Genetic algorithm for job-shop scheduling with machine unavailability and breakdowns

The paper describes an approach to solving a job-shop scheduling problem in a flexible semiconductor manufacturing system. The application refers to the production of memories in a new module built in the ST Microelectronics plant in Catania (Italy). The approach uses a genetic algorithm, which has been devised for the particular industrial application. The paper focuses on the scheduling approach and on how it improves performance of the plant.

An effective TPA-based algorithm for job-shop scheduling problem

Job shop scheduling problem has been a typical scheduling problem that has been thoroughly studied over the last few decades. It has been proven to be a NP-hard problem. The purpose of job scheduling is to assign the work pieces to each machine according to a certain sequence and accomplish the work process with the minimum time. This paper, based on the quantum algorithm theory and quantum chromosome coding knowledge as well as the traditional genetic algorithm, raises an improve quantum genetic algorithm for job shop scheduling. Under the process expression form, it suggests to present the codes as quantum chromosome in order to solve the job shop scheduling problem and make it easy for the information of the elitist to be used to control the variation and make the population to evolve towards the excellent pattern with a large probability and accelerate the convergence rate. The simulation results indicate that the algorithm has better searching and convergence performances.

This chapter first presents job shop scheduling problems (JSSP) with fuzzy processing time and fuzzy trapezoid or doublet due-date. An efficient random key genetic algorithm (RKGA) is suggested, in which a random key representation and a new decoding strategy are proposed and two-point or discrete crossover are used. Performance analyses on random key representation are done and RKGA is compared with other algorithm. Computations results validate the effectiveness of random key representation and the promising advantage of RKGA on fuzzy scheduling.

This paper reviews the theory and applications of ant algorithms, new methods of discrete optimization based on the simulation of self-organized colony of biologic ants. The colony can be regarded as a multi-agent system where each agent is functioning independently by simple rules. Unlike the nearly primitive behavior of the agents, the behavior of the whole system happens to be amazingly reasonable. The ant algorithms have been extensively studied by European researchers from the mid-1990s. These algorithms have successfully been applied to solving many complex combinatorial optimization problems, such as the traveling salesman problem, the vehicle routing problem, the problem of graph coloring, the quadratic assignment problem, the problem of network-traffic optimization, the job-shop scheduling problem, etc. The ant algorithms are especially efficient for online optimization of processes in distributed nonstationary systems (for example, telecommunication network routing).

An effective job shop scheduling (JSS) in the manufacturing industry is helpful to meet the production demand and reduce the production cost, and to improve the ability to compete in the ever increasing volatile market demanding multiple products.In so many combinatorial optimization problems, job shop scheduling problems have earned a reputation for being difficult to solve. Job-shop scheduling is essentially an ordering problem. A new encoding scheme for a classic job-shop scheduling problem is presented. The aim is to find an allocation for each job and to define the sequence of jobs on each machine so that the resulting schedule has a minimal completion time.Genetic algorithm that has demonstrated considerable success in providing efficient solutions to many non polynomial-hard optimization problems is used to solve job-shop scheduling problem. The schedules given by genetic algorithms are constructed using a priority rule and under several constraints. After a schedule is obtained a checking operation is applied to ensure that the solution is feasible. The approach is tested on a set of instances. The results validate the effectiveness of the algorithm.

In so many combinatorial optimization problems, job shop scheduling problems have earned a reputation for being difficult to solve. GA has demonstrated considerable success in providing efficient solutions to many non-polynomial-hard optimization problems. In the field of job shop scheduling, GA has been intensively researched, and there are nine kinds of methods were proposed to encoding chromosome to represent a solution. In this paper, we proposed a novel genetic chromosome encoding approach, in this encoding method, the operation of crossover and mutation was done in three-dimensional coded space. 5 selected benchmark problems were tried with the proposed three- dimensional encoding GA for validation and the results are encouraging.

The job-shop scheduling problem (JSP) is a hard combinatorial optimization problem. In this paper, the mixed programming of MATLAB and VC is considered to solve the JSP. Firstly, MATLAB is used to implement an improved genetic algorithm (GA), and then the program is packaged into a dynamic link library (DLL). Secondly, VC is used to realize the input/output interface and call the GA DLL to schedule and store the scheduling results into the ORACLE database. Finally, the simulation results show that the improved GA has good optimization performance in solving JSP and the mixed programming design is effective.

Genetic Programming based hyper-heuristics (GP-HH) for dynamic job shop scheduling (JSS) problems are approaches which aim to address the issue where heuristics are only effective for specific JSS problem domains, and that designing effective heuristics for JSS problems can be difficult. This paper is a preliminary investigation into improving the robustness of heuristics evolved by GP-HH by evolving ensembles of dispatching rules from a single population of GP individuals. The results show that the current approach does not evolve significantly better or more robust rules than a standard GP-HH approach of evolving single constituent rules.

Dynamic job shop scheduling is one of the problems that get little attention in literature as it is known as an NP-hard combinatorial optimisation problem. Few researchers handled the mathematical model and the approaches of optimising the schedule efficiency and stability. As events such as (machine breakdown, arriving new jobs or processing time variation) are hard to be formulated in a mathematical model, this research introduces a dynamic multi-objective genetic algorithm based on partial repair reactive strategy. The reactive strategy is selected to deal with the dynamic nature of job shop by applying partial repair policy for optimising the scheduling efficiency and the schedule stability simultaneously. Experimental results show that the proposed algorithm provided better solutions than key problem solutions in dynamic job shop scheduling problems published in literature.

The Flexible Job Shop scheduling Problem (FJSP) is a generalization of the classical Job Shop scheduling Problem (JSP) allowing to process operations on one machine out of a set of alternative machines. The FJSP is an NP-hard problem consisting of two sub-problems, which are the machine assignment and the operation scheduling problems. In this paper, we propose how to solve the FJSP by metaheuristics based on clustering in a holonic multiagent model. Firstly, a Neighborhood-based Genetic Algorithm (NGA) is applied by a scheduler agent for a global exploration of the search space. Secondly, a local search technique is used by a set of cluster agents to guide the research in promising regions of the search space and to improve the quality of the NGA final population. To evaluate our approach, numerical tests are made based on three sets of well known benchmark instances from the literature of the FJSP, which are Kacem, Brandimarte, Hurink. The experimental results show the efficiency of our approach in comparison to other approaches.

The classic job shop scheduling problem mainly focuses on one kind of manufacturing resources, such as machine tools, and etc. But the job shop scheduling in practical production activities always needs to consider the constraints of different manufacturing resources. In this paper, a double-resource flexible job shop scheduling problem (DFJSSP) is presented. Both machines and workers are considered in the process of job shop scheduling in this DFJSSP. And a genetic algorithm (GA) is used to solve this problem, in which a new well designed three-layer chromosome encoding method has been adopted and some effective crossover and mutation operators are designed. Finally, a case study is used to validate the method.

The job shop scheduling problem (JSSP) is a well-known difficult combinatorial optimization problem. This paper presents a hybrid genetic algorithm for the JSSP with the objective of minimizing makespan. A new generation alternation model of genetic algorithm for JSSP is designed. Every pair of randomly selected parents must pass either crossover or mutation, which are deployed in parallel. In the inner structure of crossover, mutation is partially embedded. Schedules are constructed using a procedure that generates full active schedules. In each generation, a local search heuristic based on the neighborhood structure proposed by Nowicki and Smutnicki is applied to improve the solutions. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.

In job shop scheduling problems, the previously used nonpreemptive schedules are semiactive, active, and non-delay schedules, and they are initially designed for traditional scheduling performances and do not take energy efficiency into consideration. To fill the gap, this article presents a novel class of nonpreemptive schedules by investigating the special properties of job shop scheduling problems. Then, an energy-efficient generation procedure of the novel nonpreemptive schedules and the standby policy are proposed. An improved genetic algorithm is introduced with three superior features, including the elitist selection, the improved generation alternation model, and the operator decision mechanism. The Friedman test and the Holm multiple comparison test are conducted on experimental results to determine the appropriate parameter for the proposed genetic algorithm. Finally, the computation results and two-sample t-tests show that both energy-efficient generation procedure and standby policy help to reduce energy consumption without worsening production efficiency.

A new multi-objective scheduling method based on the GA is proposed to the job-shop scheduling problem (JSP) constrained by machines, workers. Function objectives of the proposed method are to minimize the completion time, the maximum load of machines and the total expense of machines and workers. Firstly, the mathematical model is constructed. Then, on the basis of the mathematical model, the genetic algorithm (GA) based on Pareto is applied, and an optimal or suboptimal scheduling plan can be obtained. The optimal solutions are not unique due to the multi-objective of JSP. Finally, a scheduling example is employed to illustrate that the proposed method could solve multi-objective job shop scheduling problem effectively.