A GRASP for Parallel Machine Scheduling with Time Windows

  • Authors:

  • Siwate Rojanasoonthon;Jonathan Bard

  • Affiliations:

  • Graduate Program in Operations Research, Department of Mechanical Engineering, University of Texas, Austin, Texas 78712-1063, USA;Graduate Program in Operations Research, Department of Mechanical Engineering, University of Texas, Austin, Texas 78712-1063, USA

  • Venue:
  • INFORMS Journal on Computing
  • Year:
  • 2005

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Abstract

This paper presents a greedy randomized adaptive search procedure (GRASP) for scheduling n jobs on m nonhomogeneous parallel machines with time windows. An additional feature of the problem is that each job falls into one of 脧聛 priority classes. The objective is to maximize the number of jobs scheduled, where a job in a higher priority class has infinitely more value than a job in a lower priority class. The GRASP consists of two phases. The first phase produces feasible solutions by ranking each job with a greedy function and then selecting one at random from a restricted candidate list. The process is repeated until no more jobs can be scheduled. The second phase seeks a local optimum by searching over a series of neighborhoods defined by job insertions and exchanges. The algorithm is compared to a dynamic-programming heuristic that sequentially schedules the jobs in each priority class. Extensive computational results are presented based on data drawn from an application involving the use of communications relay satellites.