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Dynamic lot sizing with product returns and remanufacturing

We address the dynamic lot sizing problem for systems with product returns. The demand and return amounts are deterministic over the finite planning horizon. Demands can be satisfied by manufactured new items, but also by remanufactured returned items. The objective is to determine those lot sizes for manufacturing and remanufacturing that minimize the total cost composed of holding cost for returns and (re)manufactured products and set-up costs. Two different set-up cost schemes are considered: there is either a joint set-up cost for manufacturing and remanufacturing (single production line) or separate set-up costs (dedicated production lines). For the joint set-up cost case, we present an exact, polynomial-time dynamic programming algorithm. For both cases, we suggest modifications of the well-known Silver Meal (SM), Least Unit Cost (LUC) and Part Period Balancing (PPB) heuristics. An extensive numerical study reveals a number of insights. The key ones are that, under both set-up cost schemes: (1) the SM and LUC heuristics perform much better than PPB, (2) increased variation in the demand amounts can lead to reduced cost, showing that predictability is more important than variation, and (3) periods with more returns than demand should, if possible, be avoided by ‘matching’ demand and return.

Assessing the benefits of remanufacturing option under one-way substitution and capacity constraint

In this article, we investigate the profitability of remanufacturing option when the manufactured and remanufactured products are segmented to different markets and the production capacity is finite. A single period profit model under substitution is constructed to investigate the system conditions under which remanufacturing is profitable. We present analytical findings and computational results to show profitability of remanufacturing option under substitution policy subject to a capacity constraint of the joint manufacturing/remanufacturing facility.

A model to evaluate inventory costs in a remanufacturing environment

Abstract In this study we investigate possible benefits of remanufacturing in inventory-related costs. In order to examine the conditions on different system parameters that make the remanufacturing option cost attractive, the return ratio is considered as a decision variable. The production environment, where the manufacturing and remanufacturing require both common and separate operations, together with the suppliers operations for the new parts and useful lifetime of the product is modeled as a queuing network. A cost model is constructed under certain environmental assumptions. In this paper, the analysis of the model and computational study carried out are discussed.

An exact solution procedure for multi-item two-echelon spare parts inventory control problem with batch ordering in the central warehouse

We consider a multi-item two-echelon inventory system in which the central warehouse operates under a (Q,R) policy, and the local warehouses implement basestock policy. An exact solution procedure is proposed to find the inventory control policy parameters that minimize the system-wide inventory holding and fixed ordering cost subject to an aggregate mean response time constraint at each facility.

A Two‐Sided Cumulative Sum Chart for First‐Order Integer‐Valued Autoregressive Processes of Poisson Counts

Count data processes are often encountered in manufacturing and service industries. To describe the autocorrelation structure of such processes, a Poisson integer-valued autoregressive model of order 1, namely, Poisson INAR(1) model, might be used. In this study, we propose a two-sided cumulative sum control chart for monitoring Poisson INAR(1) processes with the aim of detecting changes in the process mean in both positive and negative directions. A trivariate Markov chain approach is developed for exact evaluation of the ARL performance of the chart in addition to a computationally efficient approximation based on bivariate Markov chains. The design of the chart for an ARL-unbiased performance and the analyses of the out-of-control performances are discussed. Copyright

Analyzing the effects of inventory cost setting rules in a disassembly and recovery environment

In this study we consider a disassembly and recovery facility receiving end-of-life products and facing demand for a specific part that is disassembled from the product and then recovered. The disassembly and recovery operations can be either performed before hand, or upon customer arrival. In the latter case, a discount on the selling price is applied to compensate the customer for waiting for the completion of the disassembly and recovery operations. One of the difficulties faced in planning for such a system is the determination of the opportunity cost associated with carrying recovered parts inventory. The difficulty arises in seeking the value added to the part given the costs incurred for maintaining the product return, disassembly and recovery costs and revenue earned from the hulk, that is the remaining product after the disassembly of the part. The main objective of the study is to investigate the effect of different rules to determine this opportunity cost on the performance of the system. Six rules are considered in the study. The performance of the rules is assessed by a computational study under an approximate inventory control policy.

Value of disruption information in an EOQ environment

We consider an infinite horizon, continuous review inventory model with deterministic stationary demand where supply is subject to disruption. The supply process alternates between two states randomly: one in which it functions normally (ON-period) and one in which it is disrupted (OFF-period). In this setting, we seek the value of disruption information which enables the buyer to place “disruption orders” at the beginning of OFF-periods. Utilizing renewal theory, we derive the total expected cost and characterize the optimal regular order-up-to level together with the order-up-to level for disruption orders. We also conduct an extensive numerical analysis and compare the results with the model with no opportunity of disruption orders. We observe that if the shortage cost is relatively high, and the disruption risk is significant (in terms of duration and/or frequency), placing a disruption order reduces the expected total cost significantly.

Heuristics for multi-item two-echelon spare parts inventory control subject to aggregate and individual service measures

We consider a multi-item two-echelon spare parts inventory system in which the central warehouse operates under a (Q, R) policy and local warehouses implement (S−1,S) policy. The objective is to find the policy parameters minimizing expected system-wide inventory holding and fixed ordering subject to aggregate and individual response time constraints. Using an exact evaluation we provide a very efficient and effective heuristic, and also a tight lower bound for real-world, large-scale two-echelon spare parts inventory problems. An extensive numerical study reveals that as the number of parts increases – which is usually the case in practice – the relative gap between the cost of the heuristic solution and the lower bound approaches zero. In line with our findings, we show that the heuristic and the lower bound are asymptotically optimal and asymptotically tight, respectively, in the number of parts. In practice, this means we can solve real-life problems with large numbers of items optimally. We propose an alternative approach between system and item approaches, which are based on setting individual and aggregate service level constraints, respectively. Using our alternative approach, we show that it is possible to keep the cost benefit of using aggregate service levels while avoiding long individual response times. We also show that the well-known sequential determination of policy parameters, i.e., determining the batch sizes first, and then finding the other policy parameters using those batch sizes, which is known for its high performance in single-item models, performs relatively poor for multi-item systems.

Remedial actions for disassembly lines with stochastic task times

We propose stopping the line, offline disassembly and hybrid line remedial actions for stochastic DLBP.For stopping the line and offline disassembly, we adopt approaches used in assembly lines.For hybrid line, we formulate the problem and propose a full enumeration scheme.For offline disassembly and hybrid line, we formulate and solve the task selection problem.Stopping the line and hybrid line, yield on average 26% higher expected profits. We suggest the incorporation of remedial actions for profit-oriented disassembly lines with stochastic task times. When task times are stochastic, there is a probability that some of the tasks are not completed within the predefined cycle time. For task incompletions in disassembly lines, pure remedial actions of stopping the line and offline disassembly are proposed along with the hybrid line which is a combination of the two pure remedial actions. The remedial actions have a significant effect on the expected cycle time as well as the expected profit due to line stoppages and offline disassembly, which together make up the incompletion costs. Stopping the line allows the line to be stopped until all incomplete tasks are completed, while in offline disassembly, incomplete tasks are completed in an offline disassembly area after the core leaves the line. The approaches used in assembly lines for quantifying the associated costs with stopping the line and offline repair for a given line balance are modified and used. Hybrid lines can implement both pure remedial actions for two different task classes: The line is stopped for Finish (F-) tasks and offline disassembly is executed for Pass (P-) tasks. For hybrid lines, we formulate the problem for given line balance so as to maximize the expected profit as a Mixed Integer Programming model. A full enumeration scheme is proposed to derive the hybrid line solution. As partial disassembly is allowed, for offline disassembly and hybrid line, we also formulate and solve the task selection problem so as to determine which incomplete P-tasks to execute during offline disassembly. Our computational study aims to show the significance of incompletion costs, analyze the effect of the base cycle time and demonstrate that hybrid lines are capable of improving the expected profit as well as expected cycle time compared to the pure remedial actions. Stopping the line and hybrid line on average yield 26% higher expected profits compared to offline disassembly.

Two-Stage Versus Single-Stage Inventory Models with or without Repair Ability

In this study, we consider an inventory system for a single item, which is being used in the military operations. In the current system, there is a two-echelon inventory setting which consists of a single stock point, the central depot, in the upper echelon and several stock points, the bases, in the lower echelon. A continuous review base-stock policy is used by all facilities. The military headquarters responsible for inventory management identified improvement opportunities such as acquiring repair ability and changing the structure of the supply chain from a two-echelon model to a single echelon model. Considering these opportunities, we investigate four different alternative inventory systems, single-echelon and two echelon models with or without repair ability. Our objective is to determine if and under what conditions each alternative results in lower costs.