A new hybrid algorithm of simulated annealing and simplex downhill for solving multiple-objective aggregate production planning on fuzzy environment

2017 ◽  
Vol 31 (6) ◽  
pp. 1823-1834 ◽  
Author(s):  
A. A. Zaidan ◽  
Bayda Atiya ◽  
M. R. Abu Bakar ◽  
B. B. Zaidan
Keyword(s):  
Simulated Annealing ◽  
Production Planning ◽  
Hybrid Algorithm ◽  
Multiple Objective ◽  
Aggregate Production Planning ◽  
Aggregate Production ◽  
Fuzzy Environment

Integrating a Weighted Additive Multiple Objective Linear Model with Possibilistic Linear Programming for Fuzzy Aggregate Production Planning Problems

2020 ◽  
Vol 9 (2) ◽  
pp. 1-30
Author(s):  
Navee Chiadamrong ◽  
Noppasorn Sutthibutr
Keyword(s):  
Linear Programming ◽  
Linear Model ◽  
Production Planning ◽  
Hybrid Approach ◽  
Optimization Method ◽  
Multiple Objective ◽  
Aggregate Production Planning ◽  
Aggregate Production ◽  
Planning Problems ◽  
Possibilistic Linear Programming

This study uses an integrated optimization method by applying a weighted additive multiple objective linear model with Possibilistic Linear Programming (PLP) to fuzzy Aggregate Production Planning (APP) problems under an uncertain environment. The uncertainty conditions include uncertainties of operating times and costs, customer demand, labor level, as well as machine capacity. The aim of this study is to minimize total costs of the plan that consist of the production cost and costs of changes in labor level. The proposed hybrid approach minimizes the most possible value of the imprecise total costs, maximizes the possibility of obtaining lower total costs, and minimizes the risk of obtaining higher total costs from PLP as multiple objectives for the fuzzy multiple objective linear model optimization. The outcome of the proposed approach shows that the solution is closer to the ideal solution obtained from Linear Programming than a typical solution obtained from PLP. There is also a higher overall satisfaction value.

scholarly journals Application of simulated annealing to solve multi-objectives for aggregate production planning

2016 ◽  
Author(s):  
Bayda Atiya ◽  
Abdul Jabbar Khudhur Bakheet ◽  
Iraq Tereq Abbas ◽  
Mohd. Rizam Abu Bakar ◽  
Lee Lai Soon ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Production Planning ◽  
Aggregate Production Planning ◽  
Aggregate Production

scholarly journals Enhanced Simulated Annealing for Solving Aggregate Production Planning

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Mohd Rizam Abu Bakar ◽  
Abdul Jabbar Khudhur Bakheet ◽  
Farah Kamil ◽  
Bayda Atiya Kalaf ◽  
Iraq T. Abbas ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Production Planning ◽  
Programming Model ◽  
Effective Means ◽  
Harmony Search ◽  
Solution Space ◽  
Search Space ◽  
Aggregate Production Planning ◽  
Aggregate Production ◽  
Real Performance

Simulated annealing (SA) has been an effective means that can address difficulties related to optimisation problems.SAis now a common discipline for research with several productive applications such as production planning. Due to the fact that aggregate production planning (APP) is one of the most considerable problems in production planning, in this paper, we present multiobjective linear programming model for APP and optimised bySA. During the course of optimising for the APP problem, it uncovered that the capability ofSAwas inadequate and its performance was substandard, particularly for a sizable controlledAPPproblem with many decision variables and plenty of constraints. Since this algorithm works sequentially then the current state will generate only one in next state that will make the search slower and the drawback is that the search may fall in local minimum which represents the best solution in only part of the solution space. In order to enhance its performance and alleviate the deficiencies in the problem solving, a modifiedSA(MSA) is proposed. We attempt to augment the search space by starting withN+1solutions, instead of one solution. To analyse and investigate the operations of the MSA with the standardSAand harmony search (HS), the real performance of an industrial company and simulation are made for evaluation. The results show that, compared toSAandHS,MSAoffers better quality solutions with regard to convergence and accuracy.

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