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Summary Reservoir systems with multiple operators can benefit from coordination of operation policies. To maximize the total benefit of these systems the literature has normally used the social planner’s approach. Based on this approach operation decisions are optimized using a multi-objective optimization model with a compound system’s objective. While the utility of the system can be increased this way, fair allocation of benefits among the operators remains challenging for the social planner who has to assign controversial weights to the system’s beneficiaries and their objectives. Cooperative game theory provides an alternative framework for fair and efficient allocation of the incremental benefits of cooperation. To determine the fair and efficient utility shares of the beneficiaries, cooperative game theory solution methods consider the gains of each party in the status quo (non-cooperation) as well as what can be gained through the grand coalition (social planner’s solution or full cooperation) and partial coalitions. Nevertheless, estimation of the benefits of different coalitions can be challenging in complex multi-beneficiary systems. Reinforcement learning can be used to address this challenge and determine the gains of the beneficiaries for different levels of cooperation, i.e., non-cooperation, partial cooperation, and full cooperation, providing the essential input for allocation based on cooperative game theory. This paper develops a game theory–reinforcement learning (GT–RL) method for determining the optimal operation policies in multi-operator multi-reservoir systems with respect to fairness and efficiency criteria. As the first step to underline the utility of the GT–RL method in solving complex multi-agent multi-reservoir problems without a need for developing compound objectives and weight assignment, the proposed method is applied to a hypothetical three-agent three-reservoir system. [ABSTRACT FROM AUTHOR]

This paper develops a novel service request sequence optimization model for tower crane operation efficiency improvement. The suggested model uses integer programming and modifies the classical Traveling Salesman Problem (TSP) formulation for optimizing construction tower crane operations. Numerical examples demonstrate that the application of the developed optimization model can result in 25–45% saving in the total travel time of the tower crane depending on the number of simultaneous requests. [ABSTRACT FROM AUTHOR]

Highlights: [•] Addressing the major gap in previous climate change and hydropower research in California. [•] Simultaneous evaluation of climate change effects on high-elevation hydropower supply and pricing. [•] Estimating climate warming effects on high-elevation hydropower operations in California. [ABSTRACT FROM AUTHOR]

As demands for limited natural resources increase, developing management institutions that ensure the sustainability of such resources is essential. Many natural resources are Common Pool Resources (CPRs), managed under different non-cooperative, cooperative, and externally imposed management frameworks. While early studies of non-cooperative CPR management suggest inevitable “tragedy of the commons,” here we discuss how users can avoid tragic outcomes by changing their decision making rationales and exploitation strategies even in a non-cooperative environment. This paper introduces and compares various types of non-cooperative institutions that are available to manage CPRs. These management institutions are then applied, using a numerical groundwater exploitation example, to determine how different planning variables are affected by the choice of management institution. Results indicate that CPR users can improve their gains by considering the externalities and developing long-term exploitation plans, as opposed to short-term plans with no consideration of externalities that result in rapid exhaustion of the resource and lead to the so-called “tragedy of the commons.” [Copyright &y& Elsevier]

This paper presents a novel approach for modeling high-elevation hydropower systems. Conservation of energy and energy flows (rather than water volume or mass flows) is used as the basis for modeling more than 135 high-elevation high-head hydropower sites throughout California. The unusual energy basis for reservoir modeling allows for development of hydropower operations models for a large number of plants to estimate large-scale system behavior without the expense and time needed to develop traditional streamflow and reservoir volume-based models in absence of storage and release capacity, penstock head, and efficiency information. Potential applications of the developed Energy-Based Hydropower Optimization Model (EBHOM) include examination of the effects of climate change and energy prices on system-wide generation and hydropower revenues. An extensive comparison of the EBHOM with a traditional hydropower optimization model used in California produced similar results and indicated good reliability of EBHOM's predictions. [ABSTRACT FROM AUTHOR]

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal. [ABSTRACT FROM AUTHOR]