Service-Level Oriented Lot Sizing Under Stochastic Demand

2008 ◽  
pp. 351-356
Author(s):  
Lars Fischer ◽  
Sascha Herpers ◽  
Michael Manitz
Keyword(s):  
Lot Sizing ◽  
Stochastic Demand ◽  
Service Level

Allocation planning in sales hierarchies with stochastic demand and service-level targets

OR Spectrum ◽  
2018 ◽  
Vol 41 (4) ◽  
pp. 981-1024 ◽  
Author(s):  
Konstantin Kloos ◽  
Richard Pibernik ◽  
Benedikt Schulte
Keyword(s):  
Stochastic Demand ◽  
Service Level ◽  
Allocation Planning

Model formulations for the capacitated lot-sizing problem with service-level constraints

OR Spectrum ◽  
2019 ◽  
Vol 41 (4) ◽  
pp. 1025-1056 ◽  
Author(s):  
Hartmut Stadtler ◽  
Malte Meistering
Keyword(s):  
Lot Sizing ◽  
Service Level ◽  
Lot Sizing Problem ◽  
Capacitated Lot Sizing

Inventory lot-sizing with supplier selection under non-stationary stochastic demand

2015 ◽  
Vol 54 (8) ◽  
pp. 2459-2469 ◽  
Author(s):  
Arun Kr. Purohit ◽  
Devendra Choudhary ◽  
Ravi Shankar
Keyword(s):  
Supplier Selection ◽  
Lot Sizing ◽  
Stochastic Demand

A reformulation for the stochastic lot sizing problem with service-level constraints

2014 ◽  
Vol 42 (2) ◽  
pp. 161-165 ◽  
Author(s):  
Huseyin Tunc ◽  
Onur A. Kilic ◽  
S. Armagan Tarim ◽  
Burak Eksioglu
Keyword(s):  
Lot Sizing ◽  
Service Level ◽  
Lot Sizing Problem

scholarly journals Capacity coordination in hybrid make-to-stock/make-to-order contexts using an enhanced multi-stage model

2017 ◽  
Vol 14 (3) ◽  
pp. 396
Author(s):  
Masoud Rabbani ◽  
Sara Motevali Haghighi ◽  
Hamed Farrokhi-Asl ◽  
Neda Manavizadeh
Keyword(s):  
Capacity Planning ◽  
Production Systems ◽  
Lot Sizing ◽  
Service Level ◽  
Stage Model ◽  
Order Acceptance ◽  
Industrial Case Study ◽  
Make To Order ◽  
Multi Stage ◽  
Make To Stock

One of the most attracting production systems that has recently been vastly explored by practitioners and academicians is hybrid make-to-stock/make-to-order. Having a hierarchical production planning structure considered, this paper develops a multi-stage model to cope with the operational decisions, including order acceptance/rejection, product lot sizing, overtime capacity planning, outsourcing, and due date setting. Moreover, the proposed framework also comprises providing alternative products for the coming orders in order to enhance service level of the firm to the customers. In order to validate the presented framework, it is applied in a real industrial case study and the obtained results approve validity of the proposed framework. 

scholarly journals Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times

2021 ◽  
Vol 11 (23) ◽  
pp. 11210
Author(s):  
Mohammed Alnahhal ◽  
Diane Ahrens ◽  
Bashir Salah
Keyword(s):  
Lot Sizing ◽  
Planning Horizon ◽  
Service Level ◽  
Mixed Integer ◽  
Lot Size ◽  
Dynamic Planning ◽  
Holding Costs ◽  
Delivery Times ◽  
Mip Model ◽  
Total Inventory

This study investigates replenishment planning in the case of discrete delivery time, where demand is seasonal. The study is motivated by a case study of a soft drinks company in Germany, where data concerning demand are obtained for a whole year. The investigation focused on one type of apple juice that experiences a peak in demand during the summer. The lot-sizing problem reduces the ordering and the total inventory holding costs using a mixed-integer programming (MIP) model. Both the lot size and cycle time are variable over the planning horizon. To obtain results faster, a dynamic programming (DP) model was developed, and run using R software. The model was run every week to update the plan according to the current inventory size. The DP model was run on a personal computer 35 times to represent dynamic planning. The CPU time was only a few seconds. Results showed that initial planning is difficult to follow, especially after week 30, and the service level was only 92%. Dynamic planning reached a higher service level of 100%. This study is the first to investigate discrete delivery times, opening the door for further investigations in the future in other industries.

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