Application of ant colony optimization for no-wait flowshop scheduling problem to minimize the total completion time

Shyu, Shyong Jian; Lin, Bertrand M.T.; Yin, Peng-Yeng

doi:10.1016/j.cie.2004.06.006

Cited by 121 publications

(44 citation statements)

References 26 publications

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“…Other important work by Shyu and Lin (2004) and Reimann and Laumanns (2006) have been conducted using ACO on the VRP. Reimann and Ulrich (2006) evaluate ACO for an expanded VRP with backhauls and time windows, but use randomly generated sets of test problems that are not representative of the spatial differences seen in the work by Ballou and Agarwaal (1990).…”

Section: Introductionmentioning

confidence: 99%

Swarm Intelligence: Application of the Ant Colony Optimization Algorithm to Logistics‐oriented Vehicle Routing Problems

Bell

Griffis

2010

J of Business Logistics

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This research evaluates a set of logistics‐oriented vehicle routing problems (VRP) taken from the logistics and supply chain literature under the widely used Clark‐Wright Savings algorithm and the newer metaheuristic method employing a type of swarm intelligence called Ant Colony Optimization (ACO). ACO simulates the decision‐making processes of colonies of ants as they forage for food and is related to other artificial intelligence techniques such as Tabu Search, Simulated Annealing and Genetic Algorithms. Experimentation shows that ACO is successful in finding solutions near the best‐known solutions for problems with up to 20 demand locations. In addition, testing for the affect of spatial patterns suggested by the logistics literature for facility locations appears to make a difference in the quality of the solutions for the two algorithms. Finally, ACO is shown to be superior to the savings algorithm found in software packages and as a result should be tested on even larger, more complex logistics‐oriented vehicle routing problems, representative of those encountered in larger industrial and retail settings.

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Section: Introductionmentioning

confidence: 99%

Swarm Intelligence: Application of the Ant Colony Optimization Algorithm to Logistics‐oriented Vehicle Routing Problems

Bell

Griffis

2010

J of Business Logistics

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“…Different genetic algorithms (GA) are applied by Chen and Neppalli [20], Aldowaisan and Allahverdi [21]. Among the other metaheuristics, one could refer the reader to particle swarm optimization (PSO) by Pan et al [22], simulated annealing (SA) by Fink and Voß [23], ant colony optimization (ACO) by Shyu et al [24] and tabu search (TS) by Grabowski and Pempera [25]. Khalili [26] proposed an iterated local search algorithm for flexible flow lines with sequence dependent setup times to minimize total weighted completion and also studied multiobjective no-wait hybrid flowshop scheduling problems to minimise both makespan and total tardiness [27].…”

Section: Literature Reviewmentioning

confidence: 99%

An electromagnetism-inspired method for a generalized flowshop problem

Khalili

2014

Manufacturing Rev.

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-There are two common simplifying assumptions made in the scheduling of operations. One is that machines are available continuously, and the other is that once a job completes one process it is immediately available for its next. Moreover, in flowshops, it is commonly assumed that jobs must visit all machines. In practice, however, these assumptions may not be justifiable in some applications. This paper deals with a generalized flow shop (GFS) problem with machine availability constraints, transportation times between stages, and machine skipping. The electromagnetism-like method (EM) has been successfully applied to some NP-hard problems and this has motivated us to apply and assess the effectiveness of an EM algorithm in the GFS scenarios. Simulated annealing (SA) and a number of other well-recognized heuristics to the given GFS scheduling problem that minimizes two independent objective functions, namely the total tardiness and the total completion time also has been applied. In order to evaluate the performance of the proposed EM and SA, a set of practical instances has been considered. The related results are analyzed in two parts; in term of the objective functions, and the observed effects on variables in our instances. Extensive experiments and statistical analyses demonstrate that our proposed EM is more efficient than SA and other heuristics with regards to the objective functions considered in this paper.

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“…As for the stopping/termination criteria, a maximum number of iterations is used in the proposed model due to its convenience and popularity [12]. The algorithm loops back for another iteration until the maximum number of iterations is reached.…”

Section: Proposed Aco Modelmentioning

confidence: 99%

Employing Ant Colony for the Optimal Reduction of Project Risk Severity

Zabel¹,

Georgy

Ibrahim

2014

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-Efforts undertaken in identifying, analyzing and assessing project risks are only made good use of when proper risk treatment strategies are decided upon and pursued. Based on the criteria established by senior management, the risk management plan goes about defining how each risk is to be handled. There are options to that end, including acceptance, avoidance, transfer and mitigation. Whilst these strategies are known to all in the industry, the decision-making process is far from easy. A research was undertaken to optimize risk treatment in construction projects, where both costs and benefits are balanced out at the project level. The paper particularly introduces Ant Colony Optimization (ACO) as a capable algorithm for the balanced selection of risk treatment strategies; that is to reduce the overall risk severity in a project at the minimum cost possible. ACO resembles the real life behavior of ants in their intelligent and guided search for food. The research is being applied in the pipeline construction sector and made use of professional knowledge and project records from a big construction company in the Middle East. The paper further presents an example project to demonstrate how ACO explores the risk treatment alternatives in a project and chooses the optimal set of strategies in such context.

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Application of ant colony optimization for no-wait flowshop scheduling problem to minimize the total completion time

Cited by 121 publications

References 26 publications

Swarm Intelligence: Application of the Ant Colony Optimization Algorithm to Logistics‐oriented Vehicle Routing Problems

Swarm Intelligence: Application of the Ant Colony Optimization Algorithm to Logistics‐oriented Vehicle Routing Problems

An electromagnetism-inspired method for a generalized flowshop problem

Employing Ant Colony for the Optimal Reduction of Project Risk Severity

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