In this paper the performance of the most recent multi-modal genetic algorithms (MMGAs) on the Job Shop Scheduling Problem (JSSP) is compared in term of efficacy, multi-solution based efficacy (the algorithm's capability to find multiple optima), and diversity in the final set of solutions. The capability of Genetic Algorithms (GAs) to work on a set of solutions allows us to reach different optima in only one run. Nevertheless, simple GAs are not able to maintain different solutions in the last iteration, therefore reaching only one local or global optimum. Research based on the preservation of the diversity through MMGAs has provided very promising results. These techniques, known as niching methods or MMGAs, allow not only to obtain different multiple global optima, but also to preserve useful diversity against convergence to only one solution (the usual behaviour of classical GAs). In previous works, a significant difference in the performance among methods was found, as well as the importance of a suitable parametrization. In this work classic methods are compared to the most recent MMGAs, grouped in three classes (sharing, clearing and species competition), for JSSP. Our experimental study found that those new MMGAs which have a certain type of replacement process perform much better (in terms of highest efficacy and multi-solution based efficacy) than classical MMGAs which do not have this type of process.

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Analysis of new niching genetic algorithms for finding multiple solutions in the job shop scheduling

Job shop scheduling problem has stronger processing constraints, and it is a kind of well-known combination optimization problem. Particle swarm optimization algorithm is employed to solve the job shop scheduling problem, and the objective is minimizing the maximum completion time of all the jobs. The particle representation based on operation-particle position sequence is proposed. In the particle representation, the mapping between the particle and the scheduling solution is established through connecting the operation sequence of all the jobs with the particle position sequence. According to processing constraints of the problem, each operation in the operation sequence of all the jobs is assigned on each machine in turn to form the scheduling solution. The particle representation can ensure that the scheduling solutions decoded are feasible and can follow the particle swam optimization algorithm model. The computational results show that particle swarm optimization algorithm can effectively solve the job shop scheduling problem.

Investigation of Particle Swarm Optimization for Job Shop Scheduling Problem

Scheduling algorithms that achieve good or near optimal solutions and can efficiently adapt them to perturbations are, in most cases, preferable to those that achieve optimal ones but that cannot implement such an adaptation. In fact, in real world, where problems are essentially of dynamic and stochastic nature, these methods or algorithms are of very little use. This is the case with most algorithms for solving the so-called static scheduling problem for different setting of both single and multi-machine systems arrangements. This paper is concerned with a scheduling system, based on Genetic Algorithms, for the resolution of the dynamic version of the same problem. An additional constraint will be considered in our approach for the JSSP, the existence of different job release and due dates and different assembly levels for the jobs.

Resource-oriented scheduling for real world manufacturing systems

OF THESIS ADAPTIVE, MULTI-OBJECTIVE JOB SHOP SCHEDULING USING GENETIC ALGORITHMS This research proposes a method to solve the adaptive, multi-objective job shop scheduling problem. Adaptive scheduling is necessary to deal with internal and external disruptions faced in real life manufacturing environments. Minimizing the mean tardiness for jobs to effectively meet customer due date requirements and minimizing mean flow time to reduce the lead time jobs spend in the system are optimized simultaneously. An asexual reproduction genetic algorithm with multiple mutation strategies is developed to solve the multi-objective optimization problem. The model is tested for single day and multi-day adaptive scheduling. Results are compared with those available in the literature for standard problems and using priority dispatching rules. The findings indicate that the genetic algorithm model can find good solutions within short computational time.

/pdf/adaptive-multi-objective-job-shop-scheduling-using-genetic-4vk3vah3su.pdf

Adaptive, multi-objective job shop scheduling using genetic algorithms

Most of the research literature concerning scheduling concentrates on the static problems, i.e problems where all input data is known and does not change over time. However, the real world scheduling problems are very seldom static. Events like machine breakdown or bottleneck in some situation impossible to predict. Dynamic scheduling is a research field, which take into consideration uncertainty and dynamic changes in the real world scheduling problem. This paper gives an overview of the real problem occured in the filed of dynamic scheduling. Then we propose a hybrid genetic algorithm for solving the dynamic job shop problem.

Towards implementing reactive scheduling for job shop problem

Due to their dynamic nature, real scheduling problems have an additional complexity in relation to static ones. In many situations these problems, even for apparently simple situations, are hard to solve, i.e. the time required to compute an optimal solution increases exponentially with the size of the problem [1]. GAs have been extensively used in the context of Job-Shop Scheduling Problems (JSSP). If all jobs are known before processing starts the JSSP is called static, while if job release times are not fixed at a single point in time, i.e. jobs arrive to the system at different times, the problem is called dynamic. Scheduling problems can also be classified as deterministic, when processing times and all other parameters are known and fixed, and stochastic, when some or all parameters are uncertain [7]. The proposed approach deals with these two cases of dynamic scheduling: deterministic and stochastic. For such class of problems, the goal is no longer to find a single optimum, but rather to continuously adapt the solution to the changing environment. The purpose of this paper is to describe an approach based on GA for solving dynamic scheduling problems, where the products (jobs) to be processed have due dates. This paper starts by presenting a scheduling system, based on Genetic Algorithms for the resolution of the dynamic version of Single Machine Scheduling Problem (SMSP). The approach used adapts the resolution of the static problem to the dynamic one in which changes may occur continually. This takes into account dynamic occurrences in a system and adapts the current population to a new regenerated population. Then, it is proposed an approach for the resolution of the Job-Shop Scheduling Problem (JSSP) in dynamic environments. The paper is structured as follows: section 2 provides a description of the considered scheduling problem. Section 3 summarises an approach for the resolution of the Dynamic Single Machine Scheduling Problem. The proposed approach for dynamic scheduling is presented in section 4. Finally, the paper concludes with a summary and some ideas for future work.

Carmo Silva

Papers

A Genetic Approach for Dynamic Job-Shop Scheduling Problems