Showing papers on "Job shop scheduling published in 2003"

A Multiple-Criterion Model for Machine Scheduling

Abstract: We consider a scheduling problem involving a single processor being utilized by two or more customers. Traditionally, such scenarios are modeled by assuming that each customer has the same criterion. In practice, this assumption may not hold. Instead of using a single criterion, we examine the implications of minimizing an aggregate scheduling objective function in which jobs belonging to different customers are evaluated based on their individual criteria. We examine three basic scheduling criteria: minimizing makespan, minimizing maximum lateness, and minimizing total weighted completion time. Although determining a minimum-cost schedule according to any one of these criteria is polynomially solvable, we demonstrate that when minimizing a mix of these criteria, the problem becomes NP-hard.

Design and Evaluation of Job Scheduling Strategies for Grid Computing

Abstract: Grid computing is intended to offer an easy and seamless access to remote resources. The importance of grid computing can be seen by the attention it gained recently in research and industry support. The scheduling task of allocating these resources automatically to user jobs is an essential part of a grid environment. In this work we discuss the evaluation and design of different scheduling strategies. To this end, we present a concept for the design process of such a scheduling system. The evaluation of scheduling algorithms for single parallel machine is done by theoretical analysis and by simulation experiments. The theoretical approach by competitive analysis lead to bounds for the worst-case scenarios. As we are especially interested in the scheduling performance in a real system installation, simulations have been applied for further evaluation. In addition to the theoretical analysis, we show that the presented preemptive scheduling algorithm is also efficient in terms of makespan and average response time in a real system scenario if compared to other scheduling algorithms. In some of the examined scenarios the algorithm could outperform other common algorithms such as backfilling. Based on these results, scheduling algorithms for the grid environment have been developed. On one hand, these methods base on modifications of the examined conventional scheduling strategies for single parallel machines. On the other hand, a scheduling strategy with a market economic approach is presented. These methods are also analyzed and compared by simulations for a real workload environment. The results show that the economic approach produces similar or even better results than the conventional strategies for common criteria as the average weighted response time. Additionally, the economic approach delivers several additional advantages, such as support for variable utility functions for users and owner of resources. As a proof of concept a possible architecture of a scheduling environment is presented, which has been used for the evaluation of the presented algorithms. The work ends with a brief conclusion on the discussed scheduling strategies and gives an outlook on future work.

Scheduling analysis of time-constrained dual-armed cluster tools

Abstract: Cluster tools, each of which consists of several single-wafer processing chambers and a wafer handling robot, have been increasingly used for diverse wafer fabrication processes. Processes such as some low pressure chemical vapor deposition processes require strict timing control. Unless a wafer processed at a chamber for such a process leaves the chamber within a specified time limit, the wafer is subject to quality problems due to residual gases and heat. We address the scheduling problem for such time-constrained dual-armed cluster tools that have diverse wafer flow patterns. We propose a systematic method of determining the schedulable process time range for which there exists a feasible schedule that satisfies the time constraints. We explain how to select the desirable process times within the schedulable process time range. We present a method of determining the tool operation schedule. For more flexible scheduling under the time constraints, we propose a modification of the conventional swap operation in order to allow wafer delay on a robot arm during a swap operation. We compare the performance of the new swap strategy with that of the conventional swap strategy.

Short-term hydrothermal generation scheduling model using a genetic algorithm

Abstract: A new model to deal with the short-term generation scheduling problem for hydrothermal systems is proposed. Using genetic algorithms (GAs), the model handles simultaneously the subproblems of short-term hydrothermal coordination, unit commitment, and economic load dispatch. Considering a scheduling horizon period of a week, hourly generation schedules are obtained for each of both hydro and thermal units. Future cost curves of hydro generation, obtained from long and mid-term models, have been used to optimize the amount of hydro energy to be used during the week. In the genetic algorithm (GA) implementation, a new technique to represent candidate solutions is introduced, and a set of expert operators has been incorporated to improve the behavior of the algorithm. Results for a real system are presented and discussed.

Genetic algorithm for the flexible job-shop scheduling problem

Abstract: In this paper, we are interested in the multiobjective optimization of the schedule performance in the flexible job shops. The flexible job shop scheduling problem (FJSP) is known in the literature as one of the hardest combinatorial optimization problems and presents many objectives to be optimized. In this way, we aim to solve such a problem according to a set of some criteria, which characterize the feasible solutions of such a problem. The studied criteria are the following: the makespan, the workload of the critical machine, and the total workload of all the machines. Our study relates to the determination of a practical method using genetic algorithm in order to obtain the best performance of the production system. The solution performance is evaluated by comparing the values of the different values of the criteria with the corresponding lower bounds.

Opportunistic fair scheduling over multiple wireless channels

Abstract: Emerging spread spectrum high-speed data networks utilize multiple channels via orthogonal codes or frequency-hopping patterns such that multiple users can transmit concurrently. In this paper, we develop a framework for opportunistic scheduling over multiple wireless channels. With a realistic channel model, any subset of users can be selected for data transmission at any time, albeit with different throughputs and system resource requirements. We first transform selection of the best users and rates from a complex general optimization problem into a decoupled and tractable formulation: a multiuser scheduling problem that maximizes total system throughput and a control-update problem that ensures long-term deterministic or probabilistic fairness constraints. We then design and evaluate practical schedulers that approximate these objectives.

Parallel GRASP with path-relinking for job shop scheduling

Abstract: In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines under certain constraints, such that the maximum completion time of the jobs is minimized. In this paper, we describe a parallel greedy randomized adaptive search procedure (GRASP) with path-relinking for the JSP. Independent and cooperative parallelization strategies are described and implemented. Computational experience on a large set of standard test problems indicates that the parallel GRASP with path-relinking finds good-quality approximate solutions of the JSP.

Generating robust and flexible job shop schedules using genetic algorithms

Abstract: The problem of finding robust or flexible solutions for scheduling problems is of utmost importance for real-world applications as they operate in dynamic environments. In such environments, it is often necessary to reschedule an existing plan due to failures (e.g., machine breakdowns, sickness of employees, deliveries getting delayed, etc.). Thus, a robust or flexible solution may be more valuable than an optimal solution that does not allow easy modifications. This paper considers the issue of robust and flexible solutions for job shop scheduling problems. A robustness measure is defined and its properties are investigated. Through experiments, it is shown that using a genetic algorithm it is possible to find robust and flexible schedules with a low makespan. These schedules are demonstrated to perform significantly better in rescheduling after a breakdown than ordinary schedules. The rescheduling performance of the schedules generated by minimizing the robustness measure is compared with the performance of another robust scheduling method taken from literature, and found to outperform this method in many cases.

Real-Time Optimization Model for Dynamic Scheduling of Transit Operations

Abstract: A new transit operating strategy is presented in which service vehicles operate in pairs with the lead vehicle providing an all-stop local service and the following vehicle being allowed to skip some stops as an express service. The underlying scheduling problem is formulated as a nonlinear integer programming problem with the objective of minimizing the total costs for both operators and passengers. A sensitivity analysis using a real-life example is performed to identify the conditions under which the proposed operating strategy is most advantageous.

Scheduling and Platforming Trains at Busy Complex Stations

Abstract: We consider the problem of train planning or scheduling for large, busy, complex train stations, which are common in Europe and elsewhere, though not in North America. We develop the constraints and objectives for this problem, but these are too computationally complex to solve by standard combinatorial search or integer programming methods. Also, the problem is somewhat political in nature, that is, it does not have a clear objective function because it involves multiple train operators with conflicting interests. We therefore develop scheduling heuristics analogous to those successfully adopted by train planners using “manual” methods. We tested the model and algorithms by applying to a typical large station that exhibits most of the complexities found in practice. The results compare well with those found by traditional methods, and take account of cost and preference trade-offs not handled by those methods. With successive refinements, the algorithm eventually took only a few seconds to run, the time depending on the version of the algorithm and the scheduling problem. The scheduling models and algorithms developed and tested here can be used on their own, or as key components for a more general system for train scheduling for a rail line or network. Train scheduling for a busy station includes ensuring that there are no conflicts between several hundred trains per day going in and out of the station on intersecting paths from multiple in-lines and out-lines to multiple platforms, while ensuring that each train is allowed at least its minimum required headways, dwell time, turnaround time and trip time. This has to be done while minimizing (costs of) deviations from desired times, platforms or lines, allowing for conflicts due to through-platforms, dead-end platforms, multiple sub-platforms, and possible constraints due to infrastructure, safety or business policy.

Different initial sequences for the heuristic of Nawaz, Enscore and Ham to minimize makespan, idletime or flowtime in the static permutation flowshop sequencing problem

Abstract: The polynomial heuristic of Nawaz/Enscore/Ham (NEH) is one of the best heuristics to minimize makespan in static-deterministic permutation flowshop problems. The NEH approach consists of two steps: (1) the generation of an initial order of jobs with respect to an indicator value and (2) the iterative insertion of jobs into a partial sequence according to the initial order of step 1. We generalize this approach to minimization of makespan, idletime and flowtime, construct 177 different initial orders, and evaluate their performance in the NEH-insertion approach. Based on a comprehensive numerical study, we propose heuristics for all three objective functions that outperform significantly the compared literature-based heuristics.

Daily imaging scheduling of an Earth observation satellite

Abstract: This work presents the development of a daily imaging scheduling system for a low-orbit, Earth observation satellite. The daily imaging scheduling problem of satellite considers various imaging requests with different reward opportunities, changeover efforts between two consecutive imaging tasks, cloud-coverage effects, and the availability of the spacecraft resource. It belongs to a class of single-machine scheduling problems with salient features of sequence-dependent setup, job assembly, and the constraint of operating time windows. The scheduling problem is formulated as an integer-programming problem, which is NP-hard in computational complexity. Lagrangian relaxation and linear search techniques are adopted to solve this problem. In order to demonstrate the efficiency and effectiveness of our solution methodology, a Tabu search-based algorithm is implemented, which is modified from the algorithm in Vasquez and Hao, 2001. Numerical results indicate that the approach is very effective to generate a near-optimal, feasible schedule for the imaging operations of the satellite. It is efficient in applications to the real problems. The Lagrangian-relaxation approach is superior to the Tabu search one in both optimality and computation time.

Use of an Artificial Immune System for Job Shop Scheduling

Abstract: In this paper, we propose an algorithm based on an artificial immune system to solve job shop scheduling problems. The approach uses clonal selection, hypermutations and a library of antibodies to construct solutions. It also uses a local selection mechanism that tries to eliminate gaps between jobs in order to improve solutions produced by the search mechanism of the algorithm. The proposed approach is compared with respect to GRASP (an enumerative approach) in several test problems taken from the specialized literature. Our results indicate that the proposed algorithm is highly competitive, being able to produce better solutions than GRASP in several cases, at a fraction of its computational cost.

A Hierarchical Multiple Criteria Mathematical Programming Approach for Scheduling General Surgery Operations in Large Hospitals

Abstract: Limited staff and equipment within surgical services require efficient use of these resources among multiple surgeon groups. In this study, a set of hierarchical multiple criteria mathematical programming models are developed to generate weekly operating room schedules. The goals considered in these models are maximum utilization of operating room capacity, balanced distribution of operations among surgeon groups in terms of operation days, lengths of operation times, and minimization of patient waiting times. Because of computational difficulty of this scheduling problem, the overall problem is broken down into manageable hierarchical stages: (1) selection of patients, (2) assignment of operations to surgeon groups, and (3) determination of operation dates and operating rooms. Developed models are tested on the data collected in College of Medicine Research Hospital at Cukurova University as well as on simulated data sets, using MPL optimization package.

Scheduling of flexible manufacturing systems: An ant colony optimization approach

Abstract: The scheduling problem forexiblemanufacturing systems (FMSs)has beenattempted inthis paper using the ant colony optimization (ACO) technique. Since the operation of a job in FMSs can be performed on more than one machine, the scheduling of the FMS is considered as a computationally hard problem. Ant algorithms are based on the foraging behaviour of real ants. The article deals with the ant algorithm with certain modi® cations that make it suitable for application to the required problem. The proposed solution procedure applies a graph-based representation technique with nodes and arcs representing operation and transfer from one stage of processing to the other. Individual ants move from the initial node to the ® nal node through all nodes desired to be visited. The solution of the algorithm is a collective outcome of the solution found by all the ants. The pheromone trail is updated after all the ants have found out their respective solutions. Various features like stagnation avoidance and prevention from quick convergence have been incorporated in the proposed algorithm so that the near-optimal solution is obtained for the FMS scheduling problem, which is considered as a non-polynomial (NP)-hard problem. The algorithm stabilizes to the solution in considerably lesser computational eA ort. Extensive computational experiments have been carried out to study the inuence of various parameters on the system performance.

On energy-optimal voltage scheduling for fixed-priority hard real-time systems

Abstract: We address the problem of energy-optimal voltage scheduling for fixed-priority hard real-time systems, on which we present a complete treatment both theoretically and practically. Although most practical real-time systems are based on fixed-priority scheduling, there have been few research results known on the energy-optimal fixed-priority scheduling problem. First, we prove that the problem is NP-hard. Then, we present a fully polynomial time approximation scheme (FPTAS) for the problem. For any e > 0, the proposed approximation scheme computes a voltage schedule whose energy consumption is at most (1 + e) times that of the optimal voltage schedule. Furthermore, the running time of the proposed approximation scheme is bounded by a polynomial function of the number of input jobs and 1/e. Given the NP-hardness of the problem, the proposed approximation scheme is practically the best solution because it can compute a near-optimal voltage schedule (i.e., provably arbitrarily close to the optimal schedule) in polynomial time. Experimental results show that the approximation scheme finds more efficient (almost optimal) voltage schedules faster than the best existing heuristic.