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...

this paper deals with the product mix problem using the concept of theory of constraints (toc). theory of constraints is one of the most efficient approaches which have been applied to solve the product mix problem heuristically. although there are numerous heuristic and meta-heuristics to solve this problem, finding the optimal solution in a reasonable time is still a challenging issue. in thi...

This article describes the origin and significant developments associated with the field of meta-heuristics as they relate to global optimization. Meta-heuristics provide a means for approximately solving complex optimization problems. These methods are designed to search for global optima; however, they cannot guarantee that the best solution found after termination criteria are satisfied is i...

the robust coloring problem (rcp) is a generalization of the well-known graph coloring problem where we seek for a solution that remains valid when extra edges are added. the rcp is used in scheduling of events with possible last-minute changes and study frequency assignments of the electromagnetic spectrum. this problem has been proved as np-hard and in instances larger than 30 vertices, meta-...

vendor selection decisions are complicated by the fact that various conflicting multi-objective factors must be considered in the decision making process. the problem of vendor selection becomes still more compli-cated with the inclusion of incremental discount pricing schedule. such hard combinatorial problems when solved using meta heuristics produce near optimal solutions. this paper propose...

this research, for the first time presents the flexible flow shop model of scheduling method for considering runway assignment and operations planning together. one of the advantages of the developed model is considering the procedures of air routes in terminal airspace and separation between consecutive aircraft which is very similar to the real world condition. there are different objective f...

The Two-Echelon Vehicle Routing Problem (2E-VRP) is an extension of the classical VRP where the delivery from a single depot to customers is managed by routing and consolidating the freight through intermediate depots called satellites. We presented several meta-heuristics based on separating the first and second level routing problems and solving iteratively the two resulting routing subproble...

We present a state-of-the-art survey of parallel meta-heuristic developments and results, discuss general design and implementation principles that apply to most meta-heuristic classes, instantiate these principles for the three meta-heuristic classes currently most extensively used—genetic methods, simulated annealing, and tabu search, and identify a number of trends and promising research dir...

The goal of this paper is to investigate to what extent a rule learning heuristic can be learned from experience. Our basic approach is to learn a large number of rules and record their performance on the test set. Subsequently, we train regression algorithms on predicting the test set performance from training set characteristics. We investigate several variations of this basic scenario, inclu...

An important problem in nonlinear science is the unknown parameters estimation in Loranz chaotic system. Clearly, the parameter estimation for chaotic systems is a multidimensional continuous optimization problem, where the optimization goal is to minimize mean squared errors (MSEs) between real and estimated responses for a number of given samples. The Bees algorithm (BA) is a new member of me...