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The demand rate for seasonable/fashionable products, in general, fluctuates through their life cycle, initially increases during the growth phase, then remains practically constant in the maturity phase, and finally decreases in the decline phase. Consequently, their demand rate can be reasonably depicted by a trapezoidal-type pattern, which includes constant, increasing, decreasing and ramp-type demand patterns as special cases. Recently, inventory models for deteriorating items with trapezoidal-type demand have been developed to minimize the total cost over the planning horizon (see Cheng & Wang, 2009; Skouri & Konstantaras, 2009; Cheng et al., 2011; Lin et al., 2014). However, in these models the purchasing cost is excluded from the total cost. As a result, this implies an underestimation of the lost sales cost. In the present paper, previous studies regarding inventory models with trapezoidal-type demand rate are extended and completed by (1) discussing two inventory systems starting with and without shortages, (2) including the purchasing cost into the total cost, (3) extending the deterioration rate to any time-varying rate, (4) taking into consideration the time value of money (5) maximizing the net present value of total profit. Therefore, many previous inventory models become special cases of the present model. [ABSTRACT FROM AUTHOR]

The resource constrained project scheduling problem with discounted cash flows (RCPSPDC) is one of the most challenging problems owing to its NP-hard characteristics. This complex combinatorial optimization problem is most relevant to project management, building and construction management, and production planning. Although several solution methods have been suggested to solve the RCPSPDC, no single method has been shown to be the best for a wide range of problems. In this study, a multi-operator immune genetic algorithm, called MO-IGA, is proposed which integrates a genetic algorithm (GA) and an immune algorithm (IA) to solve the RCPSPDC. Two different operators for each crossover, mutation, immunization, and local search operation are utilized dynamically in the MO-IGA framework. Variable insertion neighborhood search (VINS) and forward–backward improvement (FBI) are utilized for local search to enrich the searching behavior and exploration. The algorithm starts with the same probability for each crossover, mutation, immunization, and local search operator; however, the probability is updated dynamically depending on the success of each operator in producing the quality solution. An activity move rule (AMR) has been utilized to delay the task with negative cash flows as much as possible, which further improves the overall project's net present value (NPV). A standard benchmark dataset, comprising 17,280 project instances ranging from 25 to 100 activities, is used to test the performance and effectiveness of the proposed MO-IGA. The proposed MO-IGA outperforms several datasets based on both a lower value of the average percentage deviation and the number of feasible schedule generations. The proposed MO-IGA is also shown to be more effective than the multi-operator GA (MO-GA) and multi-operator IA (MO-IA). Extensive numerical analysis, statistical tests, and comparisons with state-of-the-art algorithms proves the effectiveness of the proposed approach. • Multi-operator immune genetic algorithm (MO-IGA) is proposed to solve the RCPSPDC. • Different heuristics are integrated with the proposed MO-IGA to enhance performance. • Computational experiments are conducted to verify the effectiveness of the MO-IGA. • Multi-operator concept improves the performance of simple immune genetic algorithm. • MO-IGA generates better solution than its stand alone algorithms. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present a fuzzy linguistic ontology payoff method for the valuation of real options in the aerospace industry. Using real data, we apply the fuzzy linguistic approach to determine the credibility measures and the credibilistic expected value for the fuzzy real options valuation payoff method. This approach is used to obtain a multi-scenario modeling process by envisioning three scenarios: optimistic, most likely, and pessimistic. In addition, our experience with the scenario estimates is premised on results in an operating profit forecast. This forecast corresponds to a plausible outcome within the aerospace licensing maintenance, repair, and overhaul market and provides a decision-making tool. This tool can be utilized for determining real options for project valuation of aerospace licensing revenues based on unit costs, recurring costs, and quantity of units sold. [Copyright &y& Elsevier]

Abstract: Offshore platforms oil and gas day to day operations themselves involve an undeterminable list of risk. Some of them can be prevent or at least mitigate as a result of an experience of a former disaster. Indeed, although computational mathematical technologies grows so fast helping the engineers to estimate and mitigate the risk much uncertainties remains in a way of vagueness, which is many times impossible to measure precisely due to the imprecision and the lake of data. In such cases fuzzy logic is the mathematical treatment indicated to deal with these problems. As insurance is one of the most important economic form to mitigate risk the purpose of this paper is to verify the viability of purchasing a business interruption cover (BIC) for an offshore production unit using the fuzzy net present value (FNPV) that results of the discounted cash flow (DFC) involving much vagueness and uncertainties in these available information. [Copyright &y& Elsevier]

Abstract: The main purpose of this paper is to propose a fuzzy approach for investment project valuation in uncertain environments from the aspect of real options. The traditional approaches to project valuation are based on discounted cash flows (DCF) analysis which provides measures like net present value (NPV) and internal rate of return (IRR). However, DCF-based approaches exhibit two major pitfalls. One is that DCF parameters such as cash flows cannot be estimated precisely in the uncertain decision making environments. The other one is that the values of managerial flexibilities in investment projects cannot be exactly revealed through DCF analysis. Both of them would entail improper results on strategic investment projects valuation. Therefore, this paper proposes a fuzzy binomial approach that can be used in project valuation under uncertainty. The proposed approach also reveals the value of flexibilities embedded in the project. Furthermore, this paper provides a method to compute the mean value of a project’s fuzzy expanded NPV that represents the entire value of project. Finally, we use the approach to practically evaluate a project. [Copyright &y& Elsevier]