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The Uncapacitatied Dynamic Single-Level Lot-Sizing Problem under a Time-Varying Environment and an Exact Solution Approach
被引:2
作者:
Xiao, Yiyong
[1
]
You, Meng
[1
]
Zuo, Xiaorong
[1
]
Zhou, Shenghan
[1
]
Pan, Xing
[1
]
机构:
[1] Beihang Univ, Sch Reliabil & Syst Engn, Beijing 100191, Peoples R China
基金:
中国国家自然科学基金;
关键词:
lot-sizing problem;
time-varying environment;
deteriorating setup cost;
dynamic programming;
optimization;
NEIGHBORHOOD SEARCH;
ALGORITHM;
MODELS;
PRICE;
SIZE;
D O I:
10.3390/su10113867
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
The dynamic lot-sizing problem under a time-varying environment considers new features of the production system where factors such as production setup cost, unit inventory-holding cost, and unit price of manufacturing resources may vary in different periods over the whole planning horizon. Traditional lot-sizing theorems and algorithms are no longer fit for these situations as they had assumed constant environments. In our study, we investigated the dynamic lot-sizing problem with deteriorating production setup cost, a typical time-varying environment where the production setup is assumed to consume more preparing time and manufacturing resources as the production interval lasts longer. We proposed new lot-sizing models based on the traditional lot-sizing model considering the changing setup cost as a new constraint, called uncapacitatied dynamic single-level lot-sizing under a time-varying environment (UDSLLS-TVE for short). The UDSLLS-TVE problem has a more realistic significance and higher research value as it is closer to reality and has higher computational complexity as well. We proposed two mathematical programming models to describe UDSLLS_TVE with or without nonlinear components, respectively. Properties of the UDSLLS-TVE models were extensively analyzed and an exact algorithm based on forward dynamic programming (FDP) was proposed to solve this problem with a complexity of O (n(2)). Comparative experiments with the commercial MIP solver CPLEX on synthesized problem instances showed that the FDP algorithm is a global optimization algorithm and has a high computational efficiency.
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页数:14
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