Showing papers on "Job shop scheduling published in 1998"

TGFF: task graphs for free

Abstract: We present a user-controllable, general-purpose, pseudorandom task graph generator called Task Graphs For Free (TGFF). TGFF creates problem instances for use in allocation and scheduling research. It has the ability to generate independent tasks as well as task sets which are composed of partially ordered task graphs. A complete description of a scheduling problem instance is created, including attributes for processors, communication resources, tasks, and inter-task communication. The user may parametrically control the correlations between attributes. Sharing TGFF's parameter settings allows researchers to easily reproduce the examples used by others, regardless of the platform on which TGFF is run.

Voltage scheduling problem for dynamically variable voltage processors

Abstract: This paper presents a model of dynamically variable voltage processors and basic theorems for power-delay optimization. A static voltage scheduling problem is also proposed and formulated as an integer linear programming (ILP) problem. In the problem, we assume that a core processor can vary its supply voltage dynamically, but can use only a single voltage level at a time. For a given application program and a dynamically variable voltage processor, a voltage scheduling which minimizes energy consumption under an execution time constraint can be found.

A competitive genetic algorithm for resource-constrained project scheduling

Abstract: In this paper we consider the resource-constrained project scheduling problem (RCPSP) with makespan minimization as objective. We propose a new genetic algorithm approach to solve this problem. Subsequently, we compare it to two genetic algorithm concepts from the literature. While our approach makes use of a permutation based genetic encoding that contains problem-specific knowledge, the other two procedures employ a priority value based and a priority rule based representation, respectively. Then we present the results of our thorough computational study for which standard sets of project instances have been used. The outcome reveals that our procedure is the most promising genetic algorithm to solve the RCPSP. Finally, we show that our genetic algorithm yields better results than several heuristic procedures presented in the literature. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 733–750, 1998

Effective Neighborhood Functions for the Flexible Job Shop Problem

Abstract: The flexible job shop problem is an extension of the classical job shop scheduling problem which allows an operation to be performed by one machine out of a set of machines. The problem is to assign each operation to a machine (routing problem) and to order the operations on the machines (sequencing problem), such that the maximal completion time (makespan) of all operations is minimized. To solve the flexible job shop problem approximately, we use local search techniques and present two neighbourhood functions (Nopt1, Nopt2). Nopt2 is proved to be optimum connected. Nopt1 does not distinguish between routing or sequencing an operation. In both cases, a neighbour of a solution is obtained by moving an operation which affects the makespan. Our main contribution is a reduction of the set of possible neighbours to a subset for which can be proved that it always contains the neighbour with the lowest makespan. An efficient approach to compute such a subset of feasible neighbours is presented. A tabu search procedure is proposed and an extensive computational study is provided. We show that our procedure outperforms previous approaches. Copyright © 2000 John Wiley & Sons, Ltd.

An Exact Algorithm for the Resource-Constrained Project Scheduling Problem Based on a New Mathematical Formulation

Abstract: In this paper we consider the Project Scheduling Problem with resource constraints, where the objective is to minimize the project makespan. We present a new 0-1 linear programming formulation of the problem that requires an exponential number of variables, corresponding to all feasible subsets of activities that can be simultaneously executed without violating resource or precedence constraints. Different relaxations of the above formulation are used to derive new lower bounds, which dominate the value of the longest path on the precedence graph and are tighter than the bound proposed by Stinson et al. (1978). A tree search algorithm, based on the above formulation, that uses new lower bounds and dominance criteria is also presented. Computational results indicate that the exact algorithm can solve hard instances that cannot be solved by the best algorithms reported in the literature.

Guided Local Search with Shifting Bottleneck for Job Shop Scheduling

Abstract: Many recently developed local search procedures for job shop scheduling use interchange of operations, embedded in a simulated annealing or tabu search framework. We develop a new variable depth search procedure, GLS (Guided Local Search), based on an interchange scheme and using the new concept of neighborhood trees. Structural properties of the neighborhood are used to guide the search in promising directions. While this procedure competes successfully with others even as a stand-alone, a hybrid procedure that embeds GLS into a Shifting Bottleneck framework and takes advantage of the differences between the two neighborhood structures proves to be particularly efficient. We report extensive computational testing on all the problems available from the literature.

Genetic algorithms, path relinking, and the flowshop sequencing problem

Abstract: In a previous paper, a simple genetic algorithm (GA) was developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem (PFSP). The performance of the algorithm was comparable to that of a naive neighborhood search technique and a proven simulated annealing algorithm. However, recent results have demonstrated the superiority of a tabu search method in solving the PFSP. In this paper, we reconsider the implementation of a GA for this problem and show that by taking into account the features of the landscape generated by the operators used, we are able to improve its performance significantly.

Direct load control-A profit-based load management using linear programming

Abstract: Conventional cost-based load management ignores the rate structure offered to customers. The resulting cost savings may cause revenue loss. In a deregulated power industry where utilities absorb the ultimate consequence of their decision making, reexamination of load management must be conducted. In this paper, profit-based load management is introduced to examine generic direct load control scheduling. Based upon the cost/market price function, the approach aims to increase the profit of utilities. Instead of determining the amount of energy to be deferred or to be paid back, the algorithm controls the number of groups power customer/load type to maximize the profit. In addition to the advantage of better physical feel on how the control devices should operate, the linear programming algorithm provides a relatively inexpensive and powerful approach to the scheduling problem.

Design, implementation, and evaluation of the constraint language cc (FD)

Abstract: This paper describes the design, implementation, and applications of the constraint logic language cc(FD). cc(FD) is a declarative nondeterministic constraint logic language over finite domains based on the cc framework [33], an extension of the Constraint Logic Programming (CLP) scheme [21]. Its constraint solver includes (nonlinear) arithmetic constraints over natural numbers which are approximated using domain and interval consistency. The main novelty of cc (FD) is the inclusion of a number of general-purpose combinators, in particular cardinality, constructive disjunction, and blocking implication, in conjunction with new constraint operations such as constraint entailment and generalization. These combinators significantly improve the operational expressiveness, extensibility, and flexibility of CLP languages and allow issues such as the definition of nonprimitive constraints and disjunctions to be tackled at the language level. The implementation of cc (FD) (about 40,000 lines of C) includes a WAM-based engine [44], optimal are-consistency algorithms based on AC-5 [40], and incremental implementation of the combinators. Results on numerous problems, including scheduling, resource allocation, sequencing, packing, and hamiltonian paths are reported and indicate that cc(FD) comes close to procedural languages on a number of combinatorial problems. In addition, a small cc(FD) program was able to find the optimal solution and prove optimality to a famous 10/10 disjunctive scheduling problem [29], which was left open for more than 20 years and finally solved in 1986. (C) 1998 Elsevier Science Inc. All rights reserved.

Synthesis techniques for low-power hard real-time systems on variable voltage processors

Abstract: The energy efficiency of systems-on-a-chip can be much improved if one were to vary the supply voltage dynamically at run time. We describe the synthesis of systems-on-a-chip based on core processors, while treating voltage (and correspondingly the clock frequency) as a variable to be scheduled along with the computation tasks during the static scheduling step. In addition to describing the complete synthesis design flow for these variable voltage systems, we focus on the problem of doing the voltage scheduling while taking into account the inherent limitation on the rates at which the voltage and clock frequency can be changed by the power supply controllers and clock generators. Taking these limits on rate of change into account is crucial, since changing the voltage by even a volt may take time equivalent to 100 s to 10000 s of instructions on modern processors. We present both an exact but impractical formulation of this scheduling problem as a set of nonlinear equations, as well as a heuristic approach based on reduction to an optimally solvable restricted ordered scheduling problem. Using various task mixes drawn from a set of nine real life applications, our results show that we are able to reduce power consumption to within 7% of the lower bound obtained by imposing no limit at the rate of change of voltage and clock frequencies.

Local Search Heuristics for the Single Machine Total Weighted Tardiness Scheduling Problem

Abstract: This paper presents several local search heuristics for the problem of scheduling a single machine to minimize total weighted tardiness. We introduce a new binary encoding scheme to represent solutions, together with a heuristic to decode the binary representations into actual sequences. This binary encoding scheme is compared to the usual "natural" permutation representation for descent, simulated annealing, threshold accepting, tabu search and genetic algorithms on a large set of test problems. Computational results indicate that all of the heuristics which employ our binary encoding are very robust in that they consistently produce good quality solutions, especially when multistart implementations are used instead of a single long run. The binary encoding is also used in a new genetic algorithm which performs very well and requires comparatively little computation time. A comparison of neighborhood search methods which use the permutation and binary representations shows that the permutation-based methods have a higher likelihood of generating an optimal solution, but are less robust in that some poor solutions are obtained. Of the neighborhood search methods, tabu search clearly dominates the others. Multistart descent performs remarkably well relative to simulated annealing and threshold accepting.

Utilization Bounds for N-Processor Rate MonotoneScheduling with Static Processor Assignment

Abstract: We consider the schedulability of a set of independent periodic tasks under fixed priority preemptive scheduling on homogeneous multiprocessor systems. Assuming there is no task migration between processors and each processor schedules tasks preemptively according to fixed priorities assigned by the Rate Monotonic policy, the scheduling problem reduces to assigning the set of tasks to disjoint processors in such a way that the Monotonic policy, the scheduling problem reduces to assigning the set of tasks to disjoint processors in such a way that the schedulability of the tasks on each processor can be guaranteed. In this paper we show that the worst case achievable utilization for such systems is between n(2^{1/2}-1) and (n+1)/(1+2^{1/(n+1)}), where n stands for the number of processors. The lower bound represents 41 percent of the total system capacity and the upper bound represents 50 to 66 percent depending on n. Practicality of the lower bound is demonstrated by proving it can be achieved using a First Fit scheduling algorithm.

Coordinated hospital patient scheduling

Abstract: Hospital Patient Scheduling is an inherently distributed problem because of the way real hospitals are organized. As medical procedures have become more complex, and their associated tests and treatments have become interrelated, the current ad hoc patient scheduling solutions have been observed to break down. We propose a multi-agent solution using the Generalized Partial Global Planning (GPGP) approach that preserves the existing human organization and authority structures, while providing better system-level performance (increased hospital unit throughput and decreased patient slay time). To do this, we extend GPGP with a new coordination mechanism to handle mutually exclusive resource relationships. Like the other GPGP mechanisms, the new mechanism can be applied to any problem with the appropriate resource relationship. We evaluate the this new mechanism in the context of the hospital patient scheduling problem, and examine the effect of increasing interrelations between tasks performed by different hospital units.

Techniques for Scheduling with Rejection

Abstract: We consider the general problem of scheduling a set of jobs where we may choose not to schedule certain jobs, and thereby incur a penalty for each rejected job. More specifically, we focus on choosing a set of jobs to reject and constructing a schedule for the remaining jobs so as to optimize the sum of the weighted completion times of the jobs scheduled plus the sum of the penalties of the jobs rejected. We give several techniques for designing scheduling algorithms under this criterion. Many of these techniques show how to reduce a problem with rejection to a (potentially more complex) scheduling problem without rejection. Some of the reductions are based on general properties of certain kinds of linear-programming relaxations of optimization problems, and therefore are applicable to problems outside of scheduling; we demonstrate this by giving an approximation algorithm for a variant of the facility-location problem. In the last section of the paper we consider a different notion of rejection in the context of scheduling: scheduling jobs with due dates so as to maximize the number of jobs that complete by their due dates, or equivalently to minimize the number of jobs that do not complete by their due date and that thus can be considered "rejected." We investigate the approximability of a simple version of this problem, giving approximation algorithms and characterizing integrality gaps of a class of linear-programming relaxations.

A hybrid genetic algorithm for job shop scheduling problem

Abstract: The neural network model of Job shop scheduling problem is built. The characteristics and properties of its solutions are studied. A Hybrid Genetic Algorithm, combining genetic algorithm with neural network, for Job shop scheduling problem is described. The corresponding simulation shows that our method is good for complex production scheduling, at calculation time and goodness.

Common due window size and location determination in a single machine scheduling problem

Abstract: We consider a single machine static and deterministic scheduling problem in which jobs have a common due window. Jobs completed within the window incur no penalties, other jobs incur either earliness or tardiness penalties. The objective is to find the optimal size and location of the window as well as an optimal sequence to minimise a cost function based on earliness, tardiness, window size, and window location. We propose an O(n log n) algorithm to solve the problem.

Scheduling with multiple-job-on-one-processor pattern

Abstract: Most scheduling literature considers a “one-job-on-one-processor” pattern, which assumes that a processor processes exactly one job at a time. In this paper we consider a new scheduling problem with a “multiple-job-on-one-processor” pattern, where several jobs can be processed by a single processor simultaneously, provided that the total size of the jobs being processed does not exceed the capacity of the processor at any point in time. This problem is motivated by the operation of berth allocation, which is to allocate vessels (jobs) to a berth (processor), where the vessels, if small in dimension, may share the berth with some other vessels for loading/unloading the goods. We consider the problem to minimize the makespan of the schedule. The well-known First-Fit Decreasing heuristic is generalized and applied to several variations of the problem, and the worst-case behavior of the generalized heuristics is studied. Worst-case error bounds are obtained for those models. Computational experiments are cond...