Showing papers on "Job shop scheduling published in 2010"

Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services

Abstract: We describe algorithmic enhancements to a decision-support tool that residential consumers can utilize to optimize their acquisition of electrical energy services. The decision-support tool optimizes energy services provision by enabling end users to first assign values to desired energy services, and then scheduling their available distributed energy resources (DER) to maximize net benefits. We chose particle swarm optimization (PSO) to solve the corresponding optimization problem because of its straightforward implementation and demonstrated ability to generate near-optimal schedules within manageable computation times. We improve the basic formulation of cooperative PSO by introducing stochastic repulsion among the particles. The improved DER schedules are then used to investigate the potential consumer value added by coordinated DER scheduling. This is computed by comparing the end-user costs obtained with the enhanced algorithm simultaneously scheduling all DER, against the costs when each DER schedule is solved separately. This comparison enables the end users to determine whether their mix of energy service needs, available DER and electricity tariff arrangements might warrant solving the more complex coordinated scheduling problem, or instead, decomposing the problem into multiple simpler optimizations.

TDMA scheduling algorithms for wireless sensor networks

Abstract: Algorithms for scheduling TDMA transmissions in multi-hop networks usually determine the smallest length conflict-free assignment of slots in which each link or node is activated at least once. This is based on the assumption that there are many independent point-to-point flows in the network. In sensor networks however often data are transferred from the sensor nodes to a few central data collectors. The scheduling problem is therefore to determine the smallest length conflict-free assignment of slots during which the packets generated at each node reach their destination. The conflicting node transmissions are determined based on an interference graph, which may be different from connectivity graph due to the broadcast nature of wireless transmissions. We show that this problem is NP-complete. We first propose two centralized heuristic algorithms: one based on direct scheduling of the nodes or node-based scheduling, which is adapted from classical multi-hop scheduling algorithms for general ad hoc networks, and the other based on scheduling the levels in the routing tree before scheduling the nodes or level-based scheduling, which is a novel scheduling algorithm for many-to-one communication in sensor networks. The performance of these algorithms depends on the distribution of the nodes across the levels. We then propose a distributed algorithm based on the distributed coloring of the nodes, that increases the delay by a factor of 10---70 over centralized algorithms for 1000 nodes. We also obtain upper bound for these schedules as a function of the total number of packets generated in the network.

Cost-Optimal Scheduling in Hybrid IaaS Clouds for Deadline Constrained Workloads

Abstract: With the recent emergence of public cloud offerings, surge computing –outsourcing tasks from an internal data center to a cloud provider in times of heavy load– has become more accessible to a wide range of consumers. Deciding which workloads to outsource to what cloud provider in such a setting, however, is far from trivial. The objective of this decision is to maximize the utilization of the internal data center and to minimize the cost of running the outsourced tasks in the cloud, while fulfilling the applications’ quality of service constraints. We examine this optimization problem in a multi-provider hybrid cloud setting with deadline-constrained and preemptible but non-provider-migratable workloads that are characterized by memory, CPU and data transmission requirements. Linear programming is a general technique to tackle such an optimization problem. At present, it is however unclear whether this technique is suitable for the problem at hand and what the performance implications of its use are. We therefore analyze and propose a binary integer program formulation of the scheduling problem and evaluate the computational costs of this technique with respect to the problem’s key parameters. We found out that this approach results in a tractable solution for scheduling applications in the public cloud, but that the same method becomes much less feasible in a hybrid cloud setting due to very high solve time variances.

A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems

Abstract: A Knowledge-Based Ant Colony Optimization (KBACO) algorithm is proposed in this paper for the Flexible Job Shop Scheduling Problem (FJSSP). KBACO algorithm provides an effective integration between Ant Colony Optimization (ACO) model and knowledge model. In the KBACO algorithm, knowledge model learns some available knowledge from the optimization of ACO, and then applies the existing knowledge to guide the current heuristic searching. The performance of KBACO was evaluated by a large range of benchmark instances taken from literature and some generated by ourselves. Final experimental results indicate that the proposed KBACO algorithm outperforms some current approaches in the quality of schedules.

Real-Time Scheduling for WirelessHART Networks

Abstract: WirelessHART is an open wireless sensor-actuator network standard for industrial process monitoring and control that requires real-time data communication between sensor and actuator devices. Salient features of a WirelessHART network include a centralized network management architecture, multi-channel TDMA transmission, redundant routes, and avoidance of spatial reuse of channels for enhanced reliability and real-time performance. This paper makes several key contributions to real-time transmission scheduling in WirelessHART networks: (1) formulation of the end-to-end real-time transmission scheduling problem based on the characteristics of WirelessHART, (2) proof of NP-hardness of the problem, (3) an optimal branch-and-bound scheduling algorithm based on a necessary condition for schedulability, and (4) an efficient and practical heuristic-based scheduling algorithm called Conflict-aware Least Laxity First (C-LLF). Extensive simulations based on both random topologies and real network topologies of a physical testbed demonstrate that C-LLF is highly effective in meeting end-to-end deadlines in WirelessHART networks, and significantly outperforms common real-time scheduling policies.

Dynamic capacitated lot-sizing problems: a classification and review of solution approaches

Abstract: This paper presents a review of four decades of research on dynamic lot-sizing with capacity constraints. We discuss both different modeling approaches to the optimization problems and different algorithmic solution approaches. The focus is on research that separates the lot-sizing problem from the detailed sequencing and scheduling problem. Our conceptional point of reference is the multi-level capacitated lot-sizing problem (MLCLSP). We show how different streams of research emerged over time. One result is that many practically important problems are still far from being solved in the sense that they could routinely be solved close to optimality in industrial practice. Our review also shows that currently mathematical programing and the use of metaheuristics are particularly popular among researchers in a vivid and flourishing field of research.

Towards the Design of Certifiable Mixed-criticality Systems

Abstract: Many safety-critical embedded systems are subject to certification requirements; some systems may be required to meet multiple sets of certification requirements, from different certification authorities. Certification requirements in such "mixed-criticality" systems give rise to some interesting scheduling problems, that cannot be satisfactorily addressed using techniques from conventional scheduling theory. In this paper, we propose a formal model for representing such mixed-criticality workloads. We demonstrate the intractability of determining whether a system specified in this model can be scheduled to meet all its certification requirements. For dual-criticality systems -- systems subject to two sets of certification requirements -- we quantify, via the metric of processor speedup factor, the effectiveness of 2 techniques (reservation-based scheduling and priority-based scheduling) that are widely used in scheduling such mixed-criticality systems.

Forward-chaining partial-order planning

Abstract: Over the last few years there has been a revival of interest in the idea of least-commitment planning with a number of researchers returning to the partial-order planning approaches of UCPOP and VHPOP. In this paper we explore the potential of a forward-chaining state-based search strategy to support partial-order planning in the solution of temporal-numeric problems. Our planner, POPF, is built on the foundations of grounded forward search, in combination with linear programming to handle continuous linear numeric change. To achieve a partial ordering we delay commitment to ordering decisions, timestamps and the values of numeric parameters, managing sets of constraints as actions are started and ended. In the context of a partially ordered collection of actions, constructing the linear program is complicated and we propose an efficient method for achieving this. Our late-commitment approach achieves flexibility, while benefiting from the informative search control of forward planning, and allows temporal and metric decisions to be made — as is most efficient — by the LP solver rather than by the discrete reasoning of the planner. We compare POPF with the approach of constructing a sequenced plan and then lifting a partial order from it, showing that our approach can offer improvements in terms of makespan, and time to find a solution, in several benchmark domains.

An Evaluation of SMT-Based Schedule Synthesis for Time-Triggered Multi-hop Networks

Abstract: Networks for real-time systems have stringent end-to-end latency and jitter requirements. One cost-efficient way to meet these requirements is the time-triggered communication paradigm which plans the transmission points in time of the frames off-line. This plan prevents contentions of frames on the network and is called a time-triggered schedule (tt-schedule). In general the tt-scheduling is a bin-packing problem, known to be NP-complete, where the complexity is mostly driven by the freedom in topology of the network, its associated hardware restrictions, and application-imposed constraints. Multi-hop networks, in particular, require the synthesis of path-dependent tt-schedules to maintain full determinism of time-triggered communication from sender to receiver. Our experiments using the YICES SMT solver show that the scheduling problem can be solved by YICES out-of-the-box for a few hundred random frame instances on the network. A customized tt-scheduler using YICES as a back-end solver allows to increase this number of frame instances up to tens of thousands. In terms of scheduling quality, the synthesis produces up to ninety percent maximum utilization on a communication link with schedule synthesis times of about half an hour for the biggest examples we have studied. As a nice side-effect the YICES out-of-the-box approach is immediately applicable for the verification of existing (even large-scale) tt-schedules and for debugging more sophisticated tt-schedulers.

A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems

Abstract: Article history: Received 23 January 2010 Received in revised form 23 April 2010 Accepted 26 April 2010 Available online 26 April 2010 During the past two decades, there have been increasing interests on permutation flow shop with different types of objective functions such as minimizing the makespan, the weighted mean flow-time etc. The permutation flow shop is formulated as a mixed integer programming and it is classified as NP-Hard problem. Therefore, a direct solution is not available and metaheuristic approaches need to be used to find the near-optimal solutions. In this paper, we present a new discrete firefly meta-heuristic to minimize the makespan for the permutation flow shop scheduling problem. The results of implementation of the proposed method are compared with other existing ant colony optimization technique. The preliminary results indicate that the new proposed method performs better than the ant colony for some well known benchmark problems. © 2010 Growing Science Ltd. All rights reserved.

Flexible job-shop scheduling with parallel variable neighborhood search algorithm

Abstract: Flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem. FJSP is NP-hard and mainly presents two difficulties. The first one is to assign each operation to a machine out of a set of capable machines, and the second one deals with sequencing the assigned operations on the machines. This paper proposes a parallel variable neighborhood search (PVNS) algorithm that solves the FJSP to minimize makespan time. Parallelization in this algorithm is based on the application of multiple independent searches increasing the exploration in the search space. The proposed PVNS uses various neighborhood structures which carry the responsibility of making changes in assignment and sequencing of operations for generating neighboring solutions. The results obtained from the computational study have shown that the proposed algorithm is a viable and effective approach for the FJSP.

An efficient job-shop scheduling algorithm based on particle swarm optimization

Abstract: The job-shop scheduling problem has attracted many researchers' attention in the past few decades, and many algorithms based on heuristic algorithms, genetic algorithms, and particle swarm optimization algorithms have been presented to solve it, respectively. Unfortunately, their results have not been satisfied at all yet. In this paper, a new hybrid swarm intelligence algorithm consists of particle swarm optimization, simulated annealing technique and multi-type individual enhancement scheme is presented to solve the job-shop scheduling problem. The experimental results show that the new proposed job-shop scheduling algorithm is more robust and efficient than the existing algorithms.

Competitive Two-Agent Scheduling and Its Applications

Abstract: We consider a scheduling environment with m (m ≥ 1) identical machines in parallel and two agents. Agent A is responsible for n1 jobs and has a given objective function with regard to these jobs; agent B is responsible for n2 jobs and has an objective function that may be either the same or different from the one of agent A. The problem is to find a schedule for the n1 + n2 jobs that minimizes the objective of agent A (with regard to his n1 jobs) while keeping the objective of agent B (with regard to his n2 jobs) below or at a fixed level Q. The special case with a single machine has recently been considered in the literature, and a variety of results have been obtained for two-agent models with objectives such as fmax, Σ wjCj, and Σ Uj. In this paper, we generalize these results and solve one of the problems that had remained open. Furthermore, we enlarge the framework for the two-agent scheduling problem by including the total tardiness objective, allowing for preemptions, and considering jobs with different release dates; we consider also identical machines in parallel. We furthermore establish the relationships between two-agent scheduling problems and other areas within the scheduling field, namely rescheduling and scheduling subject to availability constraints.

DAG Scheduling Using a Lookahead Variant of the Heterogeneous Earliest Finish Time Algorithm

Abstract: Among the numerous DAG scheduling heuristics suitable for heterogeneous systems, the Heterogeneous Earliest Finish Time (HEFT) heuristic is known to give good results in short time. In this paper, we propose an improvement of HEFT, where the locally optimal decisions made by the heuristic do not rely on estimates of a single task only, but also look ahead in the schedule and take into account information about the impact of this decision to the children of the task being allocated. Preliminary simulation results indicate that the lookahead variation of HEFT can effectively reduce the makespan of the schedule in most cases without making the algorithm’s execution time prohibitively high.

Clinic scheduling models with overbooking for patients with heterogeneous no-show probabilities

Abstract: Clinical overbooking is intended to reduce the negative impact of patient no-shows on clinic operations and performance. In this paper, we study the clinical scheduling problem with overbooking for heterogeneous patients, i.e. patients who have different no-show probabilities. We consider the objective of maximizing expected profit, which includes revenue from patients and costs associated with patient waiting times and physician overtime. We show that the objective function with homogeneous patients, i.e. patients with the same no-show probability, is multimodular. We also show that this property does not hold when patients are heterogeneous. We identify properties of an optimal schedule with heterogeneous patients and propose a local search algorithm to find local optimal schedules. Then, we extend our results to sequential scheduling and propose two sequential scheduling procedures. Finally, we perform a set of numerical experiments and provide managerial insights for health care practitioners.