Original scientific paper This paper presents a hybrid genetic algorithm for assembly flow-shop scheduling problem with sequence-dependent setup and transportation times, with objectives, namely the minimizing of weighted, sum of total weighted squared tardiness, makespan, and total weighted squared earliness and number of tardy jobs. Since the problem is NP-hard, we solved this problem by hybr...

This paper deals with a permutation flow-shop scheduling problem with finite intermediate storage (PFSFIS) between successive machines so as to minimize makespan. In such a problem the intermediate storage capacity constraints are considered besides the machine-related constraints usually discussed in the general permutation flow-shop. This feature adds extra difficulties to the scheduling prob...

An exact model of a simple genetic algorithm is developed for permutation based representations. Permutation based representations are used for scheduling problems and combinatorial problems such as the Traveling Salesman Problem. A remapping function is developed to remap the model to all permutations in the search space. The mixing matrices for various permutation based operators are also dev...

short term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems. although various methodologies have been applied to forecast traffic parameters, several researchers have showed that compared with the individual methods, hybrid methods provide more accurate results . these results made the hybrid tools and approaches a more common method for ...

This paper presents a hybrid genetic algorithm for the Job Shop Scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local s...

evolutionary algorithms are some of the most crucial random approaches tosolve the problems, but sometimes generate low quality solutions. on the otherhand, learning automata are adaptive decision-making devices, operating onunknown random environments, so it seems that if evolutionary and learningautomaton based algorithms are operated simultaneously, the quality of results willincrease sharpl...

 This paper present unequal-sized facilities layout solutions generated by a genetic search program named LADEGA (Layout Design using a Genetic Algorithm). The generalized quadratic assignment problem requiring pre-determined distance and material flow matrices as the input data and the continuous plane model employing a dynamic distance measure and a material flow matrix are discussed. Computa...