Showing papers on "Flow shop scheduling published in 1995"

Scheduling Algorithms

Abstract: Besides scheduling problems for single and parallel machines and shop scheduling problems, this book covers advanced models involving due-dates, sequence dependent changeover times and batching. Discussion also extends to multiprocessor task scheduling and problems with multi-purpose machines. Among the methods used to solve these problems are linear programming, dynamic programming, branch-and-bound algorithms, and local search heuristics. The text goes on to summarize complexity results for different classes of deterministic scheduling problems.

A reinforcement learning approach to job-shop scheduling

Abstract: We apply reinforcement learning methods to learn domain-specific heuristics for job shop scheduling. A repair-based scheduler starts with a critical-path schedule and incrementally repairs constraint violations with the goal of finding a short conflict-free schedule. The temporal difference algorithm TD(λ) is applied to tram a neural network to learn a heuristic evaluation function over states. This evaluation function is used by a one-step lookahead search procedure to find good solutions to new scheduling problems. We evaluate this approach on synthetic problems and on problems from a NASA space shuttle pay load processing task. The evaluation function is trained on problems involving a small number of jobs and then tested on larger problems. The TD scheduler performs better than the best known existing algorithm for this task--Zwehen's iterative repair method based on simulated annealing. The results suggest that reinforcement learning can provide a new method for constructing high-performance scheduling systems.

A GENETIC ALGORITHM FOR THE JOB SHOP PROBLEMf

Abstract: Scope and Purpoee--Job shop scheduling is a strongly NP-hard problem of combinatorial optimization and one of the most well-known machine scheduling problems. Scope of this paper is to present some improvements obtained in dealing with this problem using a heuristic technique based on Genetic Algorithms.

Implications of classical scheduling results for real-time systems

Abstract: Knowledge of complexity, fundamental limits and performance bounds-well known for many scheduling problems-helps real time designers choose a good design and algorithm and avoid poor ones. The scheduling problem has so many dimensions that it has no accepted taxonomy. We divide scheduling theory between uniprocessor and multiprocessor results. In the uniprocessor section, we begin with independent tasks and then consider shared resources and overload. In the multiprocessor section, we divide the work between static and dynamic algorithms. >

Scheduling groups of jobs on a single machine

Abstract: Economies of scale are fundamental to manufacturing operations. With respect to scheduling, this phenomenon manifests itself in efficiencies gained from grouping similar jobs together. This paper reviews the rapidly growing literature on single-machine scheduling models that incorporate benefits from job grouping. We focus on three basic models known as family scheduling with item availability, family scheduling with batch availability, and batch processing. We present known results and introduce new results, and we pay special attention to key theoretical properties and the use of these properties in optimization procedures.

Robust scheduling to hedge against processing time uncertainty in single-stage production

Abstract: Schedulers confronted with significant processing time uncertainty often discover that a schedule which is optimal with respect to a deterministic or stochastic scheduling model yields quite poor performance when evaluated relative to the actual processing times. In these environments, the notion of schedule robustness, i.e., determining the schedule with the best worst-case performance compared to the corresponding optimal solution over all potential realizations of job processing times, is a more appropriate guide to schedule selection. In this paper, we formalize the robust scheduling concept for scheduling situations with uncertain or variable processing times. To illustrate the development of solution approaches for a robust scheduling problem, we consider a single-machine environment where the performance criterion of interest is the total flow time over all jobs. We define two measures of schedule robustness, formulate the robust scheduling problem, establish its complexity, describe properties of the optimal schedule, and present exact and heuristic solution procedures. Extensive computational results are reported to demonstrate the efficiency and effectiveness of the proposed solution procedures.

A generalized permutation approach to job shop scheduling with genetic algorithms

Abstract: In order to sequence the tasks of a job shop problem (JSP) on a number of machines related to the technological machine order of jobs, a new representation technique — mathematically known as “permutation with repetition” is presented. The main advantage of this single chromosome representation is — in analogy to the permutation scheme of the traveling salesman problem (TSP) — that it cannot produce illegal operation sequences. As a consequence of the representation scheme a new crossover operator preserving the initial scheme structure of permutations with repetition will be sketched. Its behavior is similar to the well known Order-Crossover for simple permutation schemes. Actually theGOX operator for permutations with repetition arises from aGeneralisation ofOX. Computational experiments show, that GOX passes the information from a couple of parent solutions efficiently to offspring solutions. Together, the new representation and GOX support the cooperative aspect of genetic search for scheduling problems strongly.

Parallel Job Scheduling: Issues and Approaches

Abstract: Parallel job scheduling is beginning to gain recognition as an important topic that is distinct from the scheduling of tasks within a parallel job by the programmer or runtime system. The main issue is how to share the resources of the parallel machine among a number of competing jobs, giving each the required level of service. This level of scheduling is done by the operating system. The four most commonly used or advocated techniques are to use a global queue, use variable partitioning, use dynamic partitioning, and use gang scheduling. These techniques are surveyed, and the benefits and shortcomings of each are identified. Then additional requirements that are not addressed by current systems are outlined, followed by considerations for evaluating various scheduling schemes.

The one-machine problem with delayed precedence constraints and its use in job shop scheduling

Abstract: We study the one machine scheduling problem with release and delivery times and the minimum makespan objective, in the presence of constraints that for certain pairs of jobs require a delay between the completion of the first job and the start of the second (delayed precedence constraints). This problem arises naturally in the context of the Shifting Bottleneck Procedure for the general job shop scheduling problem, as a relaxation of the latter, tighter than the standard one-machine relaxation. The paper first highlights the difference between the two relaxations through some relevant complexity results. Then it introduces a modified Longest Tail Heuristic whose analysis identifies those situations that permit efficient branching. As a result, an optimization algorithm is developed whose performance is comparable to that of the best algorithms for the standard one-machine problem. Embedding this algorithm into a modified version of the Shifting Bottleneck Procedure that uses the tighter one-machine relaxa...

Solving the job shop scheduling problem with tabu search

Abstract: In the job shop scheduling problem we desire to minimize the makespan where a set of machines perform technologically ordered operations unique to each member of a set of jobs. Each operation has a fixed time duration, no machine can perform more than one operation at a time, and preemption is not allowed. In this paper, an effective tabu search approach to the job shop scheduling problem is presented. The procedure starts from the best solution rendered by a set of 14 heuristic dispatching solutions. It then makes use of the classical disjunctive network representation of the problem and iteratively moves to another feasible solution by reversing the order of two adjacent critical path operations performed by the same machine. The concepts of historical generators and search restart are used in conjunction with a contiguous spectrum of short term memory values to enhance the overall exploration strategy. Computational results are presented and areas for future investigation are suggested.

Exact solution of large-scale, asymmetric traveling salesman problems

Abstract: A lowest-first, branch-and-bound algorithm for the Asymmetric Traveling Salesman Problem is presented. The method is based on the Assignment Problem relaxation and on a subtour elimination branching scheme. The effectiveness of the algorithm derives from reduction procedures and parametric solution of the relaxed problems associated with the nodes of the branch-decision tree. Large-size, uniformly, randomly generated instances of complete digraphs with up to 2000 vertices are solved on a DECstation 5000/240 computer in less than 3 minutes of CPU time. In addition, we solved on a PC 486/33 no wait flow shop problems with up to 1000 jobs in less than 11 minutes and real-world stacker crane problems with up to 443 movements in less than 6 seconds.

Applying constraint satisfaction techniques to job shop scheduling

Abstract: In this paper, we investigate the applicability of a constraint satisfaction problem solving (CSP) model, recently developed for deadline scheduling, to more commonly studied problems of schedule optimization. Our hypothesis is twofold: (1) that CSP scheduling techniques provide a basis for developing high-performance approximate solution procedures in optimization contexts, and (2) that the representational assumptions underlying CSP models allow these procedures to naturally accommodate the idiosyncratic constraints that complicate most real-world applications. We focus specifically on the objective criterion of makespan minimization, which has received the most attention within the job shop scheduling literature. We define an extended solution procedure somewhat unconventionally by reformulating the makespan problem as one of solving a series of different but related deadline scheduling problems, and embedding a simple CSP procedure as the subproblem solver. We first present the results of an empirical evaluation of our procedure performed on a range of previously studied benchmark problems. Our procedure is found to provide strong costyperformance, producing solutions competitive with those obtained using recently reported shifting bottleneck search procedures at reduced computational expense. To demonstrate generality, we also consider application of our procedure to a more complicated, multi-product hoist scheduling problem. With only minor adjustments, our procedure is found to significantly outperform previously published procedures for solving this problem across a range of input assumptions.

Analysis of Classes of Heuristics for Scheduling a Two-Stage Flow Shop with Parallel Machines at One Stage

Abstract: This paper addresses two-stage flow shop scheduling with parallel machines at one stage. For finding a minimum makespan schedule, which is strongly NP-hard, some efficient heuristics have been proposed in the literature. In this paper we enrich the set of heuristics by introducing a few classes of heuristics, and show that the existing heuristics can be put into this classification scheme. Furthermore, we give a complete theoretical analysis of the worst-case performance of the classes. Some empirical evaluations and comparisons for the average-case performance of a few typical heuristics in the classes are also performed.

Exact methods for time constrained routing and related scheduling problems

Abstract: (11/11/2019) Exact methods for time constrained routing and related scheduling problems This dissertation presents a number of optimization methods for the Vehicle Routing Problem with Time Windows (VRPTW). The VRPTW is a generalization of the well known capacity constrained Vehicle Routing Problem (VRP), where a fleet of vehicles based at a central depot must service a set of customers. In the VRPTW customers must be serviced within a given time period a so called time window. The objective can be to minimize operating costs (e.g. distance travelled), fixed costs (e.g. the number of vehicles needed) or a combination of these component costs. During the last decade optimization methods, i.e. methods quaranteeing to find a proven optimal solution, have been studied by a number of researchers. The most successful approaches until now have been the Dantzig-Wolfe decomposition (column generation) method of Desrochers, Desrosiers and Solomon (1992) and the Variable Splitting approach of J?rnsten, Madsen and S?rensen (1986), which has been tested computationally by Halse (1992). Both methods decompose the problem into a series of time and capacity constrained shotest path problems. This yields a tight lower bound on the optimal objective, and the dual gap can often be closed by branch-and-bound methods. This work is closely related to these metods. The aim is to synthesize the research as well as develop the methods further with some original contributions. The dissertation is divided in to three parts. First, the theoretic framework is developed, and a number of methods are proposed and analyzed. Second, some of the methods are tested computationally. In the third part, generalizations of the VRPTW are considered. In the theoretic part of the dissertation, we outline the relationship between the methods based on constrained shortest path decompositions and show that the only real difference is how the coordinating master problem a concave non-differentiable maximization problem is solved. We show how the constrained shortest path problem can be solved efficiently, and present a number of different strategies for solving the master problem. The lower bound obtainable can be improved further by incorporation of valid inequalities . We show how this can be done computationally and we introduce a number of valid inequalities for the VRPTW. Finally we present a number of strategies, primarily branch-and-bound, to obtain integer solutions. In the computational part of the dissertation, we test some of the proposed methods on the well known set of benchmark problems by Solomon (1987). On basis of these experiments an optimization algorithm is constructed and executed on the benchmark problems. Of the 87 problems 70 were solved to optimality. This is to be compared to Desrochers, Desrosiers and Solomon (1992) who were able to solve 50 of the problems, and Halse (1992) who solved 33. The main reason for the success of the algorithm is the exploitation of valid inequalities. The increase in speed of computers since 1992 play only a minor role. In the last part of the dissertation a number of generalizations of the VRPTW are considered. We discuss complex routing problems with different constraints as well as important real world scheduling problems arising in transportation companies. We show how these problems can be modelled in the same framework as the VRPTW. This means, that the methods developed can be applied to a large number of important routing and scheduling problems. The dissertation gives a state-of-the-art review of optimization methods for the VRPTW based on constrained shortest path decompositions. It also contains a number of new theoretic results, and is the first application of valid inequalities on the VRPTW. The algorithm developed represents a major step forward in terms of computational ability to solve the VRPTW. Solutions to a large number of previously unsolved problems are reported.

A hierarchical bicriterion approach to integrated process plan selection and job shop scheduling

Abstract: The job shop scheduling literature deals with problems characterized by a fixed linear process plan for each job: it is assumed that the process planning problem has been solved before scheduling, and no flexibility in the process plan is considered. Our aim here is to propose a solution approach for a joint process plan selection and job shop scheduling problem, taking both operations cost and makespan into account within a multi-objective framework. Due to the complexity of the problem, a two-phase hierarchical method is proposed. In the first phase, a relaxed version of the problem is solved, yielding an approximation of the set of efficient process plans with respect to cost and load balancing objectives. Each process plan is then considered and the corresponding scheduling problem is solved by tabu search; the process plan selection is improved by a two-level hierarchical tabu search algorithm.

Scheduling Using Behavioral Templates

Abstract: This paper presents the idea of "behavioral templates" in scheduling. A behavioral template locks several operations into a relative schedule with respect to one another. This simple construct proves powerful in addressing: (1) timing constraints, (2) sequential operation modeling, (3) pre-chaining of certain operations, and (4) hierarchical scheduling. We present design examples from industry to demonstrate the importance of these issues in scheduling.

Intelligent Scheduling Systems

Abstract: Preface. Issues in Scheduling: A Survey of Intelligent Scheduling Systems D. Brown, J. Marin, W. Scherer. Schedulers & Planners: What and How Can we Learn from Them? K. McKay, F. Safayeni, J. Buzacott. Decision-Theoretic Control of Constraint Satisfaction and Scheduling O. Hansson, A. Mayer. Guided Forward Search in Tardiness Scheduling of Large One Machine Problems T. Morton, P. Ramnath. Production Scheduling: An Overview of Tabu Search Approaches to Production Scheduling Problems J. Barnes, M. Laguna, F. Glover. Measuring the Quality of Manufacturing Schedules K. Gary, R. Uzsoy, S.P. Smith, K. Kempf. A Methodology and Architecture for Reactive Scheduling S.F. Smith. Intelligent Scheduling with Machine Learning S. Park, S. Piramuthu, N. Raman, M. Shaw. Transportation Scheduling: Solving Large Integer Programs Arising from Air Traffic Flow Problems R. Burlingame, A. Boyd, K. Lindsay. Intelligent Scheduling Support for the U.S. Coastguard K. Darby-Dowman, C. Lucas, G. Mitra, R. Fink, L. Kingsley, J. Smith. Index.