Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony Ant colony optimization exploits a similar mechanism for solving optimization problems From the early nineties, when the first ant colony optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO The goal of this article is to introduce ant colony optimization and to survey its most notable applications

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Ant Colony Optimization

The author systematically examines one of the important issues of information — establishing the market price. He introduces the concept of «search» — where a buyer wanting to get a better price, is forced to question sellers. The article deals with various aspects of finding the necessary information.

Economics of Information

The talk with present the key results of the IPCC Working Group III 5th assessment report. Concluding four years of intense scientific collaboration by hundreds of authors from around the world, the report responds to the request of the world's governments for a comprehensive, objective and policy neutral assessment of the current scientific knowledge on mitigating climate change. The report has been extensively reviewed by experts and governments to ensure quality and comprehensiveness.

Climate change 2014 - Mitigation of climate change

This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.

http://www.iro.umontreal.ca/~dift6751/paper_aco.pdf

Ant algorithms for discrete optimization

http://www-lia.deis.unibo.it/Courses/SistInt/articoli/max-min-ant.pdf

MAX-MIN Ant system

This paper proposes a new model for Cluster-tools with two load locks. Cluster-tools are widely used to automate single wafer processing in semiconductor industry. The load locks are the entry points into the vacuum of the Cluster-tool's mainframe. Usually there are two of them available. Each lot being processed, is dedicated to a single load-lock. Therefore at most two different lots (with possibly different processing times and qualification) can be processed simultaneously. This restriction is one of the major potential bottlenecks. 
Capacity planning is one of the possible applications for the proposed model and the paper demonstrates the integration into a more general framework that considers different tool types and different operational modes. 
The paper also generalizes an earlier model that is limited to three processing chambers. The proposed modeling approach is based on makespan reductions by parallel processing. It turns out that the performance of the new approach is similar, when compared to the generalized model for three chambers, but the new approach outperforms the generalized model for four and more chambers.

Capacity Planning for Cluster Tools in the Semiconductor Industry

In this paper we deal with the pickup and delivery of full truckloads (the same analysis applies for the routing of containers, pallets or freight cars) under time window constraints. Our objective function is to minimize total costs.

Ants Solve Time Constrained Pickup and Delivery Problems with Full Truckloads

In optimal control problems involving nondifferentiable functions of the state variable, the adjoint differential inclusion can be formulated by either use of the Hamiltonian or the maximized Hamiltonian. In this paper, we solve a production-employment model in which the latter approach must be utilized, since the former does not enable one to determine the optimal policy.

On the use of Hamiltonian and maximized Hamiltonian in nondifferentiable control theory

In this paper we propose an ACO algorithm to optimize the total costs associated with the pickup and delivery of full truckloads under time window constraints in a hub network. We perform a thorough technical analysis of the ACO by comparing different pheromone decoding schemes, different visibility information and various population sizes. Furthermore we propose a post-optimization technique to improve the solutions. Our results show that appropriate data structures significantly improve the solution quality.

https://epub.wu.ac.at/1434/1/document.pdf

Ant colony optimization applied to the pickup and delivery problem

Abstract Carriers can remarkably reduce transportation costs and emissions when they collaborate, for example through a platform. Such gains, however, have only been investigated for relatively small problem instances with low numbers of carriers. We develop auction-based methods for large-scale dynamic collaborative pickup and delivery problems, combining techniques of multi-agent systems and combinatorial auctions. We evaluate our approach in terms of both solution quality and possibilities of strategic behaviour using a real-world data set of over 12,000 orders. Hence, this study is (to the best of our knowledge) the first to assess the benefits of large-scale carrier cooperation and to propose an approach for it. First, we use iterative single-order auctions to investigate possible collaboration gains for increasing numbers of carriers. Our results show that travel costs can be reduced by up to 77% when 1000 carriers collaborate, largely increasing the gains that were previously observed in smaller-scale collaboration. We also ensure that individual rationality is guaranteed in each auction. Next, we compare this approach of multiple local auctions with an established central combinatorial auction mechanism and observe that the proposed approach performs better on large-scale instances. Furthermore, to improve solution quality, we integrate the two approaches by allowing small bundle auctions in the multi-agent system. We analyze the circumstances under which bundling is beneficial in a large-scale decentralized system and demonstrate that travel cost gains of up to 13% can be obtained for 1000 carriers. Finally, we investigate whether the system is vulnerable to cheating: we show that misrepresentation of true values by individual participants sometimes can benefit them at the cost of the collective. Although such strategic behaviour is not straightforward, we also discuss different means to prevent it. 

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Richard F. Hartl

Papers

Capacity Planning for Cluster Tools in the Semiconductor Industry

Ants Solve Time Constrained Pickup and Delivery Problems with Full Truckloads

On the use of Hamiltonian and maximized Hamiltonian in nondifferentiable control theory

Ant colony optimization applied to the pickup and delivery problem

Large-scale collaborative vehicle routing