The most recent approaches of multi-objective optimization constitute application of meta-heuristic algorithms for which, parameter tuning is still a challenge. The present work hybridizes swarm intelligence with fuzzy operators to extend crisp values of the main control parameters into especial fuzzy sets that are constructed based on a number of prescribed facts. Such parameter-less particle ...

The bin-packing problem (BPP) and its multi-dimensional variants have many practical applications, such as packing objects in boxes, production planning, multiprocessor scheduling, etc. The classical singleobjective formulation of the two-dimensional bin packing (2D-BPP) consists of packing a set of objects (pieces) in the minimum number of bins (containers). This paper presents a new Pareto-ba...

In the science of operation research and decision theory, selection is the most important process. Selection is a process that studies multiple qualitative and quantitative criteria, related to the science of management, which are mostly incompatible with each other. The multi criteria selection of a renewable energy portfolio is one of the main issues considered in multi criteria literatur...

Multi-objective optimization of industrial styrene reactor is done using Harmony Search algorithm. Harmony search algorithm is a recently developed meta-heuristic algorithm which is inspired by musical improvisation process aimed towards obtaining the best harmony. Three objective functions – productivity, selectivity and yield are optimized to get best combination of decision variables for sty...

this paper addresses the problem of minimizing the sum of maximum earliness and tardiness on identical parallel machines scheduling problem. each job has a processing time and a due date. since this problem is trying to minimize and diminish the values of earliness and tardiness, the results can be useful for just–in-time production systems. it is shown that the problem is np-hard. using effici...

The one-dimensional cutting stock problem, has so many applications in lots of industrial processes and during the past few years has attracted so many researchers’ attention all over the world. In this paper a meta-heuristic method based on ACO is presented to solve this problem. In this algorithm, based on designed probabilistic laws, artificial ants do select various cuts and then select the...

This paper presents a new mathematical model for a hybrid flow shop scheduling problem with multiprocessor tasks in which sequence dependent set up times and preemption are considered. The objective is to minimize the weighted sum of makespan and maximum tardiness. Three meta-heuristic methods based on genetic algorithm (GA), imperialist competitive algorithm (ICA) and a hybrid approach of GA a...

Meta-heuristic algorithms inspired by the natural processes are part of the optimization algorithms that they have been considered in recent years, such as genetic algorithm, particle swarm optimization, ant colony optimization, Firefly algorithm. Recently, a new kind of evolutionary algorithm has been proposed that it is inspired by the human sociopolitical evolution process. This new algorith...

This paper proposes a new mixed-integer model for the multi-skill resource-constrained project scheduling problem (MSRCPSP). The interactions between workers are represented as undirected networks. Therefore, for each required skill, an undirected network is formed which shows the relations of human resources. In this paper, community detection in networks is used to find the most compatible wo...

This paper presents a novel population-based meta-heuristic algorithm inspired by the game of tug of war. Utilizing a sport metaphor the algorithm, denoted as Tug of War Optimization (TWO), considers each candidate solution as a team participating in a series of rope pulling competitions.  The  teams  exert  pulling  forces  on  each  other...