A computational study of constraint satisfaction for multiple capacitated job shop scheduling

We introduce the multiple capacitated job shop scheduling problem as a generalization of the job shop scheduling problem. In this problem machines may process several operations simultaneously. We present an algorithm based on constraint satisfaction techniques to handle the problem effectively. The most important novel feature of our algorithm is the consistency checking. An empirical performance analysis is performed using a well-known set of instances of the job shop scheduling problem and a newly constructed set of instances of the multiple capacitated job shop scheduling problem. We show that our algorithm performs well for both sets of instances

Scheduling with tails and deadlines

This paper discusses scheduling problems that combine tails and deadlines or, equivalently, due dates and deadlines. This approach is illustrated to be of practical interest to strengthen some lower bounds in shop scheduling problems. We show that both deadlines and tails can efficiently be modelled via a non-decreasing cost function of the completion time so that we can use the O(n2) algorithm due to Baker, Lawler, Lenstra and Rinnooy Kan for the 1pmtn,prec,rj,fmax problem to solve 1pmtn,prec,rj,dj,qjCmax. For this problem, we present an algorithm with improved complexity of O(e+nlogn) where e is the number of precedence constraints and we present some extensions of this algorithm to solve two parallel machine problems with unit execution time operations

A computational study of constraint satisfaction for multiple capacitated job shop scheduling

We introduce the multiple capacitated job shop scheduling problem as a generalization of the job shop scheduling problem. In this problem machines may process several operations simultaneously. We present an algorithm based on constraint satisfaction techniques to handle the problem effectively. The most important novel feature of our algorithm is the consistency checking. An empirical performance analysis is performed using a well-known set of instances of the job shop scheduling problem and a newly constructed set of instances of the multiple capacitated job shop scheduling problem. We show that our algorithm performs well for both sets of instances

A computational study of constraint satisfaction for multiple capacitated job shop scheduling

We introduce the multiple capacitated job shop scheduling problem as a generalization of the job shop scheduling problem. In this problem machines may process several operations simultaneously. We present an algorithm based on constraint satisfaction techniques to handle the problem effectively. The most important novel feature of our algorithm is the consistency checking. An empirical performance analysis is performed using a well-known set of instances of the job shop scheduling problem and a newly constructed set of instances of the multiple capacitated job shop scheduling problem. We show that our algorithm performs well for both sets of instances