We present Bar Systems: a family of very simple algorithms for different classes of complex optimization problems in static and dynamic environments by means of reactive multi agent systems. Bar Systems are in the same line as other Swarm Intelligence algorithms; they are loosely inspired in the behavior a staff of bartenders can show while serving drinks to a crowd of customers in a bar or pub...

Manufacturing scheduling is an important but difficult task. Building on our previous success in developing optimization-based scheduling methods using Lagrangian relaxation for practical applications, this paper presents a novel Lagrangian relaxation neural network (LRNN) optimization techniques. The convergence of LRNN for separable convex programming problems is established. For separable in...

Scheduling for the flexible job shop is very important in the fields 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 approaches owing to the high computational complexity. In this paper, dynamic scheduling in flexible job shop is considered. The...

The job shop problem is an important part of scheduling in the manufacturing industry. A new intelligent algorithm named Shuffled Complex Evolution (SCE) algorithm is proposed in this paper with the aim of getting the minimized makespan. The sequence mapping mechanism is used to change the variables in the continuous domain to discrete variables in the combinational optimization problem; the se...

Smaller production companies, such as job shops, are often incapable of committing significant human or capital resources for the improvement of manufacturing flow times. However, there is considerable demand in these organizations for solutions that offer improvements without the risk involved in expending these resources. This paper investigates the impact of scheduling rules and reorganizati...

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...

Dynamic job shop scheduling research has previously assumed that the probability of the customer placing an order is always one, implying that customers will place orders regardless of lead times quoted from the producer and customer lead time expectations. As firms increasingly compete on the basis of the delivery speed and reputation, the relative performance of quoted versus actual realized ...

We present a comprehensive complexity analysis of classical shop scheduling problems subject to various combinations of constraints imposed on the processing times of operations, the maximum number of operations per job, the upper bound on schedule length, and the problem type (taking values “open shop”, “job shop”, “mixed shop”). It is shown that in the infinite class of such problems there ex...

We survey solution methods for the job shop scheduling problem with an emphasis on local search. Both deterministic and randomized local search methods as well as the proposed neighborhoods are discussed. We compare the computational performance of the various methods in terms of their eeectiveness and eeciency on a standard set of problem instances. In the job shop scheduling problem we are gi...

A train scheduling problem in a single-track railway can be modeled as a job-shop scheduling problem. We use a mixed graph model for such a job-shop problem with appropriate criteria. There are several performance evaluations for a train schedule. Optimizing a train schedule subtends minimizing total tardiness of the trains, minimizing the sum of train transit times, minimizing the makespan for...