scholarly journals Bin Completion Algorithms for Multicontainer Packing, Knapsack, and Covering Problems

2007 ◽  
Vol 28 ◽  
pp. 393-429 ◽  
Author(s):  
A. S. Fukunaga ◽  
R. E. Korf
Keyword(s):  
Bin Packing ◽  
Optimization Problems ◽  
State Of The Art ◽  
Cutting Stock ◽  
Multi Agent Systems ◽  
Covering Problems ◽  
Combinatorial Optimization Problems ◽  
Bin Covering ◽  
Multiple Knapsack ◽  
Dominance Criterion

Many combinatorial optimization problems such as the bin packing and multiple knapsack problems involve assigning a set of discrete objects to multiple containers. These problems can be used to model task and resource allocation problems in multi-agent systems and distributed systms, and can also be found as subproblems of scheduling problems. We propose bin completion, a branch-and-bound strategy for one-dimensional, multicontainer packing problems. Bin completion combines a bin-oriented search space with a powerful dominance criterion that enables us to prune much of the space. The performance of the basic bin completion framework can be enhanced by using a number of extensions, including nogood-based pruning techniques that allow further exploitation of the dominance criterion. Bin completion is applied to four problems: multiple knapsack, bin covering, min-cost covering, and bin packing. We show that our bin completion algorithms yield new, state-of-the-art results for the multiple knapsack, bin covering, and min-cost covering problems, outperforming previous algorithms by several orders of magnitude with respect to runtime on some classes of hard, random problem instances. For the bin packing problem, we demonstrate significant improvements compared to most previous results, but show that bin completion is not competitive with current state-of-the-art cutting-stock based approaches.

scholarly journals Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1928 ◽  
Author(s):  
Alfonso González-Briones ◽  
Fernando De La Prieta ◽  
Mohd Mohamad ◽  
Sigeru Omatu ◽  
Juan Corchado
Keyword(s):  
Optimization Problems ◽  
State Of The Art ◽  
Smart Cities ◽  
Energy Optimization ◽  
Sensor Data ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Research Perspectives ◽  
Wide Range ◽  
Multi Agent

This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings.

scholarly journals Abstract Cores in Implicit Hitting Set MaxSat Solving (Extended Abstract)

2021 ◽  
Author(s):  
Jeremias Berg ◽  
Fahiem Bacchus ◽  
Alex Poole
Keyword(s):  
Linear Programming ◽  
Optimization Problems ◽  
State Of The Art ◽  
Empirical Evaluation ◽  
Boolean Satisfiability ◽  
Compact Representation ◽  
Maximum Satisfiability ◽  
Hitting Set ◽  
Combinatorial Optimization Problems ◽  
Core Extraction

Maximum satisfiability (MaxSat) solving is an active area of research motivated by numerous successful applications to solving NP-hard combinatorial optimization problems. One of the most successful approaches for solving MaxSat instances from real world domains are the so called implicit hitting set (IHS) solvers. IHS solvers decouple MaxSat solving into separate core-extraction (i.e. reasoning) and optimization steps which are tackled by a Boolean satisfiability (SAT) and an integer linear programming (IP) solver, respectively. While the approach shows state-of-the-art performance on many industrial instances, it is known that there exists instances on which IHS solvers need to extract an exponential number of cores before terminating. Motivated by the simplest of these problematic instances, we propose abstract cores, a compact representation for a potentially exponential number of regular cores. We demonstrate how to incorporate abstract core reasoning into the IHS algorithm and report on an empirical evaluation demonstrating, that including abstract cores into a state-of-the-art IHS solver improves its performance enough to surpass the best performing solvers of the 2019 MaxSat Evaluation.

scholarly journals Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

2020 ◽  
Vol 12 (2) ◽  
pp. 22 ◽  
Author(s):  
Thays A. Oliveira ◽  
Yuri B. Gabrich ◽  
Helena Ramalhinho ◽  
Miquel Oliver ◽  
Miri W. Cohen ◽  
...  
Keyword(s):  
Operations Research ◽  
Optimization Problems ◽  
State Of The Art ◽  
Smart Cities ◽  
Transportation Systems ◽  
Decision Makers ◽  
Current Generation ◽  
Combinatorial Optimization Problems ◽  
Innovation And Technology ◽  
Transportation Technologies

Cities are constantly transforming and, consequently, attracting efforts from researchers and opportunities to the industry. New transportation systems are being built in order to meet sustainability and efficiency criteria, as well as being adapted to the current possibilities. Moreover, citizens are becoming aware about the power and possibilities provided by the current generation of autonomous devices. In this sense, this paper presents and discusses state-of-the-art transportation technologies and systems, highlighting the advances that the concepts of Internet of Things and Value are providing. Decentralized technologies, such as blockchain, are been extensively investigated by the industry, however, its widespread adoption in cities is still desirable. Aligned with operations research opportunities, this paper identifies different points in which cities’ services could move to. This also study comments about different combinatorial optimization problems that might be useful and important for an efficient evolution of our cities. By considering different perspectives, didactic examples are presented with a main focus on motivating decision makers to balance citizens, investors and industry goals and wishes.

scholarly journals Dichotomous Binary Differential Evolution for Knapsack Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Hu Peng ◽  
Zhijian Wu ◽  
Peng Shao ◽  
Changshou Deng
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
State Of The Art ◽  
Experimental Studies ◽  
Continuous Optimization ◽  
Knapsack Problems ◽  
Binary Particle Swarm Optimization ◽  
Combinatorial Optimization Problems ◽  
Multidimensional Knapsack ◽  
Binary Differential Evolution

Differential evolution (DE) is one of the most popular and powerful evolutionary algorithms for the real-parameter global continuous optimization problems. However, how to adapt into combinatorial optimization problems without sacrificing the original evolution mechanism of DE is harder work to the researchers to design an efficient binary differential evolution (BDE). To tackle this problem, this paper presents a novel BDE based on dichotomous mechanism for knapsack problems, called DBDE, in which two new proposed methods (i.e., dichotomous mutation and dichotomous crossover) are employed. DBDE almost has any difference with original DE and no additional module or computation has been introduced. The experimental studies have been conducted on a suite of 0-1 knapsack problems and multidimensional knapsack problems. Experimental results have verified the quality and effectiveness of DBDE. Comparison with three state-of-the-art BDE variants and other two state-of-the-art binary particle swarm optimization (PSO) algorithms has proved that DBDE is a new competitive algorithm.

Optimization by Ant Colony Hybrid Local Search for Online Class Constrained Bin Packing Problem

2013 ◽  
Vol 311 ◽  
pp. 123-128 ◽  
Author(s):  
Tsai Duan Lin ◽  
Chiun Chieh Hsu ◽  
Li Fu Hsu
Keyword(s):  
Local Search ◽  
Bin Packing ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Packing Problem ◽  
Ant Colony ◽  
Np Hard ◽  
Combinatorial Optimization Problems ◽  
Bin Packing Problem

The on-line Class Constrained Bin Packing problem (CCBP) is one of variant version of the Bin Packing Problem (BPP). The BPP is to find the minimum numbers of bins needed to pack a given set of items of known sizes so that they do not exceed the capacity B of each bin. In the CCBP, we are given bins of capacity B with C compartments and n items of Q different classes, each item i is belong to 1,2,…,n with class qi and si. The CCBP is to pack the items into bins, where each bin contains at most Q different classes and has total items size at most B. This CCBP is known to be NP-hard combinatorial optimization problems. In this paper, we used an ant colony optimization (ACO) approach with a simple but very effective local search algorithm to resolve this NP-hard problem. After the experimental design, limited computational results show the efficiency of this scheme. It is also shown that the ACO approach can outperform some existing methods, whereas the hybrid approach can compete with the known solution methods.

An Improved Generalized Quantum-Inspired Evolutionary Algorithm for Multiple Knapsack Problem

2021 ◽  
pp. 22-50
Author(s):  
Sulabh Bansal ◽  
C. Patvardhan
Keyword(s):  
Combinatorial Optimization ◽  
Evolutionary Algorithms ◽  
Knapsack Problem ◽  
Optimization Problems ◽  
Problem Solution ◽  
Np Hard ◽  
Combinatorial Optimization Problems ◽  
Multiple Knapsack Problem ◽  
Multiple Knapsack ◽  
Decimal Numbers

This article describes how the 0/1 Multiple Knapsack Problem (MKP), a generalization of popular 0/1 Knapsack Problem, is NP-hard and harder than simple Knapsack Problem. Solution of MKP involves two levels of choice – one for selecting an item to be placed and the other for selecting the knapsack in which it is to be placed. Quantum Inspired Evolutionary Algorithms (QIEAs), a subclass of Evolutionary algorithms, have been shown to be effective in solving difficult problems particularly NP-hard combinatorial optimization problems. QIEAs provide a general framework which needs to be customized according to the requirements of a given problem to obtain good solutions in reasonable time. An existing QIEA for MKP (QIEA-MKP) is based on the representation where a Q-bit collapse into a binary number. But decimal numbers are required to identify the knapsack where an item is placed. The implementation based on such representation suffers from overhead of frequent conversion from binary numbers to decimal numbers and vice versa. The generalized QIEA (GQIEA) is based on a representation where a Q-bit can collapse into an integer and thus no inter conversion between binary and decimal is required. A set of carefully selected features have been incorporated in proposed GQIEA-MKP to obtain better solutions in lesser time. Comparison with QIEA-MKP shows that GQIEA-MKP outperforms it in providing better solutions in lesser time for large sized MKPs. The generalization proposed can be used with advantage in other Combinatorial Optimization problems with integer strings as solutions.

scholarly journals A FRAMEWORK FOR MEMETIC ALGORITHMS

2009 ◽  
Vol 12 (11) ◽  
pp. 27-38
Author(s):  
Tuan Anh Phan ◽  
Anh Tuan Duong
Keyword(s):  
Genetic Algorithm ◽  
Design Patterns ◽  
Memetic Algorithm ◽  
Optimization Problems ◽  
Object Oriented ◽  
Memetic Algorithms ◽  
Set Covering ◽  
Covering Problems ◽  
Set Covering Problems ◽  
Combinatorial Optimization Problems

Memetic algorithm, a combination of genetic algorithm with local search, is one of the most successful metaheuristics to solve complex combinatorial optimization problems. In this paper, we will introduce an object-oriented framework which allows the construction of memetic algorithms with a maximum reuse. This framework has been developed in Java using design patterns to allow its easy extension and utilization in different problem domains. Our framework has been experimented through the development of a memetic algorithm for solving set covering problems.

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