A possibilistic environment based particle swarm optimization for aggregate production planning

Chakrabortty, Ripon K.; Hasin, M. Ahsan Akhtar; Sarker, Ruhul A.; Essam, Daryl

doi:10.1016/j.cie.2015.07.021

Cited by 37 publications

(13 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deterministic Single Aghezzaf & Artiba, 1998;Silva & JoÄOoisboa, 2000;Pradenas & Pe˜nailillo, 2004;Fahimnia et al, 2005;Pipery & Vachony, 2001;Singhvi & Shenoy, 2002;Techawiboonwong & Yenradee, 2003;Mazzola et al, 1998;Sillekens et al, 2011;Zhang et al, 2012;Ramezanian et al, 2012;Chaturvedi & Bandyopadhyay, 2015;Chaturvedi, 2017Multiple Leung & Chan, 2009Leung et al, 2003;Chakrabortty & Akhtar Hasin, 2013;Entezaminia et al, 2016;Abu Bakar et al, 2016;Mehdizadeh et al, 2018 Uncertain Fuzzy Single Baykasoglu, 1999;Wang & Fang, 2000;Fung et al, 2003;Tang et al, 2000;Tang et al, 2003;Ning et al, 2006;Aliev et al, 2007;Chen & Huang, 2010;Iris & Cevikcan, 2014;Rahmani et al, 2014;Chakrabortty et al, 2015Multiple Wang & Fang, 1997Baykasoglu & Gocken, 2006;Sakallı et al, 2010;Baykasoglu & Gocken, 2010;Sadeghi et al, 2013;da Silva & Silva Marins, 2014;Gholamian et al, 2016;Fiasché et al, 2016;Chauhan et al, 2017;…”

Section: Objective Function Articlesmentioning

confidence: 99%

Aggregate production planning: A literature review and future research directions

Cheraghalikhani¹,

Khoshalhan²,

Mokhtari³

2019

10.5267/j.ijiec

View full text Add to dashboard Cite

Aggregate production planning (APP) is concerned with determining the optimum production and workforce levels for each period over the medium term planning horizon. It aims to set overall production levels for each product family to meet fluctuating demand in the near future. APP is one of the most critical areas of production planning systems. After the state-of-the-art summaries in 1992 by Nam and Logendran [ Nam, S. J., & Logendran, R. (1992). Aggregate production planning-a survey of models and methodologies. European Journal of Operational Research, 61(3), 255-272. ], which specifically summarized the various existing techniques from 1950 to 1990 into a framework depending on their abilities to either produce an exact optimal or near-optimal solution, there has not been any systematic survey in the literature. This paper reviews the literature on APP models to meet two main purposes. First, a systematic structure for classifying APP models is proposed. Second, the existing gaps in the literature are demonstrated in order to extract future directions of this research area. This paper covers a variety of APP models' characteristics including modeling structures, important issues, and solving approaches, in contrast to other literature reviews in this field which focused on methodologies in APP models. Finally some directions for future research in this research area are suggested.

show abstract

Section: Objective Function Articlesmentioning

confidence: 99%

Aggregate production planning: A literature review and future research directions

Cheraghalikhani¹,

Khoshalhan²,

Mokhtari³

2019

10.5267/j.ijiec

View full text Add to dashboard Cite

show abstract

“…An overview of the PSO along with its performance of different constituents and variants for the use in power system optimisation problems are discussed in [28]. A standard PSO algorithm has mainly two equations for the position and velocity vectors in an N -dimensional solution space [29,30]. The movement and position of each particle i can be represented as v k+1…”

Section: Proposed Solution Approachesmentioning

confidence: 99%

Energy Management of Community Microgrids Considering Uncertainty using Particle Swarm Optimisation

Ma¹,

Chakrabortty²,

Ryan³

et al. 2020

Preprint

View full text Add to dashboard Cite

Although energy management of a microgrid is generally performed using a day-ahead scheduling method, its effectiveness has been questioned by the research community due to the existence of high uncertainty in renewable power generation, power demand and electricity market. As a result, real-time energy management schemes are recently developed to minimise the operating cost of a microgrid while high uncertainty presents in the network. This paper develops modified particle swarm optimisation (MPSO) algorithms to solve optimisation problems of energy management schemes for a community microgrid and proposes a scheduling approach after taking into consideration high uncertainty to effectively minimise the operational cost of the microgrid. The optimisation problems are formulated for real-time and scheduling approaches, and solution methods are developed to solve the problems. It is observed that the scheduling program demonstrates superior performance in all the cases, including uncertainty in prediction, as compared to the other energy management approaches, although solutions have significant deviations due to prediction errors.

show abstract

“…The decisions of agricultural and logistics phases were made on a weekly planning horizon to contain the whole harvesting season and the periods between harvests. Chakrabortty et al [58] conducted a research on the PSO based on a possibilistic environment for a multi-period and multi-product APP problem. They proposed an approach that used a triangular possibility distribution to handle all the imprecise operating costs, demands, and the data related to capacity.…”

Section: Review Of the Literature On Production Planning And Appmentioning

confidence: 99%

Benders Decomposition Algorithm for Robust Aggregate Production Planning Considering Pricing Decisions in Competitive Environment: A Case Study

Aazami

Saidi‐Mehrabad

2018

Scientia Iranica

View full text Add to dashboard Cite

In operations research, bi-level programming is a mathematical modeling which has another optimization problem as a constraint. In the present research, regarding the current intense competition among large manufacturing companies for achieving a greater market share, a bi-level robust optimization model is developed as a leader-follower problem using Stackelberg game in the eld of Aggregate Production Planning (APP). The leader company with higher in uence intended to produce new products, which could replace the existing products. The follower companies, as rivals, were also seeking more sales, but they did not have the intention and ability to produce such new products. The price of the new products was determined by the presented elasticity relations between uncertain demand and price. After linearization, using the KKT conditions, the bi-level robust model was transformed into an ordinary uni-level model. Due to the NP-hard nature of the problem, Benders Decomposition Algorithm (BDA) was proposed for overcoming the computational complexities in large scale. Finally, using the real data of Sarvestan Sepahan Co as a leader company, the validity of the developed model as well as e ciency and convergence of the BDA was investigated. The computational results clearly showed the e ciency and e ectiveness of the proposed BDA.

show abstract

A possibilistic environment based particle swarm optimization for aggregate production planning

Cited by 37 publications

References 43 publications

Aggregate production planning: A literature review and future research directions

Aggregate production planning: A literature review and future research directions

Energy Management of Community Microgrids Considering Uncertainty using Particle Swarm Optimisation

Benders Decomposition Algorithm for Robust Aggregate Production Planning Considering Pricing Decisions in Competitive Environment: A Case Study

Contact Info

Product

Resources

About