The resource constraint project scheduling problem (RCPSP) is an NP-hard benchmark problem in scheduling which takes into account the limitation of resources’ availabilities in real life production processes and subsumes open-shop, job-shop, and flow-shop scheduling as special cases. We here present an application of machine learning to adapt simple greedy strategies for the RCPSP. Iterative re...

Flexibility in job shop scheduling is a main problem that has been considered by many of researchers in recent years.So Scheduling for the flexible job shop is very important in the field of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem in medium and actual size problems with traditional optimization approache...

This paper presents a meta-heuristic algorithm for solving a job shop scheduling problem involving both sequence dependent setup-times and the possibility of selecting alternative routes among the available machines. The proposed strategy is a variant of the Iterative Flattening Search (IFS) schema. This work provides three separate results: (1) a constraint-based solving procedure that extends...

Multi Objective Job Shop Scheduling Problem (MOJSP) is a problem for finding optimal operation sequences of some jobs according to more than one goal to achieve. The problem gets harder as its complexity increases. The development of optimization method has led many new methods to solve this problem. This paper offers Cross Entropy-Genetic Algorithm (CEGA) method to solve job shop scheduling pr...

In a distributed system or communication network tasks may need to be executed on more than one processor. For time-critical tasks, the timing constraints are typically given as end-to-end release-times and deadlines. This paper describes algorithms to schedule a class of systems where all the tasks execute on different processors in turn in the same order. This end-to-end scheduling problem is...

We study an inverse counterpart of the two machine flow-shop scheduling problem that arises in the context of inverse optimization. While in the forward scheduling problem all parameters are given and the objective is to find job sequence(s) for which the value of the makespan is minimum, in the inverse scheduling the exact values of processing times are unknown and they should be selected with...

The problem of maximizing the weighted number of just-in-time jobs in a two-machine ‡ow shop scheduling system is known to be NP-hard [4]. However, the question of whether this problem is strongly or ordinary NP-hard remains an open question. We provide a pseudo-polynomial time algorithm to solve this problem, proving that it is NP-hard in the ordinary sense. Moreover, we show how the pseudo-po...

In simple flow shop problems, each machine operation center includes just one machine. If at least one machine center includes more than one machine, the scheduling problem becomes a flexible flow-shop problem. Recently, group scheduling has also been proposed and discussed. In the group scheduling, each job belongs to a specific group and all the jobs are processed group by group. In this pape...

Batch and setup times are two important factors in practical job shop scheduling. This paper proposes a method to model job shop scheduling problems including batches and anticipatory sequence-dependent setup times by timed Petri nets. The general modeling method is formally presented. The free choice property of the model is proved. A case study extracted from practical scheduling is given to ...

Flexible job shop scheduling problem (FJSP) is an extended traditional job shop scheduling problem, which more approximates to real scheduling problems. This paper presents a multi-objective genetic algorithm (GA) based on immune and entropy principle to solve the multi-objective FJSP. In this improved multi-objective GA, the immune and entropy principle is used to keep the diversity of individ...