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The subject of this book is supply chain logistics planning optimization under multiple uncertainties, the key issue in supply chain management. Focusing on the strategic-alliance three-level supply chain, the model of supply chain logistics planning was established in terms of the market prices and the market requirements as random variables of manufactured goods with random expected value programming theory, and the hybrid intelligence algorithm solution model was designed. Aiming at the decentralized control supply chain, in which the nodes were unlimited expansion, the chance-constrained stochastic programming model was created in order to obtain optimal decision-making at a certain confidence level. In addition, the hybrid intelligence algorithm model was designed to solve the problem of supply chain logistics planning with the prices of the raw-materials supply market of the upstream enterprises and the prices of market demand for products of the downstream enterprises as random variables in the supply chain unit. Aimed at the three-stage mixed control supply chain, a logistics planning model was designed using fuzzy random programming theory with customer demand as fuzzy random variables and a hybrid intelligence algorithm solution was created. The research has significance both in theory and practice. Its theoretical significance is that the research can complement and perfect existing supply chain planning in terms of quantification. Its practical significance is that the results will guide companies in supply chain logistics planning in the uncertain environment.

This research introduces a two-level integration of climate-economy modelling and portfolio analysis, to simulate technological subsidisation with implications for multiple Sustainable Development Goals (SDGs), across socioeconomic trajectories and considering different levels of uncertainties. We use integrated assessment modelling outputs relevant for progress across three SDGs—namely air pollution-related mortality (SDG3), access to clean energy (SDG7) and greenhouse gas emissions (SDG13)—calculated with the Global Change Assessment Model (GCAM) for different subsidy levels for six sustainable technologies, across three Shared Socioeconomic Pathways (SSPs), feeding them into a portfolio analysis model. Optimal portfolios that are robust in the individual socioeconomic scenarios as well as across the socioeconomic scenarios are identified, by means of an SSP-robustness score. A second link between the two models is established, by feeding portfolio analysis results back into GCAM. Application in a case study for Eastern Africa confirms that most SSP-robust portfolios show smaller output ranges among scenarios. ; This work was supported by the H2020 European Commission Project "PARIS REINFORCE" under grant agreement No. 820846, and by the Spanish Ministry of Economy and Competitiveness MINECO through BC3 María de Maeztu excellence accreditation MDM-2017-0714. The sole responsibility for the content of this paper lies with the authors; the paper does not necessarily reflect the opinions of the European Commission or the Spanish Government.

Decision analysts are frequently called on to help inform decision makers in cases involving considerable uncertainty. In such situations, expert elicitation of parameter values is frequently used to supplement more conventional research. Expert elicitations typically rely on small panels of experts. However, in cases where the information needed for risk management must draw on a broad range of disciplines or types of professional backgrounds and experience, a larger, more heterogeneous expert panel is needed. In this paper we develop a formal protocol and a suite of uncertainty measures for this work. The protocol uses formal survey methods to take advantage of variation in individual expert uncertainty and heterogeneity among experts as a means of quantifying and comparing sources of uncertainty about parameters of interest. We illustrate the use of this protocol with an expert elicitation on the distribution of foodborne illness in the United States across foods. In the survey, experts are asked to attribute illnesses associated with one of eleven major foodborne pathogens to the consumption of one of eleven categories of food. Results show how the distributions of multiple measures of uncertainty (e.g., agreement of experts and uncertainty in knowledge), made feasible by use of a large panel of experts, can help identify which of several types of risk management actions may be most appropriate.

PurposeThe spatial and psychological distance within agri-food chains provides both profit and risk for supply chain members. Grounded on the transaction cost economics (TCE) and institutional theory (IT), the purpose of this paper is to test whether the adoption of multiple supply chains (MSCs), which adopt both traditional and shortened supply chains, can be used to manage uncertainty and mitigate the risk associated with a supply chain.Design/methodology/approachIn order to test the hypothesis, matched questionnaire surveys were developed to collect the data from farm managers and consumers. Completed questionnaires were received from 112 respondents. The hierarchical regression analysis was performed to test hypotheses.FindingsThe result shows the positive effects of environmental and behavioral uncertainties on MSC adoption and represents the diminished moderating effects of institutions (industrial and consumption tendency) on the relationship between uncertainties and MSA adoption.Research limitations/implicationsThis study only explored producers and their recommended consumers; future studies can undertake questionnaire designs (one producer-to-many consumers) and empirical analyses with analytic hierarchy process theory to reexamine the hypotheses proposed in this study.Practical implicationsMSC adoption is a way to manage uncertainties resulting from spatial and psychological distance in the supply chain. Producers and consumers show their risk preferences by SC adoption after considering pre-constructed societal norms. Therefore, the consumers' and producers' choice of a supply chain reflects a process of communicating risk. The adoption of a mixed governance mode (MSC adoption) and accessing information about common practices are two ways to decrease such uncertainties.Social implicationsThere are multiple goals (traceability, fairness, efficiency, well-being) in the food supply chain that may be satisfied by MSC adoption. Therefore, policymakers should understand the different values of various supply chains and facilitate the development of various supply chain modes.Originality/valueThis study integrated the undersocialized and oversocialized perspectives (TCE and IT) to understand how uncertainties of supply chains may be diminished. Based on these perspectives, it found that the adoption of the mixed governance mode and accessing of institutional information are two ways to decrease such uncertainties.

AbstractThere have been many models for portfolio selection, but most do not explicitly include uncertainty and multiple objectives. This paper presents an approach that includes these aspects using a form of stochastic integer programming with recourse.The method involves the use of a time‐based decision tree structure called a "project tree." Using this basic format, an illustrative six‐project example is presented and analyzed. Various forms of objectives are discussed, ranging from the maximization of expected portfolio value to the maximization of the minimum weighted portfolio deviation from two goals. In each case, formulated numerical problems are given, and the solutions derived are presented. The approach is shown to be very flexible and capable of handling a variety of situations and objectives.

AbstractFormal models commonly characterize interstate bargaining as dichotomous, ending in either war or peace. But there are many forms of coercion—including supporting rebel groups, sanctions, and cyberattacks. How does the availability of intermediate policy options affect the incidence of war and peace? We present an analysis of crisis bargaining models with intermediate policy options that challenges conventional results about the relationship between private information and negotiation outcomes. In our "flexible‐response" modeling framework, unlike in traditional crisis bargaining models, we find that greater private war payoffs may be associated with a lower probability of war or worse settlement values. When intermediate options are available, the relationship between the private efficacy of war and the private efficacy of these other options largely determines equilibrium outcomes. By utilizing the tools of mechanism design, we derive game‐form–free results on how private information shapes international conflict, regardless of the precise negotiating protocol.

AbstractIn a recent issue of Decision Sciences, Muhlemann, Lockett, and Gear [8] developed a multiple‐objective, stochastic linear programming formulation of the multiperiod portfolio selection problem under uncertainty. The purpose of this note is to offer some extensions to their multicriteria approach which is otherwise viewed as an excellent attempt at modeling realistic aspects of the portfolio selection problem. Further, integer goal programming combined with simulation is suggested as an alternate approach for solving the dynamic multiple‐objective problem.

AbstractHarrington and Fischer [2] discuss some of the limitations of a model presented by Muhlemann, Lockett, and Gear [8] for the portfolio selection problem in multiple‐criteria situations under uncertainty. They go on to propose integer goal programming and simulation as an alternative solution procedure. The purpose of this note is to critically examine their proposal and to contrast the two approaches. It is shown that the problem is being viewed from different decision‐making standpoints.

"I study Cournot competition under incomplete information about demand while assuming that market price must be non-negative for all demand realizations. Although this assumption is very natural, it has only rarely been made in the earlier literature. Yet it has important economic consequences: (1) multiple (symmetric, pure strategy) equilibria can exist, despite the fact that demand and cost are linear; and (2) expected total surplus can be larger when the firms do not know demand than when they do, a result which has important implications for the social desirability of information sharing. The arguments of the paper are relevant also for price competition and for uncertainty about, e.g., cost or the number of firms, and these issues are discussed." (author's abstract)

We present a novel multiple volatility real options approach (MVR) to value investment projects with embedded flexibility and affected by multiple uncertainties. A core innovation is the MVR decision tree composed of MVR synthetic tree components, each reflecting a unique binomial process that approximates a geometric Brownian motion of project value induced by one uncertainty source. MVR uses Monte Carlo simulation to generate volatility of project value log-returns arising from each uncertainty source. MVR produces a multidimensional surface, which is hidden in other approaches, representing enhanced net present value (ENPV) as a function of each uncertainty. It allows the impact of each uncertainty's volatility on ENPV to be measured through three MVR sensitivity analysis levers. To illustrate MVR, it is applied to a real-world investment project, revealing that MVR provides a more accurate valuation than alternative approaches that do not account for separate impacts of each uncertainty. MVR with its greater veracity, provides robust investment decisions through MVR decision rules.