Hybrid Linear Search, Genetic Algorithms, and Simulated Annealing for Fuzzy Non-Linear Industrial Production Planning Problems

2013 ◽  
pp. 87-109 ◽  
Author(s):  
P. Vasant
Keyword(s):  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Production Planning ◽  
Decision Maker ◽  
Optimal Solution ◽  
Optimization Approach ◽  
Product Mix ◽  
Fuzzy Environment ◽  
Level Of Satisfaction ◽  
S Curve

This chapter outlines an introduction to real-world industrial problem for product-mix selection involving eight variables and twenty one constraints with fuzzy technological coefficients, and thereafter, a formulation for an optimization approach to solve the problem. This problem occurs in production planning in which a decision maker plays a pivotal role in making decision under fuzzy environment. Decision-maker should be aware of his/her level of satisfaction as well as degree of fuzziness while making the product-mix decision. Thus, a thorough analysis is performed on a modified S-curve membership function for the fuzziness patterns and fuzzy sensitivity solution is found from the various optimization methodologies. An evolutionary algorithm is proposed to capture the optimal solutions respect to the vagueness factor and level of satisfaction. The near global optimal solution for objective function is obtained by hybrid meta-heuristics optimization algorithms such as line search, genetic algorithms, and simulated annealing.

Novel Meta-Heuristic Optimization Techniques for Solving Fuzzy Programming Problems

2012 ◽  
pp. 104-131 ◽  
Author(s):  
Pandian Vasant
Keyword(s):  
Production Planning ◽  
Decision Maker ◽  
Real World ◽  
Industrial Production ◽  
Fuzzy Programming ◽  
Industrial Sector ◽  
Optimization Techniques ◽  
Heuristic Optimization ◽  
Product Mix ◽  
Level Of Satisfaction

In this chapter, three meta-heuristic optimization techniques have been utilized to solve the fuzzy programming problems in industrial production systems. This chapter outlines an introduction to real-world industrial problem for product-mix selection involving eight variables and 21 constraints with fuzzy technological coefficients and thereafter, a formulation for an optimization approach to solve the problem. This problem occurs in production planning in which a decision maker plays a pivotal role in making decision under fuzzy environment. Decision-maker should be aware of his/her level of satisfaction as well as degree of fuzziness while making the product-mix decision. Genetic algorithms, pattern search, and mesh adaptive direct search methods have been employed to solve the large scale problems in real world industrial sector. The results for these techniques have been investigated thoroughly in the form of 2D, 3D plots, and tables. The industrial production planning problem which was illustrated in this chapter was solved successfully by these three meta-heuristic methods. The results are analyzed along with the optimal profit function (objective or fitness function) with level of satisfaction, decision variables, vagueness factor, and computational time (CPU).

scholarly journals Parallel genetic algorithms on the graphics processing units using island model and simulated annealing

2017 ◽  
Vol 9 (7) ◽  
pp. 168781401770741 ◽  
Author(s):  
Cheng-Chieh Li ◽  
Chu-Hsing Lin ◽  
Jung-Chun Liu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Graphics Processing Units ◽  
Optimal Solution ◽  
Approximate Solutions ◽  
Optimization Method ◽  
Approximate Algorithm ◽  
Parallel Genetic Algorithms ◽  
Graphics Processing

To solve a non-deterministic polynomial-hard problem, we can adopt an approximate algorithm for finding the near-optimal solution to reduce the execution time. Although this approach can come up with solutions much faster than brute-force methods, the downside of it is that only approximate solutions are found in most situations. The genetic algorithm is a global search heuristic and optimization method. Initially, genetic algorithms have many shortcomings, such as premature convergence and the tendency to converge toward local optimal solutions; hence, many parallel genetic algorithms are proposed to solve these problems. Currently, there exist many literatures on parallel genetic algorithms. Also, a variety of parallel genetic algorithms have been derived. This study mainly uses the advantages of graphics processing units, which has a large number of cores, and identifies optimized algorithms suitable for computation in single instruction, multiple data architecture of graphics processing units. Furthermore, the parallel simulated annealing method and spheroidizing annealing are also used to enhance performance of the parallel genetic algorithm.

FUZZY LINEAR PROGRAMMING FOR DECISION MAKING AND PLANNING UNDER UNCERTAINTY

2005 ◽  
Vol 04 (04) ◽  
pp. 647-662 ◽  
Author(s):  
PANDIAN M. VASANT
Keyword(s):  
Decision Maker ◽  
Manufacturing Industry ◽  
Real Life ◽  
Fuzzy Linear Programming ◽  
Final Decision ◽  
Planning Under Uncertainty ◽  
Product Selection ◽  
Level Of Satisfaction ◽  
S Curve ◽  
Industrial Problem

In this paper, the S-curve membership function methodology is used in a real life industrial problem of mix product selection. This problem occurs in the chocolate manufacturing industry whereby a decision maker, analyst and implementer play important roles in making decisions in an uncertain environment. As analysts, we try to find a solution with a higher level of satisfaction for the decision maker to make a final decision. This problem of mix product selection is considered because all the coefficients such as objective, technical and resource variables are fuzzy. This is considered as one of the sufficiently large problem involving 29 constraints and 8 variables. A decision maker can identify which vagueness (α) is suitable for achieving satisfactory optimal revenue. The decision maker can also suggest to the analyst some possible and practicable changes in fuzzy intervals for improving the satisfactory revenue. This interactive process has to go on among the analyst, the decision maker and the implementer until an optimum satisfactory solution is achieved and implemented.

Solving Fuzzy Optimization Problems of Uncertain Technological Coefficients with Genetic Algorithms and Hybrid Genetic Algorithms Pattern Search Approaches

2011 ◽  
pp. 344-368 ◽  
Author(s):  
Pandian Vasant
Keyword(s):  
Genetic Algorithms ◽  
Nonlinear Programming ◽  
Production Planning ◽  
Numerical Solutions ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Real Life ◽  
Pattern Search ◽  
Uncertain Environments ◽  
S Curve

In this chapter a solution is proposed to a certain nonlinear programming difficulties related to the presence of uncertain technological coefficients represented by vague numbers. Only vague numbers with modified s-curve membership functions are considered. The proposed methodology consists of novel genetic algorithms and a hybrid genetic algorithm pattern search (Vasant, 2008) for nonlinear programming for solving problems that arise in industrial production planning in uncertain environments. Real life application examples in production planning and their numerical solutions are analyzed in detail. The new method suggested has produced good results in finding globally near-optimal solutions for the objective function under consideration.

A Linear Programming Model for Product Mix Profit Maximization in A Small Medium Enterprise Company

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
S Mohd Baki ◽  
Jack Kie Cheng
Keyword(s):  
Linear Programming ◽  
Production Planning ◽  
Business Performance ◽  
Programming Model ◽  
Profit Maximization ◽  
Optimal Level ◽  
Linear Programming Model ◽  
Product Mix ◽  
Small Medium Enterprises ◽  
Medium Enterprises

Production planning is often challenging for small medium enterprises (SMEs) company. Most of the SMEs are having difficulty in determining the optimal level of the production output which can affect their business performance. Product mix optimization is one of the main key for production planning. Many company have used linear programming model in determining the optimal combination of various products that need to be produced in order to maximize profit. Thus, this study aims for profit maximization of a SME company in Malaysia by using linear programming model. The purposes of this study are to identify the current process in the production line and to formulate a linear programming model that would suggest a viable product mix to ensure optimum profitability for the company. ABC Sdn Bhd is selected as a case study company for product mix profit maximization study. Some conclusive observations have been drawn and recommendations have been suggested. This study will provide the company and other companies, particularly in Malaysia, an exposure of linear programming method in making decisions to determine the maximum profit for different product mix.

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