A knowledge-based approach to dynamic job-shop scheduling

The new challenges of "Agile Manufacturing" and distributed decision making entailed by decentralized organizations led to our interest in the study of computational cooperative problem solving models and coordination techniques for distributed production management. The goal of our research is to address the technical need of distributed production management and develop appropriate computational approaches to support adaptive, cost-effective responsiveness. In particular, we focus on the challenging problem of job shop scheduling, which has been one of the primary foci of production scheduling research. This paper presents a multi-agent problem solving model and an effective coordination technique for job shop scheduling. The model involves a group of agents; each agent is associated with either a job or a resource. A solution to a production scheduling problem is the result of coordinated conflict resolution in the iterative and asynchronous multi-agent decision making process. It is well known in distributed systems research that for tightly interacting, non-decomposable problems, such as job shop scheduling, the need for communicating partial solution results among parts of the system rapidly degrades system performance. On the other hand, limiting communication degrades solution quality. One can limit communication by employing shared memory, but this has the drawback that the shared memory becomes a bottleneck and, in addition, using shared memory limits decentralization. In our approach, we judiciously balance the above concerns. We limit interagent communication through a scheme that employs efficient, small and distributed shared memories, each of which is associated with and shared by a limited number of agents. We also exploit problem characteristics (e.g. disparity among subproblems) to design an effective coordination technique for the job shop scheduling problem. We have evaluated the utility of our approach through extensive experimentation on a variety of job shop constraint satisfaction and optimization problems with different optimization objectives. Our results show that our approach outperforms or gives comparative results with other state-of-the-art scheduling techniques on benchmark problems.

The Vanderbilt Schedule Optimizer Prototype (VSOP), which uses genetic algorithms as search methods for job shop scheduling problems, is discussed. A job shop is a facility that produces goods according to prespecified process plans, under several domain-dependent and common sense constraints. The scheduling of orders in a job shop is a multifaceted problem. VSOP uses domain-specific chromosome representations, recombination operators, and local enumerative search to increase efficiency. Experimental results from a fully implemented VSOP package are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In today's world of manufacturing, cost reduction becomes one of the most important issues. A successful business needs to reduce its cost to be competitive. The programming of the machine is playing an important role in production planning and control as a tool to help manufacturers reduce their costs maximizing the use of their resources. The programming problem is not only limited to the programming of the machine, but also covers many other areas such such as computer and information technology and communication. From the definition, programming is an art that involves allocating, planning the allocation and utilization of resources to achieve a goal. The aim of the program is complete tasks in a reasonable amount of time. This reasonableness is a performance measure of how well the resources are allocated to tasks. Time or time-dependent functions are always it used as performance measures. The objectives of this research are to develop Intelligent Search Heuristic algorithms (ISHA) for equal and variable size sub lot for m machine flow shop problems, to Implement Particle Swarm Optimization algorithm (PSO) in matlab, to develop PSO based Optimization program for efficient job shop scheduling problem. The work also address solution to observe and verify results of PSO based Job Shop Scheduling with help of graft chart.

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.

Constraint logic programming can be effectively applied to solve realistic job-shop scheduling problems. The role of constraints is twofold: they model dependencies among tasks and resources (e.g. temporal relations and capacities of machines), and they are used to actively prune the search space during the computation of a schedule. Since the job-shop problem is NP-complete, constraint solving techniques alone do not suffice to get efficient schedules for problems with 100 tasks and more. In order to judge a scheduling method, one has to investigate two questions: how good are the solutions in comparison to the optimum and how much search is required to find them.

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

Purpose – In a complex semiconductor manufacturing system (SMS) environment, the implementation of dynamic production scheduling and dispatching strategies is critical for SMS distributed collaborative manufacturing events to make quick and correct decisions. The purpose of this paper is to assist manufacturers in achieving the real time dispatching and obtaining integrated optimization for shop floor production scheduling. Design/methodology/approach – In this paper, an integrated model is designed under assemble to order environment and a framework of a real time dispatching (IRTD) system for production scheduling control is presented accordingly. Both of the scheduling and ordering performances are integrated into the days of inventory based dispatching algorithm, which can deal with the multiple indicators of dynamic scheduling and ordering in this system to generate the “optimal” dispatching policies. Subsequently, the platform of IRTD system is realized with four modules function embedded. Findings – The proposed IRTD system is designed to compare the previous constant work in process method in the experiment, which shows the better performance achievement of the IRTD system for shop floor production dynamic scheduling and order control. The presented framework and algorithm can facilitate real time dispatching information integration to obtain performance metrics in terms of reliability, availability, and maintainability. Research limitations/implications – The presented system can be further developed to generic factory manufacturing with the presented logic and architecture proliferation. Originality/value – The IRTD system can integrate the real time customer demand and work in process information, based on which manufacturers can make correct and timely decisions in solving dispatching strategies and ordering selection within an integrated information system.

Object-oriented programming has often been advocated as a means of improving and enhancing the facilities provided by a given programming environment. This thesis is concerned with an examination of the benefits of providing object-oriented facilities in the Logic programming language - Prolog. We consider these benefits from two different perspectives, specifically examining what benefits Prolog can gain from objects, and conversely, what benefits object-oriented programming can gain from Prolog. A previously proposed model of object execution in Prolog was used as the basis of this research. In implementing this proposal we have critically examined how well the model supports the principles of object-oriented programming, and in those areas which we consider deficient, identified alternatives for improving the model which have subsequently been implemented for the purposes of assessment. The name we have selected for our augmented system is ObLog, drawn from Ob(jects) in (Pro)log. We critically examine the suitability of ObLog in terms of object-oriented programming by implementing a series of example applications based on a Block World specification. The thesis concludes by proposing some areas in which further research might usefully be conducted.

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.

© 2020, Springer Nature Switzerland AG. Dynamic flexible job shop scheduling (DFJSS) is a very valuable practical application problem that can be applied in many fields such as cloud computing and manufacturing. In DFJSS, machine assignment and operation sequencing decisions need to be made simultaneously in dynamic environments with unpredicted events such as new job arrivals. Scheduling heuristic is an ideal candidate for solving the DFJSS problem due to its efficiency and simplicity. Genetic programming (GP) has been successfully applied to evolve scheduling heuristics for job shop scheduling automatically. However, GP has a huge search space, and the traditional search algorithms do not utilise effectively the information obtained from the evolutionary process. This paper proposes a new method to make better use of the information during the evolutionary process of GP to further enhance the ability of GP. To be specific, this paper proposes two adaptive search strategies based on the frequency of features in promising individuals to guide GP to evolve effective rules. This paper examines the proposed algorithm on six different DFJSS scenarios. The results show that the proposed GP with adaptive search can converge faster and achieve significantly better performance than the GP without adaptive search in most scenarios while no worse in all other scenarios without increasing the computational cost.

In this study, a new and simple alternate routing heuristics, which would be superior to the conventional routing strategies in terms of various system performance measures and easier to apply practically, was presented. Because of the high investment costs of FMSs, it is also definitely worth choosing the best operating policy and system configuration by analyzing the system model, and adapting to changes over time. In future research, the heuristic rule base will be extended to include other supplementary constraints such as dynamic tool allocation and sequence dependent setup times. For practical implementation of the proposed decision support system, additional research in the area of human interfaces could be useful to develop more user friendly system which automatically constructs simulation model of the system from the knowledge base of a production system.

Flexible Job Shop Scheduling (FJSS) problem has many real-world applications such as manufacturing and cloud computing, and thus is an important area of study. In real world, the environment is often dynamic, and unpredicted job orders can arrive in real time. Dynamic FJSS consists of challenges of both dynamic optimisation and the FJSS problem. In Dynamic FJSS, two kinds of decisions (so-called routing and sequencing decisions) are to be made in real time. Dispatching rules have been demonstrated to be effective for dynamic scheduling due to their low computational complexity and ability to make real-time decisions. However, it is time consuming and strenuous to design effective dispatching rules manually due to the complex interactions between job shop attributes. Genetic Programming Hyper-heuristic (GPHH) has shown success in automatically designing dispatching rules which are much better than the manually designed ones. Previous works only focused on standard job shop scheduling with only the sequencing decisions. For FJSS, the routing rule is set arbitrarily by intuition. In this paper, we explore the possibility of evolving both routing and sequencing rules together and propose a new GPHH algorithm with Cooperative Co-evolution. Our results show that co-evolving the two rules together can lead to much more promising results than evolving the sequencing rule only.

The problem of scheduling on the job shop floor is widely encountered in the manufacturing industry. However, generating good job shop schedules in a reasonable time remains a challenging problem, because of its inherent computational complexity. Dynamic job shop scheduling (DJSS) has been proven to be an intractable analytical problem and is largely solved by combinatorial analysis in traditional research. The NP-complete nature of the problem forces many assumptions to be made, which limit the use of analytical methods in practical problem solving. In this paper, the authors propose a new scheme named minimum queuing delay (MQD) for scheduling in a dynamic job shop derived from fair queuing service disciplines for asynchronous transfer mode (ATM) networks. This scheme combines the job process-time and work-content of jobs in the current queues into the dispatching methodology. The main advantage of this scheme is that the queuing delays suffered by all the jobs at every machine are minimized, leading to a minimized maximum completion time (make-span). A smaller queuing delay variation is also achieved using this algorithm. Results from experiments conducted on a testbed with several embedded controllers are presented.

An investigation of ensemble combination schemes for genetic programming based hyper-heuristic approaches to dynamic job shop scheduling

Dynamic flexible job shop scheduling (JSS) is a challenging combinatorial optimization problem due to its complex environment. In this problem, machine assignment and operation sequencing decisions need to be made simultaneously under the dynamic environments. Genetic programming (GP), as a hyperheuristic approach, has been successfully used to evolve scheduling heuristics for dynamic flexible JSS. However, in traditional GP, recombination between parents may disrupt the beneficial building blocks by choosing the crossover points randomly. This article proposes a recombinative mechanism to provide guidance for GP to realize effective and adaptive recombination for parents to produce offspring. Specifically, we define a novel measure for the importance of each subtree of an individual, and the importance information is utilized to decide the crossover points. The proposed recombinative guidance mechanism attempts to improve the quality of offspring by preserving the promising building blocks of one parent and incorporating good building blocks from the other. The proposed algorithm is examined on six scenarios with different configurations. The results show that the proposed algorithm significantly outperforms the state-of-the-art algorithms on most tested scenarios, in terms of both final test performance and convergence speed. In addition, the rules obtained by the proposed algorithm have good interpretability.