Evolving control rules for a dual-constrained job scheduling scenario

Dispatching rules are often used for scheduling in semiconductor manufacturing due to the complexity and stochasticity of the problem. In the past, simulation-based Genetic Programming has been shown to be a powerful tool to automate the time-consuming and expensive process of designing such rules. However, the scheduling problems considered were usually only constrained by the capacity of the machines. In this paper, we extend this idea to dual-constrained flow shop scheduling, with machines and operators for loading and unloading to be scheduled simultaneously. We show empirically on a small test problem with parallel workstations, re-entrant flows and dynamic stochastic job arrival that the approach is able to generate dispatching rules that perform significantly better than benchmark rules from the literature

Automatic Design of Dispatching Rules for Job Shop Scheduling with Genetic Programming

Scheduling is an important planning activity in manufacturing systems to help optimise the usage of scarce resources and improve the customer satisfaction. In the job shop manufacturing environment, scheduling problems are challenging due to the complexity of production flows and practical requirements such as dynamic changes, uncertainty, multiple objectives, and multiple scheduling decisions. Also, job shop scheduling (JSS) is very common in small manufacturing businesses and JSS is considered one of the most popular research topics in this domain due to its potential to dramatically decrease the costs and increase the throughput. Practitioners and researchers have applied different computational techniques, from different fields such as operations research and computer science, to deal with JSS problems. Although optimisation methods usually show their dominance in the literature, applying optimisation techniques in practical situations is not straightforward because of the practical constraints and conditions in the shop. Dispatching rules are a very useful approach to dealing with these environments because they are easy to implement(by computers and shop floor operators) and can cope with dynamic changes. However, designing an effective dispatching rule is not a trivial task and requires extensive knowledge about the scheduling problem. The overall goal of this thesis is to develop a genetic programming based hyper-heuristic (GPHH) approach for automatic heuristic design of reusable and competitive dispatching rules in job shop scheduling environments. This thesis focuses on incorporating special features of JSS in the representations and evolutionary search mechanisms of genetic programming(GP) to help enhance the quality of dispatching rules obtained. This thesis shows that representations and evaluation schemes are the important factors that significantly influence the performance of GP for evolving dispatching rules. The thesis demonstrates that evolved rules which are trained to adapt their decisions based on the changes in shops are better than conventional rules. Moreover, by applying a new evaluation scheme, the evolved rules can effectively learn from the mistakes made in previous completed schedules to construct better scheduling decisions. The GP method using the newproposed evaluation scheme shows better performance than the GP method using the conventional scheme. This thesis proposes a new multi-objective GPHH to evolve a Pareto front of non-dominated dispatching rules. Instead of evolving a single rule with assumed preferences over different objectives, the advantage of this GPHH method is to allow GP to evolve rules to handle multiple conflicting objectives simultaneously. The Pareto fronts obtained by the GPHH method can be used as an effective tool to help decision makers select appropriate rules based on their knowledge regarding possible trade-offs. The thesis shows that evolved rules can dominate well-known dispatching rules when a single objective and multiple objectives are considered. Also, the obtained Pareto fronts show that many evolved rules can lead to favourable trade-offs, which have not been explored in the literature. This thesis tackles one of themost challenging issues in job shop scheduling, the interactions between different scheduling decisions. New GPHH methods have been proposed to help evolve scheduling policies containing multiple scheduling rules for multiple scheduling decisions. The two decisions examined in this thesis are sequencing and due date assignment. The experimental results show that the evolved scheduling rules are significantly better than scheduling policies in the literature. A cooperative coevolution approach has also been developed to reduce the complexity of evolving sophisticated scheduling policies. A new evolutionary search mechanisms and customised genetic operations are proposed in this approach to improve the diversity of the obtained Pareto fronts

Genetic Programming Hyper-heuristics for Job Shop Scheduling

Scheduling problems arise whenever there is a choice of order in which a number of tasks should be performed; they arise commonly, daily and everywhere. A job shop is a common manufacturing environment in which a schedule for processing a set of jobs through a set of machines needs to be constructed. Job shop scheduling (JSS) has been called a fascinating challenge as it is computationally hard and prevalent in the real-world. Developing more effective ways of scheduling jobs could increase profitability through increasing throughput and decreasing costs. Dispatching rules (DRs) are one of the most popular scheduling heuristics. DRs are easy to implement, have low computational cost, and cope well with the dynamic nature of real-world manufacturing environments. However, the manual development of DRs is time consuming and requires expert knowledge of the scheduling environment. Genetic programming (GP) is an evolutionary computation method which is ideal for automatically discovering DRs. This is a hyper-heuristic approach, as GP is searching the search space of heuristic (DR) solutions rather than constructing a schedule directly. The overall goal of this thesis is to develop GP based hyper-heuristics for the efficient evolution (automatic generation) of robust, reusable and effective scheduling heuristics for JSS environments, with greater interpretability. Firstly, this thesis investigates using GP to evolve optimal DRs for the static two-machine JSS problem with makespan objective function. The results show that some evolved DRs were equivalent to an optimal scheduling algorithm. This validates both the GP based hyper-heuristic approach for generating DRs for JSS and the representation used. Secondly, this thesis investigates developing ``less-myopic'' DRs through the use of wider-looking terminals and local search to provide additional fitness information. The results show that incorporating features of the state of the wider shop improves the mean performance of the best evolved DRs, and that the inclusion of local search in evaluation evolves DRs which make better decisions over the local time horizon, and attain lower total weighted tardiness. Thirdly, this thesis proposes using strongly typed GP (STGP) to address the challenging issue of interpretability of DRs evolved by GP. Several grammars are investigated and the results show that the DRs evolved in the semantically constrained search space of STGP do not have (on average) performance that is as good as unconstrained. However, the interpretability of evolved rules is substantially improved. Fourthly, this thesis investigates using multiobjective GP to encourage evolution of DRs which are more readily interpretable by human operators. This approach evolves DRs with similar performance but smaller size. Fragment analysis identifies popular combinations of terminals which are then used as high level terminals; the inclusion of these terminals improved the mean performance of the best evolved DRs. Through this thesis the following major contributions have been made: (1) the first use of GP to evolve optimal DRs for the static two-machine job shop with makespan objective function; (2) an approach to developing less-myopic DRs through the inclusion of wider looking terminals and the use of local search to provide additional fitness information over an extended decision horizon; (3) the first use of STGP for the automatic discovery of DRs with better interpretability and semantic validity for increased trust; and (4) the first multiobjective GP approach that considers multiple objectives investigating the trade-off between scheduling behaviour and interpretability. This is also the first work that uses analysis of evolved GP individuals to perform feature selection and construction for JSS

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

Many-Objective Genetic Programming for Job-Shop Scheduling

The Job Shop Scheduling (JSS) problem is considered to be a challenging one due to practical requirements such as multiple objectives and the complexity of production flows. JSS has received great attention because of its broad applicability in real-world situations. One of the prominent solutions approaches to handling JSS problems is to design effective dispatching rules. Dispatching rules are investigated broadly in both academic and industrial environments because they are easy to implement (by computers and shop floor operators) with a low computational cost. However, the manual development of dispatching rules is time-consuming and requires expert knowledge of the scheduling environment. The hyper-heuristic approach that uses genetic programming (GP) to solve JSS problems is known as GP-based hyper-heuristic (GP-HH). GP-HH is a very useful approach for discovering dispatching rules automatically. Although it is technically simple to consider only a single objective optimization for JSS, it is now widely evidenced in the literature that JSS by nature presents several potentially conflicting objectives, including the maximal flowtime, mean flowtime, and mean tardiness. A few studies in the literature attempt to solve many-objective JSS with more than three objectives, but existing studies have some major limitations. First, many-objective JSS problems have been solved by multi-objective evolutionary algorithms (MOEAs). However, recent studies have suggested that the performance of conventional MOEAs is prone to the scalability challenge and degrades dramatically with many-objective optimization problems (MaOPs). Many-objective JSS using MOEAs inherit the same challenge as MaOPs. Thus, using MOEAs for many-objective JSS problems often fails to select quality dispatching rules. Second, although the reference points method is one of the most prominent and efficient methods for diversity maintenance in many-objective problems, it uses a uniform distribution of reference points which is only appropriate for a regular Pareto-front. However, JSS problems often have irregular Pareto-front and uniformly distributed reference points do not match well with the irregular Pareto-front. It results in many useless points during evolution. These useless points can significantly affect the performance of the reference points-based algorithms. They cannot help to enhance the solution diversity of evolved Pareto-front in many-objective JSS problems. Third, Pareto Local Search (PLS) is a prominent and effective local search method for handling multi-objective JSS optimization problems but the literature does not discover any existing studies which use PLS in GP-HH. To address these limitations, this thesis's overall goal is to develop GP-HH approaches to evolving effective rules to handle many conflicting objectives simultaneously in JSS problems. To achieve the first goal, this thesis proposes the first many-objective GP-HH method for JSS problems to find the Pareto-fronts of nondominated dispatching rules. Decision-makers can utilize this GP-HH method for selecting appropriate rules based on their preference over multiple conflicting objectives. This study combines GP with the fitness evaluation scheme of a many-objective reference points-based approach. The experimental results show that the proposed algorithm significantly outperforms MOEAs such as NSGA-II and SPEA2. To achieve the second goal, this thesis proposes two adaptive reference point approaches (model-free and model-driven). In both approaches, the reference points are generated according to the distribution of the evolved dispatching rules. The model-free reference point adaptation approach is inspired by Particle Swarm Optimization (PSO). The model-driven approach constructs the density model and estimates the density of solutions from each defined sub-location in a whole objective space. Furthermore, the model-driven approach provides smoothness to the model by applying a Gaussian Process model and calculating the area under the mean function. The mean function area helps to find the required number of the reference points in each mean function. The experimental results demonstrate that both adaptive approaches are significantly better than several state-of-the-art MOEAs. To achieve the third goal, the thesis proposes the first algorithm that combines GP as a global search with PLS as a local search in many-objective JSS. The proposed algorithm introduces an effective fitness-based selection strategy for selecting initial individuals for neighborhood exploration. It defines the GP's proper neighborhood structure and a new selection mechanism for selecting the effective dispatching rules during the local search. The experimental results on the JSS benchmark problem show that the newly proposed algorithm can significantly outperform its baseline algorithm (GP-NSGA-III)

A hyper-heuristic ensemble method for static job-shop scheduling.

We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and-conquer approach in which each heuristic solves a unique subset of the instance set considered. NELLI-GP extends an existing ensemble method called NELLI by introducing a novel heuristic generator that evolves heuristics composed of linear sequences of dispatching rules: each rule is represented using a tree structure and is itself evolved. Following a training period, the ensemble is shown to outperform both existing dispatching rules and a standard genetic programming algorithm on a large set of new test instances. In addition, it obtains superior results on a set of 210 benchmark problems from the literature when compared to two state-of-the-art hyperheuristic approaches. Further analysis of the relationship between heuristics in the evolved ensemble and the instances each solves provides new insights into features that might describe similar instances

Genetic programming for manufacturing optimisation.

A considerable number of optimisation techniques have been proposed for the solution of problems associated with the manufacturing process. Evolutionary computation methods, a group of non-deterministic search algorithms that employ the concept of Darwinian strife for survival to guide the search for optimal solutions, have been extensively used for this purpose. Genetic programming is an evolutionary algorithm that evolves variable-length solution representations in the form of computer programs. While genetic programming has produced successful applications in a variety of optimisation fields, genetic programming methodologies for the solution of manufacturing optimisation problems have rarely been reported. The applicability of genetic programming in the field of manufacturing optimisation is investigated in this thesis. Three well-known problems were used for this purpose: the one-machine total tardiness problem, the cell-formation problem and the multiobjective process planning selection problem. The main contribution of this thesis is the introduction of novel genetic programming frameworks for the solution of these problems. In the case of the one-machine total tardiness problem genetic programming employed combinations of dispatching rules for the indirect representation of job schedules. The hybridisation of genetic programming with alternative search algorithms was proposed for the solution of more difficult problem instances. In addition, genetic programming was used for the evolution of new dispatching rules that challenged the efficiency of man-made dispatching rules for the solution of the problem. An integrated genetic programming - hierarchical clustering approach was proposed for the solution of simple and advanced formulations of the cell-formation problem. The proposed framework produced competitive results to alternative methodologies that have been proposed for the solution of the same problem. The evolution of similarity coefficients that can be used in combination with clustering techniques for the solution of cell-formation problems was also investigated. Finally, genetic programming was combined with a number of evolutionary multiobjective techniques for the solution of the multiobjective process planning selection problem. Results on test problems illustrated the ability of the proposed methodology to provide a wealth of potential solutions to the decision-maker

A genetic programming hyper-heuristic for the multidimensional knapsack problem

Purpose: Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. The purpose of this paper is to investigate the suitability of using genetic programming as a hyper-heuristic methodology to generate constructive heuristics to solve the multidimensional 0-1 knapsack problem. Design/methodology/approach: Early hyper-heuristics focused on selecting and applying a low-level heuristic at each stage of a search. Recent trends in hyper-heuristic research have led to a number of approaches being developed to automatically generate new heuristics from a set of heuristic components. A population of heuristics to rank knapsack items are trained on a subset of test problems and then applied to unseen instances. Findings: The results over a set of standard benchmarks show that genetic programming can be used to generate constructive heuristics which yield human-competitive results. Originality/value: In this work the authors show that genetic programming is suitable as a method to generate reusable constructive heuristics for the multidimensional 0-1 knapsack problem. This is classified as a hyper-heuristic approach as it operates on a search space of heuristics rather than a search space of solutions. To our knowledge, this is the first time in the literature a GP hyper-heuristic has been used to solve the multidimensional 0-1 knapsack problem. The results suggest that using GP to evolve ranking mechanisms merits further future research effort. © Emerald Group Publishing Limited