editorial bioinspired computation and its applications in

EditorialBioinspired Computation and Its Applications inOperation Management

Tinggui Chen,1 Jianjun Yang,2 Kai Huang,3 and Qiang Cheng4

1 School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China2Department of Computer Science, University of North Georgia, Oakwood, GA 30566, USA3DeGroote School of Business, McMaster University, DSB 404, Hamilton, ON, Canada L8S 4M44College of Mechanical Engineering and Applied Electronics, Beijing University of Technology, Beijing 100124, China

Correspondence should be addressed to Tinggui Chen; [email protected]

Received 23 March 2014; Accepted 23 March 2014; Published 12 June 2014

Copyright © 2014 Tinggui Chen et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Bioinspired computation is an umbrella term for differentcomputational technologies that are based on principles ormodels of biological systems.This class of approaches, includ-ing evolutionary algorithm, swarm intelligence, and artificialimmune system, complements traditional ones in the sensethat the former can be applied to large and complex com-bination optimization problems, but the latter encountersdifficulties.Therefore, bioinspired technologies are becomingimportant in the face of solving discrete and dynamic prob-lems.

Recently, the bioinspired computation has attractedmuchattention of researchers and has also been widely applied tooperation management fields ranging between productionassembling, inventory control, project scheduling, humanresource management, and revenue management. However,due to complexity and uncertainty in operation managementproblems, it is very difficult to find out the optimum solutionunder the limited resources, time, and money in real-worldapplications using the bioinspired technologies. Therefore, itis necessary to develop efficient or improved algorithms tosolve operation management problems.

The main objective of this special issue is to present theoriginal research and review articles on the latest theoreticaland practical achievements that will contribute to the fieldof bioinspired computation and its applications in operationmanagement, in all branches of management science andcomputer science.

The special issue received 66 high-quality submissionsfrom different countries all over the world. All submittedmanuscripts have followed the same standard (peer-reviewedby at least three independent reviewers) as applied to regularones to “this journal.” Due to space limit, only 24 papers couldbe published (acceptance ratio of 1 : 3). Inevitably, difficultdecisions had to be made, and some high-quality submis-sions could not be included. The primary guideline was todemonstrate the wide scope of bioinspired computation andapplications in operation management. Besides, some novelresearch questions from different applications that are worthfurther investigation in the future are also included.

In the paper, “A hybrid genetic-simulated annealing algo-rithm for the location-inventory-routing problem consideringreturns under e-supply chain environment,” Y. Li et al. for-mulate a location-inventory-routing problem model withno quality defects’ returns. In addition, to solve this NP-hard problem, an effective hybrid genetic-simulated anneal-ing algorithm (HGSAA) is proposed. Results of numericalexamples show that HGSAA outperforms GA in computingtime, optimal solution, and computing stability. Moreover,the proposed model is also very useful in helping managersmake the right decisions under e-supply chain environment.

In the paper “Optimization and planning of emergencyevacuation routes considering traffic control,” G. Li et al. estab-lish two different emergency evaluation models on the basisof the maximum-flow model (MFM) and minimum-cost

Hindawi Publishing Corporatione Scientific World JournalVolume 2014, Article ID 356571, 5 pageshttp://dx.doi.org/10.1155/2014/356571

2 The Scientific World Journal

maximum-flow model (MC-MFM) and provide the cor-responding algorithms for the evacuation of one sourcenode to one designated destination (one-to-one evacuation).Besides, they also extend their model to solve one-to-manyevacuations. Finally, case analysis of evacuation optimizationand planning in Beijing is given and the efficiency of theproposed model is illustrated.

In the paper “An improved hierarchical genetic algorithmfor sheet cutting scheduling with process constraints,” Y. Rao etal. proposed an improved hierarchical genetic algorithm forsheet cutting problem which involves 𝑛 cutting patterns for𝑚 nonidentical parallel machines with process constraints.Furthermore, to speed up convergence rates and resolve localconvergence issues, a kind of adaptive crossover probabilityand a kind of mutation probability are used in this algorithm.The computational result and comparison prove that thepresented approach is quite effective for the consideredproblem.

In the paper entitled “An adaptive hybrid algorithm basedon particle swarm optimization and differential evolution forglobal optimization,” X. Yu et al. formulate a novel adaptivehybrid algorithm based on PSO and DE (HPSO-DE) bydeveloping a balanced parameter between PSO and DE.Adaptive mutation is carried out to current population whenthe population clusters around local optima. The HPSO-DEenjoys the advantages of PSO andDE andmaintains diversityof the population. Compared with PSO, DE, and theirvariants, the performance of HPSO-DE is promising andcompetitive.

In the paper “An effective hybrid self-adapting differentialevolution algorithm for the joint replenishment and location-inventory problem in a three-level supply chain,” L. Wanget al. provide an effective intelligent algorithm for a mod-ified joint replenishment and location-inventory problem(JR-LIP) where distribution centers (DCs) replenish theirdemands jointly. The problem of the JR-LIP is to deter-mine the number and locations of DCs to be opened, theassignment of retailers to DCs, the basic replenishmenttime of DCs, and the replenishment frequency of each DCsuch that the overall cost is minimized. To find an effectiveapproach for the JR-LIP, a hybrid self-adapting differentialevolution algorithm (HSDE) is designed. Comparative resultsof benchmark functions’ tests and randomly generated JR-LIPs show that HSDE outperforms GA and HDE.

In the paper entitled “Genetic algorithm application inoptimization of wireless sensor networks,” A. Norouzi and A.H. Zaim use genetic algorithm to optimize wireless sensornetworks. A fitness function with optimum formula wasobtained and the present protocols are optimized.The resultsof simulations in JPAC, MATLAB, and NS are comparedwith that of the present protocols and optimization of thetwo parameters is confirmed. It is also noticeable that thediagrams obtained from the simulations show an improve-ment in energy consumption parameters and lifetime of thenetwork; this means more ideal WSNs.

In the paper, the research of X. Cheng and Y. Lin entitled“Multiobjective robust design of the double wishbone suspen-sion system based on particle swarm optimization” proposesa robust design based on bioinspired computation. The

simulation experiment is arranged and Latin hypercubedesign is adopted to find the initial point. Then sensitivityanalysis is utilized to determine main design variables. Thekriging model is employed for fitting the mean and varianceof the quality characteristics according to the simulationresults. Furthermore, a particle swarm optimization methodbased on simple PSO is applied and the tradeoff betweenthe mean and deviation of performance is made to solve therobust optimization problem of double wishbone suspensionsystem.

In the paper entitled “A novel artificial bee colonyapproach of live virtual machine migration policy using Bayestheorem,” G. Xu et al. present a novel heuristic approachwhich is called PS-ABC. Its algorithm includes two parts. Oneis that it combines the ABC (artificial bee colony) idea withthe uniform random initialization idea and the binary searchidea and Boltzmann selection policy to achieve an improvedABC-based approach with better global exploration’s abilityand local exploitation’s ability.The other one is that it uses theBayesTheorem to further optimize the improved ABC-basedprocess to faster get the final optimal solution. As a result, thewhole approach achieves a longer-term efficient optimizationfor power saving. The experimental results demonstratethat PS-ABC evidently reduces the total incremental powerconsumption and better protects the performance of VMrunning and migrating compared with the existing research.It makes the result of live VM migration more high effectiveand meaningful.

In the paper entitled “Seven-spot ladybird optimization:a novel and efficient metaheuristic algorithm for numericaloptimization,” P. Wang et al. present a novel biologicallyinspired metaheuristic algorithm called seven-spot ladybirdoptimization (SLO).The SLO is inspired by recent discoverieson the foraging behavior of a seven-spot ladybird. In thispaper, the performance of the SLO is compared with thatof the genetic algorithm, particle swarm optimization, andartificial bee colony algorithms by using five numericalbenchmark functions with multimodality. The results showthat SLO has the ability to find the best solution with acomparative small population size and is suitable for solvingoptimization problems with lower dimensions.

In the paper entitled “Pricing resources in LTE networksthrough multiobjective optimization,” Y.-L. Lai and J.-R. Jiangstudy the pricing resources with profits and satisfaction opti-mization (PRPSO) problem in the LTE networks, consideringthe operator profit, and subscribe satisfaction at the sametime. The problem is modeled as nonlinear multiobjectiveoptimization with two optimal objectives: (1) maximizingoperator profit and (2) maximizing user satisfaction. Theypropose solving the problem based on the framework of theNSGA-II algorithm. Simulations are conducted for evaluat-ing the proposed solution.

In the paper entitled “Comparison of multiobjectiveevolutionary algorithms for operations scheduling undermachine availability constraints,” M. Frutos et al. analyzedifferent evolutionary multiobjective algorithms (MOEAs)for this kind of problems. They consider an experimentalframework in which they schedule production operations forfour real-world Job-Shop contexts using three algorithms,

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NSGAII, SPEA2, and IBEA. Using two performance indexes,hypervolume and R2, they found that SPEA2 and IBEA arethe most efficient for the tasks at hand. On the other handIBEA seems to be a better choice of tool since it yields moresolutions in the approximate Pareto frontier.

In the paper entitled “A modified decision tree algorithmbased on genetic algorithm for mobile user classification prob-lem,” D.-S. Liu and S.-J. Fan put forward a modified decisiontree algorithm for mobile user classification, which intro-duced genetic algorithm to optimize the results of the deci-sion tree algorithm.They also take the context information asa classification attributes for the mobile user and classify thecontext into public context and private context classes. Then,the processes and operators of the algorithm are analyzed. Atlast, an experiment is given so as to verify the efficiency andeffectiveness of the proposed approach.

In the paper “A location selection policy of live virtualmachine migration for power saving and load balancing,” J.Zhao et al. present the specific design and implementationof MOGA-LS such as the design of the genetic operatorsand fitness values and elitism. They introduce the Paretodominance theory and the SA (simulated annealing) idea intoMOGA-LS and present the specific process to get the finalsolution. And thus the whole approach achieves a long-termefficient optimization for power savings and load balanc-ing. The experimental results demonstrate that MOGA-LSevidently reduces the total incremental power consumption,better protects the performance of VM migration, achievesthe balancing of system load compared with the existingresearch. It makes the result of live VMmigration more higheffective and meaningful.

In the paper “ModelingMarkov switching ARMA-GARCHneural networks models and an application to forecastingstock returns,” M. E. Bildirici and O. Ersin propose a familyof regime switching GARCH neural network models tomodel volatility. Proposed MS-ARMA-GARCH-NN modelsallow MS type regime switching in both the conditionalmean and conditional variance for time series and fur-ther augmented with artificial neural networks to achieveimprovement in forecasting capabilities. In the empiricalsection, daily stock returns in ISE100 Istanbul Stock Indexare modeled and forecasted. Forecast success is evaluatedwith MAE, MSE, and RMSE criteria and Diebold-Marianotests. Result suggest that hybrid MLP and time lag recur-rent MS-ARMA-FIAPGARCH hybrid MLP andMS-ARMA-FIAPGARCH-RNN provided the best forecast and modelingperformance over the simple GARCH models and addi-tionally Gray’s MS-GARCH model. Therefore, augmentingthe forecasting capabilities with neural networks and regimeswitching provide significant gains in various economicapplications.

In the paper entitled “Full glowworm swarm optimizationalgorithm for whole-set orders scheduling in single machine,”Z. Yu and X. Yang propose a new glowworm swarm opti-mization algorithm for scheduling by analyzing the char-acteristics of whole-set orders problem and combining thetheory of glowworm swarm optimization. A new hybrid-encoding schema combiningwith two-dimensional encodingand random-key encoding is given. In order to enhance the

capability of optimal searching and speed up the convergencerate, the dynamical changed step strategy is integrated intothis algorithm. Furthermore, experimental results prove itsfeasibility and efficiency.

In the paper “Multiple R&D scheduling optimization withimproved particle swarm algorithm,” M. Liu et al. discussthe features of multiple R&D environment in customizationenterprises and demands of resources distribution, makesome improvements to the multiple project scheduling mod-els, and put forward a multiple project crashing schedulingmodel based on postpone-punishment in large scale cus-tomization environment. At the same time, based on theanalysis of particle swarm optimization algorithm and itsimprovement, a new solution of dynamic center particleswarm optimization algorithm to multiproject schedulingmodel has been arisen. The best solution can be gained afterthe experiment to the illustration example throughMATLABprogram; thus, the model in the paper and application ofalgorithm can be proved.

In the paper “Applying probability theory for the qualityassessment of a wildfire spread prediction framework based ongenetic algorithms,” A. Cencerrado et al. present a frameworkfor assessing how the existing constraints at the time ofattending an ongoing forest fire affect simulation results,both in terms of quality (accuracy) obtained and the timeneeded to make a decision. The core of this frameworkis evaluated according to the probability theory principles.Thus, a strong statistical study is presented, oriented towardsthe characterization of such an adjustment technique in orderto help the operation managers to deal with the two aspectspreviously mentioned: time and quality. The experimentalwork in this paper is based on a region in Spain which is oneof the most prone to forest fires: El Cap de Creus.

In the paper “Scheduling projects with multiskill learningeffect,” H. Zha and L. Zhang investigate the project schedulingproblem with multiskill learning effect. A new model isproposed to deal with the problem, where both autonomousand induced learning are considered. In order to obtain theoptimal solution, a genetic algorithm with specific encodingand decoding schemes is introduced. A numerical exampleis used to illustrate the proposed model. The computationalresults show that the learning effect cannot be neglected inproject scheduling. By means of determining the level ofinduced learning, the projectmanager can balance the projectmakespan with total cost.

In the paper entitled “A novel algorithm combining thefinite state method and genetic algorithm for scheduling ofcrude oil problem,” Q.-Q. Duan et al. propose a novel geneticalgorithm to solve the MINLP problem. The MINLP modelthey discussed is based on the single-operation sequencing(SOS) time representation. In this paper, based on thesequencing rules and the extension to the regular expressioncalculus, a deterministic finite state automaton (DFA) whichcaptures valid possible schedule sequences is constructed. Inthe initial population stage of GA, a population of candidatesolutions is generated on the basis of thisDFA.The rule-basedmutation strategy consists in following the sequencing ruleandmeeting the nonoverlapping constraint, which is movingtowards optimal solutions efficiently.Theoptimization results

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indicate both the effectiveness of the model and efficiency aswell as the robustness of the solution methodology.

In the paper “Improved particle swarm optimization witha collective local unimodal search for continuous optimizationproblems,” M. A. Arasomwan and A. O. Adewumi propose anew local search technique and use it to improve the perfor-mance of particle swarm optimization algorithms by address-ing the problem of premature convergence. In the proposedlocal search technique, a potential particle position in thesolution search space is collectively constructed by a numberof randomly selected particles in the swarm. The number oftimes the selection is made varies with the dimension of theoptimization problem and each selected particle donates thevalue in the location of its randomly selected dimension fromits personal best. After constructing the potential particleposition, some local search is done around its neighborhoodin comparison with the current swarm global best position.It is then used to replace the global best particle position ifit is found to be better; otherwise, no replacement is made.Using some well-studied benchmark problems with low andhigh dimensions, numerical simulations are used to validatethe performance of the improved algorithms. Comparisonsare made with four different PSO variants; two variantsimplement different local search technique, while the othertwo do not. Results show that the improved algorithms couldobtain better quality solution while demonstrating betterconvergence velocity and precision, stability, robustness, andglobal-local search ability than the competing variants.

In the paper “A new collaborative recommendationapproach based on users clustering using artificial bee colonyalgorithm,” C. Ju and C. Xu propose a novel collaborativefiltering recommendation approach based on K-means clus-tering algorithm. Firstly, they use artificial bee colony (ABC)algorithm to overcome K-means algorithm’s problems. Andthen they adopt the modified cosine similarity consideringproducts’ popularity degrees and users’ preference degrees tocompute the similarity between users in the same clusters.Finally, they generate the recommendation results for targetusers. Detailed numerical analysis on a benchmark datasetMovieLens and a real-world dataset indicates that their newcollaborative filtering approach based on users clusteringalgorithm outperforms many other recommendation meth-ods.

In the paper “Comprehensive optimization of emergencyevacuation route and departure time under traffic control,” G.Li et al. investigate the comprehensive optimization of majoremergency evacuation route and departure time, in whichevacuation propagationmechanism is considered under traf-fic control. Based onpractical assumptions, they first establisha comprehensive optimizationmodel based on the simulationof evacuation route and departure time. In order to optimizethe evacuation routes and departure time, they explore thereasonable description methods of evacuation traffic flowpropagation under traffic control, including the establish-ment of traffic flow propagation model and the design ofalgorithm which can implement simulation of evacuationtraffic flow. Finally, they propose a heuristic algorithm foroptimization of this comprehensive model. In case analysis,they take some areas in Beijing as evaluation sources to verify

the reliability of this model. Moreover, some constructivesuggestions for Beijing’s emergency evacuation are proposed,which can be applied to actual situation, especially undertraffic control.

In the paper “Collaborative scheduling model for Supply-Hub with multiple suppliers and multiple manufacturers,” G.Li et al. investigate a collaborative scheduling model thatcontains multiple suppliers, multiple manufacturers, and aSupply-Hub.They describe the operational process of Supply-Hub and formulate the basic schedulingmodel. Based on this,they consider two different scenarios: one is that the suppliersand the manufacturers make their decisions separately andthe other is that Supply-Hub makes the entire decisionswith the collaborative scheduling. Under this condition, theyprove that the scheduling model with Supply-Hub is NP-complete issue; thus, an autoadapted differential evolutionalgorithm is proposed. In the numerical analysis, they illus-trate that the performance of collaborative scheduling forSupply-Hub is superior to separate decision made by eachmanufacturer and supplier. Furthermore, they also showthat the proposed algorithm has a good convergence andreliability, in particular when it can be exerted flexibly ontocertain suppliers.

In the paper entitled “Hierarchical artificial bee colonyalgorithm for RFID network planning optimization,” L. Ma etal. present an optimization model for planning the positionsand radiated power setting of readers in the RFID network.The four nonlinear RNP objective functions are formulatedwith considering tag coverage, reader interference, economicefficiency, and network load balance as the primary require-ments of the real-world RFID system. And the combinedmeasure is also given so that the multiple objectives can beoptimized simultaneously. Finally, in order to solve the RNPmodel, a novel hierarchical artificial colony algorithm, calledHABC, is proposed by extending single artificial bee colony(ABC) algorithm to hierarchical and cooperative modeby combining the multipopulation cooperative coevolutionapproach based on vector decomposing strategy and thecomprehensive learning method. Results obtained from theproposed approach have been compared with those obtainedby ABC, PSO, CPSO, EGA, CMA ES, and CCEA.The exper-iment results show that, for all the test functions, the HABCgets significant superiority to other six algorithms. HABC isthen employed to solve the real-world RNP problem on twodifferent-scale instances, namely, Cd100 and Rd500.Throughsimulation studies, the HABC remarkably outperforms otheralgorithms. Especially, in tackling larger-scale RNP problem(i.e., Rd500 instance), the HABC performs more effectively,indicating that the HABC is more suitable for solving high-dimension RNP problem.

The study of bioinspired computation and its applicationsin operationmanagement is still in its early stage.This specialissue demonstrates the theoretical and practical importanceof further studies on bioinspired computation.

Acknowledgments

We would like to express our gratitude to all of the authorsfor their contribution and the reviewers for their effort in

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providing valuable comments and feedback. We hope thatthis special issue offers a comprehensive and timely view ofthe area of applications of bioinspired computation and thatit will offer stimulation for further research.

Tinggui ChenJianjun YangKai Huang

Qiang Cheng

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