A continuous-review, base-stock inventory model considering lost sales is proposed for general compound demands and random lead times. This model is a generalized form of the (S, d) policy, which has already been shown to be the best modified base-stock policy (MBSP) for Poisson demand and fixed lead times. In this paper, customers' inter-arrival times, demand sizes, and lead times are extended in a probabilistic situation with free distributions. Then, a hybrid simulationoptimization approach is developed to handle these generalized conditions. This approach uses design of experiments, a simulation model, and regression analysis to obtain the long-run cost function of the system under this extended MBSP. The optimal settings of this policy are achieved using a mathematical optimization model. Employing a simulation model, a cost function, and mathematical models makes this approach applicable for finding the optimal settings even in the presence of realistic restrictions and uncertainties. Moreover, a simulation-based procedure is introduced to find the optimal stock level for the traditional base-stock policy. The applicability of the proposed approach is illustrated through a real-world case study. Finally, a sensitivity analysis is applied using a series of benchmark instances, and some robustness properties are shown.
This study focuses on effectively designing reliable systems. Such systems are capable of withstanding failure events by applying multiple realistic workarounds. These workarounds include allocating redundancy, component reliability, and backup strategy that are considered concurrently as decision variables. In this novel view, the strategy and reliability of components are determined freely and independently to achieve optimality. The research problem is implemented in a general case to audit the capabilities of the proposed approach in realistic situations. This case deploys an Erlang time-to-failure probability density function together with incomplete switching. With its improved reliability and resource functions, the proposed model challenges the existing presumption regarding the superiority of the coldstandby approach in the mentioned field and provides a realistic trade-off between different redundancy strategies. This practical view reflects on reliability, cost, weight, and volume of the switch, simultaneously. The findings revealed that the proposed joint reliability-redundancy allocation problem, with an added freedom of strategy choice, outperforms the pure cold-standby counterpart. Owing to the NP-hard nature of the problem, a simplified particle swarm optimization algorithm is suggested and utilized as a solution method. The performance of the novel view is assessed using multiple benchmark instances including some typical problems from the literature. Our numerical analysis demonstrates the superiority of this approach with our maximum possible index reaching up to %96 compared to the existing results in the past works. Furthermore, the selected solution algorithm is compared with a differential evolution algorithm. We show that this simplified particle swarm optimization algorithm performs considerably better in all tested scenarios.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.