scholarly journals Hyperheuristic Based Migrating Birds Optimization Algorithm for a Fairness Oriented Shift Scheduling Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Gözde Alp ◽  
Ali Fuat Alkaya
Keyword(s):  
Computational Intelligence ◽  
Optimization Problems ◽  
Quadratic Assignment Problem ◽  
Neighborhood Search ◽  
Quadratic Assignment ◽  
Scheduling Problem ◽  
Shift Scheduling ◽  
Intelligence Algorithm ◽  
Migrating Birds Optimization ◽  
Migrating Birds

The purpose of this paper is twofold. First, it introduces a new hybrid computational intelligence algorithm to the optimization community. This novel hybrid algorithm has hyperheuristic (HH) neighborhood search movements embedded into a recently introduced migrating birds optimization (MBO) algorithm. Therefore, it is called HHMBO. Second, it gives the necessary mathematical model for a shift scheduling problem of a manufacturing company defined by including the fairness perspective, which is typically ignored especially in manufacturing industry. Therefore, we call this complex optimization problem fairness oriented integrated shift scheduling problem (FOSSP). HHMBO is applied on FOSSP and is compared with the well-known simulated annealing, hyperheuristics, and classical MBO algorithms through extended computational experiments on several synthetic datasets. Experiments demonstrate that the new hybrid computational intelligence algorithm is promising especially for large sized instances of the specific problem defined here. HHMBO has a high exploration capability and is a promising technique for all optimization problems. To justify this assertion, we applied HHMBO to the well-known quadratic assignment problem (QAP) instances from the QAPLIB. HHMBO was up to 14.6% better than MBO on converging to the best known solutions for QAP benchmark instances with different densities. We believe that the novel hybrid method and the fairness oriented model presented in this study will give new insights to the decision-makers in the industry as well as to the researchers from several disciplines.

scholarly journals Bilinear Assignment Problem: Large Neighborhoods and Experimental Analysis of Algorithms

2020 ◽  
Vol 32 (3) ◽  
pp. 730-746
Author(s):  
Vladyslav Sokol ◽  
Ante Ćustić ◽  
Abraham P. Punnen ◽  
Binay Bhattacharya
Keyword(s):  
Experimental Analysis ◽  
Assignment Problem ◽  
Heuristic Algorithms ◽  
Analysis Of Algorithms ◽  
Quadratic Assignment Problem ◽  
Point Of View ◽  
Neighborhood Search ◽  
Quadratic Assignment ◽  
Neighborhood Structures ◽  
Experimental Analysis Of Algorithms

The bilinear assignment problem (BAP) is a generalization of the well-known quadratic assignment problem. In this paper, we study the problem from the computational analysis point of view. Several classes of neighborhood structures are introduced for the problem along with some theoretical analysis. These neighborhoods are then explored within a local search and variable neighborhood search frameworks with multistart to generate robust heuristic algorithms. In addition, we present several very fast construction heuristics. Our systematic experimental analysis disclosed some interesting properties of the BAP, different from those of comparable models. We have also introduced benchmark test instances that can be used for future experiments on exact and heuristic algorithms for the problem.

Modified Migrating Birds Optimization for Solving the Low-carbon Scheduling Problem

2020 ◽  
Vol 12 (4) ◽  
pp. 63-75
Author(s):  
Zhifeng Zhang ◽  
Yusheng Sun ◽  
Yadong Cui ◽  
Haodong Zhu
Keyword(s):  
Production Scheduling ◽  
Job Shop ◽  
Optimal Solution ◽  
Scheduling Problem ◽  
Low Carbon ◽  
Scheduling Problems ◽  
Solution Quality ◽  
Migrating Birds Optimization ◽  
Initialization Scheme ◽  
Migrating Birds

Production scheduling problems have historically emphasized cycle time without involving the environmental factors. In the study, a low-carbon scheduling problem in a flexible job shop is considered to minimize the energy consumption, which mainly consists of two parts: the useful part and the wasted part. First, a mathematical model is built based on the features of the workshop. Second, a modified migrating bird's optimization (MMBO) is developed to obtain the optimal solution. In the MMBO, a population initialization scheme is designed to enhance the solution quality and convergence speed. Five types of neighborhood structures are introduced to create neighborhood solutions. Furthermore, a local search method and a reset mechanism are developed to improve the computational results. Finally, experimental results validate that the MMBO is effective and feasible.

scholarly journals An Improved Migrating Birds Optimization Algorithm for a Hybrid Flow Shop Scheduling within Steel Plants

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1661
Author(s):  
Dayong Han ◽  
Qiuhua Tang ◽  
Zikai Zhang ◽  
Zixiang Li
Keyword(s):  
Continuous Casting ◽  
Optimization Algorithm ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Hybrid Flow Shop Scheduling ◽  
Migrating Birds Optimization ◽  
Migrating Birds

Steelmaking and the continuous-casting (SCC) scheduling problem is a realistic hybrid flow shop scheduling problem with continuous-casting production at the last stage. This study considers the SCC scheduling problem with diverse products, which is a vital and difficult problem in steel plants. To tackle this problem, this study first presents the mixed-integer linear programming (MILP) model to minimize the objective of makespan. Then, an improved migrating birds optimization algorithm (IMBO) is proposed to tackle this considered NP-hard problem. In the proposed IMBO, several improvements are employed to achieve the proper balance between exploration and exploitation. Specifically, a two-level decoding procedure is designed to achieve feasible solutions; the simulated annealing-based acceptance criterion is employed to ensure the diversity of the population and help the algorithm to escape from being trapped in local optima; a competitive mechanism is developed to emphasize exploitation capacity by searching around the most promising solution space. The computational experiments demonstrate that the proposed IMBO obtains competing performance and it outperforms seven other implemented algorithms in the comparative study.

scholarly journals Garden optimization problems for benchmarking quantum annealers

2021 ◽  
Vol 20 (9) ◽  
Author(s):  
Carlos D. Gonzalez Calaza ◽  
Dennis Willsch ◽  
Kristel Michielsen
Keyword(s):  
Real World ◽  
Assignment Problem ◽  
Optimization Problems ◽  
Quadratic Assignment Problem ◽  
Quadratic Assignment ◽  
New Class ◽  
Qubit System ◽  
Hybrid Solver ◽  
Qubo Formulation ◽  
D Wave

AbstractWe benchmark the 5000+ qubit system coupled with the Hybrid Solver Service 2 released by D-Wave Systems Inc. in September 2020 by using a new class of optimization problems called garden optimization problems known in companion planting. These problems are scalable to an arbitrarily large number of variables and intuitively find application in real-world scenarios. We derive their QUBO formulation and illustrate their relation to the quadratic assignment problem. We demonstrate that the system and the new hybrid solver can solve larger problems in less time than their predecessors. However, we also show that the solvers based on the 2000+ qubit system sometimes produce more favourable results if they can solve the problems.

scholarly journals Constrained Nets for Graph Matching and Other Quadratic Assignment Problems

1991 ◽  
Vol 3 (2) ◽  
pp. 268-281 ◽  
Author(s):  
Petar D. Simić
Keyword(s):  
Graph Matching ◽  
Optimization Problems ◽  
Quadratic Assignment Problem ◽  
Semiclassical Approximation ◽  
Geometric Optimization ◽  
Elastic Net ◽  
Physical Models ◽  
Quadratic Assignment ◽  
Matching Problem ◽  
Special Case

Some time ago Durbin and Willshaw proposed an interesting parallel algorithm (the “elastic net”) for approximately solving some geometric optimization problems, such as the Traveling Salesman Problem. Recently it has been shown that their algorithm is related to neural networks of Hopfield and Tank, and that they both can be understood as the semiclassical approximation to statistical mechanics of related physical models. The main point of the elastic net algorithm is seen to be in the way one deals with the constraints when evaluating the effective cost function (free energy in the thermodynamic analogy), and not in its geometric foundation emphasized originally by Durbin and Willshaw. As a consequence, the elastic net algorithm is a special case of the more general physically based computations and can be generalized to a large class of nongeometric problems. In this paper we further elaborate on this observation, and generalize the elastic net to the quadratic assignment problem. We work out in detail its special case, the graph matching problem, because it is an important problem with many applications in computational vision and neural modeling. Simulation results on random graphs, and on structured (hand-designed) graphs of moderate size (20-100 nodes) are discussed.

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