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In this paper, an intensive search evolutionary algorithm is proposed to solve single machine total weighted tardiness scheduling problems. A specialised locally improved random swap mutation operator and an ordered crossover operator are used for evolution. The proposed algorithm starts with a pair of sequences: one generated by a greedy heuristic, namely, a backward phase heuristic acts as one parent, and a randomly generated sequence acts as the other. A computational experiment is conducted by applying the mutation operator on the backward phase sequence and the proposed algorithm with the same number of generations as the termination criteria. A total of 125 benchmark instances for sizes 40, 50 and 100 available in the OR library are solved and the results are compared with the available best-known results. It is observed that the proposed evolutionary algorithm provides better results than others . [ABSTRACT FROM AUTHOR]

Many optimization problems from the industrial engineering world, in particular the manufacturing systems, are very complex in nature and quite hard to solve by conventional optimization techniques. There has been increasing interest in imitating living beings to solve such kinds of hard optimization problems. Simulating the natural evolutionary process of human beings results in stochastic optimization tech niques called evolutionary algorithms, which can often outperform conventional optimization methods when applied to difficult real-world problems. There are currently three main avenues of this research: genetic algorithms (GAs), evolutionary programming (EP) and evolution strategies (ESs). Among them, genetic algorithms are perhaps the most widely known types of evolutionary algorithms today. During the past years, several GAs for the job-shop scheduling problems have been proposed, each with different chromosome representation. In this paper, the different GAs are collected from the literature and an attempt has been made to evaluate them. The benchmark problems available in open literature are used for evaluation and the performance measure considered is makespan. The algorithms are coded in C+ +. [ABSTRACT FROM AUTHOR]

A number of optimization techniques are available to solve the scheduling problems. Most of the scheduling problems are combinatorial optimization problems which are too difficult to be solved optimally, and hence heuristics are used to obtain 'good' solutions in reasonable time. Scheduling deals with the allocation of resources. Scheduling is a decision-making process that has, as a goal, the optimization of two or more objectives. The specific goal of this paper is to perform a comparative evaluation of the exisiting constructive heuristic algorithms and to propose an improvement heuristic algorithm. The improvement heuristic algorithm proposed is a genetic algorithm. The benchmark problems are used to evaluate these algorithms. The genetic algorithm (GA) is a potentially powerful tool for solving complex optimization problems. In this paper, the heuristic procedures considered for evaluation are Palmer's algorithm, Gupta's algorithm, C D S algorithm, Dannenbring's R A algorithm, N E H algorithm and the proposed genetic algorithm. The algorithms are coded in G language. The performance measure considered for evaluation is the makespan. [ABSTRACT FROM AUTHOR]