Finding equitable policy solutions is critical for developing sustainable energy use. This paper presents a system-of-systems (SoS) formalism for addressing the equity issue in multi-actor policymaking. In a SoS, the control of the overall system performance is shared among a network of actors. In contrast to a single optimal solution that aggregates objectives of actors, the solution concept of iso-performance is formulated and employed to illuminate multiple solutions and hence the ‘space’ for actors to compromise. By specifically accounting for the equity issue, the level of sacrifice each actor makes for each iso-performance solution is computed. To demonstrate the approach, a case study is presented about policymaking to reduce fuel life cycle aviation emissions in the United States based on the year 2020 reduction target, involving government, airlines, jet fuel refinery companies, and aircraft and engine manufacturers. A resource allocation mixed integer programming model is employed to calculate carbon emissions resulting from airlines’ deployment of aircraft fleet to meet changing air transport demand. The paper discusses three iso-performance solutions; each of them requires a different level of sacrifice from each actor. Such an insight can inform policymaking in determining the magnitude of compensation required when a particular solution is pursued.

In this paper, the optimal policy is considered when the buyer faces two supply sources: one is the contract supplier from which the buyer orders over a specific contract period (say, a year) at a pre-agreed price, and the other is the spot market. However, when ordering from the contract supplier, the buyer must fulfill a pre-determined total order quantity, or the so-called definite total order quantity commitment, over the whole contract period. In other words, the commitment secures the buyer a fixed price but obliges him/her a total order quantity over the contract period. Although the spot market gives the buyer more flexibility in terms of order quantities, its prices are volatile. Such a combination of contract and spot procurements is often observed in practice. Within the contract period, there are multiple sub-periods, during each of which the buyer reviews the inventory, issues an individual order, and uses the on-hand inventory to meet the random demand. Thus, in each (ordering) period, the buyer will weigh between the current known spot price (by procuring from the spot market) and a lower future price (by waiting while consuming the remaining commitment). An optimal dual ordering policy is characterized for each period, depending on the on-hand inventory level, the spot price, and the remaining commitment quantity. The optimal policy in each period is also shown to be independent of the contract price. Through a numerical study, the inventory cost is demonstrated to be (1) insensitive to the contract price when the total commitment quantity is lower than the total expected demand over the contract period and (2) non-increasing in the variability of spot prices.

Large and complex construction projects face risk from various sources and the successful completion of such projects depends on effective risk management. This study investigates the risk faced by Chinese firms participating in constructing AP1000 nuclear power plants in China. AP1000 nuclear reactors are new, Generation III+ reactors designed by Westinghouse and to be built first in China. The semi-structured interview approach is used to elicit information from experts involved in the AP1000 projects in China. Based on the interviews, various sources of risk are identified. In addition to general risks that megaprojects normally face, there are unique risks that arise from various sources such as technological, political, organizational, and individual personnel risks. Risk management strategies are proposed to manage general and unique risks identified in the study. The findings of this study would be helpful for Chinese companies involved in the construction of AP1000 nuclear power plants to mitigate the risks associated with the projects.

Based on game theory and principal-agent theory, this paper focuses on how to control product quality and design quality contract in supply chain when moral hazard exists. We set up the supplier and buyer’s expected profits function model, in which the supplier makes production process investment-level decision and decides on the product quality prevention level, whereas the buyer makes quality evaluation decision and decides on the product quality inspection level. The supplier with a moral hazard of reducing investment level may lack investment in the production process; thus, the buyer will pay the information rent to incentivize the supplier to improve the investment level. The buyer creates the moral hazard of exaggerating the product quality defective rate, who may overinvest in the inspection process. We use the optimal condition to solve supplier’s first-best investment level, product quality prevention level, and buyer’s first-best quality inspection level, internal penalty, and apportionment ratio of external failure cost. We also conduct a simulation test that shows the following: When the supplier improves its investment level, its product quality prevention level will increase, and the buyer’s quality inspection level will decrease. With the improvement in the buyer’s product quality inspection level, its internal penalty will increase, and the supplier’s external failure cost will also increase while its expected profits will decrease. Hence, the buyer will design an incentive contract, the expected profits of which will increase, and the whole supply chain’s joint expected profits function may become an inverse U shape. Finally, we develop a simulation example and propose suggestions for quality control strategy and contract design in the supply chain under the conditions of asymmetric information.

The intent of this paper is to schedule short-term hydrothermal system probabilistically considering stochastic operating cost curves for thermal power generation units and uncertainties in load demand and reservoir water inflows. Therefore, the stochastic multi-objective hydrothermal generation scheduling problem is formulated with explicit recognition of uncertainties in the system production cost coefficients and system load, which are treated as random variable. Fuzzy methodology has been exploited for solving a decision making problem involving multiplicity of objectives and selection criterion for best compromised solution. A real-coded genetic algorithm with arithmetic-average-bound-blend crossover and wavelet mutation operator is applied to solve short-term variable-head hydrothermal scheduling problem. Initial feasible solution has been obtained by implementing the random heuristic search. The search is performed within the operating generation limits. Equality constraints that satisfy the demand during each time interval are considered by introducing a slack thermal generating unit for each time interval. Whereas the equality constraint which satisfies the consumption of available water to its full extent for the whole scheduling period is considered by introducing slack hydro generating unit for a particular time interval. Operating limit violation by slack hydro and slack thermal generating unit is taken care using exterior penalty method. The effectiveness of the proposed method is demonstrated on two sample systems.

We consider the preventive maintenance of a production system that is deteriorated by random shocks and the production process itself. The degree of deterioration is modeled by discrete and finite states. Shocks arrive according to a Poisson process and deteriorate the system by random amounts. The system may deteriorate whenever it produces an item. The system is continuously monitored and repaired if the system state is at or above a predetermined level for maintenance. We analyze the lifetime, product quantity, average cost, and average profit considering revenue from the product and cost due to setup, operation, and repair. Assuming a structure of system parameters and costs, using numerical examples, we investigate the impact of production and shock arrivals on the average profit and the optimal maintenance level that maximizes the average profit. The proposed model is applicable to manufacturing tasks in which machines wear due to production, for example, press processes, milling, turning, punching, and drilling.

This paper deals with single-machine scheduling problems with a more general learning effect based on sum-of-processing-time. In this study, sum-of-processing-time-based learning effect means that the processing time of a job is defined by a decreasing function of the total normal processing time of jobs that come before it in the sequence. Results show that even with the introduction of the sum-of-processing-time-based learning effect to job processing times, single-machine makespan minimization problems remain polynomially solvable. The curves of the optimal schedule of a total completion time minimization problem are V-shaped with respect to job normal processing times.