Optimal reorder level and lot size decisions for an inventory system with defective items

2021 ◽  
Vol 92 ◽  
pp. 651-668
Author(s):  
G. Karakatsoulis ◽  
K. Skouri
2021 ◽  
Author(s):  
Mehmood Khan

A common measure of quality for a buyer or a vendor is the defect rate. Defects may represent an attribute, a dimension or a quantity. They may be classified as product quality defects or process quality defects. Product quality defects may be caused by human error which can de due to fatigue, lack of proper training, or other reasons. For example, an inspector may misclassify a defective fuel tank of a car as good. On the other hand, process quality defects maybe caused by a machine going out-of-control. While many researchers assume that the screening processes which separate the defective items are error-free, it would be realistic to consider misclassification errors in this process. Beside inspection errors, learning is another human factor that brings in enhancement in the overall performance of a supply chain. Learning is inherent when there are workers involved in a repetitive type of production process. Learning and forgetting are even more important in manufacturing environments that emphasize on flexibility where workers are cross-trained to do different tasks and where products have a short life cycle. Inventory management with learning in quality, inspection and processing time will be the focus of this thesis. A number of models will be developed for a buyer and/or a two level supply chain to incorporate these human factors. The key findings of this work may be summarized as 1. Inspection errors significantly affect the annual profit. 2. An increase in the unit screening cost reduces the annual profit to a great extent at slower rates of learning. 3. For the two-level supply chain we investigated, learning in production drops the annual cost significantly while the learning in supplier's quality results in a situation where there are no defectives from the suppliers. 4. Type II error may seem to be beneficial for a two level supply chain as the order/lot size goes down and thus affects the costs of ordering, production and screening. 5. Consignment stocking policy performs better than conventional stocking when holding costs go higher than a threshold value.


Author(s):  
Shyamal Kumar Mondal

In this chapter, a multi-storage inventory system has been considered to develop a deterministic inventory model in finite planning horizon. Realistically, it is shown that due to large stock and insufficient space of existing own warehouse (OW); excess items are stored in single rented warehouse (RW). Due to different preserving facilities and storage environment, inventory holding cost is considered to be different in different warehouses. Here, the replenishment cycle lengths are of equal length, the demand rate is a continuous linear increasing function of time and partially backlogged shortages are allowed in all cycles. In each cycle, the replenishment cost is assumed to be dependent linearly on lot size and the stocks of RW are also transported to OW in continuous release pattern. The model is formulated as a constrained non-linear mixed integer cost objective function under single management. Finally, results with a sensitivity analysis have been shown with the help of a real coded GA.


2015 ◽  
Vol 39 (4) ◽  
pp. 555-566 ◽  
Author(s):  
Achin Srivastav ◽  
Sunil Agrawal

This paper studies a multi-objective mixture inventory problem for a pharmaceutical distributor. The work starts with a discussion of a mixture inventory model and three objectives, namely the minimization of: 1) ordering and holding costs, 2) number of units that stockout and 3) frequency of stockout occasions. Multi-objective particle swarm optimization (MOPSO) is used to determine the non-dominated solutions and generate Pareto curves for the inventory system. Two variants of MOPSO are proposed, based on the selection of inertia weight. The performance of the proposed MOPSO algorithms is evaluated in comparison with two robust algorithms like non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective cuckoo search (MOCS). The metrics that are used for the performance measurement of the algorithms are error ratio, spacing and maximum spread. Furthermore, the technique of order preference by similarity to ideal solution (TOPSIS) is used to rank the non-dominated solutions and determine the best compromise solution among them. A factorial analysis develops the linear regression expressions of optimal cost, service level measures, lot size and safety stock factor for practitioners. Lastly, the results of the regression equations are compared using a MOPSO–TOPSIS approach and the validity of the developed equations are checked.


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