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Operations and maintenance management for renewable energy (RE) projects has become increasingly important in improving energy system models' precision. Specifically, some elements are uncertain and difficult to predict, such as temperature conditions or system reliability, and these result in more complicated RE projects. Suitable maintenance and insurance policies are vital to reduce risks for RE system operators. This paper formulates a profit model that integrates system reliability, predictive maintenance, and green insurance into electricity pricing and capacity problems. A non-linear optimization solution procedure is proposed to determine the optimal electricity price, capacity, investment plan, predictive maintenance budget, and insurance level while maximizing company profit. The theoretical results indicate that RE systems' increased reliability decreases the insurance level. However, an increase in the insurance level decreases RE project investments. Therefore, companies can attract more investments for RE projects if RE system reliability increases while insurance costs decrease. Additionally, this work not only presents a numerical analysis with examples and a sensitivity analysis to illustrate the model, but also discusses the opportunities this work offers for managers and analysts in practice. [ABSTRACT FROM AUTHOR]

• Analyze and discuss the company decision-making when banks support to generate renewable energy and demand uncertain. • The repayment ability of the power company depends on sales revenue. • A single newsvendor model is used to determine the optimal renewable energy price, capacity and amount of loan from bank. • Discuss how company responds to the changes of the different situation of energy market. The costs of renewable energy technologies are highly sensitive to financing terms. Power companies are willing to invest in renewable energy generation because of clean and sustainability concerns. However, renewable energy generation poses a potentially high risk, both in generation and consumption. In this study, we consider how a power company makes its decisions if a credit bank willing to lend to it for renewable energy generation. The repayment ability of the company depends on sales revenue under demand uncertainty. We formulate a mathematical model to help the company determine the optimal electricity price, renewable energy capacity, and amount of loan while maximizing the total expected profit. A searching algorithm is proposed to solve the problem. Several numerical examples are provided to illustrate the proposed model and solution approach. Managerially, our study suggests that increasing renewable energy investments through financing institutions requires careful attention to pricing, capacity, and financing policies. [ABSTRACT FROM AUTHOR]

Optimizing the order-fulfillment process is crucial to the logistics industry. Replacing experience-based judgment with intelligent methods in the packing process not only improves work efficiency but also eliminates resource wastage. This study focuses on the three-dimensional multiple bin-size bin packing problem with compatible categories (3D-MBSBPPCC) and different size of the boxes. An algorithm is developed to achieve the best packing pattern while minimizing the number of boxes required and the total unused space in the boxes. In the algorithm, we first use the first-fit decreasing algorithm to find an initial solution. Next, the carton configuration algorithm is applied to perform packing and evaluate the solution. Subsequently, a simulated annealing (SA)-based algorithm is employed to improve the solution by moving the products among the subsets of the solution. Finally, hybrid metaheuristics based on both SA and the genetic algorithm, are proposed to improve the solution quality. We tested the proposed packing algorithms on real-world scenarios. The experimental results demonstrate that the proposed algorithms are effective in solving 3D-MBSBPPCC. [ABSTRACT FROM AUTHOR]

In practice, a vendor may offer a retailer an advance-cash-credit (ACC) payment scheme. This implies the buyer needs to pay a fraction of the purchasing cost before he receives the item (advance payment), some cash when the item is arrived (cash on delivery), and then the remaining purchasing cost after a credit period (credit payment). This paper studies the effect of payment schemes on the supply chain network design. The network consists of an outside supplier, multiple distribution centers (DCs), and multiple retailers. The DCs receive an upstream ACC payment from the supplier. This study attempts to determine the optimal location, allocation, as well as the inventory cycle time while minimizing total cost. A continuous approximation is used in the model development and the time value of money concept is considered, since different payment schemes related to time change the cash value. Numerical studies are adopted to demonstrate the solution approach and the effects of changing parameters on decisions and total cost. This study provides novel management implications for supply chain network designers' reference. The results show that the number of DCs to be opened, the joint replenishment cycle time of DC, and the total cost increase as the interest earned decreases and the interest charged increases. [ABSTRACT FROM AUTHOR]

We develop an economic production quantity model on the basis of radio frequency identification (RFID) adoption, trade credit, and reworking of imperfect products. We determine the optimal values of production cycle time and RFID investment levels for ordering, just-in-time, and operating efficiencies. On the basis of the relationship between the demand and reworking rates, we classify the model into two cases. Furthermore, on the basis of the relationship between production cycle time and credit period, we divide each case into four subcases. We derive the total manufacturing cost for the subcases and develop an algorithm to solve the four-branch piecewise nonlinear problem. Finally, we illustrate the solution approach and perform numerical analyses to discuss the influences of system parameters on decisions. [ABSTRACT FROM AUTHOR]