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In a microgrid, an efficient energy storage system is necessary to maintain a balance between uncertain supply and demand. Distributed energy storage system (DESS) technology is a good choice for future microgrids. However, it is a challenge in determining the optimal capacity, location, and allocation of storage devices (SDs) for a DESS. This paper proposes a two-stage approach to solve these SD decision-making problems in a microgrid. In the first stage, a continuous approximation approach was used to formulate the SD location, capacity, and renewable energy investment problems from a long-term perspective. In the second stage, the dispatch quantity problems are addressed to minimize the operational cost based on the results of Stage 1. Some examples and real data are used to illustrate the model and solution approaches. The results show that our approach can achieve the cost minimization. The optimal SD location and capacity strategy and the renewable energy investment level are close to the real data obtained from Liu et al. (2018) after 20 runs. • Designing a two-stage approach to solve these SD decision-making problems in a microgrid. • A continuous approximation approach is used to formulate the SD location, capacity, and renewable energy investment problems from a long-term perspective at the first stage. • The dispatch quantity problems are addressed to minimize the operational cost based on the results of Stage 1 at Stage 2. [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]

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]