scholarly journals Effective Investment to Reduce Setup Cost in a Mixture Inventory Model Involving Controllable Backorder Rate and Variable Lead Time with a Service Level Constraint

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Hsien-Jen Lin

This paper investigates the impact of setup cost reduction on an inventory policy for a continuous review mixture inventory model involving controllable backorder rate and variable lead time with a service level constraint, in which the order quantity, setup cost, and lead time are decision variables. Our objective is to develop an algorithm to determine the optimal order quantity, setup cost, and lead time simultaneously, so that the total expected annual cost incurred has a minimum value. Furthermore, four numerical examples are provided to illustrate the results, and the effects of system parameters are also included for decision making.

2020 ◽  
Vol 54 (3) ◽  
pp. 653-673
Author(s):  
Selvaraj Hemapriya ◽  
Ramasamy Uthayakumar

This paper explores a neoteric approach to geometric shipment policy and concerns the impact of controllable lead time, setup cost reduction, lost sales caused by stock-out and freight cost within an integrated vendor–buyer supply chain configuration using service-level constraint. In particular, the transportation cost is a function of shipping weight, distance and transportation modes. In other words, truckload (TL) and less-than-truckload (LTL) shipments. A heuristic model is developed to minimize the joint expected total cost (JETC), when the mode of transportation is limited to TL and LTL shipments. Numerical examples including the sensitivity analysis with some managerial insights of system parameters is implemented to endorse the outcome of the supply chain models.


Mathematics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 328 ◽  
Author(s):  
Bikash Dey ◽  
Biswajit Sarkar ◽  
Sarla Pareek

This model investigates the variable production cost for a production house; under a two-echelon supply chain management where a single vendor and multi-retailers are involved. This production system goes through a long run system and generates an out-of-control state due to different issues and produces defective items. This model considers the reduction of the defective rate and setup cost through investment. A discrete investment for setup cost reduction and a continuous investment is considered to reduce the defective rate and to increase the quality of products. Setup and processing time are dependent on lead time in this model. The model is solved analytically to find the optimal values of the production rate, safety factors, optimum quantity, lead time length, investment for setup cost reduction, and the probability of the production process going out-of-control. An efficient algorithm is constructed to find the optimal solution numerically and sensitivity analysis is given to show the impact of different parameters. A case study and different cases are also given to validate the model.


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