Production planning with remanufacturing and back-ordering in a cooperative multi-factory environment

被引:19
作者
Jing, Yi [1 ]
Li, Wenchuan [2 ]
Wang, Xu [3 ]
Deng, Lei [3 ]
机构
[1] Chongqing Univ Technol, Coll Management, Chongqing, Peoples R China
[2] Nanchang Hangkong Univ, Sch Econ & Management, Nanchang, Peoples R China
[3] Chongqing Univ, Coll Mech Engn, Chongqing 630044, Peoples R China
基金
中国国家自然科学基金;
关键词
multi-factory; back-ordering; remanufacturing; SAGA-PD; production planning; HYBRID GENETIC ALGORITHM; LOT-SIZING PROBLEM; SUPPLY CHAIN; MODEL; OPTIMIZATION; RETURNS;
D O I
10.1080/0951192X.2015.1068450
中图分类号
TP39 [计算机的应用];
学科分类号
080201 [机械制造及其自动化];
摘要
Remanufacturing has attracted a lot of attention as the best form of resource utilisation for used products and the important way of energy saving and emission reduction. In this article, a version of production planning problems with remanufacturing and back-ordering is discussed, in which there are multiple factories in a cooperative relationship to produce new products, or remanufactured ones, or both. And all products are transported to different demand sites, but back-ordering is allowable. For this planning problem, three different models are proposed. The first model is a basic model, the objective of which is to maximise the total profit of all factories without considering the individual profit of each factory. The second model introduces the individual profit constraints of each factory, so as to maximise the total profit on the basis of achieving the individual profit matching with the production scale of each factory. The third model allows a kind of appropriate compromise form between the total profit and the individual profit. Then, the solution approach for these models is designed based on self-adaptive genetic algorithm with population division (SAGA-PD). Finally, a numerical example is suggested to demonstrate the applicability and effectiveness of the proposed models and solution approach, and solution results of three models are compared and analysed.
引用
收藏
页码:692 / 708
页数:17
相关论文
共 36 条
[1]
A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty [J].
Al-e-hashem, S. M. J. Mirzapour ;
Malekly, H. ;
Aryanezhad, M. B. .
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2011, 134 (01) :28-42
[2]
Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management [J].
Aliev, R. A. ;
Fazlollahi, B. ;
Guirimov, B. G. ;
Aliev, R. R. .
INFORMATION SCIENCES, 2007, 177 (20) :4241-4255
[3]
A proposed mathematical model for closed-loop network configuration based on product life cycle [J].
Amin, Saman Hassanzadeh ;
Zhang, Guoqing .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2012, 58 (5-8) :791-801
[4]
Multi-objective integrated production and distribution planning of perishable products [J].
Amorim, P. ;
Guenther, H. -O ;
Almada-Lobo, B. .
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2012, 138 (01) :89-101
[5]
Optimal cash flow and operational planning in a company supply chain [J].
Bertel, S. ;
Fenies, P. ;
Roux, O. .
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2008, 21 (04) :440-454
[6]
Bilevel model for production-distribution planning solved by using ant colony optimization [J].
Calvete, Herminia I. ;
Gale, Carmen ;
Oliveros, Maria-Jose .
COMPUTERS & OPERATIONS RESEARCH, 2011, 38 (01) :320-327
[7]
A hybrid genetic algorithm for production and distribution [J].
Chan, FTS ;
Chung, SH ;
Wadhwa, S .
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, 2005, 33 (04) :345-355
[8]
Production planning for remanufactured products [J].
Depuy, G. W. ;
Usher, J. S. ;
Walker, R. L. ;
Taylor, G. D. .
PRODUCTION PLANNING & CONTROL, 2007, 18 (07) :573-583
[9]
Dhaenens-Flipo C, 2001, IIE TRANS, V33, P705, DOI 10.1080/07408170108936867
[10]
Generic production planning model for remanufacturing systems [J].
Doh, H-H ;
Lee, D-H .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE, 2010, 224 (B1) :159-168