A Capacitated Production Planning Problem for Closed-Loop Supply Chain

2010 ◽  
pp. 243-251 ◽  
Author(s):  
Jian Zhang ◽  
Xiao Liu
Keyword(s):  
Supply Chain ◽  
Production Planning ◽  
Closed Loop ◽  
Planning Problem ◽  
Closed Loop Supply Chain ◽  
Production Planning Problem

scholarly journals Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS

2014 ◽  
Vol 24 (3) ◽  
pp. 669-682 ◽  
Author(s):  
D. Thresh Kumar ◽  
Hamed Soleimani ◽  
Govindan Kannan
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Supply Chain Design ◽  
Second Phase ◽  
Planning Problem ◽  
Two Phase ◽  
Closed Loop Supply Chain ◽  
Inference System ◽  
Forecasting Method ◽  
Phase Optimization

Abstract Interests in Closed-Loop Supply Chain (CLSC) issues are growing day by day within the academia, companies, and customers. Many papers discuss profitability or cost reduction impacts of remanufacturing, but a very important point is almost missing. Indeed, there is no guarantee about the amounts of return products even if we know a lot about demands of first products. This uncertainty is due to reasons such as companies’ capabilities in collecting End-of-Life (EOL) products, customers’ interests in returning (and current incentives), and other independent collectors. The aim of this paper is to deal with the important gap of the uncertainties of return products. Therefore, we discuss the forecasting method of return products which have their own open-loop supply chain. We develop an integrated two-phase methodology to cope with the closed-loop supply chain design and planning problem. In the first phase, an Adaptive Network Based Fuzzy Inference System (ANFIS) is presented to handle the uncertainties of the amounts of return product and to determine the forecasted return rates. In the second phase, and based on the results of the first one, the proposed multi-echelon, multi-product, multi-period, closed-loop supply chain network is optimized. The second-phase optimization is undertaken based on using general exact solvers in order to achieve the global optimum. Finally, the performance of the proposed forecasting method is evaluated in 25 periods using a numerical example, which contains a pattern in the returning of products. The results reveal acceptable performance of the proposed two-phase optimization method. Based on them, such forecasting approaches can be applied to real-case CLSC problems in order to achieve more reliable design and planning of the network

Modeling and Analysis of a Closed-Loop Supply Chain in Consideration of Extra Demand

2018 ◽  
Vol 12 (4) ◽  
pp. 469-481
Author(s):  
Ayako Okuda ◽  
Aya Ishigaki ◽  
Tetsuo Yamada ◽  
Surendra M. Gupta ◽  
◽  
...  
Keyword(s):  
Supply Chain ◽  
Production Planning ◽  
Closed Loop ◽  
Chain Model ◽  
Manufacturing Companies ◽  
Closed Loop Supply Chain ◽  
Modeling And Analysis ◽  
Supply Chain Model ◽  
Manufactured Products ◽  
Demand Fluctuations

In recent years, activities undertaken to reduce environmental impacts – such as recycling and reusing – have been increasing in popularity. For manufacturing companies, designing and using a closed-loop supply chain can help meet social responsibility objectives and enhance competitiveness. A closed-loop supply chain requires the accurate prediction of not only demand but also returned products; however, in the literature, the quantity of returned products is assumed to be dependent on demand. Importantly, the quantity of returned products is influenced by past demand and use periods. Further, some returned products may be treated as end-of-life products, because of quality deterioration. The purpose of this study is to design a closed-loop supply chain model in the context of returned product quantities as affected by past demand, use period, and extra demand, in order to analyze system performance. Herein, the quantity of demand influences the quantity of returned products, and hence the quantity of reusable products. Moreover, the dynamics of returned products, demand, and reusable products will also significantly influence production planning. In this study, fluctuations in the quantity of returned products influence not only production planning but also future demand fluctuations. The results of numerical examples derived from using the model proposed in this study clarify that the quantities of reusable products and manufactured products will fluctuate depending on the return rate, given policies that prioritize the sale of reusable products. This finding suggests that manufacturers need to consider reducing their environmental impact as well as establishing production planning and inventory control policies that contain fluctuations in the quantities of reusable and manufactured products.

scholarly journals Production Planning Problem of a Two-Level Supply Chain with Production-Time-Dependent Products

2021 ◽  
Vol 11 (20) ◽  
pp. 9687
Author(s):  
Jun-Hee Han ◽  
Ju-Yong Lee ◽  
Bongjoo Jeong
Keyword(s):  
Supply Chain ◽  
Production Planning ◽  
Programming Model ◽  
Planning Horizon ◽  
Mixed Integer ◽  
Planning Problem ◽  
Manufacturing Plant ◽  
Production Lines ◽  
Multiple Production ◽  
Production Planning Problem

This study considers a production planning problem with a two-level supply chain consisting of multiple suppliers and a manufacturing plant. Each supplier that consists of multiple production lines can produce several types of semi-finished products, and the manufacturing plant produces the finished products using the semi-finished products from the suppliers to meet dynamic demands. In the suppliers, different types of semi-finished products can be produced in the same batch, and products in the same batch can only be started simultaneously (at the same time) even if they complete at different times. The purpose of this study is to determine the selection of suppliers and their production lines for the production of semi-finished products for each period of a given planning horizon, and the objective is to minimize total costs associated with the supply chain during the whole planning horizon. To solve this problem, we suggest a mixed integer programming model and a heuristic algorithm. To verify performance of the algorithm, a series of tests are conducted on a number of instances, and the results are presented.

Sign in / Sign up

Export Citation Format

Share Document