittently. It is assumed that cases are not interrupted while in process and that each clerk can process only one case at a time. Presently, a clerk works …