Genetic Programming Hyper-Heuristic with Cooperative Coevolution for Dynamic Flexible Job Shop Scheduling

Production scheduling is a critical requirement in today’s production systems. The production scheduling problem focuses on the rational allocation of production resources, determining the processing order of production tasks and operations, satisfying production constraints arising from the production environment such as the number of machines and task duration, and achieving optimal production goals, such as completing all tasks in the fastest way possible. The Flexible Job Shop Scheduling (FJSP) problem is a classical production scheduling issue, an extension of the Job Shop Scheduling (JSP) problem. The JSP problem involves allocating a set of jobs to a set of machines, where each job consists of multiple operations that must adhere to a precedence order. Each machine can process only one operation at a time for each job, and the time required for each operation on the corresponding machine is predetermined. The challenge in the JSP problem is to determine an appropriate order for processing operations or assignments, based on the production objective. FJSP represents a more complex and flexible variant of this problem, where at least one operation of a task can be performed on a set of alternative machines. Given that FJSP problems are NP-hard, solutions tend to be complex, varied, and often have limited applicability. Many FJSP scenarios require targeted modeling. In this paper, I propose a modeling approach for the static scheduling problem of job shops, considering low flexibility and demonstrating its usability by comparing it with other scheduling approaches, such as those based on genetic algorithm modeling. The paper is organized as follows:The first part of this paper offers a brief introduction to the FJSP problem, including background, research motivation, and main contributions, the second part provides a literature review,including a brief review and introduction of recent solutions to the FJSP problem, the third part describes a specific modeling approach, the fourth part details the test experiments and results of the modeling approach and includes comparisons with some other approaches, the fifth part analyzes the results from the fourth part and discusses the advantages and disadvantages of the modeling approach relative to others, and the sixth part concludes with a summary of findings and recommendations.

In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artificial Bee Colony algorithm, is proposed for a dynamic flexible job-shop scheduling (DFJSP) problem. This problem consists of n jobs that should be processed by m machines and the processing time of jobs deviates from estimated times. The objective is near-optimal scheduling after any change in tasks in order to minimise the maximal completion time (Makespan). In the proposed model, first, scheduling is done according to the estimated processing times and then re-scheduling is performed after determining the exact ones considering machine set-up. In order to evaluate the performance of the proposed model, some numerical experiments are designed in small, medium and large sizes in different levels of changes in processing times and statistical results illustrate the efficiency of the proposed algorithm.

This paper presents an optimization via simulation approach to solve dynamic flexible job shop scheduling problems. In most real-life problems, certain operation of a part can be processed on more than one machine, which makes the considered system (i.e., job shops) flexible. On one hand, flexibility provides alternative part routings which most of the time relaxes shop floor operations. On the other hand, increased flexibility makes operation machine pairing decisions (i.e., the most suitable part routing) much more complex. This study deals with both determining the best process plan for each part and then finding the best machine for each operation in a dynamic flexible job shop scheduling environment. In this respect, a genetic algorithm approach is adapted to determine best part processing plan for each part and then select appropriate machines for each operation of each part according to the determined part processing plan. Genetic algorithm solves the optimization phase of solution methodology. Then, these machine-operation pairings are utilized by discrete-event system simulation model to estimate their performances. These two phases of the study follow each other iteratively. The goal of methodology is to find the solution that minimizes total of average flowtimes for all parts. The results reveal that optimization via simulation approach is a good way to cope with dynamic flexible job shop scheduling problems, which usually takes NP-Hard form.

This paper focuses on multi-objective dynamic flexible job shop scheduling problem (MODFJSP) with machine breakdown. First, a multi-objective dynamic scheduling model is established, with objectives to minimize makespan and total machine workload. Second, according to the processing status of faulty machine, a hybrid rescheduling strategy including transfer rescheduling strategy and complete rescheduling strategy is proposed to react to stochastic machine breakdown. The performance of two rescheduling strategies is analyzed in terms of the scheduling efficiency and its stability, from the delay extent and initial scheduling deviation, respectively. Besides, the optimal adaptation conditions of both scheduling strategies are obtained. Furthermore, the non-dominated sorting genetic algorithm (NSGA-II) is employed to solve the constructed model. Experimental results demonstrate the effectiveness of the proposed strategies on reducing the impact of machine breakdown in real scheduling.

Dynamic flexible job shop scheduling (DFJSS) is a critical and challenging problem in production scheduling such as order picking in the warehouse. Given a set of machines and a number of jobs with a sequence of operations, DFJSS aims to generate schedules for completing jobs to minimise total costs while reacting effectively to dynamic changes. Genetic programming, as a hyper-heuristic approach, has been widely used to learn scheduling heuristics for DFJSS automatically. A scheduling heuristic in DFJSS includes a routing rule for machine assignment and a sequencing rule for operation sequencing. However, existing studies assume that the routing and sequencing are equally important, which may not be true in real-world applications. This paper aims to propose an importance-aware GP algorithm for automated scheduling heuristics learning in DFJSS. Specifically, we first design a rule importance measure based on the fitness improvement achieved by the routing rule and the sequencing rule across generations. Then, we develop an adaptive resource allocation strategy to give more resources for learning the more important rules. The results show that the proposed importance-aware GP algorithm can learn significantly better scheduling heuristics than the compared algorithms. The effectiveness of the proposed algorithm is realised by the proposed strategies for detecting rule importance and allocating resources. Particularly, the routing rules play a more important role than the sequencing rules in the examined DFJSS scenarios.KeywordsImportance-aware scheduling heuristics learningGenetic programmingHyper-heuristicDynamic flexible job shop scheduling

In this paper, an intelligent approach based on gene expression programming (GEP) is proposed to discover scheduling strategies for dynamic flexible job shop scheduling problem (DFJSP). In the approach, an indirect encoding and decoding scheme is designed in which the concept of automatically defined functions (ADF) is introduced. In the evaluation of the proposed GEP-based approach, simulation experiments are conducted with respect to the objective of minimizing mean tardiness. The results show that GEP-based approach can automatically find more efficient scheduling strategies for DFJSP under a big range of processing conditions.

A linguistic-based meta-heuristic modeling and solution approach for solving the flexible job shop scheduling problem (FJSSP) is presented in this study. FJSSP is an extension of the classical job-shop scheduling problem. The problem definition is to assign each operation to a machine out of a set of capable machines (the routing problem) and to order the operations on the machines (the sequencing problem), such that predefined performance measures are optimized. In this research, the scope of the problem is widened by taking into account the alternative process plans for each part (process plan selection problem). Probabilistic selection of alternative process plans and machines are also considered. The FJSSP is presented as a grammar and the productions in the grammar are defined as controls (Baykasoglu, 2002). Using these controls and Giffler and Thompson's (1960) priority rule-based heuristic along with the multiple objective tabu search algorithm of Baykasoglu et al. (1999) FJSSP is solved. This novel approach simplifies the modeling process of the FJSSP and enables usage of existing job shop scheduling algorithms for its fast solution. Instead of scheduling job shops with inflexible algorithms that cannot take into account the flexibility which is available in the job shop, the present algorithm is developed which can take into account the flexibility during scheduling. Such an approach will considerably increase the responsiveness of the job shops.

Multitasking learning has been successfully used in handling multiple related tasks simultaneously. In reality, there are often many tasks to be solved together, and the relatedness between them is unknown in advance. In this paper, we focus on multitask genetic programming for the dynamic flexible job shop scheduling problems, and address two challenges. The first is how to measure the relatedness between tasks accurately. The second is how to select task pairs to transfer knowledge during the multitask learning process. To measure the relatedness between dynamic flexible job shop scheduling tasks, we propose a new relatedness metric based on the behaviour distributions of the variable-length genetic programming individuals. In addition, for more effective knowledge transfer, we develop an adaptive strategy to choose the most suitable assisted task for the target task based on the relatedness information between tasks. The findings show that in all of the multitask scenarios studied, the proposed algorithm can substantially increase the effectiveness of the learned scheduling heuristics for all the desired tasks. The effectiveness of the proposed algorithm has also been verified by the analyses of task relatedness and structures of the evolved scheduling heuristics, and the discussions of population diversity and knowledge transfer.

Research on Design Method of Dynamic Shop Floor Scheduling System Based on Human-computer Interaction

Scheduling for the flexible job shop is very important in both fields of production management and combinatorial optimization. However, when we attempt to formulate job shop scheduling problems which closely describe and represent the real world problem, various factors involved are often imprecisely or ambiguously known to the analyst. Also, in the real situations, machines are not continuously available due to preventive maintenance activity. Besides, in the production system, there are many users and machines in various priorities. Often, user satisfactions, machine stability and schedule itself are in conflict. By considering the imprecise or fuzzy nature of the data in real world problem, job shop scheduling with uncertain processing time and machine available constraint is introduced. Under the fuzzy circumstance, we attempt to make a feasible solution for flexible job shop scheduling problem (FJSSP) based on a hybrid optimization approach with multi-objective which not only to minimize the makespan, but also maximize the user satisfaction and machine stability. In the experiment, we make the comparisons with some other algorithms, such as genetic algorithm, to demonstrate the hybrid approach is much better on efficient search and stability with three benchmark examples. Sensitivity analysis is conducted to study the impact, in term of total objective, when the proportion of machine stability is varied.

The Flexible Job Shop Scheduling Problem (FJSP) is an extension of the classical job shop scheduling problem, which is characterized by the fact that each process can be processed on multiple candidate machines, and needs to solve the two subproblems of machine allocation and process sequencing simultaneously. Since FJSP is an NP-hard problem, its complexity and multi-objective characteristics make the traditional exact methods inefficient. At the same time, the existing intelligent optimization algorithms are prone to falling into local optimums, which makes it difficult to balance global exploration and local exploitation capabilities. To this end, this study proposed a Levy flight-based Harmony Search algorithm (LHS), which effectively avoids premature convergence by dynamically and adaptively adjusting the Harmony Memory Considering Rate (HMCR), the probability of Pitch Adjusting Rate (PAR), and the arbitrary distance Bandwidth(BW), and by introducing a Levy flight mechanism to perturb the parameters to broaden the search space and enhance the diversity of the population. We validate the experiments using 8 × 8, 10 × 10, and 10 benchmark instances proposed by Brandimarte in the literature, and the experimental results show that the Harmony search algorithm based on Levy flight outperforms the other comparative algorithms in terms of the solution quality and the convergence speed, demonstrating the effectiveness of its solution for FJSP.

Flexible job shop scheduling problem (FJSP) has been extensively investigated and objectives are often related to time. Energy-related objective should be considered fully in FJSP with the advent of green manufacturing. In this study, FJSP with the minimisation of workload balance and total energy consumption is considered and the conflicting between two objectives is analysed. A shuffled frog-leaping algorithm (SFLA) is proposed based on a three-string coding approach. Population and a non-dominated set are used to construct memeplexes according to tournament selection and the search process of each memeplex is done on its non-dominated member. Extensive experiments are conducted to test the search performance of SFLA and computational results show the conflicting between two objectives of FJSP and the promising advantages of SFLA on the considered FJSP.

The classical job shop scheduling (JSS) problem can be extended by allowing processing of an operation by any machine from a given set. This type of scheduling is known as flexible job shop scheduling (FJSS) problem. It incorporates all the difficulties and complexities of its predecessor classical problem. However, it is more complex as it is required to determine the assignment of operations to the machine. Swarm intelligence techniques proved their effectiveness in solving a wide range of complex NP-Hard real world problems. One of these techniques is the meerkat clan algorithm (MCA) that has been successfully applied to various optimization problems. This paper presents a modified MCA for solving the FJSS problem. The modification is based on using harmony search (HS). The introduction of HS provides more exploitation and intensification. HS generates various solutions, which are provided to the MCA. As a result, the exploitation of the local optimum is increased, which in turn increases the convergence rate. The experimental results show that the improved method achieves higher quality schedules. Additionally, the convergence rate is speeded up compared with the standalone algorithm. This gives the proposed method the superiority over the original algorithm.

In this paper, 4S auto maintenance shop scheduling with multi-constraint machines is of concern. In 4S auto maintenance shop, it may appear temporary bottlenecks except for the long-term bottlenecks, and they form multi-constraint machines jointly with the task size reaching the peak. Through scheduling the constraint machines effectively, it can improve overall performance of the system to satisfy the customers’ requirements. First, we describe and construct a model for the scheduling problem which can be designed as the dynamic flexible job shop scheduling problem (FJSP) with multi-constraint machines for the goal of minimizing customers’ waiting time. Then, putting an emphasis on the constraint machines, we apply the theory of constraint to decompose and simplify the complex system and also construct the coordination mechanism between constraint machines and non-constraint machines. After that, an improved constraint-guided heuristic algorithm is proposed to solve the constraint machine scheduling problem, while different dispatching rules are selected for solving the non-constraint machine scheduling according to the location of the non-bottleneck in the system. What is more, we design the rescheduling rules combing characteristics of the problem to realize dynamic scheduling with multi-constraint machines. Finally, 4S auto maintenance shop scheduling with high workload during the rush hour (on the eve of the holiday) served as the actual cases, and the proposed algorithm is compared with three different dispatching rules under various size of problems. The result obtained from the computational study has shown that the proposed algorithm is much better.

Research on flexible job shop scheduling under finite transportation conditions for digital twin workshop