Using a Hybrid Genetic Algorithm to Minimize the Number of Tardy Jobs in the Flow Shop

2011 ◽  
Vol 201-203 ◽  
pp. 1070-1074
Author(s):  
Jing Jing Wu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Flow Shop ◽  
Hybrid Approach ◽  
Hybrid Genetic Algorithm ◽  
Solution Space ◽  
Fine Tuning ◽  
Due Dates ◽  
Number Of Tardy Jobs ◽  
Tardy Jobs

Numerous real-world problems relating to flow shops scheduling are complex. The main problem is that the solution space is very large and therefore the set of feasible solutions cannot be enumerated one by one. Current approaches to solve these problems are metaheuristics techniques, which fall in two categories: population-based search and trajectory-based search. Because of their complexity, recent research has turned to genetic algorithms to address such problems. In this paper we present an effective hybrid approach based on genetic algorithm (GA) for minimizing the number of tardy jobs in a flow shop consisting of m machines. Jobs with processing times and due dates randomly arrive to the system. We assume that job arrival or release dates are not known in advance. The objective is to minimize the number of tardy jobs. Although genetic algorithms have been proven to facilitate the entire space search, they lack in fine-tuning capability for obtaining the global optimum. Therefore the proposed approach incorporates a fitness functions and a population trained by a local improvement search based on tabu search with a candidate list strategy into GA for the problem which belongs to NP-hard class. Experimentation results show that the number of cells and the crossover strategy adapted affect the number of tardy jobs found. The results also indicate that hybrid genetic algorithm approach improves the solution quality drastically.

RDF Query Path Optimization Using Hybrid Genetic Algorithms

2022 ◽  
Vol 12 (1) ◽  
pp. 1-16
Author(s):  
Qazi Mudassar Ilyas ◽  
Muneer Ahmad ◽  
Sonia Rauf ◽  
Danish Irfan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Execution Time ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Solution Space ◽  
Path Optimization ◽  
Initial Population ◽  
Description Framework ◽  
The Cost

Resource Description Framework (RDF) inherently supports data mergers from various resources into a single federated graph that can become very large even for an application of modest size. This results in severe performance degradation in the execution of RDF queries. As every RDF query essentially traverses a graph to find the output of the Query, an efficient path traversal reduces the execution time of RDF queries. Hence, query path optimization is required to reduce the execution time as well as the cost of a query. Query path optimization is an NP-hard problem that cannot be solved in polynomial time. Genetic algorithms have proven to be very useful in optimization problems. We propose a hybrid genetic algorithm for query path optimization. The proposed algorithm selects an initial population using iterative improvement thus reducing the initial solution space for the genetic algorithm. The proposed algorithm makes significant improvements in the overall performance. We show that the overall number of joins for complex queries is reduced considerably, resulting in reduced cost.

Cell Loading and Family Scheduling for Jobs with Individual Due Dates

2012 ◽  
pp. 1201-1219
Author(s):  
Gürsel A. Süer ◽  
Emre M. Mese
Keyword(s):  
Mathematical Modeling ◽  
Genetic Algorithm ◽  
Cellular Manufacturing ◽  
Optimal Solution ◽  
Performance Measure ◽  
Good Strategy ◽  
Due Dates ◽  
Cell Loading ◽  
Job Splitting ◽  
Tardy Jobs

In this chapter, cell loading and family scheduling in a cellular manufacturing environment is studied. What separates this study from others is the presence of individual due dates for every job in a family. The performance measure is to minimize the number of tardy jobs. Family splitting among cells is allowed but job splitting is not. Even though family splitting increases number of setups, it increases the possibility of meeting individual job due dates. Two methods are employed in order to solve this problem, namely Mathematical Modeling and Genetic Algorithms. The results showed that Genetic Algorithm found the optimal solution for all problems tested. Furthermore, GA is efficient compared to the Mathematical Modeling especially for larger problems in terms of execution times. The results of experimentation showed that family splitting was observed in all multi-cell solutions, and therefore, it can be concluded that family splitting is a good strategy.

Two-Machine Ordered Flow Shop Scheduling with Generalized Due Dates

2020 ◽  
Vol 37 (01) ◽  
pp. 1950032
Author(s):  
Myoung-Ju Park ◽  
Byung-Cheon Choi ◽  
Yunhong Min ◽  
Kyung Min Kim
Keyword(s):  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Due Dates ◽  
Shop Scheduling ◽  
Processing Times ◽  
Specific Position ◽  
Polynomially Solvable ◽  
Definition Of ◽  
Tardy Jobs ◽  
Np Hardness

We consider a two-machine flow shop scheduling with two properties. The first is that each due date is assigned for a specific position different from the traditional definition of due dates, and the second is that a consistent pattern exists in the processing times within each job and each machine. The objective is to minimize maximum tardiness, total tardiness, or total number of tardy jobs. We prove the strong NP-hardness and inapproximability, and investigate some polynomially solvable cases. Finally, we develop heuristics and verify their performances through numerical experiments.

A Hybrid Approach for Shape Retrieval Using Genetic Algorithms and Approximate Distance

2021 ◽  
pp. 205-222
Author(s):  
Saliha Mezzoudj ◽  
Kamal Eddine Melkemi
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Hybrid Approach ◽  
Shape Context ◽  
Similar Shape ◽  
Complex Shapes ◽  
Ga Algorithm ◽  
Approximate Distance ◽  
Multi Agent ◽  
Part Structure

This article describes how the classical algorithm of shape context (SC) is still unable to capture the part structure of some complex shapes. To overcome this insufficiency, the authors propose a novel shape-based retrieval approach that is called HybMAS-GA using a multi-agent system (MAS) and a genetic algorithm (GA). They define a new distance called approximate distance (AD) to define a SC method by AD, which called approximate distance shape context (ADSC) descriptor. Furthermore, the authors' proposed HybMAS-GA is a star architecture where all shape context agents, N, are directly linked to a coordinator agent. Each retrieval agent must perform either a SC or an ADSC method to obtain a similar shape, started from its own initial configuration of sample points. This combination increases the efficiency of the proposed HybMAS-GA algorithm and ensures its convergence to an optimal images retrieval as it is shown through experimental results.

Towards a Hybrid Solver: Integration of a Genetic Algorithm Within “DSIDES”

2002 ◽  
Author(s):  
Shuiwei Xie ◽  
Warren F. Smith
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Linear Programming ◽  
Decision Support ◽  
Hybrid Genetic Algorithm ◽  
Systems Design ◽  
Nonlinear Problems ◽  
The Body ◽  
Hybrid Solver ◽  
Decision Based Design

In contributing to the body of knowledge for decision-based design, the work reported in this paper has involved steps towards building a hybrid genetic algorithm to address systems design. Highlighted is a work in progress at the Australian Defence Force Academy (ADFA). A genetic algorithm (GA) is proposed to deal with discrete aspects of a design model (e.g., allocation of space to function) and a sequential linear programming (SLP) method for the continuous aspects (e.g., sizing). Our historical Decision Based Design (DBD) tool has been the code DSIDES (Decision Support In the Design of Engineering Systems). The original functionality of DSIDES was to solve linear and non-linear goal programming styled problems using linear programming (LP) and sequential (adaptive) linear programming (SLP/ALP). We seek to enhance DSIDES’s solver capability by the addition of genetic algorithms. We will also develop the appropriate tools to deal with the decomposition and synthesis implied. The foundational paradigm for DSIDES, which remains unchanged, is the Decision Support Problem Technique (DSPT). Through introducing genetic algorithms as solvers in DSIDES, the intention is to improve the likelihood of finding the global minimum (for the formulated model) as well as the ability of dealing more effectively with nonlinear problems which have discrete variables, undifferentiable objective functions or undifferentiable constraints. Using some numerical examples and a practical ship design case study, the proposed GA based method is demonstrated to be better in maintaining diversity of populations, preventing premature convergence, compared with other similar GAs. It also has similar effectiveness in finding the solutions as the original ALP DSIDES solver.

Analysis of Pavement Management Activities Programming by Genetic Algorithms

1998 ◽  
Vol 1643 (1) ◽  
pp. 1-6 ◽  
Author(s):  
T. F. Fwa ◽  
W. T. Chan ◽  
K. Z. Hoque
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Solution Space ◽  
Pavement Management ◽  
Practical Significance ◽  
Optimization Analysis ◽  
Large Solution ◽  
Pavement Management System ◽  
Pavement Maintenance ◽  
Operational Characteristics

The application of genetic algorithms to programming of pavement maintenance activities at the network level is demonstrated. The operational characteristics of the genetic algorithm technique and its relevance to solving the programming problem in a Pavement Management System (PMS) are discussed. The robust search capability of genetic algorithms enables them to effectively handle the highly constrained problem of pavement management activities programming, which has an extremely large solution space of astronomical scale. Examples are presented to highlight the versatility of genetic algorithms in accommodating different objective function forms. This versatility makes the algorithms an effective tool for planning in PMS. It is also demonstrated that composite objective functions that combine two or more different objectives can be easily considered without having to reformulate the genetic algorithm computer program. Another useful feature of genetic algorithm solutions is the availability of near-optimal solutions besides the "best" solution. This has practical significance as it gives the users the flexibility to examine the suitability of each solution when practical constraints and factors not included in the optimization analysis are considered.

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