Intelligent bionic genetic algorithm (IB-GA) and its convergence

Li, Fachao; Xu, Li Da; Jin, Chi; Wang, Hong

doi:10.1016/j.eswa.2011.01.091

Cited by 51 publications

(19 citation statements)

References 19 publications

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“…Genetic algorithms, introduced by Holland (1975), refer to a class of adaptive search procedures based on the principles derived from natural evolution and genetics. GA has several variants (Jiang et al 2009, Kakousis et al 2010, Li et al 2011a, 2011b. The GA for solving the serial SCND problem was the virtual gene genetic algorithm, which is a generalisation of traditional genetic algorithms that use binary linear chromosomes (Valenzuela-Rendo´n 2003).…”

Section: Solution Methods Based On the Genetic Algorithmmentioning

confidence: 99%

The impact of the cost of quality on serial supply‐chain network design

Castillo-Villar

Smith

Simonton

2012

International Journal of Production Research

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This paper presents a model for supply-chain design that considers the Cost of Quality as well as the traditional manufacturing and distribution costs (SC-COQ model). It includes three main contributions: (1) the SC-COQ model internally computes quality costs for the whole supply chain considering the interdependencies among business entities, whereas previous works have assumed exogenously given Cost of Quality functions; (2) the SC-COQ model can be used at a strategic planning level to design a logistic route that achieves a maximum profit while considering the overall quality level within a supply chain; and (3) we provide two solution methods based on simulated annealing and a genetic algorithm and perform computational experiments on test instances. IntroductionCost of Quality (COQ), or quality cost, represents a powerful measurement system that translates the implications of poor quality, activities of a quality programme, and quality-improvement efforts into a monetary language for managers. COQ is a language that every stakeholder can understand; it affects operating costs, profitability, and consumer need (Srivastava 2008). Although COQ has been applied mostly within enterprises, it is crucial to extend COQ as an external measure and integrate these costs into supply-chain (SC) modelling.This study aims to develop a strategic-level model for computing the COQ in a formulation of a single-product, multi-stage, serial supply-chain network design (SCND) problem. This paper follows the tradition of strategic-level models that are mainly concerned with the design of the supply chain. Hierarchical models allow the integration of decision levels. The PLOT model developed by Jayaraman and Ross (2003) is an example of a hierarchical model. The first model is a strategic model that is to be run whenever the distribution network design has to be updated. The second model is an operational model that aims to determine the optimal flow of product from warehouses to customers. Another example is the model constructed by Beamon and Sabri (2000). Their executional model includes, among other operational variables, inventory variables. The SC-COQ model could be expanded to a hierarchical model (Section 7) but is presented here only as a strategic-level model.The problem addressed in this paper is to select among several potential suppliers, several manufacturing plants, and several potential retailers to generate a logistic route that achieves the maximum profit while attaining a required quality level at the minimum cost. This paper presents two solution procedures for the resulting nonlinear mixed-integer optimisation problem based on well-known heuristics: simulated annealing (SA) and a genetic algorithm (GA).A few studies have provided comprehensive models to ensure quality in multi-stage SC (Li and Warfield 2011). Das (2011) proposed a multi-stage global SC mathematical model for preventing recall risks by first integrating a quality-based supply management system to affiliate prospective suppliers and then integrat...

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Section: Solution Methods Based On the Genetic Algorithmmentioning

confidence: 99%

The impact of the cost of quality on serial supply‐chain network design

Castillo-Villar

Smith

Simonton

2012

International Journal of Production Research

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“…PSO was inspired by swarm intelligence in guiding the swarms, such as bird flock and fish school to the most promising directions in the search space (Kennedy et al 2001;Hsu et al 2011;Lin et al 2010). During the past two decades, the application of the evolutionary algorithms including PSO have been successfully applied in many challenging and complex optimization problems, where other methodologies are found difficult to cope with (Fritzsche et al 2012;Tang and Bagchi 2010;Wang et al 2010Wang et al , 2011aLi et al 2011a, b;Jiang et al 2009;Zhu et al 2008;Xu et al 2008a). An evolutional algorithm such as PSO usually results in faster convergence rates and better results compared with other optimization methods .…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 95%

A new methodology to support group decision-making for IoT-based emergency response systems

Sun

et al. 2013

Inf Syst Front

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An emergency response system (ERS) can assist a municipality or government in improving its capabilities to respond urgent and severe events. The responsiveness and effectiveness of an ERS relies greatly on its data acquisition and processing system, which has been evolved with information technology (IT). With the rapid development of sensor networks and cloud computing, the emerging Internet of things (IoT) tends to play an increasing role in ERSs; the networks of sensors, public services, and experts are able to interact with each other and make scientific decisions to the emergencies based on real-time data. When group decision making is required in an ERS, one critical challenge is to obtain the good understanding of massive and diversified data and make consensus group decisions under a high-level stress and strict time constraint. Due to the nature of unorganized data and system complexity, an ERS depends on the perceptions and judgments of experts from different domains; it is challenging to assess the consensus of understanding on the collected data and response plans before appropriate decisions can be reached for emergencies. In this paper, the group decision-making to emergency situations is formulated as a multiple attribute group decision making (MAGDM) problem, the consensus among experts is modeled, and a new methodology is proposed to reach the understanding of emergency response plans with the maximized consensus in course of decision-making. In the implementation, the proposed methodology in integrated with computer programs and encapsulated as a service on the server. The objectives of the new methodology are (i) to enhance the comprehensive group cognizance on emergent scenarios and response plans and (ii) to accelerate the consensus for decision making with an intelligent clustering algorithm, (iii) to adjust the experts' opinions without affecting the reliability of the decision when the consensus cannot be reached from the preliminary decision-making steps. Partitioning Around Medoids (PAM) has been applied as the clustering algorithm, Particle Swarm Optimization (PSO) is deployed to adjust evaluation values automatically. The methodology is applied in a case study to illustrate its effectiveness in converging group opinions and promoting the consensus of understanding on emergencies.

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“…As a result, the IP-based approaches can hardly fulfill the requirement of scalability for realistic problems with large search spaces. Therefore, a lot of heuristic algorithms with polynomial or pseudo-polynomial time complexity are designed to find acceptable near-to-optimal solutions, such as particle swarm-based approaches [22], tabu search and simulated annealing integrated approaches [54], and genetic algorithm (GA)-based approaches [51,[55][56][57]. With regard to the GA-based approaches, many improved algorithms were proposed, such as GA combined with other heuristic algorithms [51] and matrix-coded genetic algorithm (MCGA) [58].…”

Section: Related Workmentioning

confidence: 99%

QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups

Liu

Zhou

2016

Int J Adv Manuf Technol

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Cloud manufacturing (CMfg) has drawn extensive attentions from industrial community and academia. Quality of service (QoS)-aware service composition is critical to the on-demand use of distributed manufacturing resources and capabilities in CMfg systems. However, most previous work plainly composed composite services by the approach of oneto-one mapping-based service composition (OOM-SC), which leads to drawbacks to both the overall QoS of composite services and the success rate of service composition. To circumvent this, an approach of synergistic elementary service group-based service composition (SESG-SC) is proposed in this paper. It releases the assumption of one-to-one mapping between elementary services and subtasks, allowing a free combination of multiple functionally equivalent elementary services into a synergistic elementary service group (SESG) to perform each subtask collectively, thereby bettering the overall QoS and achieving more acceptable success rate. To introduce an optimal construction of SESGs into the optimization model of QoS-aware service composition, three kinds of redundant structures within SESGs are discussed and the corresponding QoS evaluation formulas are also proposed. To deal with the increasing computing complexity of the optimization model, an algorithm named matrix-coded genetic algorithm with collaboratively evolutional populations (MCGA-CEP) is designed in the current study. The experimental results indicate that the proposed SESG-SC approach significantly outperforms the previous approaches, and the proposed MCGA-CEP is sound on performance-wise.

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Intelligent bionic genetic algorithm (IB-GA) and its convergence

Cited by 51 publications

References 19 publications

The impact of the cost of quality on serial supply‐chain network design

The impact of the cost of quality on serial supply‐chain network design

A new methodology to support group decision-making for IoT-based emergency response systems

QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups

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