TY - GEN
T1 - Reinforcement learning approaches for the parallel machines job shop scheduling problem
AU - Martinez Jimenez, Yailen
AU - Tony, Wauters
AU - Nowe, Ann
AU - Patrick, De Causmaecker
AU - Verbeeck, Katja
PY - 2010/5
Y1 - 2010/5
N2 - This paper addresses the application of AI techniques in a practical OR problem, i.e. scheduling. Scheduling is a scientific domain concerning the allocation of tasks to a limited set of resources over time. The goal of scheduling is to maximize (or minimize) different optimization criteria such as the makespan (i.e. the completion time of the last operation in the schedule), the occupation rate of a machine or the total tardiness. In this area, the scientific community usually classifies the problems according to the characteristics of the systems studied. Important characteristics are: the number of machines available (one machine, parallel machines), the shop type (Job Shop, Open Shop or Flow Shop), the job characteristics (such as preemption allowed or not, equal processing times or not) and so on [1]. In this work we present two Reinforcement Learning approaches for the Parallel Machines Job Shop Scheduling Problem (JSP-PM).
AB - This paper addresses the application of AI techniques in a practical OR problem, i.e. scheduling. Scheduling is a scientific domain concerning the allocation of tasks to a limited set of resources over time. The goal of scheduling is to maximize (or minimize) different optimization criteria such as the makespan (i.e. the completion time of the last operation in the schedule), the occupation rate of a machine or the total tardiness. In this area, the scientific community usually classifies the problems according to the characteristics of the systems studied. Important characteristics are: the number of machines available (one machine, parallel machines), the shop type (Job Shop, Open Shop or Flow Shop), the job characteristics (such as preemption allowed or not, equal processing times or not) and so on [1]. In this work we present two Reinforcement Learning approaches for the Parallel Machines Job Shop Scheduling Problem (JSP-PM).
KW - Reinforcement Learning
KW - Parallel Machines Job Shop Scheduling
M3 - Conference paper
T3 - Proceedings of the International conference on interdisciplinary research on technology, education and communication, itec2010
BT - Proceedings of the International conference on interdisciplinary research on technology, education and communication, itec2010
T2 - International conference on interdisciplinary research on technology, education and communication
Y2 - 23 May 2010 through 29 May 2010
ER -