Showing papers on "Job shop scheduling published in 2011"

A survey of problems, solution techniques, and future challenges in scheduling semiconductor manufacturing operations

Abstract: In this paper, we discuss scheduling problems in semiconductor manufacturing. Starting from describing the manufacturing process, we identify typical scheduling problems found in semiconductor manufacturing systems. We describe batch scheduling problems, parallel machine scheduling problems, job shop scheduling problems, scheduling problems with auxiliary resources, multiple orders per job scheduling problems, and scheduling problems related to cluster tools. We also present important solution techniques that are used to solve these scheduling problems by means of specific examples, and report on known implementations. Finally, we summarize some of the challenges in scheduling semiconductor manufacturing operations.

An effective genetic algorithm for the flexible job-shop scheduling problem

Abstract: In this paper, we proposed an effective genetic algorithm for solving the flexible job-shop scheduling problem (FJSP) to minimize makespan time. In the proposed algorithm, Global Selection (GS) and Local Selection (LS) are designed to generate high-quality initial population in the initialization stage. An improved chromosome representation is used to conveniently represent a solution of the FJSP, and different strategies for crossover and mutation operator are adopted. Various benchmark data taken from literature are tested. Computational results prove the proposed genetic algorithm effective and efficient for solving flexible job-shop scheduling problem.

Scheduling Power Consumption With Price Uncertainty

Abstract: The problem of causally scheduling power consumption to minimize the expected cost at the consumer side is considered. The price of electricity is assumed to be time-varying. The scheduler has access to past and current prices, but only statistical knowledge about future prices, which it uses to make an optimal decision in each time period. The scheduling problem is naturally cast as a Markov decision process. Algorithms to find decision thresholds for both noninterruptible and interruptible loads under a deadline constraint are then developed. Numerical results suggest that incorporating the statistical knowledge into the scheduling policies can result in significant savings, especially for short tasks. It is demonstrated with real price data from Commonwealth Edison that scheduling with mismatched modeling and online parameter estimation can still provide significant economic advantages to consumers.

Tramp ship routing and scheduling with speed optimization

Abstract: Tramp shipping companies are committed to transport a set of contracted cargoes and try to derive additional revenue from carrying optional spot cargoes. Traditionally, models for ship routing and scheduling problems are based on fixed speed and a given fuel consumption rate for each ship. However, in real life a ship’s speed is variable within an interval, and fuel consumption per time unit can be approximated by a cubic function of speed. Here we present the tramp ship routing and scheduling problem with speed optimization, where speed on each sailing leg is introduced as a decision variable. We present a multi-start local search heuristic to solve this problem. To evaluate each move in the local search we have to determine the optimal speed for each sailing leg of a given ship route. To do this we propose two different algorithms. Extensive computational results show that the solution method solves problems of realistic size and that taking speed into consideration in tramp ship routing and scheduling significantly improves the solutions.

Scheduling smart home appliances using mixed integer linear programming

Abstract: This paper considers the minimum electricity cost scheduling problem of smart home appliances. Operation characteristics, such as expected duration and peak power consumption of the smart appliances, can be adjusted through a power profile signal. The optimal power profile signal minimizes cost, while satisfying technical operation constraints and consumer preferences. Constraints such as enforcing uninterruptible and sequential operations are modeled in the proposed framework using mixed integer linear programming (MILP). Several realistic scenarios based on actual spot price are considered, and the numerical results provide insight into tariff design. Computational issues and extensions of the proposed scheduling framework are also discussed.

Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems

Abstract: This paper presents a hybrid Pareto-based discrete artificial bee colony algorithm for solving the multi-objective flexible job shop scheduling problem. In the hybrid algorithm, each solution corresponds to a food source, which composes of two components, i.e., the routing component and the scheduling component. Each component is filled with discrete values. A crossover operator is developed for the employed bees to learn valuable information from each other. An external Pareto archive set is designed to record the non-dominated solutions found so far. A fast Pareto set update function is introduced in the algorithm. Several local search approaches are designed to balance the exploration and exploitation capability of the algorithm. Experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.

Load Balanced Min-Min Algorithm for Static Meta-Task Scheduling in Grid Computing

Abstract: Grid computing has become a real alternative to traditional supercomputing environments for developing parallel applications that harness massive computational resources. However, the complexity incurred in building such parallel Grid-aware applications is higher than the traditional parallel computing environments. It addresses issues such as resource discovery, heterogeneity, fault tolerance and task scheduling. Load balanced task scheduling is very important problem in complex grid environment. So task scheduling which is one of the NP-Complete problems becomes a focus of research scholars in grid computing area. The traditional Min-Min algorithm is a simple algorithm that produces a schedule that minimizes the makespan than the other traditional algorithms in the literature. But it fails to produce a load balanced schedule. In this paper a Load Balanced Min-Min (LBMM) algorithm is proposed that reduces the makespan and increases the resource utilization. The proposed method has two-phases. In the first phase the traditional Min-Min algorithm is executed and in the second phase the tasks are rescheduled to use the unutilized resources effectively.

Temperature-Aware Scheduling and Assignment for Hard Real-Time Applications on MPSoCs

Abstract: Increasing integrated circuit (IC) power densities and temperatures may hamper multiprocessor system-on-chip (MPSoC) use in hard real-time systems. This paper formalizes the temperature-aware real-time MPSoC assignment and scheduling problem and presents an optimal phased steady-state mixed integer linear programming-based solution that considers the impact of scheduling and assignment decisions on MPSoC thermal profiles to directly minimize the chip peak temperature. We also introduce a flexible heuristic framework for task assignment and scheduling that permits system designers to trade off accuracy for running time when solving large problem instances. Finally, for task sets with sufficient slack, we show that inserting idle times between task executions can further reduce the peak temperature of the MPSoC quite significantly.

Scheduling in mapreduce-like systems for fast completion time

Abstract: Large-scale data processing needs of enterprises today are primarily met with distributed and parallel computing in data centers. MapReduce has emerged as an important programming model for these environments. Since today's data centers run many MapReduce jobs in parallel, it is important to find a good scheduling algorithm that can optimize the completion times of these jobs. While several recent papers focused on optimizing the scheduler, there exists very little theoretical understanding of the scheduling problem in the context of MapReduce. In this paper, we seek to address this problem by first presenting a simplified abstraction of the MapReduce scheduling problem, and then formulate the scheduling problem as an optimization problem.We devise various online and offline algorithms to arrive at a good ordering of jobs to minimize the overall job completion times. Since optimal solutions are hard to compute (NP-hard), we propose approximation algorithms that work within a factor of 3 of the optimal. Using simulations, we also compare our online algorithm with standard scheduling strategies such as FIFO, Shortest Job First and show that our algorithm consistently outperforms these across different job distributions.

Chemical Reaction Optimization for Task Scheduling in Grid Computing

Abstract: Grid computing solves high performance and high-throughput computing problems through sharing resources ranging from personal computers to supercomputers distributed around the world. One of the major problems is task scheduling, i.e., allocating tasks to resources. In addition to Makespan and Flowtime, we also take reliability of resources into account, and task scheduling is formulated as an optimization problem with three objectives. This is an NP-hard problem, and thus, metaheuristic approaches are employed to find the optimal solutions. In this paper, several versions of the Chemical Reaction Optimization (CRO) algorithm are proposed for the grid scheduling problem. CRO is a population-based metaheuristic inspired by the interactions between molecules in a chemical reaction. We compare these CRO methods with four other acknowledged metaheuristics on a wide range of instances. Simulation results show that the CRO methods generally perform better than existing methods and performance improvement is especially significant in large-scale applications.

An iterated greedy algorithm for the flowshop scheduling problem with blocking

Abstract: This paper proposes an iterated greedy algorithm for solving the blocking flowshop scheduling problem for makespan minimization. Moreover, it presents an improved NEH-based heuristic, which is used as the initial solution procedure for the iterated greedy algorithm. The effectiveness of both procedures was tested on some of Taillard’s benchmark instances that are considered to be blocking flowshop instances. The experimental evaluation showed the efficiency of the proposed algorithm, in spite of its simple structure, in comparison with a state-of-the-art algorithm. In addition, new best solutions for Taillard’s instances are reported for this problem, which can be used as a basis of comparison in future studies.

A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem.

Abstract: A novel hybrid tabu search algorithm with a fast public critical block neighborhood structure (TSPCB) is proposed in this paper to solve the flexible job shop scheduling problem with the criterion to minimize the maximum completion time (makespan). First, a mix of four machine assignment rules and four operation scheduling rules is developed to improve the quality of initial solutions to empower the hybrid algorithm with good exploration capability. Second, an effective neighborhood structure to conduct local search in the machine assignment module is proposed, which integrates three adaptive approaches. Third, a speedup local search method with three kinds of insert and swap neighborhood structures based on public critical block theory is presented. With the fast neighborhood structure, the TSPCB algorithm can enhance its exploitation capability. Simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is concluded that the proposed TSPCB algorithm is superior to several recently published algorithms in terms of solution quality, convergence ability, and efficiency.

Dynamic scheduling of a multiclass make-to-stock queue

Abstract: Motivated by make-to-stock production systems, we consider a scheduling problem for a single server queue that can process a variety of different job classes. After jobs are processed, they enter a finished goods inventory that services customer demand. The scheduling problem is to dynamically decide which job class, if any, to serve next in order to minimize the long-run expected average cost incurred per unit of time, which includes linear costs which may differ by class for backordering and holding finished goods inventory. Under the heavy traffic condition that the server must be busy the great majority of the time in order to satisfy customer demand, the scheduling problem is approximated by a dynamic control problem involving Brownian motion. The Brownian control problem is solved, and its solution is interpreted in terms of the queueing system to obtain a scheduling policy. A simulation experiment is performed that demonstrates the policy's effectiveness.

Bi-Criteria Scheduling of Surgical Services for an Outpatient Procedure Center

Abstract: Uncertainty in the duration of surgical procedures can cause long patient wait times, poor utilization of resources, and high overtime costs. We compare several heuristics for scheduling an Outpatient Procedure Center. First, a discrete event simulation model is used to evaluate how 12 different sequencing and patient appointment time-setting heuristics perform with respect to the competing criteria of expected patient waiting time and expected surgical suite overtime for a single day compared with current practice. Second, a bi-criteria genetic algorithm (GA) is used to determine if better solutions can be obtained for this single day scheduling problem. Third, we investigate the efficacy of the bi-criteria GA when surgeries are allowed to be moved to other days. We present numerical experiments based on real data from a large health care provider. Our analysis provides insight into the best scheduling heuristics, and the trade-off between patient and health care provider-based criteria. Finally, we summarize several important managerial insights based on our findings.

Optimal charging of plug-in hybrid electric vehicles in smart grids

Abstract: Plug-in hybrid electric vehicles (PHEVs) play an important role in making a greener future. Given a group of PHEVs distributed across a power network equipped with the smart grid technology (e.g. wireless communication devices), the objective of this paper is to study how to schedule the charging of the PHEV batteries. To this end, we assume that each battery must be fully charged by a pre-specified time, and that the charging rate can be time-varying at discrete-time instants. The scheduling problem for the PHEV charging can be augmented into the optimal power flow (OPF) problem to obtain a joint OPF-charging (dynamic) optimization. A solution to this highly nonconvex problem optimizes the network performance by minimizing the generation and charging costs while satisfying the network, physical and inelastic-load constraints. A global optimum to the joint OPF-charging optimization can be found efficiently in polynomial time by solving its convex dual problem whenever the duality gap is zero for the joint OPF-charging problem. It is shown in a recent work that the duality gap is expected to be zero for the classical OPF problem. We build on this result and prove that the duality gap is zero for the joint OPF-charging optimization if it is zero for the classical OPF problem. The results of this work are applied to the IEEE 14 bus system.

Bi-objective optimization research on integrated fixed time interval preventive maintenance and production for scheduling flexible job-shop problem

Abstract: Research highlights? Integrated flexible job-shop and preventive maintenance is probed in this study. ? Makespan as an objective for production part and reliability as an objective for maintenance part are optimized simultaneously. ? A mathematic model and a solving method are introduced in this paper and results are compared carefully. This paper investigates integrated flexible job shop problem (FJSP) with preventive maintenance (PM) activities under the multi-objective optimization approaches. Finding compromise solutions between the production objectives and maintenance ones is under consideration. In order to carry out the maintenance activities, reliability models are employed. This paper attempts to simultaneously optimize two objectives: the minimization of the makespan for the production part and the minimization of the system unavailability for the maintenance part. For doing it so, two decisions are taken at the same time: finding the appropriate assignment of n jobs on m machines in order to minimize the makespan and deciding when to execute the PM activities in order to minimize the system unavailability. Both the maintenance activity numbers and maintenance intervals are not fixed in advance. Four multi-objective optimization methods are compared to find the Pareto-optimal front in the flexible job-shop problem case. Promising the obtained results, a benchmark with a large number of test instances (more than 4800) and meticulous care is employed.

Multiple timescale dispatch and scheduling for stochastic reliability in smart grids with wind generation integration

Abstract: Integrating volatile renewable energy resources into the bulk power grid is challenging, due to the reliability requirement that the load and generation in the system remain balanced all the time. In this study, we tackle this challenge for smart grid with integrated wind generation, by leveraging multi-timescale dispatch and scheduling. Specifically, we consider smart grids with two classes of energy users - traditional energy users and opportunistic energy users (e.g., smart meters or smart appliances), and investigate pricing and dispatch at two timescales, via day-ahead scheduling and real-time scheduling. In day-ahead scheduling, with the statistical information on wind generation and energy demands, we characterize the optimal procurement of the energy supply and the day-ahead retail price for the traditional energy users; in real-time scheduling, with the realization of wind generation and the load of traditional energy users, we optimize real-time prices to manage the opportunistic energy users so as to achieve system-wide reliability. More specifically, when the opportunistic users are non-persistent, we obtain closed-form solutions to the two-level scheduling problem. For the persistent case, we treat the scheduling problem as a multi-timescale Markov decision process. We show that it can be recast, explicitly, as a classic Markov decision process with continuous state and action spaces, the solution to which can be found via standard techniques.

Meta-heuristics implementation for scheduling of trucks in a cross-docking system with temporary storage

Abstract: Cross-docking is an approach in inventory management which can reduce inventories, lead times and customer response time. In this strategy, products and shipments are unloaded from inbound trucks, sorted and categorized based on their characteristics, moved and loaded onto outbound trucks for delivery to demand points in a distribution network. The important fact is that, the items are stored in the inventory for a period which is primarily less than the actual time allocated to keep these items in a typical warehouse. Therefore, total cost and space requirement for inventory can be cut down. One of the most important targets in such systems is to establish coordination between the performance of inbound and outbound trucks in that these trucks can be scheduled, and the product items can be allocated to trucks effectively. This paper addresses some meta-heuristics to find the best sequence of inbound and outbound trucks, so that the objective, minimizing the total operation time called makespan, can be satisfied. Furthermore, not only the efficiency and capability of the algorithms' parameters are assessed and analyzed by some performance measures, but also these meta-heuristics are compared with each other in order to find out the set of homogeneous algorithms among all proposed algorithms. By this analysis, it can be shown that the suitability of these meta-heuristics is quite sensible especially for the cross-docking systems with large sizes in which a high volume of inbound or outbound trucks transmit the product items.

A novel imperialist competitive algorithm for bi-criteria scheduling of the assembly flowshop problem

Abstract: This paper deals with the two-stage assembly flowshop scheduling problem with minimisation of weighted sum of makespan and mean completion time as the objective. The problem is NP-hard, hence we proposed a meta-heuristic named imperialist competitive algorithm (ICA) to solve it. Since appropriate design of the parameters has a significant impact on the algorithm efficiency, we calibrate the parameters of this algorithm using the Taguchi method. In comparison with the best algorithm proposed previously, the ICA indicates an improvement. The results have been confirmed statistically.

An efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problems

Abstract: The hybrid flow shop scheduling with multiprocessor task (HFSMT) problem is a substantial production scheduling problem for minimizing the makespan, and there exist many difficulties in solving large scale HFSMT problems which include many jobs, machines and tasks. The HFSMT problems known as NP-hard and the proposal of an efficient genetic algorithm (GA) were taken into consideration in this study. The numerical results prove that the computational performance of a GA depends on the factors of initial solution, reproduction, crossover, and mutation operators and probabilities. The implementation details, including a new mutation operator, were described and a full factorial experimental design was determined with our GA program by using the best values of the control parameters and the operators. After a comparison was made with the studies of Oguz [1], Oguz and Ercan [2] and Kahraman et al. [3] related to the HFSMT problems, the computational results indicated that the proposed genetic algorithm approach is very effective in terms of reduced total completion time or makespan (C"m"a"x) for the attempted problems.