Analysis of Admission and Inventory Control Policies for Production Networks

Abstract

Problems of inventory control and customer admission control are considered for a manufacturing system that produces one product to meet random demand. Four admission policies are investigated: lost sales, complete backordering, randomized admission, and partial backordering. These policies are combined with an integral inventory control policy, which releases raw items only when an incoming order is accepted and keeps the inventory position (total inventory minus outstanding orders) constant. The objective is to determine the inventory level and the maximum number of backorders, as well as the admission probability that maximize the mean profit rate of the system. The system is modeled as a closed queueing network and its performance is computed analytically. The optimal parameters for each policy are found using exhaustive search and convex analysis. Numerical results show that managing inventory levels and sales jointly through partial backordering achieves higher profit than other control policies.Note to Practitioners-In stochastic production networks, decisions concerning when to produce and whether to accept or reject an incoming order are often made separately or sequentially. For example, inventory control is often applied assuming that all demand during a stockout period is either completely backlogged or lost. This paper studies two partial backordering policies jointly with an integral inventory control policy, and explores the benefits of coordinated decision making.

Single-stage Kanban system with deterioration failures and condition-based preventive maintenance

“…If there are no finished jobs in the buffer at the time of the job arrival then the order is discarded, i.e. no backorders are allowed ( [25] Mean time to repair the manufacturing station operate (hard failure). If the deterioration failure takes place when the machine is in the good-as-new-state or the ith, iod deterioration stage then it transists to the first and (i þ1)th deterioration stage, respectively.…”

“…(27) is the non-linear constraint imposed on the throughput, whereas Eqs. (23)- (25) are the bound constraints imposed on the decision variables. Note that the objectives of minimizing inventory and maximizing throughput are conflicting (refer to Section 6).…”

“…(29) is the non-linear constraint imposed on the utilization, whereas Eqs. (23)- (25) are bound constraints which are explained in the previous Section 7. In this problem, the aim is to find the combination of decision variables that correspond to the best trade-off between preventing machine failures and keeping the system functional.…”

Dynamic service contracting for on-demand asset delivery

“…They have proposed an inventory control approach to support product aggregation in the distributed environment. To support rapid responsiveness to ad-hoc customer demands, Ioannidis et al have summarised a set of customer admission policies, which are combined with an integral inventory control policy [9]. These policies determine the inventory level and the maximum number of backorders, as well as the admission probability that maximises the mean profit rate of the system.…”

Supply Chain Optimization: Centralized vs Decentralized Planning and Scheduling

“…This is a partial backordering policy (PB). Although it has received little attention in the past, PB is frequently applied in practice because it is more profitable than other policies such as lost sales or complete backordering (Kouikoglou and Phillis 2002;Ioannidis et al, 2008).…”