Biogeography-based optimization algorithm for large-scale multistage batch plant scheduling

•A time key representation is developed to encode scheduling scheme.•The elite solution is improved to enhance the search accuracy.•An improved time key biogeography-based optimization algorithm is presented to solve problem.•Results have shown that the proposed algorithm is efficient. The batch pro...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Expert systems with applications Ročník 162; s. 113776
Hlavní autoři: Zhang, Yaya, Gu, Xingsheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 30.12.2020
Elsevier BV
Témata:
Batch plant
Biogeography
Biogeography-based optimization algorithm
Fine chemicals
Genetic algorithms
Heuristic methods
Heuristic rules
Mathematical analysis
Mathematical programming
Metallurgy
Multistage multiproduct
Optimization
Optimization algorithms
Representations
Scheduling
Solution space
Vectors (mathematics)
SIV
SDs
MILP
Batch plant
DPSO
BCSF
BBO
RKBBO
SPGA
SD
Multistage multiproduct
MMSP
MPGA
GA
Biogeography-based optimization algorithm
OBBBO
LCA
CP
PSO
ACO
NEH
HIS
CV
TG
MIP
Improved-TKBBO
TKBBO
DA
Heuristic rules
ISSN:0957-4174, 1873-6793
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:•A time key representation is developed to encode scheduling scheme.•The elite solution is improved to enhance the search accuracy.•An improved time key biogeography-based optimization algorithm is presented to solve problem.•Results have shown that the proposed algorithm is efficient. The batch process is characterized by many varieties, small batches, redundant production equipment, flexible production process, and high-added-value products. This process is widely used in chemical, plastic, rubber, pharmaceutical, fine chemical, metallurgical, steel, food, and other industries. The optimized scheduling scheme of the batch process can effectively enhance enterprise competitiveness and improve economic benefits. Multistage Multiproduct Scheduling Problem (MMSP) is an important branch of batch scheduling problems. It is difficult to solve MMSP within a reasonable time by traditional mathematical programming, because once the scale of scheduling problems increases, the solution space expands exponentially. This study proposes a metaheuristic approach based on a time key biogeography-based optimization algorithm to solve MMSP. This new time key representation contains two vectors, which represent the processing sequence and equipment allocation of orders respectively. In accordance with the time information in the new representation, we add the preference of equipment processing time to migration and calculate the probability of every mutation value. In addition, the elite solution is combined with the active scheduling technique and modified Nawaz-Enscore-Ham (NEH) algorithm to improve the search accuracy of the proposed algorithm. To test the performance of Improved Time Key Biogeography-Based Optimization (Improved-TKBBO) algorithm, its results are compared with computational results of mathematical programming, Genetic Algorithm (GA), and Line-up Competition Algorithm (LCA). Simulation results show that the proposed Improved-TKBBO can solve the large-scale MMSP with non-identical parallel units effectively.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2020.113776