Ants Systems, A State Of The Art Overview: Applications To Industrial Plants Problems.
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paper_title: A Study of Some Properties of Ant-Q

paper_content:

Ant-Q is an algorithm belonging to the class of ant colony based methods, that is, of combinatorial optimization methods in which a set of simple agents, called ants, cooperate to find good solutions to combinatorial optimization problems. The main focus of this article is on the experimental study of the sensitivity of the Ant-Q algorithm to its parameters and on the investigation of synergistic effects when using more than a single ant. We conclude comparing Ant-Q with its ancestor Ant System, and with other heuristic algorithms.
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paper_title: A Study of Some Properties of Ant-Q

paper_content:

Ant-Q is an algorithm belonging to the class of ant colony based methods, that is, of combinatorial optimization methods in which a set of simple agents, called ants, cooperate to find good solutions to combinatorial optimization problems. The main focus of this article is on the experimental study of the sensitivity of the Ant-Q algorithm to its parameters and on the investigation of synergistic effects when using more than a single ant. We conclude comparing Ant-Q with its ancestor Ant System, and with other heuristic algorithms.
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paper_title: A New Rank Based Version of the Ant System - A Computational Study

paper_content:

The ant system is a new meta-heuristic for hard combinatorial optimization problems. It is a population-based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem (TSP), but has been also successfully applied to problems such as quadratic assignment, job-shop scheduling, vehicle routing and graph coloring.In this paper we introduce a new rank based version of the ant system and present results of a computational study, where we compare the ant system with simulated annealing and a genetic algorithm on several TSP instances. It turns out that our rank based ant system can compete with the other methods in terms of average behavior, and shows even better worst case behavior. (author's abstract)
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paper_title: A Study of Some Properties of Ant-Q

paper_content:

Ant-Q is an algorithm belonging to the class of ant colony based methods, that is, of combinatorial optimization methods in which a set of simple agents, called ants, cooperate to find good solutions to combinatorial optimization problems. The main focus of this article is on the experimental study of the sensitivity of the Ant-Q algorithm to its parameters and on the investigation of synergistic effects when using more than a single ant. We conclude comparing Ant-Q with its ancestor Ant System, and with other heuristic algorithms.
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paper_title: The Ant System: Optimization by a colony of cooperating agents

paper_content:

An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
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paper_title: Ants can colour graphs

paper_content:

In the last few years researchers have shown how insect colonies can be seen as a natural model of collective problem solving. The analogy between the behaviour of ants looking for food and the well known travelling salesman problem has recently given rise to promising solution methods. We present in this paper an evolutionary search procedure for tackling assignment type problems. The algorithm repeatedly constructs feasible solutions of the problem under study by taking account of two complementary notions, namely the trace factor and the desirability factor. The use of such search principles will be illustrated for graph colouring problems. The results obtained have proven satisfactory when compared with those existing in the literature.
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paper_title: An island model based ant system with lookahead for the shortest supersequence problem

paper_content:

In this paper we introduce an Ant Colony Optimisation (ACO) algorithm for the Shortest Common Supersequence (SCS) problem, which has applications in production system planning, mechanical engineering and molecular biology. The ACO algorithm is used to find good parameters for a heuristic for the SCS problem. An island model with several populations of ants is used for the ACO algorithm. Besides we introduce a lookahead function which makes the decisions of the ants dependent on the state arrived after the decision.
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paper_title: An ant colony optimization for single-machine tardiness scheduling with sequence-dependent setups

paper_content:

In many real-world production systems, it requires an explicit consideration of sequence-dependent setup times when scheduling jobs. As for the scheduling criterion, the weighted tardiness is always regarded as one of the most important criteria in practical systems. While the importance of the weighted tardiness problem with sequence-dependent setup times has been recognized, the problem has received little attention in the scheduling literature. In this paper, we present an ant colony optimization (ACO) algorithm for such a problem in a single-machine environment. The proposed ACO algorithm has several features, including introducing a new parameter for the initial pheromone trail and adjusting the timing of applying local search, among others. The proposed algorithm is experimented on the benchmark problem instances and shows its advantage over existing algorithms. As a further investigation, the algorithm is applied to the unweighted version of the problem. Experimental results show that it is very competitive with the existing best-performing algorithms.
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paper_title: Beam-ACO - hybridizing ant colony optimization with beam search: an application to open shop scheduling

paper_content:

Ant colony optimization (ACO) is a metaheuristic approach to tackle hard combinatorial optimization problems. The basic component of ACO is a probabilistic solution construction mechanism. Due to its constructive nature, ACO can be regarded as a tree search method. Based on this observation, we hybridize the solution construction mechanism of ACO with beam search, which is a well-known tree search method. We call this approach Beam-ACO. The usefulness of Beam-ACO is demonstrated by its application to open shop scheduling (OSS). We experimentally show that Beam-ACO is a state-of-the-art method for OSS by comparing the obtained results to the best available methods on a wide range of benchmark instances.
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paper_title: An ant-colony optimization algorithm for minimizing the completion-time variance of jobs in flowshops

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Abstract The problem of scheduling in permutation flowshops with the objective of minimizing the completion-time variance of jobs is considered and solved by making use of ant-colony optimization (ACO) algorithms. ACO is an algorithmic approach, inspired by the foraging behavior of real ants, which can be applied to solve combinatorial optimization problems. A new ant-colony algorithm (NACO) has been developed in this paper to solve the flowshop scheduling problem. The objective is to minimize the completion-time variance of jobs. Two existing ant-colony algorithms and the proposed ant-colony algorithm have been compared with an existing heuristic for scheduling with the objective of minimizing the completion-time variance of jobs. It is found that the proposed ant-colony algorithm gives promising and better results, on an average, as compared to those solutions given by the existing ant-colony algorithms and the existing heuristic for the permutation flowshop scheduling problem under study.
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paper_title: Ant-colony algorithms for permutation flowshop scheduling to minimize makespan / total flowtime of jobs

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The problem of scheduling in permutation flowshops is considered with the objective of minimizing the makespan, followed by the consideration of minimization of total flowtime of jobs. Two ant-colony optimization algorithms are proposed and analyzed for solving the permutation flowshop scheduling problem. The first algorithm extends the ideas of the ant-colony algorithm by Stuetzle [Proceedings of the 6th European Congress on Intelligent Techniques and Soft Computing (EUFIT 98),vol. 3,Verlag Mainz,Aachen,Germany,1998,p. 1560],called max–min ant system (MMAS), by incorporating the summation rule suggested by Merkle and Middendorf [Proceedings of the EvoWorkshops 2000, Lecture Notes in Computer Science No. 1803,Springer-Verlag,Berlin,2000,p. 287] and a newly proposed local search technique. The second ant-colony algorithm is newly developed. The proposed ant-colony algorithms have been applied to 90 benchmark problems taken from Taillard [European Journal of Operational Research 64 (1993) 278]. First,a comparison of the solutions yielded by the MMAS and the two ant-colony algorithms developed in this paper,with the heuristic solutions given by Taillard [European Journal of Operational Research 64 (1993) 278] is undertaken with respect to the minimization of makespan. The comparison shows that the two proposed ant-colony algorithms perform better,on an average,than the MMAS. Subsequently,by considering the objective of minimizing the total flowtime of jobs,a comparison of solutions yielded by the proposed ant-colony algorithms with the best heuristic solutions known for the benchmark problems,as published in an extensive study by Liu and Reeves [European Journal of Operational Research 132 (2001) 439],is carried out. The comparison shows that the proposed ant-colony algorithms are clearly superior to the heuristics analyzed by Liu and Reeves. For 83 out of 90 problems considered,better solutions have been found by the two proposed ant-colony algorithms,as compared to the solutions reported by Liu and Reeves. 2003 Elsevier B.V. All rights reserved.
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paper_title: An Ant Algorithm with a new Pheromone Evaluation Rule for Total Tardiness Problems

paper_content:

Ant Colony Optimization is an evolutionary method that has recently been applied to scheduling problems. We propose an ACO algorithm for the Single Machine Total Weighted Tardiness Problem. Compared to an existing ACO algorithm for the unweighted Total Tardiness Problem our algorithm has several improvements. The main novelty is that in our algorithm the ants are guided on their way to good solutions by sums of pheromone values. This allows the ants to take into account pheromone values that have already been used for making earlier decisions.
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paper_title: CAPE applications in special glasses manufacturing

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The development and consolidation of CAPE - computer aided production engineering - as an innovative engineering approach for the design, modeling and simulation of production processes allows to obtain plant configurations organically planned and rationally sized. We show the result obtained by means of the use of eM-Plant by Tecnomatix Technologies for the solution of problems concerning the operating management of a production system working upon order. By knowing the quality and quantity of the lots to be produced in advance, analyzing the resources to be employed and simulating the various production phases, we obtained the management plant definition allowing to optimize the operating sequence and ensuring a considerable cut in production time and costs
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paper_title: Multi-objective flexible job shop schedule: Design and evaluation by simulation modeling

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Flexible job shop schedule is very important in both fields of combinatorial optimization and production management. In this paper, a simulation model is presented to solve the multi-objective flexible job shop scheduling problem. The proposed model has been coded by Matlab which is a special mathematical computation language. After modeling the pending problem, the model is validated by five representative instances based on practical data. The results obtained from the computational study have shown that the proposed approach is a feasible and effective approach for the multi-objective flexible job shop scheduling problem.
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Title: Ants Systems, A State Of The Art Overview: Applications To Industrial Plants Problems

Section 1: INTRODUCTION
Description 1: Introduce the significance of studying ants' behavior and provide an overview of how Ant's Theory has inspired algorithms for solving optimization problems. Summarize the structure of the paper.

Section 2: ANT'S THEORY ALGORITHMS
Description 2: Discuss the origin of Ant's Theory algorithms, including Ant System and Ant Colony Optimization (ACO) meta-heuristic. Describe different classes of combinatorial optimization problems addressed by these algorithms.

Section 3: Traveling salesman problem
Description 3: Detail the application of various Ant's Theory algorithms to the traveling salesman problem (TSP), including AS, MMAS, ACS, and Ant-Q, and compare their performances.

Section 4: Quadratic Assignment Problem
Description 4: Explain the use of Ant's Theory algorithms to solve the quadratic assignment problem (QAP) and highlight the differences from TSP algorithms.

Section 5: Job scheduling problem
Description 5: Describe the application of Ant's Theory to job scheduling problems, focusing on algorithm constraints and performance.

Section 6: Vehicle Routing
Description 6: Illustrate how Ant's Theory has been applied to solve vehicle routing problems and discuss different algorithmic adaptations.

Section 7: Sequential Ordering
Description 7: Explain the sequential ordering problem and how the HAS-SOP algorithm utilizing Ant's Theory is used to address it.

Section 8: Graph Colouring
Description 8: Summarize the application of Ant's Theory to graph colouring problems, including the ANTCOL algorithm.

Section 9: Shortest Common Supersequence
Description 9: Discuss the shortest common supersequence (SCS) problem and describe the AS-SCS algorithm used to solve it.

Section 10: ANT'S THEORY APPLIED TO PRODUCTION SCHEDULINGS IN INDUSTRIAL PLANTS
Description 10: Focus on the application of Ant's Theory algorithms to production scheduling problems within industrial plants, covering single machine, open shop, flow shop, and job shop scheduling problems.

Section 11: Single Machine
Description 11: Elaborate on the scheduling of jobs on a single machine and the specific ACO algorithms developed to optimize this process.

Section 12: Open Shop
Description 12: Define the open shop scheduling problem and discuss the Beam-ACO algorithm used to find optimal solutions.

Section 13: Flow Shop
Description 13: Describe the flow shop scheduling problem for two and multiple machines, and detail various ACO algorithms developed, such as SACO, M-MMAS, and NACO.

Section 14: Job Shop
Description 14: Address the complexities of job shop scheduling problems and the innovative ACO solutions proposed, including extensions to flexible job shop scheduling.

Section 15: ACO AND MODELLING AND SIMULATION
Description 15: Discuss the integration of ACO algorithms with modelling and simulation techniques for job shop scheduling in real industrial environments.

Section 16: AN APPLICATION EXAMPLE
Description 16: Present a specific case study combining modelling and simulation with ACO meta-heuristics for a job shop scheduling problem in a manufacturing plant.

Section 17: CONCLUSIONS
Description 17: Summarize the state-of-the-art in Ant Systems and their applications to industrial plant problems. Highlight ongoing research and potential future directions.