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Due to successful applications of revenue management in the airline industry, in recent years, there has been a growing interest to adopt revenue management in make-to-order (MTO) manufacturing systems. Several interrelated decision problems such as order acceptance/rejection, short-term capacity planning, due date assignment, and order scheduling need to be studied simultaneously in order to manage revenues effectively in MTO manufacturing systems. Both the producer's and customer's requirements need to be taken into account through some negotiation mechanisms that are sensitive to the service-level reputation of the manufacturing companies. In this article, we propose a new dynamic bid price–based revenue management model that considers all of the aforementioned decision problems simultaneously. A simulation optimization approach is utilized in order to determine the best possible values of control parameters for bid price, due date assignment, and price increment/reduction mechanisms. The performance of the proposed integrated revenue management model is tested on both a hypothetical example and a real problem of a bridal gown company. The computational results show that the proposed model provides significant improvements in total revenue compared to other static and dynamic bid price policies. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy-stochastic optimization model is developed for an intermodal fleet management system of a large international transportation company. The proposed model integrates various strategic, tactical and operational level decisions simultaneously. Since real-life fleet planning problems may involve different types of uncertainty jointly such as randomness and fuzziness, a hybrid chance-constrained programming and fuzzy interactive resolution-based approach is employed. Therefore, stochastic import/export freight demand and fuzzy transit times, truck/trailer availabilities, the transport capacity of Ro-Ro vessels, bounds on block train services, etc. can also be taken into account concurrently. In addition to minimize overall transportation costs, optimization of total transit times and CO2 emission values are also incorporated in order to provide sustainable fleet plans by maximizing customer satisfaction and environmental considerations. Computational results show that effective and efficient fleet plans can be produced by making use of the proposed optimization model. [ABSTRACT FROM AUTHOR]

Making accurate accept/reject decisions on dynamically arriving customer requests for different combinations of resources is a challenging task under uncertainty of competitors' pricing strategies. Because customer demand may be affected by a competitor's pricing action, changes in customer interarrival times should also be considered in capacity control procedures. In this article, a simulation model is developed for a bid price–based capacity control problem of an airline network revenue management system by considering the uncertain nature of booking cancellations and competitors' pricing strategy. An improved bid price function is proposed by considering competitors' different pricing scenarios that occur with different probabilities and their effects on the customers' demands. The classical deterministic linear program (DLP) is reformulated to determine the initial base bid prices that are utilized as control parameters in the proposed self-adjusting bid price function. Furthermore, a simulation optimization approach is applied in order to determine the appropriate values of the coefficients in the bid price function. Different evolutionary computation techniques such as differential evolution (DE), particle swarm optimization (PSO), and seeker optimization algorithm (SOA), are utilized to determine these coefficients along with comparisons. The computational experiments show that promising results can be obtained by making use of the proposed metaheuristic-based simulation optimization approach. [ABSTRACT FROM PUBLISHER]