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Showing papers on "Job shop scheduling published in 2004"


Book
27 Apr 2004
TL;DR: This book discusses Real-Time Scheduling Problems, Scheduling Models, Stochastic Scheduling, and Online Deterministic Scheduling as well as some basic Scheduling Algorithms and Complexity.
Abstract: Introduction Introduction and Notation, Joseph Y-T. Leung A Tutorial on Complexity, Joseph Y-T. Leung Some Basic Scheduling Algorithms, Joseph Y-T. Leung Classical Scheduling Problems Elimination Rules for Job-shop Scheduling Problem: Overview and Extensions, Jacques Carlier, Laurent Peridy, Eric Pinson, and David Rivreau Flexible Hybrid Flowshops, George Vairaktarakis Open Shop Scheduling, Teofilo F. Gonzalez Cycle Shop Scheduling, Vadim G. Timkovsky Reducibility among Scheduling Classes, Vadim G. Timkovsky Parallel Scheduling for Early Completion, Bo Chen Minimizing the Maximum Lateness, Hans Kellerer Approximation Algorithms for Minimizing Average Weighted Completion Time, Chandra Chekuri and Sanjeev Khanna Minimizing the Number of Tardy Jobs, Marjan van den Akker and Han Hoogeveen Branch-and-Bound Algorithms for Total Weighted Tardiness, Antoino Jouglet, Philippe Baptiste, and Jacques Carlier Scheduling Equal Processing Time Jobs, Philippe Baptiste and Peter Brucker Online Scheduling, Kirk Pruhs, Jiri Sgall, and Eric Torng Convex Quadratic Relaxations in Scheduling, Jay Sethuraman Other Scheduling Models The Master/Slave Scheduling Model, Sartaj Sahni and George Vairaktarakis Scheduling in Bluetooth Networks, Yong Man Kim and Ten H. Lai Fair Sequences, Wieslaw Kubiak Due-Date Quotation Models and Algorithms, Philip Kaminsky and Dorit Hochbaum Scheduling with Due-Date Assignment, Valery S. Gordon, Jean-Marie Proth, and Vitaly A. Strusevich Machine Scheduling with Availability Constraints, Chung-Yee Lee Scheduling with Discrete Resource Constraints, J. B_lazewicz, N. Brauner, and G. Finke Scheduling with Resource Constraints-Continuous Resources, Joanna J'ozefowska and Jan Weglarz Scheduling Parallel Tasks-Algorithms and Complexity, M. Drozdowski Scheduling Parallel Tasks Approximation Algorithms, Pierre-Franc' ois Dutot, Gr'egory Mouni'e, and Denis Trystram Real-Time Scheduling The Pinwheel: A Real-Time Scheduling Problem, Deji Chen and Aloysivs Mok Scheduling Real-Time Tasks: Algorithms and Complexity, Sanjay Baruah and Joael Goossens Real Time Synchronization Protocols, Lui Sha and Marco Caccamo Fair Scheduling of Real-Time Tasks on Multiprocessors, James Anderson, Philip Holman, and Anand Srinivasan A Categorization of Real-Time Multiprocessor Scheduling Problems and Algorithms, John Carpenter, Shelby Funk, Philip Holman, Anand Srinivasan, James Anderson, and Sanjoy Baruah Approximation Algorithms for Scheduling Time-Critical Jobs on Multiprocessor System, Sudarshan K. Dhall Scheduling Overloaded Real-Time Systems with Competitive/Worst Case Guarantees, Gilad Koren and Dennis Shasha Minimizing TotalWeighted Error for Imprecise Computation Tasks and Related Problems, Joseph Y-T. Leung Dual Criteria Optimization Problems for Imprecise Computation Tasks, Kevin I-J Ho Periodic Reward-Based Scheduling and Its Application to Power-Aware Real-Time Systems, Hakan Aydin, Rami Melhem, and Daniel Mosse Routing Real-Time Messages on Networks, G. Young Stochastic Scheduling and Queueing Networks Offline Deterministic Scheduling, Stochastic Scheduling, and Online Deterministic Scheduling: A Comparative Overview, Michael Pinedo Stochastic Scheduling with Earliness and Tardiness Penalties, Xiaoqiang Cai and Xian Zhou Developments in Queueing Networks with Tractable Solutions, Xiuli Chao Scheduling in Secondary Storage Systems, Alexander Thomasian Selfish Routing on the Internet, Artur Czumaj Applications Scheduling of Flexible Resources in Professional Service Firms, Yalcin Akcay, Anantaram Balakrishnan, and Susan H. Xu Novel Metaheuristic Approaches to Nurse Rostering Problems in Belgian Hospitals, Edmund Kieran Burke, Patrick De Causmaecker and Greet Vanden Berghe University Timetabling, Sanja Petrovic and Edmund Burke Adapting the GATES Architecture to Scheduling Faculty, R. P. Brazile and K. M. Swigger Constraint Programming for Scheduling, John J. Kanet, Sanjay L. Ahire, and Michael F. Gorman Batch Production Scheduling in the Process Industries, Karsten Gentner, Klaus Neumann, Christoph Schwindt, and Norbert Trautmann A Composite Very-Large-Scale Neighborhood Search Algorithm for the Vehicle Routing Problem, Richa Agarwal, Ravinder K. Ahuja, Gilbert Laporte, and Zuo-Jun "Max" Shen Scheduling Problems in the Airline Industry, Xiangtong Qi, Jian Yang and Gang Yu Bus and Train Driver Scheduling, Raymond S. K. Kwan Sports Scheduling, Kelly Easton, George Nemhauser, and Michael Trick Index

1,003 citations


Journal ArticleDOI
TL;DR: An overview of developments in the scheduling of multiproduct/multipurpose batch and continuous processes is presented and various continuous-time models have been proposed in the literature and their strengths and limitations are examined.

614 citations


Journal ArticleDOI
TL;DR: This work presents a comparison of 25 methods, ranging from the classical Johnson's algorithm or dispatching rules to the most recent metaheuristics, including tabu search, simulated annealing, genetic algorithms, iterated local search and hybrid techniques, for the well-known permutation flowshop problem with the makespan criterion.

544 citations


Journal ArticleDOI
TL;DR: This study proposes a branch and bound (B & B) method to obtain the optimal solution of the QC scheduling problem and a heuristic search algorithm, called greedy randomized adaptive search procedure (GRASP), to overcome the computational difficulty of the B & B method.

485 citations


Proceedings ArticleDOI
01 Jan 2004
TL;DR: This work proposes a dual optimization based approach through which the rate control problem and the scheduling problem can be decomposed and demonstrates via both analytical and numerical results that the proposed mechanism can fully utilize the capacity of the network, maintain fairness, and improve the quality of service to the users.
Abstract: We study the joint problem of allocating data rates and finding a stabilizing scheduling policy in a multihop wireless network. We propose a dual optimization based approach through which the rate control problem and the scheduling problem can be decomposed. We demonstrate via both analytical and numerical results that the proposed mechanism can fully utilize the capacity of the network, maintain fairness, and improve the quality of service to the users.

444 citations


Journal ArticleDOI
TL;DR: A comparison of solutions yielded by the proposed ant-colony algorithms with the best heuristic solutions known for the benchmark problems, as published in an extensive study by Liu and Reeves is carried out.

441 citations


Journal ArticleDOI
TL;DR: A new solution to the thermal unit-commitment (UC) problem based on an integer-coded genetic algorithm (GA) that achieves significant chromosome size reduction compared to the usual binary coding.
Abstract: This paper presents a new solution to the thermal unit-commitment (UC) problem based on an integer-coded genetic algorithm (GA). The GA chromosome consists of a sequence of alternating sign integer numbers representing the sequence of operation/reservation times of the generating units. The proposed coding achieves significant chromosome size reduction compared to the usual binary coding. As a result, algorithm robustness and execution time are improved. In addition, generating unit minimum up and minimum downtime constraints are directly coded in the chromosome, thus avoiding the use of many penalty functions that usually distort the search space. Test results with systems of up to 100 units and 24-h scheduling horizon are presented.

403 citations


Journal ArticleDOI
TL;DR: This paper focuses on a runtime system for guarantee-based scheduling of hard real-time tasks, formulate the scheduling problem for the 1D and 2D resource models and present two heuristics, the horizon and the stuffing technique, to tackle it.
Abstract: Today's reconfigurable hardware devices have huge densities and are partially reconfigurable, allowing for the configuration and execution of hardware tasks in a true multitasking manner. This makes reconfigurable platforms an ideal target for many modern embedded systems that combine high computation demands with dynamic task sets. A rather new line of research is engaged in the construction of operating systems for reconfigurable embedded platforms. Such an operating system provides a minimal programming model and a runtime system. The runtime system performs online task and resource management. In this paper, we first discuss design issues for reconfigurable hardware operating systems. Then, we focus on a runtime system for guarantee-based scheduling of hard real-time tasks. We formulate the scheduling problem for the 1D and 2D resource models and present two heuristics, the horizon and the stuffing technique, to tackle it. Simulation experiments conducted with synthetic workloads evaluate the performance and the runtime efficiency of the proposed schedulers. The scheduling performance for the 1D resource model is strongly dependent on the aspect ratios of the tasks. Compared to the 1D model, the 2D resource model is clearly superior. Finally, the runtime overhead of the scheduling algorithms is shown to be acceptably low.

302 citations


Journal ArticleDOI
TL;DR: This paper deals with a classic flow-shop scheduling problem with makespan criterion and proposes a new very fast local search procedure based on a tabu search approach.

295 citations


Journal ArticleDOI
TL;DR: Experimental results show that the artificial immune system algorithm is an effective and efficient method for solving HFS problems.

264 citations


Journal ArticleDOI
Annie S. Wu1, Han Yu1, S. Jin1, Kuo-Chi Lin, Guy A. Schiavone 
TL;DR: A genetic algorithm approach to the problem of task scheduling for multiprocessor systems that requires minimal problem specific information and no problem specific operators or repair mechanisms and is able to automatically adapt to changing targets.
Abstract: We have developed a genetic algorithm (GA) approach to the problem of task scheduling for multiprocessor systems. Our approach requires minimal problem specific information and no problem specific operators or repair mechanisms. Key features of our system include a flexible, adaptive problem representation and an incremental fitness function. Comparison with traditional scheduling methods indicates that the GA is competitive in terms of solution quality if it has sufficient resources to perform its search. Studies in a nonstationary environment show the GA is able to automatically adapt to changing targets.

Journal ArticleDOI
TL;DR: This paper examines scheduling in flexible flow lines with sequence-dependent setup times to minimize makespan, and finds an application of the Random Keys Genetic Algorithm to be very effective for the problems examined.

Journal ArticleDOI
TL;DR: This paper reviews and classify the main contributions regarding this topic and discusses future research issues on makespan minimization in permutation flow-shop scheduling.
Abstract: Makespan minimization in permutation flow-shop scheduling is an operations research topic that has been intensively addressed during the last 40 years. Since the problem is known to be NP-hard for ...

Journal ArticleDOI
TL;DR: Strong NP-hardness of the makespan minimization problem for two different models of job processing time is proved for makespan, total completion time and total weighted completion time.
Abstract: The paper is devoted to some single machine scheduling problems, where job processing times are defined by functions dependent on their positions in the sequence. It is assumed that each job is available for processing at its ready time. We prove some properties of the special cases of the problems for the following optimization criteria: makespan, total completion time and total weighted completion time. We prove strong NP-hardness of the makespan minimization problem for two different models of job processing time. The reductions are done from the well-known 3-Partition Problem. In order to solve the makespan minimization problems, we suggest the Earliest Ready Date algorithms, for which the worst-case ratios are calculated. We also prove that the makespan minimization problem with job ready times is equivalent to the maximum lateness minimization problem.

Journal ArticleDOI
TL;DR: An ant colony optimization approach that uses a strong non-delay guidance for constructing solutions and which employs black-box local search procedures to improve the constructed solutions is developed, which is the first competitive ant colonies optimization approach for job shop scheduling instances.
Abstract: We deal with the application of ant colony optimization to group shop scheduling, which is a general shop scheduling problem that includes, among others, the open shop scheduling problem and the job shop scheduling problem as special cases. The contributions of this paper are twofold. First, we propose a neighborhood structure for this problem by extending the well-known neighborhood structure derived by Nowicki and Smutnicki for the job shop scheduling problem. Then, we develop an ant colony optimization approach, which uses a strong non-delay guidance for constructing solutions and which employs black-box local search procedures to improve the constructed solutions. We compare this algorithm to an adaptation of the tabu search by Nowicki and Smutnicki to group shop scheduling. Despite its general nature, our algorithm works particularly well when applied to open shop scheduling instances, where it improves the best known solutions for 15 of the 28 tested instances. Moreover, our algorithm is the first competitive ant colony optimization approach for job shop scheduling instances.

Proceedings ArticleDOI
19 Jun 2004
TL;DR: A heuristic rule, the smallest position value (SPV) rule, is developed to enable the continuous particle swarm optimization algorithm to be applied to all classes of sequencing problems, which are NP-hard in the literature.
Abstract: In This work we present a particle swarm optimization algorithm to solve the single machine total weighted tardiness problem. A heuristic rule, the smallest position value (SPV) rule, is developed to enable the continuous particle swarm optimization algorithm to be applied to all classes of sequencing problems, which are NP-hard in the literature. A simple but very efficient local search method is embedded in the particle swarm optimization algorithm. The computational results show that the particle swarm algorithm is able to find the optimal and best-known solutions on all instances of widely used benchmarks from the OR library.

Journal ArticleDOI
TL;DR: This research proposes a simulated annealing approach to minimize makespan for a single batch-processing machine and outperforms CPLEX on all the instances.

Journal ArticleDOI
TL;DR: A rescheduling methodology is proposed that uses a multiobjective performance measures that contain both efficiency and stability measures and is tested on a simulated job shop to determine the impact of the key parameters on the performance measures.

Journal ArticleDOI
TL;DR: A new heuristic algorithm for solving the bi-objective vehicle routing and scheduling problem with time windows is presented and has been applied to several benchmark problems.

Journal ArticleDOI
TL;DR: Packing integer programs capture a core problem that directly relates to both vector scheduling and vector bin packing, namely, the problem of packing a maximum number of vectors in a single bin of unit height.
Abstract: We study the approximability of multidimensional generalizations of three classical packing problems: multiprocessor scheduling, bin packing, and the knapsack problem. Specifically, we study the vector scheduling problem, its dual problem, namely, the vector bin packing problem, and a class of packing integer programs. The vector scheduling problem is to schedule n d-dimensional tasks on m machines such that the maximum load over all dimensions and all machines is minimized. The vector bin packing problem, on the other hand, seeks to minimize the number of bins needed to schedule all n tasks such that the maximum load on any dimension across all bins is bounded by a fixed quantity, say, 1. Such problems naturally arise when scheduling tasks that have multiple resource requirements. Finally, packing integer programs capture a core problem that directly relates to both vector scheduling and vector bin packing, namely, the problem of packing a maximum number of vectors in a single bin of unit height. We obtain a variety of new algorithmic as well as inapproximability results for these three problems.

Journal ArticleDOI
TL;DR: This paper is the first to apply ACS for the n/m/P/Cmax problem, an NP-hard sequencing problem which is used to find a processing order of n different jobs to be processed on m machines in the same sequence with minimizing the makespan.

Journal Article
TL;DR: This paper describes a procedure that uses particle swarm optimization (PSO) combined with the Lagrangian Relaxation (LR) framework to solve a power-generator scheduling problem known as the unit commitment problem (UCP).
Abstract: This paper describes a procedure that uses particle swarm optimization (PSO) combined with the Lagrangian Relaxation (LR) framework to solve a power-generator scheduling problem known as the unit commitment problem (UCP). The UCP consists of determining the schedule and production amount of generating units within a power system subject to operating constraints. The LR framework is applied to relax coupling constraints of the optimization problem. Thus, the UCP is separated into independent optimization functions for each generating unit. Each of these sub-problems is solved using Dynamic Programming (DP). PSO is used to evolve the Lagrangian multipliers. PSO is a population based search technique, which belongs to the swarm intelligence paradigm that is motivated by the simulation of social behavior to manipulate individuals towards better solution areas. The performance of the PSO-LR procedure is compared with results of other algorithms in the literature used to solve the UCP. The comparison shows that the PSO-LR approach is efficient in terms of computational time while providing good solutions.

Journal ArticleDOI
TL;DR: It is shown that a heuristic reduction of the search space can help the algorithm to find better solutions in a shorter computation time.

Journal ArticleDOI
TL;DR: In this paper, employee tour scheduling literature published since 1990 is reviewed and classified to identify broad classifications, present typical mathematical models, compare the different methods, and identify future research directions.
Abstract: The employee tour scheduling problem involves the determination of both work hours of the day and workdays of the week for each employee. This problem has proven difficult to solve optimally due to its large size and pure integer nature. During the last decade, numerous approaches for modeling and solving this problem have been proposed. In this paper, employee tour scheduling literature published since 1990 is reviewed and classified. Solution techniques are classified into ten categories: (1) manual solution, (2) integer programming, (3) implicit modeling, (4) decomposition, (5) goal programming, (6) working set generation, (7) LP-based solution, (8) construction and improvement, (9) metaheuristics, and (10) other methods. The objective is to identify broad classifications, present typical mathematical models, compare the different methods, and identify future research directions.

Journal ArticleDOI
TL;DR: The new methods outperform the NEH algorithm, currently the best constructive heuristic for this problem, in problems with up to 500 jobs and 20 machines.

Book
01 Feb 2004
TL;DR: This work considers the two-machine open shop and two- machine flow shop scheduling problems in which each machine has to be maintained exactly once during the planning period, and the duration of each of these intervals depends on its start time.
Abstract: We consider the two-machine open shop and two-machine flow shop scheduling problems in which each machine has to be maintained exactly once during the planning period, and the duration of each of these intervals depends on its start time. The objective is to minimize the maximum completion time of all activities to be scheduled. We resolve complexity and approximability issues of these problems. The open shop problem is shown to be polynomially solvable for quite general functions defining the length of the maintenance intervals. By contrast, the flow shop problem is proved binary NP-hard and pseudopolynomially solvable by dynamic programming. We also present a fully polynomial approximation scheme and a fast 3/2-approximation algorithm.

10 Nov 2004
TL;DR: This thesis is an in-depth journey through the ACO metaheuristic, during which it is tried to get a clear understanding of its potentialities, limits, and relationships with other frameworks and with its biological background, and identified in dynamic problems in telecommunication networks the most appropriate domain of application for theACO ideas.
Abstract: In ant societies, and, more in general, in insect societies, the activities of the individuals, as well as of the society as a whole, are not regulated by any explicit form of centralized control On the other hand, adaptive and robust behaviors transcending the behavioral repertoire of the single individual can be easily observed at society level These complex global behaviors are the result of self-organizing dynamics driven by local interactions and communications among a number of relatively simple individualsThe simultaneous presence of these and other fascinating and unique characteristics have made ant societies an attractive and inspiring model for building new algorithms and new multi-agent systems In the last decade, ant societies have been taken as a reference for an ever growing body of scientific work, mostly in the fields of robotics, operations research, and telecommunicationsAmong the different works inspired by ant colonies, the Ant Colony Optimization metaheuristic (ACO) is probably the most successful and popular one The ACO metaheuristic is a multi-agent framework for combinatorial optimization whose main components are: a set of ant-like agents, the use of memory and of stochastic decisions, and strategies of collective and distributed learningIt finds its roots in the experimental observation of a specific foraging behavior of some ant colonies that, under appropriate conditions, are able to select the shortest path among few possible paths connecting their nest to a food site The pheromone, a volatile chemical substance laid on the ground by the ants while walking and affecting in turn their moving decisions according to its local intensity, is the mediator of this behaviorAll the elements playing an essential role in the ant colony foraging behavior were understood, thoroughly reverse-engineered and put to work to solve problems of combinatorial optimization by Marco Dorigo and his co-workers at the beginning of the 1990'sFrom that moment on it has been a flourishing of new combinatorial optimization algorithms designed after the first algorithms of Dorigo's et al, and of related scientific eventsIn 1999 the ACO metaheuristic was defined by Dorigo, Di Caro and Gambardella with the purpose of providing a common framework for describing and analyzing all these algorithms inspired by the same ant colony behavior and by the same common process of reverse-engineering of this behavior Therefore, the ACO metaheuristic was defined a posteriori, as the result of a synthesis effort effectuated on the study of the characteristics of all these ant-inspired algorithms and on the abstraction of their common traitsThe ACO's synthesis was also motivated by the usually good performance shown by the algorithms (eg, for several important combinatorial problems like the quadratic assignment, vehicle routing and job shop scheduling, ACO implementations have outperformed state-of-the-art algorithms)The definition and study of the ACO metaheuristic is one of the two fundamental goals of the thesis The other one, strictly related to this former one, consists in the design, implementation, and testing of ACO instances for problems of adaptive routing in telecommunication networksThis thesis is an in-depth journey through the ACO metaheuristic, during which we have (re)defined ACO and tried to get a clear understanding of its potentialities, limits, and relationships with other frameworks and with its biological background The thesis takes into account all the developments that have followed the original 1999's definition, and provides a formal and comprehensive systematization of the subject, as well as an up-to-date and quite comprehensive review of current applications We have also identified in dynamic problems in telecommunication networks the most appropriate domain of application for the ACO ideas According to this understanding, in the most applicative part of the thesis we have focused on problems of adaptive routing in networks and we have developed and tested four new algorithmsAdopting an original point of view with respect to the way ACO was firstly defined (but maintaining full conceptual and terminological consistency), ACO is here defined and mainly discussed in the terms of sequential decision processes and Monte Carlo sampling and learningMore precisely, ACO is characterized as a policy search strategy aimed at learning the distributed parameters (called pheromone variables in accordance with the biological metaphor) of the stochastic decision policy which is used by so-called ant agents to generate solutions Each ant represents in practice an independent sequential decision process aimed at constructing a possibly feasible solution for the optimization problem at hand by using only information local to the decision stepAnts are repeatedly and concurrently generated in order to sample the solution set according to the current policy The outcomes of the generated solutions are used to partially evaluate the current policy, spot the most promising search areas, and update the policy parameters in order to possibly focus the search in those promising areas while keeping a satisfactory level of overall explorationThis way of looking at ACO has facilitated to disclose the strict relationships between ACO and other well-known frameworks, like dynamic programming, Markov and non-Markov decision processes, and reinforcement learning In turn, this has favored reasoning on the general properties of ACO in terms of amount of complete state information which is used by the ACO's ants to take optimized decisions and to encode in pheromone variables memory of both the decisions that belonged to the sampled solutions and their qualityThe ACO's biological context of inspiration is fully acknowledged in the thesis We report with extensive discussions on the shortest path behaviors of ant colonies and on the identification and analysis of the few nonlinear dynamics that are at the very core of self-organized behaviors in both the ants and other societal organizations We discuss these dynamics in the general framework of stigmergic modeling, based on asynchronous environment-mediated communication protocols, and (pheromone) variables priming coordinated responses of a number of ``cheap' and concurrent agentsThe second half of the thesis is devoted to the study of the application of ACO to problems of online routing in telecommunication networks This class of problems has been identified in the thesis as the most appropriate for the application of the multi-agent, distributed, and adaptive nature of the ACO architectureFour novel ACO algorithms for problems of adaptive routing in telecommunication networks are throughly described The four algorithms cover a wide spectrum of possible types of network: two of them deliver best-effort traffic in wired IP networks, one is intended for quality-of-service (QoS) traffic in ATM networks, and the fourth is for best-effort traffic in mobile ad hoc networksThe two algorithms for wired IP networks have been extensively tested by simulation studies and compared to state-of-the-art algorithms for a wide set of reference scenarios The algorithm for mobile ad hoc networks is still under development, but quite extensive results and comparisons with a popular state-of-the-art algorithm are reported No results are reported for the algorithm for QoS, which has not been fully tested The observed experimental performance is excellent, especially for the case of wired IP networks: our algorithms always perform comparably or much better than the state-of-the-art competitorsIn the thesis we try to understand the rationale behind the brilliant performance obtained and the good level of popularity reached by our algorithms More in general, we discuss the reasons of the general efficacy of the ACO approach for network routing problems compared to the characteristics of more classical approaches Moving further, we also informally define Ant Colony Routing (ACR), a multi-agent framework explicitly integrating learning components into the ACO's design in order to define a general and in a sense futuristic architecture for autonomic network controlMost of the material of the thesis comes from a re-elaboration of material co-authored and published in a number of books, journal papers, conference proceedings, and technical reports The detailed list of references is provided in the Introduction

Journal ArticleDOI
TL;DR: This research attempts to minimize total weighted tardiness on parallel batch machines with incompatible job families by proposing two different versions of a genetic algorithm (GA), each consisting of three different phases, and shows that algorithms of the first version of the GA outperform traditional dispatching rules with respect to solution quality and computation time.
Abstract: This research is motivated by a scheduling problem found in the diffusion and oxidation areas of semiconductor wafer fabrication, where the machines can be modelled as parallel batch processors. We attempt to minimize total weighted tardiness on parallel batch machines with incompatible job families. Given that the problem is NP-hard, we propose two different versions of a genetic algorithm (GA), each consisting of three different phases. The first version forms fixed batches, then assigns batches to the machines using a GA, and finally sequences the batches on individual machines. The second version assigns jobs to machines using a GA, then forms batches on each machine for the jobs assigned to it, and finally sequences these batches. Heuristics are used for the batching phase and the sequencing phase. For both these versions an additional fourth phase can be included wherein the sequenced batches are modified using pairwise swapping techniques. Using stochastically generated test data we show that algorithms of the first version of the GA outperform (1) traditional dispatching rules with respect to solution quality and (2) the algorithms of the second version with respect to both solution quality and computation time.

Journal ArticleDOI
TL;DR: It is shown that the efficiency of GAs in solving a flowshop problem can be improved significantly by tailoring the various GA operators to suit the structure of the problem by presenting empirical evidence via extensive simulation studies supported by statistical tests of improvement in efficiency.

Journal ArticleDOI
TL;DR: A resource allocation model that protects a given baseline schedule against activity duration variability is presented and a branch-and-bound algorithm is developed that solves the proposed resource allocation problem.
Abstract: The majority of resource-constrained project scheduling efforts assume perfect information about the scheduling problem to be solved and a static deterministic environment within which the precomputed baseline schedule is executed. In reality, project activities are subject to considerable uncertainty, which generally leads to numerous schedule disruptions. In this paper, we present a resource allocation model that protects a given baseline schedule against activity duration variability. A branch-and-bound algorithm is developed that solves the proposed resource allocation problem. We report on computational results obtained on a set of benchmark problems.