<em>Job shop scheduling problem (JSSP) is considered to belong to the class of NP-hard combinatorial optimization problem. Finding a solution to this problem is equivalent to solving different problems of various fields such as industry and logistics. The objective of this work is to optimize the makespan in JSSP using Golden Ball algorithm. In this paper we propose an efficient adaptation of Golden Ball algorithm to the JSSP. Numerical results are presented for 36 instances of OR-Library. The computational results show that the proposed adaptation is competitive when compared with other existing methods in the literature; it can solve the most of the benchmark instances.</em>

-The Flow Shop Scheduling Problem (FSSP) is notoriously NP-hard combinatorial optimization problem. The goal is to find a schedule that minimizes the makespan. This paper proposes an adaptation of a new approach called Golden Ball Algorithm (GBA). The proposed algorithm has been never tested with FSSP; it's based on soccer concept to obtain the optimal solution. Numerical results are presented for 22 instances of ORLibrary. The computational results indicate that this approach is practical for small OR-Library instances.

T HE quadratic assignment problem (QAP) is one of the known classical combinatorial optimization problems, in 1976 Sahni and Gonzalez [1] proved that the QAP belongs to the class of NP-hard problems [1]. It was introduced for the first time by Koopmans and Beckmann in 1957 [2]; its purpose is to assign n facilities to n fixed locations with a given flow matrix of facilities and distance matrix of locations in order to minimize the total assignment cost. This problem is applied in various fields such as hospital layout [3], scheduling parallel production lines [4] and analyzing chemical reactions for organic compounds [5].Many recent hybrid approaches have improved performance in solving QAP such as genetic algorithm hybridized with tabu search method [6], ant colony optimization mixed with local search method [7] and ant colony optimization combined with genetic algorithm and local search method [8]. Recently the hybrid algorithms are much proposed and used by many researchers to find optimal or near optimal solutions for the QAP.In this paper we propose a new competitive approach when compared with other existing methods in the literature. The golden ball algorithm mixed with simulated annealing (GBSA) is considered here as a hybrid metaheuristic to apply in the quadratic assignment problem.This work presents an efficient adaptation of GBSA algorithm to the quadratic assignment problem (QAP). This algorithm is based on the concept of soccer; it guides the search by simulated annealing [9] to escape from the local optima. The suggested technique has never been proposed or tested with QAP. In this research we use some small, medium and large test problems for comparing our approach to other recent methods from literature. Our approach is able to explore effectively the search space; it reaches the known optimal solutions in less time.The rest of this paper is structured as follows: In section I, Introduction. In section II, Quadratic assignment problem formulation. In section III, Methods. In section IV, Results and discussion. In section V, Conclusion. KeywordsCombinatorial Optimization, Golden Ball Algorithm, Simulated Annealing, Quadratic Assignment Problem. AbstractThe Quadratic Assignment Problem (QAP) is a combinatorial optimization problem; it belongs to the class of NP-hard problems. This problem is applied in various fields such as hospital layout, scheduling parallel production lines and analyzing chemical reactions for organic compounds. In this paper we propose an application of Golden Ball algorithm mixed with Simulated Annealing (GBSA) to solve QAP. This algorithm is based on different concepts of football. The simulated annealing search can be blocked in a local optimum due to the unacceptable movements; our proposed strategy guides the simulated annealing search to escape from the local optima and to explore in an efficient way the search space. To validate the proposed approach, numerous simulations were conducted on 64 instances of QAPLIB to compare GBSA with existing algorithms in the literatu...

No abstract