A new approach towards integrated cell formation and inventory lot sizing in an unreliable cellular manufacturing system

This paper presents a comprehensive mathematical model for integrated cell formation and inventory lot sizing problem. The proposed model seeks to minimize cell formation costs as well as the costs associated with production, while dynamic conditions, alternative routings, machine capacity limitation, operations sequences, cell size constraints, process deterioration, and machine breakdowns are also taken into account. The total cost consists of machine procurement, cell reconfiguration, preventive and corrective repairs, material handling (intra-cell and inter-cell), machine operation, part subcontracting, finished and unfinished parts inventory cost, and defective parts replacement costs. With respect to the multiple products, multiple process plans for each product and multiple routing alternatives for each process plan which are assumed in the proposed model, the model is combinatorial. Moreover, unreliability conditions are considered, because moving from “in-control” state to “out-of-control” state (process deterioration) and machine breakdowns make the model more practical and applicable. To conquer the breakdowns, preventive and corrective actions are adopted. Finally, a Particle Swarm Optimization (PSO)-based meta-heuristic is developed to overcome NP-completeness of the proposed model.     

Hierarchical production control of a flexible manufacturing system

A hierarchical production control framework for a flexible manufacturing system is proposed. The machines in the system are subject to failures in a wide spectrum band. At first, failures are clustered near some discrete points on the failure spectrum in order to define the hierarchical model. Each level in the hierarchy corresponds to a discrete point on the failure spectrum. At each level, faster varying failures are modelled by their mean behaviour, and more slowly varying failures are treated as static. Then, a hierarchical controller of multiple time scale type is proposed. System control at each level is based on the work of Kimemia and Gershwin. Simulation results conclude the paper.     

Cost-effective production policy for a stochastic unreliable manufacturing system

^** Email: dohi{at}rel.hiroshima-u.ac.jp The paper deals with an economic manufacturing quantity (EMQ)problem for an unreliable manufacturing system in both continuous-and discrete-time settings. The time to machine failure andcorrective and preventive repair times of the production facilityare assumed to follow arbitrary probability distributions. Thetraditional method of determining the EMQ policy for a failure-pronemanufacturing system is based on the minimization of the long-runaverage cost in the steady state. In this paper, an alternativecriterion of optimality called cost effectiveness is introduced.The criteria for the existence and uniqueness of the optimalproduction time maximizing the cost effectiveness are derivedanalytically under general failure and specific repair (correctiveand preventive) time distributions. The optimal cost-effectiveand average cost production policies are numerically calculatedand compared in terms of their performances.     

Optimal control policy for a standing order inventory system

In this paper, we consider a standing order inventory system in which an order of fixed size arrives in each period. Since demand is stochastic, such a system must allow for procurement of extra units in the case of an emergency and sell-offs of excess inventory. Assuming the average-cost criterion, Rosenshine and Obee (Operations Research 24 (1976) 1143–1155) first studied such a system and devised a 4-parameter inventory control policy that is not generally optimal. The current paper uses dynamic programming to determine the optimal control policy for a standing order system, which consists of only two operational parameters: the dispose-down-to level and order-up-to level. Either the average-cost or discounted-cost criterion can be assumed in the proposed model. Also, both the backlogged and lost-sales problems are investigated in this paper. By using a convergence theorem, we stop the dynamic programming computation and obtain the two optimal parameters.     

Optimal policy and holding cost stability regions in a periodic review inventory system with manufacturing and remanufacturing options

In this paper a periodic review inventory model with finite horizon and remanufacturing, manufacturing options is studied. It is assumed that demand and cost parameters are constant and a sufficiently large quantity of used products is available at the beginning of the horizon. The model is studied within the class of policies with given remanufacturing and manufacturing set up and the optimal policy is obtained within this class. The policy specifies the period of switching from remanufacturing to manufacturing (switching period), the periods where remanufacturing and manufacturing activities take place and the corresponding lot sizes. An explicit formula for the cost function and some of its properties are established. Based on these, an algorithm which partitions the set of holding cost parameters into subsets, computes the optimal policy and constructs its corresponding stability regions on every such subset is proposed.     

Enhanced lateral transshipments in a multi-location inventory system

In managing an inventory network, two approaches to the pooling of stock have been proposed. Reactive transshipments respond to shortages at a location by moving inventory from elsewhere within the network, while proactive stock redistribution seeks to minimize the chance of future stockouts. This paper is the first to propose an enhanced reactive approach in which individual transshipments are viewed as an opportunity for proactive stock redistribution. We adopt a quasi-myopic approach to the development of a strongly performing enhanced reactive transshipment policy. In comparison to a purely reactive approach to transshipment, service levels are improved while a reduction in safety stock levels is achieved. The aggregate costs incurred in managing the system are significantly reduced, especially so for large networks. Moreover, an optimal policy is determined for small networks and it is shown that the enhanced reactive policy substantially closes the gap to optimality.     

Inventory management in truck manufacturing —A case study

In a case study of inventory management at a truck manufacturing company, it is shown that automation sets the pace for improvements in material requirements planning, that many stochastic problems often outlive solutions offered by efficiency studies.     

Stability and optimization of a production inventory system under prioritized base-stock control

Email: dongping.song{at}plymouth.ac.uk Received on 19 July 2007. Accepted on 1 May 2008. A make-to-stock system producing two part-types with stochasticprocessing times and random demands is considered. A prioritizedbase-stock policy is used to control the production to meetexogenous Poisson demands, where the unmet high-priority demandsare partially backordered due to their limited patience andthe unmet low-priority demands are fully backordered. Basedon the matrix analytical method, a necessary and sufficientcondition for the stability of the system is provided. The explicitstationary distribution is derived using the spectral expansionapproach. Interesting steady-state performance measures suchas stock-out probabilities and lost-sale fraction are then calculated.By investigating the structural properties of the objectivefunctions, simple procedures are presented to find the optimalbase-stock levels either to minimize the expected cost or tosatisfy the stock-out probability and the lost-sale fractionconstraints. In addition, the optimization problem with respectto the maximum backlog level for part-type one and two base-stocklevels is addressed and a solution procedure is presented. Numericalexamples are given to demonstrate the results.     

Inventory control as a discrete system control for the fixed-order quantity system

This paper shows how to model a problem to find optimal number of replenishments in the fixed-order quantity system as a basic problem of optimal control of the discrete system. The decision environment is deterministic and the time horizon is finite. A discrete system consists of the law of dynamics, control domain and performance criterion. It is primarily a simulation model of the inventory dynamics, but the performance criterion enables various order strategies to be compared. The dynamics of state variables depends on the inflow and outflow rates. This paper explicitly defines flow regulators for the four patterns of the inventory: discrete inflow – continuous/discrete outflow and continuous inflow – continuous/discrete outflow. It has been discussed how to use suggested model for variants of the fixed-order quantity system as the scenarios of the model. To find the optimal process, the simulation-based optimization is used.     

Decomposition in multi-item inventory control

This paper considers a Lagrangian decomposition approach to a stochastic demand multi-item inventory control problem with a single resource constraint. The work is a generalization of existing decomposition methods.Three decomposition methods are proposed, and bounds on the loss of optimality for each are given in terms of the Lagrange multiplier used. One method allows the calculation of the complete decision rule in advance of the realization of the states, but is expected to perform worse than the other two methods. The second and third method allow the determination of decisions as an optimization problem as the states are realized. Since, in any problem with many states, only a small proportion will actually be realized even in a large time-horizon problem, there may be some advantage in taking this approach.     

Optimal control of a finite-capacity inventory system with setup cost and lost sales

One of the most fundamental results in inventory theoryis the optimality of (s, S) policy for inventory systems withsetup cost. This result is established based on a key assumptionof infinite production/ordering capacity. Several studies haveshown that, when there is a finite production/ordering capacity,the optimal policy for the inventory system is very complicatedand indeed, only partial characterization for the optimal policyis possible. In this paper, we consider a continuous reviewinventory system with finite production/ordering capacity andsetup cost, and show that the optimal control policy for thissystem has a very simple structure. We also develop efficientalgorithms to compute the optimal control parameters.     

Stochastic leadtimes in a one-warehouse,N-retailer inventory system with the warehouse not carrying stock

This study extends upon a multi-echelon inventory model developed by Graves, introducing in the one-warehouse, N-retailer case—as Graves suggested—stochastic leadtimes between the warehouse and the retail sites in place of the original deterministic leadtimes. Effects of stochastic leadtimes on required base stock levels at the retail sites in the case where the warehouse carries no stock (e.g., serves as a cross-dock point) were investigated analytically. Two alternative treatments of stochastic leadtime distributions were considered. Using as a baseline Graves’ computational study under deterministic leadtimes, results of the current study suggest that it may be better to use the deterministic model with an accurately estimated mean leadtime than a stochastic model with a poorly estimated mean leadtime.     

Production control of an unreliable manufacturing system under the assumption of no backlog

This paper deals with an unreliable manufacturing system in which no backlog is allowed. The hedging point policy is used to control production. This paper's objective is to find the optimum hedging point so as to minimize the average inventory cost. Firstly, for a tentative hedging point, we derive the limiting distribution of the inventory level. This derivation is accomplished by comparison between this system and a finite storage-production system. Based on it we calculate the average inventory cost and find the optimum hedging point.This research was supported in part by the National Natural Science Foundation of China. Bin Liu was partially supported by ITDC, contract No. 105-82150.     

Leadtime management in a periodic-review inventory system: A state-dependent base-stock policy

We examine a single-item, periodic-review inventory system with stochastic leadtimes, in which a replenishment order is delivered immediately or one period later, depending probabilistically on costly effort. The objective is to determine a joint inventory policy and effort-choice strategy that minimizes the expected total costs. Our analytical and computational analysis suggests that (i) a state-dependent base-stock policy is optimal, (ii) the optimal effort strategy is such that the marginal cost of effort is equal to the value of immediate delivery, and (iii) the cost impact of leadtime reduction can be very large. We also provide some counter-intuitive results, compared with the traditional multi-period newsvendor model.     

An algorithm for repairable item inventory system with depot spares and general repair time distribution

We consider the problem of determining the initial spare inventory level for a multi-echelon repairable item inventory system. We extend the previous results to the system, which has an inventory at the central depot as well as at bases and with a general repair time distribution. We propose an algorithm which finds spare inventory level to minimize the total expected cost and simultaneously to satisfy a specified minimum service rate. Extensive computational experiments show that the algorithm is accurate and efficient.     

A simulation optimization approach for a two-echelon inventory system with service level constraints

In this paper, we present a simulation optimization algorithm for solving the two-echelon constrained inventory problem. The goal is to determine the optimal setting of stocking levels to minimize the total inventory investment costs while satisfying the expected response time targets for each field depot. The proposed algorithm is more adaptive than ordinary optimization algorithms, and can be applied to any multi-item multi-echelon inventory system, where the cost structure and service level function resemble what we assume. Empirical studies are performed to compare the efficiency of the proposed algorithms with other existing simulation algorithms.     

An application of a hybrid approach to modeling a flexible manufacturing system

Both analytic and simulation models were used to analyze the capabilities and requirements of an automated circuit card manufacturing system. Analytic models were used to determine the sensitivity of the measures of effectiveness (MOEs) to various design parameters. This analysis gave approximate results and bounded the range of input parameters for the simulation model. A detailed simulation model was required for use during both the design and production phases of the project. This simulation model incorporated only those variables to which the MOEs are most sensitive, and provided additional features to observe system behavior. The benefits and appropriate uses for each class of models are discussed.     

A solution approach to the inventory routing problem in a three-level distribution system

We consider the infinite horizon inventory routing problem in a three-level distribution system with a vendor, a warehouse and multiple geographically dispersed retailers. In this problem, each retailer faces a demand at a deterministic, retailer-specific rate for a single product. The demand of each retailer is replenished either from the vendor through the warehouse or directly from the vendor. Inventories are kept at both the retailers and the warehouse. The objective is to determine a combined transportation (routing) and inventory strategy minimizing a long-run average system-wide cost while meeting the demand of each retailer without shortage. We present a decomposition solution approach based on a fixed partition policy where the retailers are partitioned into disjoint and collectively exhaustive sets and each set of retailers is served on a separate route. Given a fixed partition, the original problem is decomposed into three sub-problems. Efficient algorithms are developed for the sub-problems by exploring important properties of their optimal solutions. A genetic algorithm is proposed to find a near-optimal fixed partition for the problem. Computational results show the performance of the solution approach.     

Queueing analysis on a single-station make-to-stock/make-to-order inventory-production system

We consider a one-station production system that produces standard products for ordinary demands and custom products for specific demands. In this system, the workstation has two manufacturing modes. In mode 1, it produces standard products and, in mode 2, it produces custom products by performing the additional alternating works on one existing finished standard product. Base-stock control policy is applied to control the production of standard products. The fill rate of the ordinary demand and the on-time-delivery-rate of the specific demand are considered as the measures of the qualities of service. By assuming an Markovian system, qualities of service under base-stock policy are obtained; furthermore, the optimal base-stock level can be obtained numerically under the requirements on the qualities of services.