Most of the scheduling problems are NP-hard. In the literature, several heuristics and dispatching rules are proposed to solve such hard combinatorial optimization problems and genetic algorithm (GA) ranks among the most preferred ones in view of its characteristics such as high adaptability, near optimization and easy realization. But, even though it is a common problem in the industry, only a small number of studies deal with non-identical parallel machines. In this paper, the authors propose a new "crossover operator" and a new "optimality criterion" in order to adapt the GA to non-identical parallel machine scheduling problem. New algorithm is tested on a numerical example by implementing it in a simulation software and computational results are compared to those obtained with LPT (Longest Processing Time) dispatching rule; results were promising. Findings show that, in addition to its high computational speed for larger scale problem, the GA proposed here fits the non-identical parallel machine scheduling problem of minimizing the maximum completion time (makespan).

Non-identical parallel machine scheduling using genetic algorithm

Parallel machine scheduling with fuzzy processing times using a robust genetic algorithm and simulation

Intelligent scheduling covers various tools and techniques for successfully and efficiently solving the scheduling problems. In this paper, we provide a survey of intelligent scheduling systems by categorizing them into five major techniques containing fuzzy logic, expert systems, machine learning, stochastic local search optimization algorithms and constraint programming. We also review the application case studies of these techniques.

A state of the art review of intelligent scheduling

Development of Multiple Regression and Neural Network Models for Assessment of Blasting Dust at a Large Surface Coal Mine

This work proposes a unified heuristic algorithm for a large class of earliness–tardiness (E–T) scheduling problems. We consider single/parallel machine E–T problems that may or may not consider some additional features such as idle time, setup times and release dates. In addition, we also consider those problems whose objective is to minimize either the total (average) weighted completion time or the total (average) weighted flow time, which arise as particular cases when the due dates of all jobs are either set to zero or to their associated release dates, respectively. The developed local search-based metaheuristic framework is quite simple, but at the same time relies on sophisticated procedures for efficiently performing local search according to the characteristics of the problem. We present efficient move evaluation approaches for some parallel machine problems that generalize the existing ones for single machine problems. The algorithm was tested in thousands of instances of several E–T problems and particular cases. The results obtained show that our unified heuristic is capable of producing high-quality solutions when compared to the best ones available in the literature that were obtained by specific methods. Moreover, we provide an extensive annotated bibliography on the problems related to those considered in this work, where we not only indicate the approach(es) used in each publication, but we also point out the characteristics of the problem(s) considered. Beyond that, we classify the existing methods in different categories so as to have a better idea of the popularity of each type of solution procedure.

A unified heuristic and an annotated bibliography for a large class of earliness–tardiness scheduling problems

Manufacturing industries frequently face the problem of reducing earliness and tardiness penalties, owing to the emerging concept of the just-in-time production philosophy. The problem studied in this work is the Non-identical Parallel-machine?Earliness-Tardiness Non-common Due Date?Sequence-dependent Set-up Time Scheduling Problem (NPETNDDSP) for jobs with varying processing times, where the objective is to minimise the sum of the absolute deviations of job completion times from their corresponding due dates for the different weighted earliness and tardiness combinations. A Genetic Algorithm-Fuzzy Logic Approach (GA-Fuzzy) has been proposed to select the optimal weighted earliness-tardiness combinations in a non-identical parallel-machine environment. The performance of the combined objective function obtained by the proposed GA-Fuzzy technique has been compared with the solutions yielded by the Genetic Algorithm (GA) techniques available in literature, known as GA with Partially Mapped Crossover Operator (GA-PMX) and GA with Multi-Component Uniform Order-based Crossover Generator (GA-MCUOX). The comparison shows that the proposed GA-Fuzzy technique outperforms both the GA-PMX and GA-MCUOX.

Non-identical parallel-machine scheduling using genetic algorithm and fuzzy logic approach

Just-in-time production philosophy has led to the growing interest in scheduling problems considering both earliness and tardiness penalties. This research work considers the uniform parallel machine earliness-tardiness non-common due date sequence-dependent set-up time scheduling problem (UPETNDDSP) for jobs with varying processing times, where the objective is to minimize the sum of the absolute deviations of job completion times from their corresponding due dates. A simulated annealing (SA) -fuzzy logic approach has been proposed to select the optimal weighted earliness-tardiness combinations in a non-identical parallel machine environment. The SA algorithm identifies the best sequences for the different weighted combinations of earliness and tardiness measures for each given set of jobs. Fuzzy logic is then used to select the optimal weighted combination, which satisfies the combined objective function to a larger extent. The performance of the combined objective function and individual objective measure obtained by the proposed SA-fuzzy technique has been compared with the solutions yielded by the genetic algorithm technique known as the genetic algorithm with partially mapped cross-over operator (GA-PMX) and with those of the results reported in literature. The comparison shows that the proposed SA-fuzzy technique outperforms the existing available techniques.

Earliness—tardiness scheduling on uniform parallel machines using simulated annealing and fuzzy logic approach:

Quality Function Deployment in Agile Parallel Machine Scheduling Through Neural Network Technique

The problem of flexible hybrid flow shop scheduling with identical and non-identical parallel machines in one or more stages has been considered with the objective of minimising the makespan. A Simulated Annealing (SA) approach has been developed to select the best solutions in a flexible hybrid flow shop scheduling. The SA approach identifies the best sequences for the given set of jobs in a 2-stage/2-machine and 2-stage/3-machine hybrid flow shop problems. The extent of deviation of the performance measures owing to the flexibility in the arrangement of machines in the two stages has been analysed. The proposed SA algorithm has been applied to benchmark problems taken from Taillard (1993). A comparison of the solutions yielded by the ant-colony algorithm by Stuetzle (1998), called Max-Min Ant System (MMAS) and the SA algorithm developed in this paper, with the heuristic solutions given by Taillard is undertaken with respect to the minimisation of makespan. The comparison shows that the proposed SA algorithm performs better on the average, than the MMAS.

K. Raja

Papers

Non-identical parallel-machine scheduling using genetic algorithm and fuzzy logic approach

Earliness—tardiness scheduling on uniform parallel machines using simulated annealing and fuzzy logic approach:

Quality Function Deployment in Agile Parallel Machine Scheduling Through Neural Network Technique

Agility in two-stage hybrid flow shop parallel machine scheduling through simulated annealing