Decision support system for the batching problems of steelmaking and continuous-casting production

This paper investigates two batching problems for steelmaking and continuous-casting (SCC) production in an integrated iron and steel enterprise. The tasks of the problems are to make the decisions as how to consolidate ordered slabs into charges, and then how to group charges into casts. The effective decisions on these batching problems can help to balance the requirements of materials in downstream production lines, improve the customer satisfaction levels, and reduce production costs (including reduction of open ordered slabs, less slabs quality upgrading, reduction of steel-grade changeovers, and reduction of inefficient utilization of tundishes lives). We first formulate the problems as integer-programming models by consider practical constraints and requirements, and then develop the two heuristic algorithms for the corresponding batching problems. By embedding above models and algorithms, we develop decision support system (DSS) software with interactive planning editor. The DSS has been tested by using practical data set collected from the steelmaking plant in Baosteel which is one of the most advanced iron and steel enterprises in China. Computational experiments demonstrate that the models and algorithms developed can generate the satisfactory solutions when they work together with the planning editor in the DSS.     

Farmers’ Risk‐Based Decision Making Under Pervasive Uncertainty: Cognitive Thresholds and Hazy Hedging

Researchers in judgment and decision making have long debunked the idea that we are economically rational optimizers. However, problematic assumptions of rationality remain common in studies of agricultural economics and climate change adaptation, especially those that involve quantitative models. Recent movement toward more complex agent‐based modeling provides an opportunity to reconsider the empirical basis for farmer decision making. Here, we reconceptualize farmer decision making from the ground up, using an in situ mental models approach to analyze weather and climate risk management. We assess how large‐scale commercial grain farmers in South Africa (n = 90) coordinate decisions about weather, climate variability, and climate change with those around other environmental, agronomic, economic, political, and personal risks that they manage every day. Contrary to common simplifying assumptions, we show that these farmers tend to satisfice rather than optimize as they face intractable and multifaceted uncertainty; they make imperfect use of limited information; they are differently averse to different risks; they make decisions on multiple time horizons; they are cautious in responding to changing conditions; and their diverse risk perceptions contribute to important differences in individual behaviors. We find that they use two important nonoptimizing strategies, which we call cognitive thresholds and hazy hedging, to make practical decisions under pervasive uncertainty. These strategies, evident in farmers' simultaneous use of conservation agriculture and livestock to manage weather risks, are the messy in situ performance of naturalistic decision‐making techniques. These results may inform continued research on such behavioral tendencies in narrower lab‐ and modeling‐based studies.     

The robustness of linear models for decision-making

This paper elaborates on some issues discussed by Moskowitz, who presented evidence that linear multiple regression models, estimated from decisions made by individuals, often outperform the individuals themselves. In discussing his results, Moskowitz (1) suggested that inconsistency in information utilization by individuals may account for the relative superiority of regression models, and (2) expressed concern over the robustness of linear regression models to changes in (a) information environments, (b) weighting parameters, and (c) functional form of the model. This paper discusses reasons (in addition to inconsistency) for the relative superiority of model over man, and it summarizes recent research in psychology concerning the robustness of linear regression models (and linear models in general). This paper is supportive, rather than critical, of Moskowitz's research.     

Game Theory and Risk Analysis

Risk analysts often analyze adversarial risks from terrorists or other intelligent attackers without mentioning game theory. Why? One reason is that many adversarial situations—those that can be represented as attacker‐defender games, in which the defender first chooses an allocation of defensive resources to protect potential targets, and the attacker, knowing what the defender has done, then decides which targets to attack—can be modeled and analyzed successfully without using most of the concepts and terminology of game theory. However, risk analysis and game theory are also deeply complementary. Game‐theoretic analyses of conflicts require modeling the probable consequences of each choice of strategies by the players and assessing the expected utilities of these probable consequences. Decision and risk analysis methods are well suited to accomplish these tasks. Conversely, game‐theoretic formulations of attack‐defense conflicts (and other adversarial risks) can greatly improve upon some current risk analyses that attempt to model attacker decisions as random variables or uncertain attributes of targets (“threats”) and that seek to elicit their values from the defender's own experts. Game theory models that clarify the nature of the interacting decisions made by attackers and defenders and that distinguish clearly between strategic choices (decision nodes in a game tree) and random variables (chance nodes, not controlled by either attacker or defender) can produce more sensible and effective risk management recommendations for allocating defensive resources than current risk scoring models. Thus, risk analysis and game theory are (or should be) mutually reinforcing.     

Comparing methods for weighting decision criteria

Several metbods have been proposed to weight decision criteria. In this field study a decision group, like that used by public agencies, was formed to establish project priorities. The decision group used a variety of criteria weighting methods to study how these methods influence the magnitude of the weights assigned by members of the decision group. Direct criteria weighting methods (scaling, rank-weight, point assignment and odds procedures) and an indirect approach (weights derived from hypothetical projects) were considered. The study compared the methods in terms of the magnitude of the weights assigned, the variance in weights among members of the decision group, and the similarity of the decisions based on weights assigned by each method. The time to apply each method and the preferences of the decision makers were also determined. The linear rating scale that defined the meaning of scale increments, called an ‘anchored ratin scale’, was found to be favorably received and efficient and led to the most defensible decisions.     

Nonstationary Binary Choice

This paper develops an asymptotic theory for time series binary choice models with nonstationary explanatory variables generated as integrated processes. Both logit and probit models are covered. The maximum likelihood (ML) estimator is consistent but a new phenomenon arises in its limit distribution theory. The estimator consists of a mixture of two components, one of which is parallel to and the other orthogonal to the direction of the true parameter vector, with the latter being the principal component. The ML estimator is shown to converge at a rate of n^3/4 along its principal component but has the slower rate of n^1/4 convergence in all other directions. This is the first instance known to the authors of multiple convergence rates in models where the regressors have the same (full rank) stochastic order and where the parameters appear in linear forms of these regressors. It is a consequence of the fact that the estimating equations involve nonlinear integrable transformations of linear forms of integrated processes as well as polynomials in these processes, and the asymptotic behavior of these elements is quite different. The limit distribution of the ML estimator is derived and is shown to be a mixture of two mixed normal distributions with mixing variates that are dependent upon Brownian local time as well as Brownian motion. It is further shown that the sample proportion of binary choices follows an arc sine law and therefore spends most of its time in the neighborhood of zero or unity. The result has implications for policy decision making that involves binary choices and where the decisions depend on economic fundamentals that involve stochastic trends. Our limit theory shows that, in such conditions, policy is likely to manifest streams of little intervention or intensive intervention.     

A COMPARISON OF WEIGHTING METHODS IN POWER PLANT SITING*

The insufficient attention that too often is paid to the characteristics required of attribute weights prevents multiattribute decision rules from accurately representing preferences. The weighting summation or linear model requires weights to be proportional to the relative value of unit changes in their attribute value functions. Only then will weights accurately reflect the trade-offs decision makers are willing to make. A number of methods of choosing weights are critiqued from this perspective. Applications of weighting methods in power plant siting are surveyed, and a siting study of western Maryland is presented. Two weighting methods were used, one deriving weights from trade-offs made by decision makers and the other asking decision makers to choose weights on a scale of 0 to 10. The locations picked by the two methods differ strikingly.     

Regression models of behavior for managerial decision making

A principal problem in systems studies concerns the development of models that will be accepted and used by decision makers in organizations. Regression models derived from managers' past behavior offer promise in overcoming this problem for many repetitive types of decisions. Employing a simulated production planning environment, this paper discusses both the potential usefulness and limitations of such models for understanding and improving decision making in practical applications.     

Risk Communication,Values Clarification,and Vaccination Decisions

Many health‐related decisions require choosing between two options, each with risks and benefits. When presented with such tradeoffs, people often make choices that fail to align with scientific evidence or with their own values. This study tested whether risk communication and values clarification methods could help parents and guardians make evidence‐based, values‐congruent decisions about children's influenza vaccinations. In 2013–2014 we conducted an online 2×2 factorial experiment in which a diverse sample of U.S. parents and guardians (n = 407) were randomly assigned to view either standard information about influenza vaccines or risk communication using absolute and incremental risk formats. Participants were then either presented or not presented with an interactive values clarification interface with constrained sliders and dynamic visual feedback. Participants randomized to the risk communication condition combined with the values clarification interface were more likely to indicate intentions to vaccinate (β = 2.10, t(399) = 2.63, p < 0.01). The effect was particularly notable among participants who had previously demonstrated less interest in having their children vaccinated against influenza (β = –2.14, t(399) = –2.06, p < 0.05). When assessing vaccination status reported by participants who agreed to participate in a follow‐up study six months later (n = 116), vaccination intentions significantly predicted vaccination status (OR = 1.66, 95%CI (1.13, 2.44), p < 0.05) and rates of informed choice (OR = 1.51, 95%CI (1.07, 2.13), p < 0.012), although there were no direct effects of experimental factors on vaccination rates. Qualitative analysis suggested that logistical barriers impeded immunization rates. Risk communication and values clarification methods may contribute to increased vaccination intentions, which may, in turn, predict vaccination status if logistical barriers are also addressed.     

A NOTE ON INTEGRATING INVENTORY AND PRODUCT SALES-MIX MODELS

In a recent issue of Decision Sciences, Raiborn and Harris [2] discussed the integration of inventory and product sales-mix models. While they formulated a model which reflected some of the interactions between product sales-mix and inventory decisions, the purpose of this note is to point out deficiencies in their decision rule analysis for solving such integrated models.     

Coordination of a multidivisional organization through two levels of management

This paper develops the bilevel multidivisional programming problem (BMPP) as a model for a decentralized organization. In particular, a hierarchical arrangement comprising one superior unit and M subordinate units is proposed where each unit is assumed to control a unique set of decision variables defined over jointly dependent strategy sets. Individual but interdependent objective functions are also assumed so that each unit may influence but not control another. In the formulation, decisions are made in two stages with top management given the first choice followed by the concurrent responses of the divisions. In this way it is possible to account for production externalities at the lower level and coordination activities at the higher level. As such, after outlining the major geometry of the linear case, the usefulness of the general model is discussed from a management perspective. Ramifications relating to opportunity costs, capacity utilization and computational requirements are presented and highlighted through a variety of examples.     

Impact of Demand Uncertainty on Stability of Supplier Alliances in Assembly Models

In their recent paper, Nagarajan and Sošić study an assembly supply chain in which n suppliers sell complementary components to a downstream assembler, who faces a price‐sensitive deterministic demand. Suppliers may form alliances, and each alliance then sells a kit of components to the assembler and determines the price for that kit. The assembler buys the components (kits) from the alliances and sets the selling price of the product. Nagarajan and Sošić consider three modes of competition—supplier Stackelberg, vertical Nash (VN), and assembler Stackelberg models—which correspond to different power structures in the market, and study stable supplier alliances when the assembler faces linear and isoelastic demand. In this paper, we study the impact that demand uncertainty has on stability results obtained in Nagarajan and Sošić. We first analyze models in which all decisions are made before the uncertainty is resolved, and show that the alliance of all suppliers remains stable when demand is isoelastic, or under Stackelberg models when demand is linear. However, demand uncertainty may change stability results when both parties make decisions simultaneously (VN model) and demand is linear. We then extend our results by considering scenarios in which some decisions may be postponed and made after the actual demand is known. When the ordering quantity can be determined after observing the true demand, we show that stable outcomes correspond to those obtained in the deterministic case and uncertainty has no impact on coalition stability; if only the assembler's pricing decision is postponed, we need additional conditions for stability results to carry over in the additive demand model.     

THE TIMING OF LABOR TRANSFERS IN DUAL RESOURCE-CONSTRAINED SYSTEMS: “PUSH” VS. “PULL” RULES*

Altough the dual resource-constrained (DRC) system has been studied, the decision rule used to determine when workers are eligible for transfer largely has been ignored. Some earlier studies examined the impact of this rule [5] [12] [15] but did not include labor-transfer times in their models. Gunther [6] incorporated labour-transfer times into his model, but the model involved only one worker and two machines. No previous study has examined decision rules that initiate labor transfers based on labor needs (“pull” rules). Labor transfers always have been initiated based on lack of need (“push” rules). This study examines three “pull” variations of the “When” labor-assignment decision rule. It compares their performances to the performances of two “push” rules and a comparable machine-limited system. A nonparametric statistical test, Jonckheere's S statistic, is used to test for significance of the rankings of the rules: a robust parametric multiple-comparison statistical test, Tukey's B statistic, is used to test the differences. One “pull” and one “push” decision rule provide similar performances and top the rankings consistently. Decision rules for determining when labor should be transferred from one work area to another are valuable aids for managers. This especially is true for the ever-increasing number of managers operating in organizations that recognize the benefits of a cross-trained work force. Recently there has been much interest in cross-training workers, perhaps because one of the mechanisms used in just-in-time systems to handle unbalanced work loads is to have cross-trained workers who can be shifted as demand dictates [8]. If management is to take full advantage of a cross-trained work force, it need to know when to transfer workers.     

A Comparative Analysis of PRA and Intelligent Adversary Methods for Counterterrorism Risk Management

In counterterrorism risk management decisions, the analyst can choose to represent terrorist decisions as defender uncertainties or as attacker decisions. We perform a comparative analysis of probabilistic risk analysis (PRA) methods including event trees, influence diagrams, Bayesian networks, decision trees, game theory, and combined methods on the same illustrative examples (container screening for radiological materials) to get insights into the significant differences in assumptions and results. A key tenent of PRA and decision analysis is the use of subjective probability to assess the likelihood of possible outcomes. For each technique, we compare the assumptions, probability assessment requirements, risk levels, and potential insights for risk managers. We find that assessing the distribution of potential attacker decisions is a complex judgment task, particularly considering the adaptation of the attacker to defender decisions. Intelligent adversary risk analysis and adversarial risk analysis are extensions of decision analysis and sequential game theory that help to decompose such judgments. These techniques explicitly show the adaptation of the attacker and the resulting shift in risk based on defender decisions.     

A Note on the Relationship Among Capacity,Pricing, and Inventory in a Make‐to‐Stock System

We address the simultaneous determination of pricing, production, and capacity investment decisions by a monopolistic firm in a multi‐period setting under demand uncertainty. We analyze the optimal decision with particular emphasis on the relationship between price and capacity. We consider models that allow for either bi‐directional price changes or models with markdowns only, and in the latter case we prove that capacity and price are strategic substitutes.     

Positioning a company in enterprise collaborations: Vertical integration and make-or-buy decisions

During any strategic development, a firm has to position itself in its supply chain. The decision as to how many activities in the chain to occupy is known as vertical integration and is termed as make-or-buy when an individual activity is considered. Because of its very nature, this category of decisions may be made only within the framework of an extended enterprise. To support such a decision-making process, a model is described that defines all key elements needed to make such critical decisions. More precisely, a set of rules must present all items which have to be defined and valued, and the relationships between them, in order to conclude if an activity must be processed internally (Make) or supplied externally (Buy). This paper details such a hierarchy of rules.     

Consortium for Atlantic Regional Assessment: Information Tools for Community Adaptation to Changes in Climate or Land Use

To inform local and regional decisions about protecting short-term and long-term quality of life, the Consortium for Atlantic Regional Assessment (CARA) provides data and tools (for the northeastern United States) that can help decision makers understand how outcomes of their decisions could be affected by potential changes in both climate and land use. On an interactive, user-friendly website, CARA has amassed data on climate (historical records and future projections for seven global climate models), land cover, and socioeconomic and environmental variables, along with tools to help decision makers tailor the data for their own decision types and locations. CARA Advisory Council stakeholders help identify what information and tools stakeholders would find most useful and how to present these; they also provide in-depth feedback for subregion case studies. General lessons include: (1) decision makers want detailed local projections for periods short enough to account for extreme events, in contrast to the broader spatial and temporal observations and projections that are available or consistent at a regional level; (2) stakeholders will not use such a website unless it is visually appealing and easy to find the information they want; (3) some stakeholders need background while others want to go immediately to data, and some want maps while others want text or tables. This article also compares what has been learned across case studies of Cape May County, New Jersey, Cape Cod, Massachusetts, and Hampton Roads, Virginia, relating specifically to sea-level rise. Lessons include: (1) groups can be affected differently by physical dangers compared with economic dangers; (2) decisions will differ according to decision makers' preferences about waiting and risk tolerance; (3) future scenarios and maps can help assess the impacts of dangers to emergency evacuation routes, homes, and infrastructure, and the natural environment; (4) residents' and decision makers' perceptions are affected by information about potential local impacts from global climate change.     

Individual differences in numerical representations of risk in health decision making: A fuzzy-trace theory approach

Fuzzy-trace theory predicts that decisionmakers process numerical information about risk at multiple levels in parallel: the simplest level, nominal (categorical some-none) gist, and at more fine-grained levels, involving relative comparison (ordinal less-more gist) and exact quantities (verbatim representations). However, little is known about how individual differences in these numerical representations relate to judgments and decisions, especially involving health tradeoffs and relative risks. To investigate these differences, we administered measures of categorical and ordinal gist representations of number, objective numeracy, and intelligence in two studies (Ns = 978 and 956). In both studies, categorical and ordinal gist representations of number predicted risk judgments and decisions beyond objective numeracy and intelligence. Participants with higher scores in categorical gist were more likely to choose options to avoid cancer recurrence risks; those who were higher in ordinal gist of numbers were more likely to discriminate relative risk of skin cancer; and those with higher scores in objective numeracy were more likely to choose options that were numerically superior overall in terms of relative risk of skin cancer and of genetic risks of breast cancer (e.g., lower numerical probability of cancer). Results support parallel-processing models that assume multiple representations of numerical information about risk, which vary in precision, and illustrate how individual differences in numerical representations are relevant to tradeoffs and risk comparisons in health decisions. These representations cannot be reduced to one another and explain psychological variations in risk processing that go beyond low versus high levels of objective numeracy.