Metaheuristics for scheduling in industrial and manufacturing applications
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Bibliographic Information
Metaheuristics for scheduling in industrial and manufacturing applications
（Studies in computational intelligence, v. 128）
Springer, c2008
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Description and Table of Contents
Description
During the past decades scheduling has been among the most studied op- mization problemsanditisstillanactiveareaofresearch!Schedulingappears in many areas of science, engineering and industry and takes di?erent forms depending on the restrictions and optimization criteria of the operating en- ronments [8]. For instance, in optimization and computer science, scheduling has been de?ned as "the allocation of tasks to resources over time in order to achieve optimality in one or more objective criteria in an e?cient way" and in production as "production schedule, i. e. , the planning of the production or the sequence of operations according to which jobs pass through machines and is optimal with respect to certain optimization criteria. " Although there is a standardized form of stating any scheduling problem, namely "e?cient allocation ofn jobs onm machines -which can process no more than one activity at a time- with the objective to optimize some - jective function of the job completion times", scheduling is in fact a family of problems. Indeed, several parameters intervene in the problem de?nition: (a) job characteristics (preemptive or not, precedence constraints, release dates, etc. ); (b) resource environment (single vs. parallel machines, un- lated machines, identical or uniform machines, etc. ); (c) optimization criteria (minimize total tardiness, the number of late jobs, makespan, ?owtime, etc. ; maximize resource utilization, etc. ); and, (d) scheduling environment (static vs. dynamic,intheformerthenumberofjobstobeconsideredandtheirready times are available while in the later the number of jobs and their charact- istics change over time).
Table of Contents
Exact, Heuristic and Meta-heuristic Algorithms for Solving Shop Scheduling Problems.- Scatter Search Algorithms for Identical Parallel Machine Scheduling Problems.- On the Effectiveness of Particle Swarm Optimization and Variable Neighborhood Descent for the Continuous Flow-Shop Scheduling Problem.- A Dynamical Ant Colony Optimization with Heuristics for Scheduling Jobs on a Single Machine with a Common Due Date.- Deterministic Search Algorithm for Sequencing and Scheduling.- Sequential and Parallel Variable Neighborhood Search Algorithms for Job Shop Scheduling.- Solving Scheduling Problems by Evolutionary Algorithms for Graph Coloring Problem.- Heuristics and meta-heuristics for lot sizing and scheduling in the soft drinks industry: a comparison study.- Hybrid Heuristic Approaches for Scheduling in Reconfigurable Manufacturing Systems.- A Genetic Algorithm for Railway Scheduling Problems.- Modelling Process and Supply Chain Scheduling Using Hybrid Meta-heuristics.- Combining Simulation and Tabu Search for Oil-derivatives Pipeline Scheduling.- Particle Swarm Scheduling for Work-Flow Applications in Distributed Computing Environments.
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