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Computational Intelligence in Scheduling, 2007. SCIS '07. IEEE Symposium on

Date 1-5 April 2007

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Displaying Results 1 - 25 of 43
  • The 2007 IEEE Computational Intelligence in Scheduling Symposium(CISsched)

    Publication Year: 2007 , Page(s): nil1
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  • [Table of contents]

    Publication Year: 2007 , Page(s): nil2 - nil4
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  • A Genetic Algorithm with Injecting Artificial Chromosomes for Single Machine Scheduling Problems

    Publication Year: 2007 , Page(s): 1 - 6
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (331 KB) |  | HTML iconHTML  

    A genetic algorithm with injecting artificial chromosomes was developed for solving the single machine scheduling problems with the objective to minimize the total deviation. Artificial chromosomes are generated according to a probability matrix which was transformed from the gene structure of an elite base. A roulette wheel selection method was applied to generate an artificial chromosome by assigning genes onto each position according to the probability matrix. The higher the probability is, the more possible that the gene will show up in that particular position. By injecting these artificial chromosomes, the genetic algorithm will speed up the convergence of the evolutionary processes. Intensive experimental results indicate that the proposed algorithm is very encouraging and it can improve the solution quality significantly View full abstract»

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  • A New Meta-heuristic Approach for Combinatorial Optimization and Scheduling Problems

    Publication Year: 2007 , Page(s): 7 - 14
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (8100 KB) |  | HTML iconHTML  

    This study presents a new metaheuristic approach that reasonably combines different features of several well-know heuristics. The core component of the proposed algorithm is a simulated annealing that utilizes three types of memories, two short-term memories and one long-term memory. The purpose of the two short-term memories is to guide the search toward good solutions. While the aim of the long term memory is to provide means for the search to escape local optima through increasing the diversification phase in a logical manner. The long-term memory is considered as a population list. In specific circumstances, members of the population might be employed to generate a new population from which a new initial solution for the simulated annealing component is generated. Job shop scheduling problem has been used to test the performance of the proposed method View full abstract»

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  • A New Lower Bound to the Traveling Tournament Problem

    Publication Year: 2007 , Page(s): 15 - 18
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4283 KB) |  | HTML iconHTML  

    Optimization in sports is a field of increasing interest. The traveling tournament problem abstracts certain characteristics of sports scheduling problems. We propose a new method for determining a lower bound to this problem. The new bound improves upon the previously best known lower bound. Numerical results on benchmark instances showed reductions as large as 38.6% in the gaps between lower and upper bounds. View full abstract»

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  • An Ant Based Hyper-heuristic for the Travelling Tournament Problem

    Publication Year: 2007 , Page(s): 19 - 26
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (8311 KB) |  | HTML iconHTML  

    The travelling tournament problem is a challenging sports timetabling problem which is widely believed to be NP-hard. The objective is to establish a feasible double round robin tournament schedule, with minimum travel distances. This paper investigates the application of an ant based hyper-heuristic algorithm for this problem. Ant algorithms, a well known meta-heuristic, have been successfully applied to various problems. Whilst hyper-heuristics are an emerging technology, which operate at a higher level of abstraction than meta-heuristics. This paper presents a framework which employs ant algorithms as a hyper-heuristic. We show that this approach produces good quality solutions for the traveling tournament problem when compared with results from the literature View full abstract»

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  • Test Machine Scheduling and Optimization for z/ OS

    Publication Year: 2007 , Page(s): 27 - 34
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (164 KB) |  | HTML iconHTML  

    We describe a system for solving a complex scheduling problem faced by software product test organizations. Software testers need time on test machines with specific features and configurations to perform the test tasks assigned to them. There is a limited number of machines with any given configuration, and this makes the machines scarce resources. Deadlines are always short. Thus, testers must reserve time on machines. Managing a schedule for a large test organization is a difficult task to perform manually. Requirements change frequently, making the task even more onerous, yet scheduling is done by hand in most teams. Our scheduling system is able to take into account the many and varied constraints and preferences that a team of human users inevitably has View full abstract»

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  • Super 14 Rugby Fixture Scheduling Using a Multi-Objective Evolutionary Algorithm

    Publication Year: 2007 , Page(s): 35 - 42
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (10474 KB) |  | HTML iconHTML  

    Super 14 Rugby is not only a popular game, but also a hugely profitable business. However, determining a schedule for games in the competition is very difficult, as a number of different, often conflicting, factors must be considered. We propose the use of a multi-objective evolutionary algorithm for deciding such a schedule. We detail the technical details needed to apply a multi-objective evolutionary algorithm to this problem and report on experiments that show the effectiveness of this approach. We compare solutions found by our approach with recent fixtures employed by the organising authority; our results showing significant improvements over the existing solutions View full abstract»

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  • An Ant Colony Optimization Approach to the Minimum Tool Switching Instant Problem in Flexible Manufacturing System

    Publication Year: 2007 , Page(s): 43 - 48
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6055 KB) |  | HTML iconHTML  

    Efficient tool management is very important for the productivity in flexible manufacturing systems. This paper proposes an ant colony approach to minimize the number of tool switching instants in flexible manufacturing systems for the first time. The proposed approach is compared to optimal results from the literature, and very promising results are reported. View full abstract»

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  • Solving a Bi-Criteria Permutation Flow Shop Problem Using Immune Algorithm

    Publication Year: 2007 , Page(s): 49 - 56
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (246 KB) |  | HTML iconHTML  

    A flow shop problem as a typical manufacturing challenge has gained wide attention in academic fields. In this paper, we consider a bi-criteria permutation flow shop scheduling problem, in which the weighted mean completion time and the weighted mean tardiness are to be minimized simultaneously. Since a flow shop scheduling problem has been proved to be NP-hard in strong sense, an effective multi-objective immune algorithm (MOIA) is proposed for searching locally Pareto-optimal frontier for the given problem. To prove the efficiency of the proposed algorithm, a number of test problems are solved and the efficiency of the proposed algorithm, based on some comparison metrics, is compared with a distinguished multi-objective genetic algorithm, i.e. SPEA-II. The computational results show that the proposed MOIA performs better than the above genetic algorithm, especially for large-sized problems View full abstract»

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  • Efficient Scheduling Focusing on the Duality of MPL Representation

    Publication Year: 2007 , Page(s): 57 - 64
    Cited by:  Papers (87)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6778 KB) |  | HTML iconHTML  

    A max-plus linear (MPL) representation for describing the behavior of a repetitious execution system with a MIMO-FIFO structure is proposed. A conventional MPL form is required to recalculate the representation matrices by each job when applied to systems whose processing times differ by each job. Approximately twice the calculation volume is required to obtain the earliest and latest times using conventional MPL compared with our proposed MPL representation. This work assigns the state variables to events other than conventional ones, reduces the number of independent system parameters in representation matrices, and improves the form to schedule efficiently, even when applied to systems whose processing times differ by each job. The derived equations are similar to the dual system in modern control theory, which means that the calculation load for scheduling can be reduced remarkably comparing with the conventional method View full abstract»

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  • Global Estimations for Multiprocessor Job-Shop

    Publication Year: 2007 , Page(s): 65 - 71
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6221 KB) |  | HTML iconHTML  

    Classical job-shop scheduling problem (JSP) is one of the heaviest (strongly) NP-hard scheduling problems, which is very difficult to solve in practice. No approximation algorithms with a guaranteed performance exist. We deal with a natural generalization of this problem allowing parallel processors instead of each single processor in JSP, and an arbitrary task graph (without cycles) instead of a serial-parallel task graph in JSP. Parallel processors might be identical, uniform or unrelated. The whole feasible solution space grows drastically compared to JSP. However, as it turned out, parallel processors can also be used to reduce the solution space to a subspace, which is essentially smaller than even the corresponding solution space for JSP. For large problem instances, this space still may remain too big. Here we propose different global estimations which allow us to reduce it further. By applying our bounds to the reduced solution space, a class of exact and approximation algorithms are obtained. We are in the process of the implementation of our reduction algorithm and the bounds. Then we aim to carry out the experimental study comparing the behavior and the efficiency of the proposed bounds in practice View full abstract»

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  • Rolling Partial Rescheduling Driven by Disruptions on Single-machine Based on Genetic Algorithm

    Publication Year: 2007 , Page(s): 72 - 78
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6965 KB) |  | HTML iconHTML  

    This paper discusses large-scale single-machine rescheduling problems with efficiency and stability as bi-criterion, where more than one disruption arises during the execution of an initial schedule. Partial rescheduling (PR), which involves only partial unfinished schedules, is adopted in response to each disruption and forms a PR sub-problem. The remaining unfinished schedule is just right-shifted or not following the solution of PR sub-problem. During the process of schedule execution, a rolling PR strategy is driven by disruption events. Each global rescheduling consisting of two segments of local rescheduling revises the original schedule into a new schedule, which is exactly the next original schedule. Two types of local objective functions are designed for PR sub-problems locating in the process or the terminal of original schedules respectively, where the global information of bi-criterion problems is reflected to an extent. The analytical results demonstrate that each local PR objective is consistent to the global one. For PR sub-problems with such a particular criteria, a genetic algorithm is used to solve it. Extensive computational experiments were performed. Computational results show that the rolling PR can greatly improve schedule stability with a little sacrifice in schedule efficiency and consistently outperforms the rolling right-shift rescheduling. The rolling PR strategy is effective to address large-scale rescheduling problems with more disruptions View full abstract»

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  • Local Rescheduling - A Novel Approach for Efficient Response to Schedule Disruptions

    Publication Year: 2007 , Page(s): 79 - 86
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (257 KB) |  | HTML iconHTML  

    Whenever an unforeseen disturbance occurs during the execution of scheduled operations, rescheduling might be necessary: Beside temporal shifts and the allocation of alternative resources, also potential switches from one process variant to another one have typically to be considered. In realistic scenarios of operational disruption management (DM) the high number of potential options makes the provision of online decision support complex. It is thus necessary to significantly reduce the size of the regarded (search) problems which can for instance be achieved by applying methods of partial rescheduling. However, existing approaches such as affected operations rescheduling (AOR) or matchup scheduling (MUP) focus on production-specific problems and can not be applied to more generic problem classes. To overcome this limitation, we introduce a novel approach to partial rescheduling in this paper: local rescheduling (LRS) is based on the incremental extension of a time window which is regarded for potential schedule modifications. We discuss how this time window can be initialized, extended and used for rescheduling. Moreover, we illustrate the superior performance of LRS in comparison with full rescheduling and MUP View full abstract»

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  • A Hybrid GA-based Scheduling Algorithm for Heterogeneous Computing Environments

    Publication Year: 2007 , Page(s): 87 - 92
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (131 KB) |  | HTML iconHTML  

    We design a hybrid algorithm to schedule the execution of a group of dependent tasks for heterogeneous computing environments. The algorithm consists of two elements: a genetic algorithm (GA) to map tasks to processors, and a heuristic-based approach to assign the execution order of tasks. This algorithm takes advantage of both the exploration power of GA and the heuristics embedded in the scheduling problem, so it can effectively reduce the search space while not sacrificing the search quality. The experiments show that this algorithm performs consistently better than heterogeneous earliest-finish-time (HEFT) without incurring much computational cost. Multiple runs of the algorithm can further improve the search result. View full abstract»

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  • A GA based Intelligent Traffic Signal Scheduling Model

    Publication Year: 2007 , Page(s): 93 - 97
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4665 KB) |  | HTML iconHTML  

    A GA based intelligent traffic signal scheduling model is proposed in this paper. There are two layers in this model. The upper layer decides which direction of intersection should have the priority to go. The intersection signal controller in the lower layer will execute its instruction. The upper layer has to make a decision in a very short time limit, or the signal in the lower layer will response to a wrong traffic pattern. It is as if what the fixed time signal scheduling strategy did before. This paper shows this idea through a simulation model. Our simulation results show that it saves 71 seconds from the fixed time signal scheduling strategy. The lost time might be even higher in our real world. If one intersection is jam-packed, our simulation result also shows that all cars will be redirected within a short time. This model can bring the travelers a better experience of traffic facility for keeping their transportation efficient View full abstract»

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  • A Genetic Algorithm with Dominance Properties for Single Machine Scheduling Problems

    Publication Year: 2007 , Page(s): 98 - 104
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (324 KB) |  | HTML iconHTML  

    This paper considers a single machine scheduling problem in which n jobs are to be processed and a machine setup time is required when the machine switches jobs from one to the other. All jobs have a common due date that has been predetermined using the median of the set of sequenced jobs. The objective is to find an optimal sequence of the set of n jobs to minimize the sum of the job's setups and the cost of tardy or early jobs related to the common due date. Dominance properties are developed according to the sequence swapping of two neighborhood jobs. These dominance properties are further embedded in the simple genetic algorithm to improve the efficiency and effectiveness of the global searching procedure. Analytical results in benchmark problems are presented and computational algorithms are developed View full abstract»

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  • Scheduling through Group Decision Support with Adaptive Hypermedia

    Publication Year: 2007 , Page(s): 105 - 112
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (9472 KB) |  | HTML iconHTML  

    This paper aims is to present an ongoing project which proposes a new methodology and architecture for collaborative scheduling through adaptive hypermedia and group decision support. The approach to the problem is new in a sense that the techniques of user modelling, adaptive system and group decision support will be used and adapted to the scheduling process in manufacturing environments. A scheduling module outputs a set of candidate scheduling solutions, each generated based on specific criteria and/or by a particular method. Scheduling is a multi-criteria decision problem in practice where different schedulers may agree on key objectives but differ greatly on their relative importance in a particular situation. The selection of a scheduling solution is achieved through the interaction among scheduling actors which is supported by a group decision support module considering the different necessities and the diversity of information source of each group or individual user View full abstract»

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  • Greedy Scheduling with Complex Obejectives

    Publication Year: 2007 , Page(s): 113 - 120
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (9735 KB) |  | HTML iconHTML  

    We present a methodology for automatically generating an online scheduling process for an arbitrary objective with the help of evolution strategies. The scheduling problem comprises independent parallel jobs and multiple identical machines and occurs in many real massively parallel processing systems. The system owner defines the objective that may consider job waiting times and priorities of user groups. Our scheduling process is a variant of the simple and commonly used greedy scheduling algorithm in combination with a repeated sorting of the waiting queue. This sorting uses a criterion whose parameters are evolutionary optimized. We evaluate our new scheduling process with real workload data and compare it to the best offline solutions and to the online results of the standard EASY backfill algorithm. To this end, we partition the user of the workloads into groups and select an exemplary objective that prioritizes some of those groups over others View full abstract»

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  • A starting-time-based approach to production scheduling with Particle Swarm Optimization

    Publication Year: 2007 , Page(s): 121 - 128
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (173 KB) |  | HTML iconHTML  

    This paper provides a generic formulation for the complex scheduling problems of Optimatix, a South African company specializing in supply chain optimization. To address the complex requirements of the proposed problem, various additional constraints were added to the classical job shop scheduling problem. These include production downtime, scheduled maintenance, machine breakdowns, sequence-dependent set-up times, release dates and multiple predecessors per job. Differentiation between primary resources (machines) and auxiliary resources (labour, tools and jigs) were also achieved. Furthermore, this paper applies particle swarm optimization (PSO), a stochastic population based optimization technique originating from the study of social behavior of birds and fish, to the proposed problem. Apart from the significance of the paper in that the proposed problem has not been addressed before, the benefit of an improved production schedule can be generalized to include cost reduction, customer satisfaction, improved profitability and overall competitive advantage View full abstract»

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  • Modelling Alternatives in Temporal Networks

    Publication Year: 2007 , Page(s): 129 - 136
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (9732 KB) |  | HTML iconHTML  

    Temporal networks play an important role in solving planning problems and they are also used, though not as frequently, when solving scheduling problems. In this paper we propose an extension of temporal networks by parallel and alternative branching. This extension supports modelling of alternative paths in the network; in particular, it is motivated by modelling alternative process routes in manufacturing scheduling. We show that deciding which nodes can be consistently included in this extended temporal network is an NP-complete problem. To simplify solving this problem, we propose a pre-processing step whose goal is to identify classes of equivalent nodes. The ideas are presented using precedence networks, but we also show how they can be extended to simple temporal networks View full abstract»

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  • Scheduling Coupled-Tasks on a Single Machine

    Publication Year: 2007 , Page(s): 137 - 142
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    In this paper, we consider the coupled-task scheduling problem, to schedule n jobs on a single machine. Each job consists of two coupled tasks which have to be processed in a predetermined order and at exactly a specified interval apart. The objective is to minimize the makespan. The problem was shown to be NP-hard in the strong sense even for some special cases. We analyze some heuristics with worst-case bounds for some NP-hard cases. In addition, we present a tabu search meta-heuristic for solving the general case. Computational results show that the meta-heuristic is efficient to solve the problem in terms of solution quality and running time View full abstract»

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  • A Genetic Algorithm for Scheduling Parallel Non-identical Batch Processing Machines

    Publication Year: 2007 , Page(s): 143 - 150
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (197 KB) |  | HTML iconHTML  

    In this paper, we study the scheduling problem of minimizing makespan on parallel non-identical batch processing machines. We formulate the scheduling problem into an integer programming model. Due to the difficulty of the problem, it is hard to solve the problem with standard mathematical programming software. We propose a genetic algorithm based on random keys encoding to address this problem. Computational results show that this genetic algorithm consistently finds a solution in a reasonable amount of computation time View full abstract»

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  • Refinery Scheduling Optimization using Genetic Algorithms and Cooperative Coevolution

    Publication Year: 2007 , Page(s): 151 - 158
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (710 KB) |  | HTML iconHTML  

    Oil refineries are one of the most important examples of multiproduct continuous plants, that is, a continuous processing system that generates a number of products simultaneously. A refinery processes various crude oil types and produces a wide range of products. It is a complex optimization problem, mainly due to the number of different tasks involved and different objective criteria. In addition, some of the tasks have precedence constraints that require other tasks to be scheduled first. In this paper the refinery scheduling problem is addressed using genetic algorithms and cooperative coevolution. A simple refinery, with commonly found types of equipments, tasks and constraints of a real refinery, was created. Three test scenarios were designed with different sizes, demands and constraints. In all of them, the results obtained were far better than the ones obtained through random search View full abstract»

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  • Multi-Objective Semiconductor Manufacturing Scheduling: A Random Keys Implementation of NSGA-II

    Publication Year: 2007 , Page(s): 159 - 164
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6371 KB) |  | HTML iconHTML  

    We examine a complex, multi-objective semiconductor manufacturing scheduling problem involving two batch processing steps linked by a timer constraint. This constraint requires that any job completing the first processing step must be started on the succeeding second machine within some allowable time window; otherwise, the job must repeat its processing on the first step. We present a random keys implementation of NSGA-II (nondominated sorting genetic algorithm) for our problem of interest and investigate the efficacy of different batching policies in terms of the number of approximate efficient solutions that are produced by NSGA-II over a wide range of experimental problem instances. Experimental results suggest a full batch policy can produce superior solutions as compared to greedy batching policies under the experimental conditions examined View full abstract»

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