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In "Operations Research: Topics, Impact and Trends from 1952–2019," A. Calma, W. Ho, L. Shao, and H. Li retrospectively look at 68 years of publication of the Operations Research. Using 5,440 journal articles, they highlight the top contributing countries and authors and top research methods and problems investigated. Mathematical programming is the most common research method, whereas inventory is the most investigated problem. Investigations related to pricing are growing significantly. The United States, Canada, and the United Kingdom publish the most papers, with the United States and Canada having similar publication profiles per capita. Inventory is the most popular research problem studied by North American, Asian, and Middle Eastern countries, whereas European countries focus on scheduling problems. Network visualizations of the journal's last 10 years show dynamic programming as the most used method and pricing as the most studied problem. Coauthor networks on collaborations on both dynamic programming and pricing are also shown. This paper is a retrospective look at 68 years of publication output of Operations Research, revealing changes in its publications, its authors, and their impact over time and how these changes might affect researchers and practitioners in the present. A total of 5,440 journal articles from its inception in 1952 to 2019 are used. The analysis initially focuses on the most studied topics and then continues with the top research methods and research problems investigated. The top contributing countries and authors to the most investigated research problems are also studied. The results indicate that mathematical programming is the most common research method, whereas inventory is the most investigated problem. However, investigations related to pricing are growing significantly. The United States, Canada, and the United Kingdom publish the most papers, with the United States and Canada having similar publication profiles per capita. Inventory is the most popular research problem studied by North American, Asian, and Middle Eastern countries, whereas European countries focus on scheduling problems. In order to understand the latest research trend, we visualize the networks of the last 10 years of Operations Research that show dynamic programming as the most used method and pricing as the most studied problem. We further visualize the coauthor networks on both dynamic programming and pricing to identify the most significant clusters of researchers and the topics these research clusters collaborate on. Finally, we provide researchers information about where Operations Research is and where it is likely heading. [ABSTRACT FROM AUTHOR]

Interconnection wide electric transmission system has been envisioned to facilitate the growth of renewable energy, enhance reliability, improve system operating efficiency, and reduce emissions. In this paper, we propose an explicit planning approach for transmission design at the national level. It has been applied to the U.S. system to design transmission overlays for multiple future generation scenarios. Associated simulation results suggest that a national transmission overlay provides economic, environmental, and system performance benefits. [ABSTRACT FROM PUBLISHER]

The electricity industry is undergoing a substantial process of restructuring, with an emphasis on the introduction of competition in the generation sector. Traditional planning methods are not necessarily appropriate for this new environment. This paper extends a previously developed linear programming model to the problem of optimal expansion planning in the face of uncertainty. The model explicitly accounts for equipment availability and load duration curves in selecting optimal investment. The use of the model is illustrated with a simple 2-region problem calibrated with data from Hydro-Québec and the northeastern United States that suggests how the LP model can help decision makers. [ABSTRACT FROM AUTHOR]

The article presents information about the Daniel H. Wagner Prize for Excellence in Operations Research Practice, established in memory of the mathematician Daniel H. Wagner. Dan Wagner earned his Ph.D. in mathematics from Brown University in 1951. His dissertation "On free products of groups" was published in the Transactions of the American Mathematical Society in 1957. In 1957, Dan Wagner and John D. Kettelle formed the partnership of Kettelle and Wagner, which was dissolved in 1963. In 1963, he formed a new company, Daniel H. Wagner Associates Inc. During his years as president and principal owner of Daniel H. Wagner Associates, Dan brought many high-quality mathematicians into the operations research community. After retirement from the firm, Dan held various teaching and research positions with the U.S. Naval Postgraduate School and the U.S. Naval Academy. The idea for this prize began at Dan's memorial service, where many of his former colleagues gathered. Following agreements made on that day, the firms Metron Inc., Daniel H. Wagner Associates Inc. and Applied Mathematics Inc., have pledged generous endowments totaling $51,000, ensuring the availability of a cash award in perpetuity.

This paper first presents a brief reveiw of the multicriteria problems faced by the United States Army, Navy, and Marine Corps in making enlisted personnel assignment decisions. Then for the most complex of these military assignment problems, the Marine Corps problem, a new modeling and solution approach is presented with computational results for a problem having eleven criteria and approximately 10,000 constraints and 780,000 variables. Other contributions of this paper include: (a) New insights into the effects on model structure and solution efficiency of handling preemptive multiple criteria sequentially versus simultaneously. (b) Derivation of an excellent convex proportional fill function. (c) Providing multicriteria researchers with a class of large-scale test problems. [ABSTRACT FROM AUTHOR]

This paper presents a two-stage multi-period decision model for allocation of the individual's savings into several investment plans. Although the U.S. economy is used as the background, the modelling methods are general enough to accommodate any tax law. The first stage of the model uses an asset-allocation method based on the single-index model. Because this method is static and does not provide for tax considerations and other constraints, it alone is not enough. The output of this optimal selection is used as exogenous parameters and controls for the second stage of the model which is an integer program. The IP includes fixed charges, statutory and budgetary constraints, a discount rate, and the risk level. We provide an example of this approach to illustrate how an individual can achieve his goals of terminal accumulations while maintaining the risk level, measured by the aggregate beta, he prefers. A linear programming relaxation of the IP model is utilized for sensitivity analysis to examine whether future adjustments in investment strategies are required. The model remains tractable enough for implementation by individuals who may not be experts in mathematical programming and financial planning. [ABSTRACT FROM AUTHOR]

A model for measuring the efficiency of Decision Making Units (= DMU's) is presented, along with related methods of implementation and interpretation. The term DMU is intended to emphasize an orientation toward managed entities in the public and/or not-for-profit sectors. The proposed approach is applicable to the multiple outputs and designated inputs which are common for such DMU's. A priori weights, or imputations of a market-price-value character are not required. A mathematical programming model applied to observational data provides a new way of obtaining empirical estimates of extremal relations -- such as the production functions and/or efficient production possibility surfaces that are a cornerstone of modern economics. The resulting extremal relations are used to envelop the observations in order to obtain the efficiency measures that form a focus of the present paper. An illustrative application utilizes data from Program Follow Through (= PFT). A large scale social experiment in public school education, it was designed to test the advantages of PFT relative to designated NFT (= Non-Follow Through) counterparts in various parts of the U.S. It is possible that the resulting observations are contaminated with inefficiencies due to the way DMU's were managed en route to assessing whether PFT (as a program) is superior to its NFT alternative. A further mathematical programming development is therefore undertaken to distinguish between "management efficiency" and "program efficiency." This is done via procedures referred to as Data Envelopment Analysis (= DEA) in which one first obtains boundaries or envelopes from the data for PFT and NFT, respectively. These boundaries provide a basis for estimating the relative efficiency of the DMU's operating under these programs. These DMU's are then adjusted up to their program boundaries, after which a new inter-program envelope is obtained for evaluating the PFT and NFT programs with the estimated managerial inefficiencies eliminated. The claimed superiority of PFT fails to be validated in this illustrative application. Our DEA approach, however, suggests the additional possibility of new approaches obtained from PFT-NFT combinations which may be superior to either of them alone. Validating such possibilities cannot be done only by statistical or other modelings. It requires recourse to field studies, including audits (e.g., of a U.S. General Accounting Office variety) and therefore ways in which the results of a DEA approach may be used to guide such further studies (or audits) are also indicated. [ABSTRACT FROM AUTHOR]

Coal is the second-largest source for electricity generation in the United States. However, the burning of coal produces dangerous gas emissions, such as carbon dioxide and Green House Gas (GHG) emissions. One alternative to decrease these emissions is biomass co-firing. To establish biomass as a viable option, the optimization of the biomass supply chain (BSC) is essential. Although most of the research conducted has focused on optimization models, the purpose of this paper is to incorporate machine-learning (ML) algorithms into a stochastic Mixed-Integer Linear Programming (MILP) model to select potential storage depot locations and improve the solution in two ways: by decreasing the total cost of the BSC and the computational burden. We consider the level of moisture and level of ash in the biomass from each parcel location, the average expected biomass yield, and the distance from each parcel to the closest power plant. The training labels (whether a potential depot location is beneficial or not) are obtained through the stochastic MILP model. Multiple ML algorithms are applied to a case study in the northeast area of the United States: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Multi-Layer Perceptron (MLP) Neural Network. After applying the hybrid methodology combining ML and optimization, it is found that the MLP outperforms the other algorithms in terms of selecting potential depots that decrease the total cost of the BSC and the computational burden of the stochastic MILP model. The LR and the DT also perform well in terms of decreasing total cost. [ABSTRACT FROM AUTHOR]

• We analyze time-of-use rates with respect to model predictive controls. • We show that thermal storage potential can vary greatly for differing climates. • We build a model predictive control framework for indoor temperature setpoints. • We analyze of the impacts of different variable electric rates. • The framework is capable of reducing cooling electricity costs by 30%. Heating, ventilation and air conditioning for residential and commercial buildings requires a substantial share of electric energy, and ultimately drives summer peak demand in the United States. Variable electric rates are becoming more common in the residential market, as utilities try to encourage users to shift their energy demand. Model predictive controls, one method of reducing energy usage, employ an optimization model to minimize peak demand, energy usage, or electricity costs. This paper details the development of a co-simulation framework to rapidly model and simulate building energy use and optimize cooling setpoint controls. The framework integrates commercially available software to: (i) simulate all energy interactions between the building, internal gains, outdoor environment, and heating and cooling systems via a building energy simulation program (EnergyPlus), (ii) algebraically formulate an optimization problem (with AMPL) using a black-box, reduced-order model for rapid calculations, (iii) employ Simulink as the environment that links calls to EnergyPlus and AMPL, and (iv) solve the optimization model (with CPLEX) to minimize electricity costs and user discomfort. Variable electric time-of-use rates are analyzed in the context of total cooling electricity costs, thermal comfort of users, and peak demand shedding. The framework uses a model predictive control formulation capable of reducing cooling electricity costs by up to 30%; however, cost savings and peak demand shedding are highly dependent on the time-of-use electricity rate schedule. [ABSTRACT FROM AUTHOR]

The airline industry currently has a $40-billion plus market and is expected to grow rapidly with the population growth and growth in the overall economy. Everyday, thousands of aircrafts undergo maintenance, repair, and overhaul. The aircraft maintenance problem is one of the important logistic problems in the airline industry. It is aimed at scheduling the aircrafts' routing so that enough maintenance opportunities are provided to every aircraft in the fleet. In this paper, we present a new compact network representation of the aircraft maintenance routing problem (AMR) and propose a new mixed-integer linear programming formulation to solve the problem. The quality of this model was assessed on four real test instances from a major U.S. carrier, and compared with the flight string model proposed in the literature. The computational results show that the proposed model is able to obtain the optimal solutions to all test instances in reasonable time. This study suggests that this model can be applied to integrated problems of the AMR and other planning problems such as the fleet assignment problem and crew pairing problem. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to compare the abilities of two competing analytical techniques —mathematical programming and statistical regression—to shed light on the union/nonunion productivity differential in U.S. surface coal mining. The programming approach has the virtues of being nonparametric (and thus extremely flexible) and of being able to provide a decomposition of productivity differentials into three components—differences in technical efficiency, differences in scale efficiency, and differences in congestion. Identification of these three components provides an aid to management in its search for the sources of, and remedies for, productivity gaps. The econometric approach, on the other hand, is neither flexible nor does it provide such a decomposition; its chief virtue lies in the fact that, being stochastic, it allows for the presence of noise and measurement error that plagues most if not all empirical data. Thus, the two approaches have complementary virtues. The two techniques are used to investigate productivity in two samples of U.S. surface coal mines. The programming approach finds a large and significant positive union/nonunion productivity differential. This differential is due primarily to greater congestion occurring in the smaller nonunion mines in one sample, and to scale inefficiency in the smaller nonunion mines in the other sample. The econometric analysis finds nearly the same union/nonunion productivity differential, but sheds no light on the composition of, and thus the cure for, the differential. [ABSTRACT FROM AUTHOR]

In this paper we present a Nash dynamic game model of interactions among oil inventory and tariff policies for oil importing countries. The inventory policies consist of the rates for building up and drawing down strategic oil reserves, while the tariff policies consist of setting tariffs as a function of the state of the world oil market. The model represents a stochastic game in which countries are grouped into two players, where each player in the game chooses its individual policies to maximize expected social welfare. We use the model to present empirical results about the sizes of the stockpiles as a function of disruption probabilities, tariff policies and cooperation versus noncooperation between nations. In addition to results regarding the buildup and draw down rates as a function of the amount of disruption or probability of future disruption our central conclusion is that build-up and drawdown policies are essentially the same with or without international cooperation, due to the current inventory limits on U.S. and Foreign Strategic Petroleum Reserves. [ABSTRACT FROM AUTHOR]

An operations research study of fire company location in Denver, Colorado, has been conducted, resulting in recommendations that may reduce annual costs by more than one million dollars without a substantial change in the level of fire-suppression capability. One development resulting from this study is a hierarchical objective function for the set-covering problem; this objective function permits the simultaneous minimization of the number of fire stations and the maximization of the number of existing fire stations within the minimum total number of stations. This paper includes discussions of the use of mathematical programming, station configuration information models, the synergism of judgment and analysis, and the action that resulted from the recommendations. [ABSTRACT FROM AUTHOR]

This paper examines real-time decision rules for a U.S. Air Force inventory system where items are repaired rather than "used up." The problem is to decide which user in the system has the greatest need for the newly available inventory items coming out of repair. The system is modeled as a Markov decision process and a heuristic rule is presented. This rule, the Transportation Time Look Ahead policy, is justified theoretically by showing that the rule is optimal for a modified model. Thus we have a theoretical justification of a decision rule in a large scale dynamic programming application. [ABSTRACT FROM AUTHOR]

We study an optimal matching problem in the context of dual‐donor organ exchange, where a portion of two living donors' organs are transplanted to a single patient. This dual‐donor transplant technique is becoming more widespread for lung and liver transplants. However, multiple medical compatibility criteria pose a serious challenge for matching a patient with two compatible donors. In the United States and many other countries, laws prohibit commercial (for‐profit) deals for human organs, so donor exchanges are run by nonprofit organizations connecting donors with people in need of organs, with the goal of increasing transplant matches. We propose a simple chain mechanism in dual‐donor organ exchange to increase the number of patient–dual‐donor matches, which would maximize the number of patients receiving transplants. Based on this objective, we propose a general simple chain optimization framework for finding the maximum patient matching, taking into account multiple compatibility criteria (e.g., blood type and weight), and determine the complexity status of the problem. We provide theoretical results on the structures of simple chains, as well as a polynomial time algorithm to obtain the maximum patient matching simple chain with blood type compatibility. Through a numerical study for multiple compatibility criteria, we show that in many scenarios, a simple chain substantially increases the number of patients matched with dual donors for transplants, as opposed to exchange cycles. We also address the problem of maximizing the number of patients matched for dual‐donor organ transplants via two‐way and three‐way exchange cycles, subject to donors' and recipients' medical compatibility criteria, along with a discussion of their computational complexity. Finally, we characterize the general configurations of large n‐way exchange cycles and provide theoretical insights for their structural properties. Our findings provide general optimization models for dual‐donor organ exchange operators to increase the number of patients matched for transplant, given multiple compatibility criteria. In addition, we show how exchange operators, using simple chains, can increase patient matches and reduce simultaneous surgical resource requirements over exchange cycles. [ABSTRACT FROM AUTHOR]

This paper presents the development of a mathematical programming model for evaluating options for the continuance of the Essential Air Services (EAS) program, a provision of the Airline Deregulation Act of 1978. The EAS program was established to guarantee continued air service to small communities that suffered air service losses as a result of carrier withdrawals permitted by the Deregulation Act. The model has been designed to aid decision makers in allocating subsidized air services to communities in a region, given the goal of maximizing access to the air transportation system. In order to provide information on optimal service locations, service characteristics, and patterns of access, the authors have utilized a maximal covering formulation and incorporated elements of hierarchical and weighted benefit models. In addition, a stopover service option has been included in the model to permit evaluation of potential gains in economic efficiency that may be realized by utilizing stopover service, when appropriate, in place of more costly nonstop service. In addition to the coverage objective, a second objective of systemwide cost minimization is employed. By using a multiobjective approach, the trade-offs between the conflicting objectives of coverage and cost can be explored and the spatial patterns of service associated with each combination of coverage level and cost can be examined. The model is applied to a sample study of small community air service in North and South Dakota. [ABSTRACT FROM AUTHOR]

A number of large scale models have been proposed and implemented m recent years to study the anticipated expansion of coal production and utilization in the United States. This paper reports on the application of mathematical programming decomposition methods to the constructive integration and optimization of these models. In particular, it was found that an implemented hybrid decomposition approach, part resource directed and part price directed, exhibited fast convergence to an optimal solution. [ABSTRACT FROM AUTHOR]

Examines the operating characteristics of congressional committee assignments in the U.S. Optimization model with institutional constraints; Responsibilities of political parties in committee assignments; Features of a programming model of committee assignments.

We propose an integrated bond portfolio optimization model based on the popular cash-flow matching and immunization strategies. The underlying mathematical program, not only minimizes the initial required capital for the creation of the least cost bond portfolio, satisfying the connected series of liabilities, but also handles the uncertainty of parallel and symmetric shifts on the term structure of short rates. Moreover, a complete investment policy statement regarding the bond portfolio’s structure, the level of diversification, the amount of transaction costs and the lot sizes, is precisely formulated. We verify the validity of the proposed approach through an illustrative empirical testing application, using a well-diversified investment universe of US and European corporate bonds, as well as international sovereign bonds. The qualitative and technical conclusions we report, document the model’s effectiveness and usability. [ABSTRACT FROM AUTHOR]

Mathematical programming models are widely used in agricultural sector analysis. However, the lack of micro-level data, as well as computational requirements, necessitate the aggregation of individual producers into representative units when working at the sectoral level. This usually leads to unrealistic extreme specialization in supply responses. In 1982, McCarl introduced the “historical crop mixes” approach to avoid extreme specialization. We extend this approach by generating additional synthetic crop mixes using supply response elasticities and systematically varied commodity prices. In addition to avoiding extreme specialization, this approach provides flexibility when future supply responses can be vastly different from past responses. An application to U.S. biofuel policy analysis is presented. [ABSTRACT FROM AUTHOR]

Scheduling policy which depends on selection of dispatching rules is one of the reasons which affect FMS’ productivity. Selection of a dispatching rule can only be based on one or two criteria such as processing time, due date, or system components at traditional methods such as mathematical programming, simulation, and heuristic algorithms. However, traditional methods have some weakness; the first one is about selection criteria based on only one dispatching rule or a few combination of the rules, the second one is related with not considering most of the manufacturing system information, and the last one is to deal with selection decision is not a dynamic structure. In this study, an analytical network process (ANP) model has been developed in order to eradicate these weaknesses. The model based on multiple criteria decision-making process. In addition, most of manufacturing system information have been employed, and it consists of a dynamic decision-making process. The proposed model has a comprehensive structure which consists of a company’s manufacturing criteria and macro-strategies. Although interdependence is accepted among criteria in ANP, non-interaction is assumed among them (i.e., additive measure). Because of existence of interactivities among criteria, a choquet integral has been adapted in an ANP model. [ABSTRACT FROM AUTHOR]

The article discusses the research on computational mathematics for undergraduate students. It is said that Christopher P. Bendel from the University of Wisconsin-Stout has received a research grant from the National Science Foundation. Bendel has involved students in his study, having them make mathematical computations through the use of computer. It is cited that computational tools were developed by people who have programming skills and a graduate-level mathematical knowledge to understand mathematical ideas.

We developed a model of interrelated timber markets in the U.S. West to assess the impacts of large-scale fuel reduction programs on these markets, and concomitant effects of the market on the fuel reduction programs. The linear programming spatial equilibrium model allows interstate and international trade with western Canada and the rest of the world, while accounting for price effects of introducing softwood logs to the market. The model maximizes area treated, given fire regime-condition class priorities, maximum increases in softwood processing capacity, maximum rates of annual treatments, prohibitions on exports of U.S. and Canadian softwood logs from public lands and a fixed annual treatment budget. Results show that the loss to U.S. private timber producers is less than the gains for timber consumers (mills). States receiving more treatments when spending is not constrained by state proportions include Idaho, Montana, New Mexico and Oregon. When only the wildland-urban interface is treated, California, Oregon and Washington receive more treatments. Utah and Colorado receive more treatments when low risk stands are included. [ABSTRACT FROM AUTHOR]

Because of environmental concerns, many states are regulating animal manure applications to crops. A conceptual model is presented to describe manure demand for crop nutrient application under alternative regulatory standards. Demand relationships are incorporated into a spatial equilibrium model to estimate welfare costs of phosphorus and nitrogen regulatory standards for manure applications. Model estimates for Virginia indicate that regulatory standards for manure application achieve large reductions in excess nitrogen and phosphorus and result in 5–15% welfare losses excluding nonmarket environmental valuation. [ABSTRACT FROM AUTHOR]

The recently implemented court decision to break up Bell (= American Telephone & Telegraph Co.) to accord with U.S. anti-trust laws represents a highly significant policy decision which is proving to be influential in other countries as well as the U.S. The telecommunication industry is of such size and importance that even relatively small economies that might be lost with Bell's breakup as a "natural monopoly" could involve substantial welfare losses to consumers and producers. Studies commissioned by the U.S. Justice Department that approached this topic by econometric methods reported that the evidence failed to support the contention that Bell was a natural monopoly. Here a goal programming/constrained regression, as developed in the Management Science literature, uses the same functional form and the same data but nevertheless reverses the main findings of the econometric studies in every one of the 20 years covered. This kind of difference in results obtained by two different methods of analysis points up a need for drawing on persons from different disciplines who are capable of checking each other's methodologies when important policy decisions may be influenced by results that depend on the methodologies that these disciplines customarily use. Advantages of doing this are further illustrated by data deficiencies that escape detection by the econometric methods employed in the Justice Department-commissioned studies. [ABSTRACT FROM AUTHOR]

The Emergency Medical Service (EMS) Act specifies the fraction of all demands for service that must be reached in a given amount of time for urban and rural areas. The conditions have traditionally been interpreted to apply to the case in which all EMS vehicles are available to respond to demands. A model that considers the probability of a vehicle being busy is formulated and model properties are briefly discussed. The model is then applied to two problems: a 55-node test case and a 33-node census tract representation of Austin, Texas. The implications of the new model for EMS system design are discussed as are the limitations of the modeling approach. [ABSTRACT FROM AUTHOR]

Investigates overfishing in the Texas shrimp fishing industry by time period and fishing area. Determination of an optimal harvesting pattern using a mathematical programming model; Fishery management modeling; Model specification and data; Model validation.

Since 1991, the US Department of the Army has been using a heuristic programming model to better plan its housing construction and leasing. It obtained substantial economic and management benefits from using the original model. We enhanced the original model and developed a new delivery system to yield additional benefits. [ABSTRACT FROM AUTHOR]

The nine universities in the Atlantic Coast Conference (ACC) have a basketball competition in which each school plays home and away games against each other over a nine-week period. The creation of a suitable schedule is a very difficult problem with a myriad of conflicting requirements and preferences. We develop an approach to scheduling problems that uses a combination of integer programming and enumerative techniques. Our approach yields reasonable schedules very quickly and gave a schedule that was accepted by the ACC for play in 1997-1998. [ABSTRACT FROM AUTHOR]

A goal programming model for planning officer accessions to the U.S. Navy from various commissioning sources is developed and described. Present and future requirements for different career specialty areas in the Navy are considered in terms of years of commissioned service and related to various "choke points" where inventories fall short of requirements in officer force structure. An illustration of the use of this model is provided which involves assessments of the effects of phasing out one comissioning source. Other uses and possible further extensions are also indicated for this model, which now forms a part of the Navy's manpower planning procedures. [ABSTRACT FROM AUTHOR]

A nonlinear model is developed for allocating treatment requirements along a stream so as to meet stream dissolved oxygen standards at each point in the stream while minimizing the total annual cost of all discharging activities. An application of the model to several reaches of the Upper Hudson River is considered, and some alternative pollution abatement policies are analyzed. [ABSTRACT FROM AUTHOR]