This paper considers an inventory and production-planning problem for a contract manufacturer who anticipates an order of a single product, but with uncertain quantity. To meet the challenges of long component procurement lead times and limited assembly capacity, which may render production time insufficient to assemble total order quantity, the manufacturer may need to procure components or even assemble some quantities of the final product before receiving the confirmation of the actual order quantity. We present profit-maximization models that make optimal inventory and production decisions in the above assemble-to-order environment. We also consider the option of outsourcing, so that the manufacturer can outsource part of his production to an external facility that also has limited capacity. We establish structural properties of optimal solutions and develop efficient solution procedures for the proposed problems. We also provide sensitivity analysis of the optimal decisions, and some managerial insights.

We consider a component acquisition problem for a contract manufacturer who faces a one-time stochastic demand for a single product consisting of a number of components. Components can be ordered early at normal prices before the demand is revealed, or they can be replenished later at higher prices due to expediting. In addition, the customer pays a price for the final product that is nonincreasing in the delivery lead time to discourage late delivery. We propose a cost-minimization model and develop an efficient polynomial time algorithm to solve the problem.

In this article, we consider the performance evaluation of a multicomponent, multiproduct assemble-to-order (ATO) system. Each component is managed independently using a base-stock policy at a supply facility with limited production capacity and an infinite buffer. The arrivals of demands follow a multivariate Poisson process and unfilled demands are backlogged. Because exact analysis of the proposed system is not feasible, we propose two approximation methods which provide upper and lower bounds for various performance measures such as fill rate, average waiting time, and average number of backorders of the proposed system. Our computational experiments demonstrate the effectiveness of the two approximation methods under various system settings. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011

Recently, crossdocking techniques have been successfully applied in responsive supply chain management. However, most researches focused on physical layout of a crossdock, or scheduling operations within a crossdock. In this paper, we study a multi-crossdock transshipment service problem with both soft and hard time windows. The flows from suppliers to customers via the crossdocks are constrained by fixed transportation schedules. Cargos can be delayed and consolidated in crossdocks, and both suppliers and customers have specific hard time windows. In addition to hard time windows, customers also have less-restrictive time windows, called soft time windows. The problem to minimize the total cost of the multi-crossdock distribution network, including transportation cost, inventory handling cost and penalty cost, can be proved to be NP-hard in the strong sense and hence efficient heuristics are desired. We propose two types of meta-heuristic algorithms, called Adaptive Tabu Search and Adaptive Genetic Algorithm, respectively, to solve the problem efficiently. We conduct extensive experiments and the results show that both of them outperform CPLEX solver and provide fairly good solutions within realistic timescales. We also perform sensitivity analysis and obtain a number of managerial insights.

We consider a manufacturer facing single period inventory planning problem with uncertain demand and multiple options of expediting. The demand comes at a certain time in the future. The manufacturer may order the product in advance with a relatively low cost. She can order additional amount by expediting after the demand is realized. There are a number of expediting options, each of which corresponds to a certain delivery lead time and a unit procurement price. The unit procurement price is decreasing over delivery lead time. The selling price is also decreasing over time. In this paper, we assume that the manufacturer must deliver all products to the customer in a single shipment. The problem can be formulated as a profit maximization problem. We develop structural properties and show how the optimal solution can be identified efficiently. In addition, we compare our model with the classical newsvendor model and obtain a number of managerial insights. Highlights ► We consider a newsvendor problem with multiple options of expediting. ► We develop a number of structural properties that help to solve the problem efficiently. ► We examine how the demand variation affects the optimal decisions and profits.