Binary optimization problems are in the most case the NP-hard problems that call to satisfy an objective function with or without constraints. Various optimization problems can be formulated in binary expression whither they can be resolved in easier way. Optimization literature supplies a large number of approaches to find solutions to binary hard problems. However, most population-based algor...

Job shop scheduling problem (JSSP), as one of the NP-Hard combinatorial optimization problems, has attracted the attention of many researchers during the last four decades. The overall purpose regarding this problem is to minimize maximum completion time of jobs, known as makespan. This paper addresses an approach to evolving Cellular Learning Automata (CLA) in order to enable it to solve the J...

Recall that the approximation ratio for an approximation algorithm is a measure to evaluate the approximation performance of the algorithm. The closer the ratio to 1 the better the approximation performance of the algorithm. It is notable that there is a class of NP-hard optimization problems, most originating from scheduling problems, for which there are polynomial time approximation algorithm...

this paper presented a mixed integer programming for integrated scheduling and process planning. the presented process plan included some orders with precedence relations similar to multiple traveling salesman problem (mtsp), which was categorized as an np-hard problem. these types of problems are also called advanced planning because of simultaneously determining the appropriate sequence and m...

The original Extremal Optimization (EO) algorithm and its modified versions have been successfully applied to a variety of NP-hard optimization problems. However, there exists a problem that almost all existing EO-based algorithms have overlooked the inherent structural properties behind the optimization problems, e.g., the backbone information. This paper proposes a novel stochastic search met...

Many optimization problems are fundamentally hard. Essentially, a ‘hard’ problem is one for which we cannot guarantee to find the best solution in a reasonable amount of time. In practice, however, the quest to solve hard problems is not quite so hopeless as this definition suggests. This is due to the use of approximate methods. An approximate method is an algorithm that we use to try to find ...

In this paper, teaching–learning-based optimization (TLBO) is proposed to solve flexible job shop scheduling problem (FJSP) based on the integrated approach with an objective to minimize makespan. An FJSP is an extension of basic job-shop scheduling problem. There are two sub problems in FJSP. They are routing problem and sequencing problem. If both the sub problems are solved simultaneously, t...