An interactive memetic algorithm for production and manufacturing problems modelled as a multi-objective travelling salesman problem

Samanlıoğlu, Funda; Ferrell, William G.; Kurz, Mary E.

doi:10.1080/00207543.2011.593578

Cited by 6 publications

(3 citation statements)

References 38 publications

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“…The evolutionary algorithm has shown substantial potential to solve NP-complete problems, such as the satisfaction problem (SAT) (OnoltIshdorj et al 2010;Pan and Martin-Vide 2006); the 0-1 knapsack problem (Pan and Martin-Vide 2005), the PSPACE-complete problem (Alhazov, Martin-Vide, and Pan 2003), the tripartite matching problem (Niu et al 2011) and the travelling-salesman problem (TSP) (Samanlioglua, Ferrellb, and Kurzb 2012). In 1996, a quantum evolutionary algorithm (QEA) was introduced by Narayanan and Moore (1996), and the algorithm's practical form was proposed by Han and Kim in Han and Kim (2000).…”

Section: Introductionmentioning

confidence: 99%

An adaptive quantum swarm evolutionary algorithm for partner selection in virtual enterprise

Xiao

Liu

Huang

et al. 2013

International Journal of Production Research

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The selection of cooperative partners is one of the most important factors when creating a virtual enterprise, and has been extensively researched in recent years. It is difficult to solve the selection problem using the traditional optimisation methods because the simultaneous consideration of many aspects of the problem is required, such as the running cost, the reaction time and the running risk. In this paper, an adaptive quantum swarm evolutionary algorithm with time-varying acceleration coefficients is proposed for the partner selection optimisation problem. Simulation and experimental results demonstrate that the improved algorithm is valid and outperforms other evolutionary algorithms.

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Section: Introductionmentioning

confidence: 99%

An adaptive quantum swarm evolutionary algorithm for partner selection in virtual enterprise

Xiao

Liu

Huang

et al. 2013

International Journal of Production Research

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“…Other applications to scheduling problems using memetic algorithms and random keys were proposed by Li and Yin [24] and Ghrayeb and Damodaran [25], among others. Although this combination of memetic algorithms and random keys have been used to solve sequencing problems, such as in the traveling salesman problem [26], this combination has not been used to solve complex routing problems such as the one presented in this paper.…”

Section: Introductionmentioning

confidence: 99%

A Memetic Algorithm for the Cumulative Capacitated Vehicle Routing Problem Including Priority Indexes

Nucamendi-Guillén¹,

Flores-Díaz²,

Olivares-Benítez³

et al. 2020

Applied Sciences

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This paper studies the Cumulative Capacitated Vehicle Routing Problem, including Priority Indexes, a variant of the classical Capacitated Vehicle Routing Problem, which serves the customers according to a certain level of preference. This problem can be effectively implemented in commercial and public environments where customer service is essential, for instance, in the delivery of humanitarian aid or in waste collection systems. For this problem, we aim to minimize two objectives simultaneously, the total latency and the total tardiness of the system. A Mixed Integer formulation is developed and solved using the AUGMECON2 approach to obtain true efficient Pareto fronts. However, as expected, the use of commercial software was able to solve only small instances, up to 15 customers. Therefore, two versions of a Memetic Algorithm with Random Keys (MA-RK) were developed to solve the problem. The computational results show that both algorithms provided good solutions, although the second version obtained denser and higher quality Pareto fronts. Later, both algorithms were used to solve larger instances (20–100 customers). The results were mixed in terms of quality but, in general, the MA-RK v2 consistently outperforms the first version. The models and algorithms proposed in this research provide useful insights for the decision-making process and can be applied to solve a wide variety of business situations where economic, customer service, environmental, and social concerns are involved.

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“…AGV's and tow trucks used for transportation between the different locations within the factory area as single or multiple loads. [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Routing of Mobile Resources with PSO using Chaotic Randomness (Chaotic-PSO) for Unexpected Delivery Failures in Manufacturing

Özpınar

Kucukasci

2016

Mar. J. Pure Appl. Sci.

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Dealing with short term and long term production planning and scheduling has already been solved with different optimization and artificial methods and approaches. Under normal manufacturing conditions supply and demand progress controlled and supported by decision support systems, ERP and MRP software packages aiming maximum utilization of resources and minimizing the stocks aiming for JIT. These software packages becoming even more intelligent and proactive based on the data in the database systems. However all these systems start with initial assumption of under normal conditions of flow time ordering to delivering. In case of unplanned stops, failures, malfunctions, shortages of unplanned inventory levels alter these initial conditions and progress to a diverging outcomes and consequences. This paper aiming a dynamic allocation and routing of mobile resources in a manufacturing plant by reallocating them by using a modified Particle Swarm Optimization using Chaotic Randomness.

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An interactive memetic algorithm for production and manufacturing problems modelled as a multi-objective travelling salesman problem

Cited by 6 publications

References 38 publications

An adaptive quantum swarm evolutionary algorithm for partner selection in virtual enterprise

An adaptive quantum swarm evolutionary algorithm for partner selection in virtual enterprise

A Memetic Algorithm for the Cumulative Capacitated Vehicle Routing Problem Including Priority Indexes

Routing of Mobile Resources with PSO using Chaotic Randomness (Chaotic-PSO) for Unexpected Delivery Failures in Manufacturing

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