Showing papers on "Job shop published in 2007"

Mathematical modeling and heuristic approaches to flexible job shop scheduling problems

Abstract: Scheduling for the flexible job shop is very important in both fields of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem in medium and actual size problem with traditional optimization approaches owing to the high computational complexity. For solving the realistic case with more than two jobs, two types of approaches have been used: hierarchical approaches and integrated approaches. In hierarchical approaches assignment of operations to machines and the sequencing of operations on the resources or machines are treated separately, i.e., assignment and sequencing are considered independently, where in integrated approaches, assignment and sequencing are not differentiated. In this paper, a mathematical model and heuristic approaches for flexible job shop scheduling problems (FJSP) are considered. Mathematical model is used to achieve optimal solution for small size problems. Since FJSP is NP-hard problem, two heuristics approaches involve of integrated and hierarchical approaches are developed to solve the real size problems. Six different hybrid searching structures depending on used searching approach and heuristics are presented in this paper. Numerical experiments are used to evaluate the performance of the developed algorithms. It is concluded that, the hierarchical algorithms have better performance than integrated algorithms and the algorithm which use tabu search and simulated annealing heuristics for assignment and sequencing problems consecutively is more suitable than the other algorithms. Also the numerical experiments validate the quality of the proposed algorithms.

Flexible job shop scheduling with tabu search algorithms

Abstract: This paper presents a tabu search algorithm that solves the flexible job shop scheduling problem to minimize the makespan time. As a context for solving sequencing and scheduling problems, the flexible job shop model is highly complicated. Alternative operation sequences and sequence-dependent setups are two important factors that frequently appear in various manufacturing environments and in project scheduling. In this paper, we present a model for a flexible job shop scheduling problem while considering those factors simultaneously. The purpose of this paper is to minimize the makespan time and to find the best sequence of operations and the best choice of machine alternatives, simultaneously. The proposed tabu search algorithm is composed of two parts: a procedure that searches for the best sequence of job operations, and a procedure that finds the best choice of machine alternatives. Randomly generated test problems are used to evaluate the performance of the proposed algorithm. Results of the algorithm are compared with the optimal solution using a mathematical model solved by the traditional optimization technique (the branch and bound method). After modeling the scheduling problem, the model is verified and validated. Then the computational results are presented. Computational results indicate that the proposed algorithm can produce optimal solutions in a short computational time for small and medium sized problems. Moreover, it can be applied easily in real factory conditions and for large size problems. The proposed algorithm should thus be useful to both practitioners and researchers.

A simulated annealing-based optimization approach for integrated process planning and scheduling

Abstract: A job shop needs to deal with a lot of make-to-order business, in which the orders are usually diverse in types but each one is small in volume. To increase the flexibility and responsiveness of the job shop in the more competitive market, process planning and scheduling modules have been actively developed and deployed. The functions of the two modules are usually complementary. It is ideal to integrate them more tightly to achieve the global optimization of product development and manufacturing. In this paper, a unified representation model and a simulated annealing-based approach have been developed to facilitate the integration and optimization process. In the approach, three strategies, including processing flexibility, operation sequencing flexibility and scheduling flexibility, have been used for exploring the search space to support the optimization process effectively. Performance criteria, such as makespan, the balanced level of machine utilization, job tardiness and manufacturing cost, have been systematically defined to make the algorithm adaptive to meet various practical requirements. Case studies under various working conditions and the comparisons of this approach with two modern evolutionary approaches are given. The merits and characteristics of the approach are thereby highlighted.

Design rules for implementing the Toyota Production System

Abstract: The Toyota Motor Company has risen to a place of world prominence in the automotive industry by redesigning the mass production system into the Toyota Production System (TPS), or what is now known worldwide as lean production. In redesigning the mass production system, they changed the final assembly into a mixed model final assembly system to level the demand on their suppliers, converted the linear subassembly lines into U-shaped subassembly cells and redesigned the job shop into manufacturing cells. Final assembly operates with a takt time, and the cells are designed to have a cycle time slightly less than the takt time and to operate on a ‘make one, check one and move one on’ (MO-CO-MOO basis). Single-cycle machine tools are used with built-in devices to check parts (poka-yokes). Between the machines are devices (decouplers) designed to assist the standing, walking workers producing the parts in the manufacturing cells. This paper will discuss four design rules for implementing the TPS.

Using dispatching rules for job shop scheduling with due date-based objectives

Abstract: This paper addresses the job shop-scheduling problem with due date-based objectives including the tardy rate, mean tardiness and maximum tardiness. The focused approach is the dispatching rules. Eighteen dispatching rules are selected from the literature, and their features and design concepts are discussed. Then a dispatching rule is proposed with the goal of achieving a good and balanced performance when more than one objective is concerned at the same time. First, three good design principles are recognized from the existing rules. Second, it introduces a due date extension procedure to solve a problem of negative allowance time. Third, a job candidate reduction mechanism is developed to make the rule computationally efficient. Lastly, a comprehensive simulation study is conducted with the 18 existing rules as the benchmarks. The experimental results verify the superiority of the proposed rule, especially on the tardy rate and mean tardiness.

Assignment and Scheduling in Flexible Job-Shops by Hierarchical Optimization

Abstract: In this paper, we propose a new hierarchical method for the flexible job-shop scheduling problem (FJSP). This approach is mainly adapted to a job-shop problem (JSP) with high flexibility and is based on the decomposition of the problem in an assignment subproblem and a sequencing subproblem. For the first subproblem, we propose two methods: the first one is based successively on a heuristic approach and a local search; the second one, however, is based on a branch-and-bound algorithm. The quality of the assignment is evaluated by a lower bound. For the second subproblem we apply a hybrid genetic algorithm to deal with the sequencing problem. Computational tests are finally presented.

Mathematical Models of Flow Shop and Job Shop Scheduling Problems

Abstract: In this paper, mathematical models for permutation flow shop scheduling and job shop scheduling problems are proposed. The first problem is based on a mixed integer programming model. As the problem is NP-complete, this model can only be used for smaller instances where an optimal solution can be computed. For large instances, another model is proposed which is suitable for solving the problem by stochastic heuristic methods. For the job shop scheduling problem, a mathematical model and its main representation schemes are presented. Keywords—Flow shop, job shop, mixed integer model, representation scheme.

A Complete Multiagent Framework for Robust and Adaptable Dynamic Job Shop Scheduling

Abstract: This paper presents a complete multiagent framework for dynamic job shop scheduling, with an emphasis on robustness and adaptability. It provides both a theoretical basis and some experimental justifications for such a framework: a job dispatching procedure for a completely reactive scheduling approach, combining real-time and predictive decision making. It resolves various disruptions as flexibly as dispatching rules while providing more stability. It is ready to be implemented in a distributed environment where agents have minimum global information thereby improving system fault tolerance. Computational experiments on dynamic job arrivals provide the experimental justification of the framework. First, a comparison of computational results on unpredictable job arrivals among the presented framework and commonly used dispatching rules is presented to show the effectiveness and robustness of the developed framework. Then, a comparison of the computational results among four cases of dynamic job arrivals is presented to demonstrate the effects of making full use of available uncertain information about disruptions using this framework for the enhancement of scheduling robustness.

Complexity of shop-scheduling problems with fixed number of jobs: a survey

Abstract: The paper surveys the complexity results for job shop, flow shop, open shop and mixed shop scheduling problems when the number n of jobs is fixed while the number r of operations per job is not restricted. In such cases, the asymptotical complexity of scheduling algorithms depends on the number m of machines for a flow shop and an open shop problem, and on the numbers m and r for a job shop problem. It is shown that almost all shop-scheduling problems with two jobs can be solved in polynomial time for any regular criterion, while those with three jobs are NP-hard. The only exceptions are the two-job, m-machine mixed shop problem without operation preemptions (which is NP-hard for any non-trivial regular criterion) and the n-job, m-machine open shop problem with allowed operation preemptions (which is polynomially solvable for minimizing makespan).

Decision support tool for multi-objective job shop scheduling problems with linguistically quantified decision functions

Abstract: This paper presents a new tool for multi-objective job shop scheduling problems. The tool encompasses an interactive fuzzy multi-objective genetic algorithm (GA) which considers aspiration levels set by the decision maker (DM) for all the objectives. The GA's decision (fitness) function is defined as a measure of truth of a linguistically quantified statement, imprecisely specified by the DM using linguistic quantifiers such as most, few, etc., that refer to acceptable distances between the achieved objective values and the aspiration levels. The linguistic quantifiers are modelled using fuzzy sets. The developed tool is used to analyse and solve a real-world problem defined in collaboration with a pottery company. The tool provides a valuable support in performing various what-if analyses, for example, how changes of batch sizes, aspiration levels, linguistic quantifiers and the measure of acceptable distances affect the final schedule.

Using A Bee Colony Algorithm For Neighborhood Search In Job Shop Scheduling Problems

Abstract: This paper describes a population-based approach that uses a honey bees foraging model to solve job shop scheduling problems. The algorithm applies an efficient neighborhood structure to search for feasible solutions and iteratively improve on prior solutions. The initial solutions are generated using a set of priority dispatching rules. Experimental results comparing the proposed honey bee colony approach with existing approaches such as ant colony, tabu search and shifting bottleneck procedure on a set of job shop problems are presented. The results indicate the performance of the proposed approach is comparable to other efficient scheduling approaches.

A note on the two machine job shop with the weighted late work criterion

Abstract: The paper presents a dynamic programming approach for the two-machine nonpreemptive job-shop scheduling problem with the total weighted late work criterion and a common due date $$(J2\,|\,n_i \le 2,d_i = d\,|\,Y_w )$$ , which is known to be NP-hard. The late work performance measure estimates the quality of an obtained solution with regard to the duration of late parts of tasks not taking into account the quantity of this delay. Providing a pseudopolynomial time method for the problem mentioned we can classify it as binary NP-hard.

A Case Study on Integrated Production Planning and Scheduling in a Three-Stage Manufacturing System

Abstract: This paper presents an integrated optimization model of production planning and scheduling for a three-stage manufacturing system, which is composed of a forward chain of three kinds of workshops: a job shop, a parallel flow shop consisting of parallel production lines, and a single machine shop. As the products at the second stage are assembled from the parts produced in its upstream workshop, a complicated production process is involved. On the basis of the analysis of the batch production, a dynamic batch splitting and amalgamating algorithm is proposed. Then, a heuristic algorithm based on a genetic algorithm (known as the integrated optimization algorithm) is proposed for solving the problem. Note to Practitioners-This paper presents a method for integrated production planning and scheduling in a three-stage manufacturing system consisting of a forward chain of three kinds of workshops, which is common in such enterprises as producers of automobiles and household electric appliances, as in the case of an autobody plant usually with the stamping workshop, the welding and assembling workshop, and the painting workshop. Herein, the production planning and scheduling problems are simultaneously addressed in the way that a feasible production plan can be obtained and the inventory reduced. A batch splitting and amalgamating algorithm is proposed for balancing the production time of the production lines. And a case study of the integrated planning and scheduling problem in a real autobody plant verifies the effectiveness of our method

Filtered-beam-search-based algorithm for dynamic rescheduling in FMS

Abstract: In the practical production process of a flexible manufacturing system (FMS), unexpected disturbances such as rush orders arrival and machine breakdown may inevitably render the existing schedule infeasible. This makes dynamic rescheduling necessary to respond to the disturbances and to improve the efficiency of the disturbed FMS. Compared with the static scheduling, the dynamic rescheduling relies on more effective and robust search approaches for its critical requirement of real-time optimal response. In this paper, a filtered-beam-search (FBS) -based heuristic algorithm is proposed to solve the dynamic rescheduling problem in a large and complicated job shop FMS environment with realistic disturbances. To enhance its performance, the proposed algorithm makes improvement in the local/global evaluation functions and the generation procedure of branches. With respect to a due date-based objective (weighted quadratic tardiness), computational experiments are studied to evaluate the performance of the proposed algorithm in comparison with those of other popular methods. The results show that the proposed FBS-based algorithm performs very well for dynamic rescheduling in terms of computational efficiency and solution quality.

A Memetic Approach to Fuzzy Job Shop Based on Expectation Model

Abstract: In the sequel we consider a job shop problem with uncertain processing times modelled using triangular fuzzy numbers. A scheduling model based on the expected value of the makespan is introduced. Later, a genetic algorithm based on codification of permutations with repetitions, a decoding algorithm to generate possibly active schedules and a local search schema are defined in order to solve the job shop problem. Experimental results illustrate the potential of the proposed methods.

A Disjunctive Graph for the Job-Shop with several robots

Abstract: This paper addresses the scheduling problem in a job-shop where the jobs have to be transported between the machines by several transport robots. The problem can be efficiently modeled by a disjunctive graph and any solution can be fully defined by an orientation of the graph. The objective is to determine a schedule of machine and transport operations as well as an assignment of robots to transport operations with minimal makespan. We present: (i) a problem representation using an appropriate disjunctive graph; (ii) a solution representation based on 3 vectors consisting of machine disjunctions, transport disjunctions and robots assignments; (iii) a new problem-specific properties to define local search algorithms. Computational results are presented for test data arising from Bilge and Uluzoy’s benchmark instances enlarged by transportation, empty moving times for each robot. This paper is a step forward definition of an efficient approach arising a job-shop with several robots and it is an attempt to extend the Hurink and Knust proposal dedicated to one single transport robot.

A reinforcement learning approach for developing routing policies in multi-agent production scheduling.

Abstract: Most recent research studies on agent-based production scheduling have focused on developing negotiation schema for agent cooperation. However, successful implementation of agent-based approaches not only relies on the cooperation among the agents, but the individual agent’s intelligence for making good decisions. Learning is one mechanism that could provide the ability for an agent to increase its intelligence while in operation. This paper presents a study examining the implementation of the Q-learning algorithm, one of the most widely used reinforcement learning approaches, for use by job agents when making routing decisions in a job shop environment. A factorial experiment design for studying the settings used to apply Q-learning to the job routing problem is carried out. This study not only investigates the effects of this Q-learning application but also provides recommendations for factor settings and useful guidelines for future applications of Q-learning to agent-based production scheduling.

A comparison of cross-training policies in different job shops

Abstract: This research compares a set of cross-training policies represented by different numbers of cross-trained workers, additional skills per cross-trained worker, and additional machines. The policies are evaluated in job shops, represented by different efficiency losses, labour utilization, variability in processing times, and worker absenteeism. Our results show that adding one machine in each department and cross-training one or two workers from each department with one additional skill is generally sufficient to realize most of the benefits of cross-training. Cross-training is thus beneficial in most job shops, unless the cost of the minimal training and spare machines is high. Our results also show that the value of cross-training and adding machines depends very much on the environment, and it is better to spread cross-training over more workers than to train a few workers with more skills.