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Highlights • A multi-stage fuzzy stochastic model was developed in this research. • The developed model was applied for supporting water resources management. • Ecological water demand was considered and reflected in the developed model. • Multiple ways were adopted for dealing with multiple uncertainties. • It can contribute to in-depth analysis and sustainable development of water-resource- and eco- systems. Abstract In this paper, a multi-stage fuzzy stochastic programming (MFSP) method is introduced to deal with uncertainties presented as fuzzy sets and probability distributions. Moreover, it is able to reflect dynamics of uncertainties and the related decision processes through constructing a series of representative scenarios within a multi-stage context under a set of fuzzy α-cut levels. A management problem about long-term planning of water resources system has been studied to illustrate applicability of the proposed approach. With ecological water demand being considered, the framework solves the complex problems that can hardly be solved in previous individual model research and promotes sustainable development. The results indicate that the dynamic and complexity of water resources allocation can be reflected through the multilayer discrete context tree. Moreover, real-time correction for reducing the risk of water shortage and low economic penalty can be presented. They can also help identify satisfaction degree of the goal and feasibility degree of constraints in an interactive way, enabling decision makers to generate a series of alternatives under various system conditions. Overall, it can not only contribute to decision makers for in-depth analysis, but also for sustainable development of ecosystem. [ABSTRACT FROM AUTHOR]

An enhanced radial interval programming approach was developed for supporting the identification of sound agricultural production plans. It was designed to tackle dual uncertainties in both the economic and environmental subsystems as well as reflect differential aspiration levels for 'immunity' against such uncertainty. By adding protection functions to both the objective functions and constraints susceptible to dual uncertainties without any distributions, it improved upon previous methods through simultaneously measuring and manipulating expected return risk and resource-environmental compliance risk. Moreover, this study made novel attempts to explore system-wide optimal levels of robustness based on proposed performance evaluation criteria and investigations over the interactive effects of varied robustness aspirations for different model components. Based on analyzed tradeoffs among system optimality, robustness and uncertainty degree of solutions pertaining to the study case, a unified protection level of 13 (or 10) was recommended for risk-averse (or risk-neutral) decision making. The model outputs were compared to those from three alternatives, revealing that it could achieve a high level of system reliability at a minor price of economic return. This approach could be generalized for wide-scale applications. [ABSTRACT FROM AUTHOR]

Concerns over water conflicts between human beings and ecosystems are increasing. Also, wastewater discharged by manufacturing industries is causing multiple ecological and environmental impacts in many regions. In this research, to comprehensively assess ecological and environmental impacts of wastewater discharged from large-scale industries, a hybrid life-cycle and fuzzy-set-pair analyses (HLCA-FSPA) approach was proposed. This approach represented an integration of life cycle analysis, set pair analysis, and fuzzy sets theory. The developed method could improve previous studies in systematically reflecting impacts of industrial wastewater in terms of multiple dimensions, and considering uncertain parameters in the evaluation process. It could give a complete and robust assessment of wastewater environmental and ecological impacts based on life cycle inventory/database and uncertainty analyses. The developed HLCA-FSPA method was then applied to a pulp and paper mill in Shandong Province of China. The results indicated that the impact of wastewater at the stage of pulp production was under the limit of China's wastewater discharge standard (i.e., level III) with the connection degree of 0.47. Comparatively, the impact level of the entire life cycle of copying paper production was III with a slightly decreased connection degree (i.e., 0.38). Such a difference reflected possibility variations of the impacts at different stages. The results also indicated that the developed method can be expanded to other areas based on the corresponding LCA database. Thus, the results could provide scientific bases for supporting decision-making in industrial wastewater management to mitigate the associated ecological and environmental impacts. [ABSTRACT FROM AUTHOR]

The processes and factors involved in evacuation activities are associated with a variety of uncertainties, posing major challenges to evacuation planners. This study represents an attempt to employ inexact optimization techniques for addressing uncertainties in evacuation practices. In this study, an interval-parameter fuzzy evacuation management (IF-EM) model is developed for supporting environment-oriented evacuation management under uncertainty. Through IF-EM, uncertainties in the model's stipulations and coefficients which are expressed as fuzzy sets and interval numbers can be directly communicated into the optimization process, greatly enhancing the robustness of the optimization system. The model is then applied to a case study and solved through a two-step interactive algorithm. A number of evacuation schemes can be generated by adjusting decision variables within their solution intervals according to projected planning conditions, reflecting various decision policies and a compromise between system optimality and stability. The relationships among vehicle allocation pattern, evacuation time, system satisfaction level, and system reliability level can be effectively reflected, facilitating more in-depth analyses of interactions among system efficiency, environmental protection, and economic cost. Results from the case study suggest that the proposed IF-EM model is applicable to practical evacuation problems that are associated with uncertainties and complexities. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The intensification of heat stress in a changing climate poses great threats to both human health and labor productivity. It is of great practical importance to assess the impacts of climate-induced heat stress on labor productivity and to develop effective adaptation strategies. In this paper, an integrated optimization-based productivity restoration modeling framework is proposed for the first time to develop the optimal policies for adaptation to climate change. To address underlying uncertainties associated with climate and labor management systems, we take into account ensemble projections from five global climate models (GCMs) under two Representative Concentration Pathways (RCP2.6 and RCP8.5) and inexact system costs. The system costs, including direct and indirect costs such as management costs, energy costs, and labor costs, are presented as interval numbers due to inherent uncertainty caused by population growth, technology development, and other social-economic factors. Uncertain information can be effectively communicated into the optimization processes in this study to generate optimal and reliable decision alternatives. We find that the increased Wet-Bulb Globe Temperature (WBGT) will lead to a large reduction in labor capacities over China except for the Tibetan Plateau under both RCPs by the end of the 21st century. The less developed regions tend to achieve the minimum system cost by having labor productivity recovered through working overtime due to the relatively low cost of overtime. This could result in more heat-related work injuries in the less developed regions. Since the less developed regions are not heat-prone areas in China, the changing climate would be a more dangerous threat and cause more damages to these regions where the residents are less acclimatized to heat stress. Moreover, we obtain a range of minimum system costs from 1.86 to 8.97 billion dollars under RCP2.6 and from 9.42 to 32.31 billion dollars under RCP8.5 (about 0.2% of China's GDP in 2019, 0.01% of China's GDP projected in 2100 under a sustainable socio-economic development scenario) for the restoration of labor productivity in a warming climate. We argue that urgent actions are needed to mitigate global warming impacts on labor productivity. • An integrated optimization modeling framework is developed to achieve optimal labor productivity restoration in a warming climate. • Various uncertainties are addressed through ensemble climate projections and interval programming models. • Intensification of heat stress leads to a large decrease in labor capacity over China except for the Tibetan Plateau. • More losses in working hours are projected for moderate labor activities compared to light and heavy labor activities. • Less developed regions tend to have more working hours recovered by working overtime than air conditioning. [ABSTRACT FROM AUTHOR]

Inflow forecast is an important input for reservoir operation. Due to limited forecast techniques, the inflow forecast always has uncertainties (usually in the form of forecast errors). Handling inflow forecast uncertainties effectively is important for optimizing reservoir operation. The magnitude of this uncertainty depends on both the forecast lead times and the hydrological periods (i.e., wet season vs. dry season). Two-stage Bayesian stochastic dynamic programming (TBSDP) is an effective way to address the forecast uncertainties. However, current TBSDP methods pay more attention to the uncertainty differences caused by forecast lead times, neglecting the uncertainty differences in wet and dry seasons. Starting with a TBSDP model, we proposed a new two-stage and two-period version of the model (TTBSDP), which can not only consider the forecast uncertainty differences caused by forecast lead times, but also separately accounts for forecast uncertainty in wet and dry seasons. Because dealing separately with the uncertainties in the two periods increases the computational complexity, we further explored the conditions under which degree of forecast uncertainties this separation is necessary. We compared our new model's results (in terms of cumulative annual power generation) with the previous Bayesian stochastic dynamic programming models. With increasing forecast uncertainty in the first stage of the overall forecast horizon (in the wet season), the TTBSDP model produced superior results at higher uncertainty, with the most stable performance (the smallest variation of cumulative annual power generation when the forecast uncertainty coefficient increases). The proposed new model is benefit for hydropower operations when the magnitude of forecast uncertainty in the wet seasons is large. • A two-stage and two-period Bayesian stochastic dynamic programming model was developed. • The new model dealt separately with forecast uncertainties not only in two stages but also in two hydrological periods. • The TTBSDP model produced superior results at higher uncertainty, with the smallest variation of power generation. [ABSTRACT FROM AUTHOR]