A Research on Flowshop Scheduling Problems With Column Generation

2012 ◽  
Author(s):  
H. Tanohata ◽  
T. Kaihara ◽  
N. Fujii
Keyword(s):  
Combinatorial Optimization ◽  
Local Search ◽  
Column Generation ◽  
Optimization Problems ◽  
New Method ◽  
Duality Gap ◽  
Scheduling Problems ◽  
Flowshop Scheduling ◽  
Feasible Schedule ◽  
Combinatorial Optimization Problems

Column generation is a method to calculate lowerbound for combinatorial optimization problems, although a feasible schedule is generally obtained with the upperbound. Therefore, in this paper, a new method is proposed to solve the flowshop scheduling problems with column generation, which is composed of the local search and duality gap termination condition. The neighborhood of the local search is composed of columns, and the method is applied in column generation to improve the upperbound and lowerbound. The effectiveness of the proposed method is verified by computational experiments.

scholarly journals Applying the big bang-big crunch metaheuristic to large-sized operational problems

2020 ◽  
Vol 10 (3) ◽  
pp. 2484 ◽  
Author(s):  
Yousef K. Qawqzeh ◽  
Ghaith Jaradat ◽  
Ali Al-Yousef ◽  
Anmar Abu-Hamdah ◽  
Ibrahim Almarashdeh ◽  
...  
Keyword(s):  
Combinatorial Optimization ◽  
Job Shop ◽  
Optimization Problems ◽  
Big Bang ◽  
Quadratic Assignment ◽  
Scheduling Problems ◽  
High Quality ◽  
Combinatorial Optimization Problems ◽  
Big Crunch ◽  
The Big Bang

In this study, we present an investigation of comparing the capability of a big bang-big crunch metaheuristic (BBBC) for managing operational problems including combinatorial optimization problems. The BBBC is a product of the evolution theory of the universe in physics and astronomy. Two main phases of BBBC are the big bang and the big crunch. The big bang phase involves the creation of a population of random initial solutions, while in the big crunch phase these solutions are shrunk into one elite solution exhibited by a mass center. This study looks into the BBBC’s effectiveness in assignment and scheduling problems. Where it was enhanced by incorporating an elite pool of diverse and high quality solutions; a simple descent heuristic as a local search method; implicit recombination; Euclidean distance; dynamic population size; and elitism strategies. Those strategies provide a balanced search of diverse and good quality population. The investigation is conducted by comparing the proposed BBBC with similar metaheuristics. The BBBC is tested on three different classes of combinatorial optimization problems; namely, quadratic assignment, bin packing, and job shop scheduling problems. Where the incorporated strategies have a greater impact on the BBBC's performance. Experiments showed that the BBBC maintains a good balance between diversity and quality which produces high-quality solutions, and outperforms other identical metaheuristics (e.g. swarm intelligence and evolutionary algorithms) reported in the literature.

scholarly journals Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems

2020 ◽  
Vol 34 (02) ◽  
pp. 1603-1610 ◽  
Author(s):  
Jayanta Mandi ◽  
Emir Demirovi? ◽  
Peter J. Stuckey ◽  
Tias Guns
Keyword(s):  
Combinatorial Optimization ◽  
Objective Function ◽  
Large Scale ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Machine Learning Techniques ◽  
Scheduling Problems ◽  
Combinatorial Optimization Problems ◽  
Main Challenge ◽  
Linear Programming Problems

Combinatorial optimization assumes that all parameters of the optimization problem, e.g. the weights in the objective function, are fixed. Often, these weights are mere estimates and increasingly machine learning techniques are used to for their estimation. Recently, Smart Predict and Optimize (SPO) has been proposed for problems with a linear objective function over the predictions, more specifically linear programming problems. It takes the regret of the predictions on the linear problem into account, by repeatedly solving it during learning. We investigate the use of SPO to solve more realistic discrete optimization problems. The main challenge is the repeated solving of the optimization problem. To this end, we investigate ways to relax the problem as well as warm-starting the learning and the solving. Our results show that even for discrete problems it often suffices to train by solving the relaxation in the SPO loss. Furthermore, this approach outperforms the state-of-the-art approach of Wilder, Dilkina, and Tambe. We experiment with weighted knapsack problems as well as complex scheduling problems, and show for the first time that a predict-and-optimize approach can successfully be used on large-scale combinatorial optimization problems.

scholarly journals A Multi-Agent Based Optimization Method for Combinatorial Optimization Problems

2018 ◽  
Vol 27 (05) ◽  
pp. 1850021 ◽  
Author(s):  
Ines Sghir ◽  
Ines Ben Jaafar ◽  
Khaled Ghédira
Keyword(s):  
Combinatorial Optimization ◽  
Local Search ◽  
Decision Maker ◽  
Optimization Problems ◽  
Optimization Method ◽  
Quadratic Assignment ◽  
Agent Based ◽  
Combinatorial Optimization Problems ◽  
Multidimensional Knapsack ◽  
Multi Agent

This paper introduces a Multi-Agent based Optimization Method for Combinatorial Optimization Problems named MAOM-COP. In this method, a set of agents are cooperatively interacting to select the appropriate operators of metaheuristics using learning techniques. MAOM-COP is a flexible architecture, whose objective is to produce more generally applicable search methodologies. In this paper, the MAOM-COP explores genetic algorithm and local search metaheuristics. Using these metaheuristics, the decision-maker agent, the intensification agents and the diversification agents are seeking to improve the search. The diversification agents can be divided into the perturbation agent and the crossover agents. The decision-maker agent decides dynamically which agent to activate between intensification agents and crossover agents within reinforcement learning. If the intensification agents are activated, they apply local search algorithms. During their searches, they can exchange information, as they can trigger the perturbation agent. If the crossover agents are activated, they perform recombination operations. We applied the MAOM-COP to the following problems: quadratic assignment, graph coloring, winner determination and multidimensional knapsack. MAOMCOP shows competitive performances compared with the approaches of the literature.

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