METAHEURISTIC APPROACH FOR THE MULTI-DEPOT VEHICLE ROUTING PROBLEM

Distribution logistics comprises all activities related to the provision of finished products and merchandise to a customer. The focal point of distribution logistics is the shipment of goods from the manufacturer to the consumer. The products can be delivered to a customer directly either from the production facility or from the trader's stock located close to the production site or, probably, via additional regional distribution warehouses. These kinds of distribution logistics are mathematically represented as a vehicle routing problem (VRP), a well-known nondeterministic polynomial time (NP)-hard problem of operations research. VRP is more suited for applications having one warehouse. In reality, however, many companies and industries possess more than one distribution warehouse. These kinds of problems can be solved with an extension of VRP called multi-depot VRP (MDVRP). MDVRP is an NP-hard and combinatorial optimization problem. MDVRP is an important and challenging problem in logistics management. It can be solved using a search algorithm or metaheuristic and can be viewed as searching for the best element in a set of discrete items. In this article, cluster first and route second methodology is adapted and metaheuristics genetic algorithms (GA) and particle swarm optimization (PSO) are used to solve MDVRP. A hybrid particle swarm optimization (HPSO) for solving MDVRP is also proposed. In HPSO, the initial particles are generated based on the k-means clustering and nearest neighbor heuristic (NNH). The particles are decoded into clusters and multiple routes are generated within the clusters. The 2-opt local search heuristic is used for optimizing the routes obtained; then the results are compared with GA and PSO for randomly generated problem instances. The home delivery pharmacy program and waste-collection problem are considered as case studies in this paper. The algorithm is implemented using MATLAB 7.0.1.     

基于最具影响粒子群优化的BP神经网络训练

系统地介绍了粒子群优化算法,将粒子群优化算法用于BP神经网络的学习训练,提出了一种改进的粒子群算法——最具影响粒子PSO算法BIPSO,并利用复合适应度即均方误差和误差均匀度之和作为BIPSO训练神经网络的指标,并对它与其他的神经网络训练算法诸如BP算法、GA算法、PSO算法进行了比较。实验结果表明:BIPSO性能优于其他算法,更容易找到全局最优解,具有更好的收敛性。     

A binary ABC algorithm based on advanced similarity scheme for feature selection

Feature selection is the basic pre-processing task of eliminating irrelevant or redundant features through investigating complicated interactions among features in a feature set. Due to its critical role in classification and computational time, it has attracted researchers’ attention for the last five decades. However, it still remains a challenge. This paper proposes a binary artificial bee colony (ABC) algorithm for the feature selection problems, which is developed by integrating evolutionary based similarity search mechanisms into an existing binary ABC variant. The performance analysis of the proposed algorithm is demonstrated by comparing it with some well-known variants of the particle swarm optimization (PSO) and ABC algorithms, including standard binary PSO, new velocity based binary PSO, quantum inspired binary PSO, discrete ABC, modification rate based ABC, angle modulated ABC, and genetic algorithms on 10 benchmark datasets. The results show that the proposed algorithm can obtain higher classification performance in both training and test sets, and can eliminate irrelevant and redundant features more effectively than the other approaches. Note that all the algorithms used in this paper except for standard binary PSO and GA are employed for the first time in feature selection.     

Modeling customer satisfaction for new product development using a PSO-based ANFIS approach

When developing new products, it is important to understand customer perception towards consumer products. It is because the success of new products is heavily dependent on the associated customer satisfaction level. If customers are satisfied with a new product, the chance of the product being successful in marketplaces would be higher. Various approaches have been attempted to model the relationship between customer satisfaction and design attributes of products. In this paper, a particle swarm optimization (PSO) based ANFIS approach to modeling customer satisfaction is proposed for improving the modeling accuracy. In the approach, PSO is employed to determine the parameters of an ANFIS from which better customer satisfaction models in terms of modeling accuracy can be generated. A notebook computer design is used as an example to illustrate the approach. To evaluate the effectiveness of the proposed approach, modeling results based on the proposed approach are compared with those based on the fuzzy regression (FR), ANFIS and genetic algorithm (GA)-based ANFIS approaches. The comparisons indicate that the proposed approach can effectively generate customer satisfaction models and that their modeling results outperform those based on the other three methods in terms of mean absolute errors and variance of errors.     

An efficient optimal algorithm for the quantity discount problem in material requirement planning

An optimal algorithm based on branch-and-bound approach is presented in this paper to determine lot sizes for a single item in material requirement planning environments with deterministic time-phased demand and constant ordering cost with zero lead time, where all-units discounts are available from vendors and backlog is not permitted. On the basis of the proven properties of optimal order policy, a tree-search procedure is presented to construct the sequence of optimal orders. Some useful fathom rules have been proven, which make the algorithm very efficient. To compare the performance of this algorithm with the other existing optimal algorithms, an experimental design with various environments has been developed. Experimental results show that the performance of our optimal algorithm is much better than the performance of other existing optimal algorithms. Considering computational time as the performance measure, this algorithm is considered the best among the existing optimal algorithms for real problems with large dimensions (i.e. large number of periods and discount levels).     

A market-oriented hierarchical scheduling strategy in cloud workflow systems

A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. One of the most important aspects which differentiate a cloud workflow system from its other counterparts is the market-oriented business model. This is a significant innovation which brings many challenges to conventional workflow scheduling strategies. To investigate such an issue, this paper proposes a market-oriented hierarchical scheduling strategy in cloud workflow systems. Specifically, the service-level scheduling deals with the Task-to-Service assignment where tasks of individual workflow instances are mapped to cloud services in the global cloud markets based on their functional and non-functional QoS requirements; the task-level scheduling deals with the optimisation of the Task-to-VM (virtual machine) assignment in local cloud data centres where the overall running cost of cloud workflow systems will be minimised given the satisfaction of QoS constraints for individual tasks. Based on our hierarchical scheduling strategy, a package based random scheduling algorithm is presented as the candidate service-level scheduling algorithm and three representative metaheuristic based scheduling algorithms including genetic algorithm (GA), ant colony optimisation (ACO), and particle swarm optimisation (PSO) are adapted, implemented and analysed as the candidate task-level scheduling algorithms. The hierarchical scheduling strategy is being implemented in our SwinDeW-C cloud workflow system and demonstrating satisfactory performance. Meanwhile, the experimental results show that the overall performance of ACO based scheduling algorithm is better than others on three basic measurements: the optimisation rate on makespan, the optimisation rate on cost and the CPU time.     

Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization

This work presents particle swarm optimization (PSO), a collaborative population-based meta-heuristic algorithm for solving the Cardinality Constraints Markowitz Portfolio Optimization problem (CCMPO problem). To our knowledge, an efficient algorithmic solution for this nonlinear mixed quadratic programming problem has not been proposed until now. Using heuristic algorithms in this case is imperative. To solve the CCMPO problem, the proposed improved PSO increases exploration in the initial search steps and improves convergence speed in the final search steps. Numerical solutions are obtained for five analyses of weekly price data for the following indices for the period March, 1992 to September, 1997: Hang Seng 31 in Hong Kong, DAX 100 in Germany, FTSE 100 in UK, S&P 100 in USA and Nikkei 225 in Japan. The test results indicate that the proposed PSO is much more robust and effective than existing PSO algorithms, especially for low-risk investment portfolios. In most cases, the PSO outperformed genetic algorithm (GA), simulated annealing (SA), and tabu search (TS).     

异构云中面向集群负载均衡的任务调度策略

负载均衡是提高资源利用率和系统稳定性的重要手段。基于改进的自适应变异粒子群算法,提出了一种异构环境下面向集群负载均衡的任务调度策略。在调度策略的设计中,融入了经济学“二八”定律,通过把握用户对集群节点安全性和可靠性的偏好程度并预估任务的负载信息,在保证系统负载尽量均衡的前提下,最小化任务执行时间的同时提高大客户满意度。仿真实验显示,改进的自适应变异粒子群算法比未改进的自适应变异粒子群算法和基本粒子群算法在收敛速度和跳出局部最优两个方面都有更好的表现。结果表明,改进的自适应变异粒子群算法在保证集群负载均衡的同时可以更好地提高云服务提供商的利润空间。     

Two storage inventory model of a deteriorating item with variable demand under partial credit period

In this paper, a two-warehouse inventory model for deteriorating item with stock and selling price dependent demand has been developed. Above a certain (fixed) ordered label, supplier provides full permissible delay in payment per order to attract more customers. But an interest is charged by the supplier if payment is made after the said delay period. The supplier also offers a partial permissible delay in payment even if the order quantity is less than the fixed ordered label. For display of goods, retailer has one warehouse of finite capacity at the heart of the market place and another warehouse of infinite capacity (that means capacity of second warehouse is sufficiently large) situated outside the market but near to first warehouse. Units are continuously transferred from second warehouse to first and sold from first warehouse. Combining the features of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) a hybrid heuristic (named Particle Swarm-Genetic Algorithm (PSGA)) is developed and used to find solution of the proposed model. To test the efficiency of the proposed algorithm, models are also solved using another two established heuristic techniques and results are compared with those obtained using proposed PSGA. Here order quantity, refilling point at first warehouse and mark-up of selling price of fresh units are decision variables. Models are formulated for both crisp and fuzzy inventory parameters and illustrated with numerical examples.     

Vehicle routing problem with uncertain demands: An advanced particle swarm algorithm

The Vehicle Routing Problem (VRP) has been thoroughly studied in the last decades. However, the main focus has been on the deterministic version where customer demands are fixed and known in advance. Uncertainty in demand has not received enough consideration. When demands are uncertain, several problems arise in the VRP. For example, there might be unmet customers’ demands, which eventually lead to profit loss. A reliable plan and set of routes, after solving the VRP, can significantly reduce the unmet demand costs, helping in obtaining customer satisfaction. This paper investigates a variant of an uncertain VRP in which the customers’ demands are supposed to be uncertain with unknown distributions. An advanced Particle Swarm Optimization (PSO) algorithm has been proposed to solve such a VRP. A novel decoding scheme has also been developed to increase the PSO efficiency. Comprehensive computational experiments, along with comparisons with other existing algorithms, have been provided to validate the proposed algorithms.     

A charging-scheme decision model for electric vehicle battery swapping station using varied population evolutionary algorithms

This paper proposes a new battery swapping station (BSS) model to determine the optimized charging scheme for each incoming Electric Vehicle (EV) battery. The objective is to maximize the BSS’s battery stock level and minimize the average charging damage with the use of different types of chargers. An integrated objective function is defined for the multi-objective optimization problem. The genetic algorithm (GA), differential evolution (DE) algorithm and three versions of particle swarm optimization (PSO) algorithms have been implemented to solve the problem, and the results show that GA and DE perform better than the PSO algorithms, but the computational time of GA and DE are longer than using PSO. Hence, the varied population genetic algorithm (VPGA) and varied population differential evolution (VPDE) algorithm are proposed to determine the optimal solution and reduce the computational time of typical evolutionary algorithms. The simulation results show that the performances of the proposed algorithms are comparable with the typical GA and DE, but the computational times of the VPGA and VPDE are significantly shorter. A 24-h simulation study is carried out to examine the feasibility of the model.     

Recency-based storage assignment and warehouse configuration for recurrent demands

Storage assignment is an important decision problem in warehouse operation management. In conventional problem settings of distribution warehouses, stock items are stored in bulk but retrieved in small quantities. Storage assignment methods typically make use of demand attribute information of order quantity, order frequency and correlation between demands. In this paper, we address a different problem in which the request for the same stock items is stochastically recurrent. The problem arises when the items are needed in production and, after production, are returned to warehouses for later reuse. Examples of such items include tooling in factory, books in library and digital objects in data warehouses. Utilizing the recurrent characteristics, a salient recency-based storage assignment policy and an associated cascaded warehouse configuration are proposed and analyzed in this paper. This paper has four parts. In the first part, a model of recurrent demand is described. In the second part, the efficiency of the recency-based policy and a traditional ID-based policy is analyzed. In the third part, a mathematical programming model for optimal configuration of cascaded warehouses is presented. Finally, a case study of hospital visits is presented. This paper concludes with recommendations on cascading and zoning the warehouse for applying the recency-based policy.     

云计算资源的动态随机扰动的粒子群优化策略

云计算环境中的资源具有动态性和异构性,大规模任务资源分配的目标是最小化完成时间和资源占用,同时具有尽可能好的负载均衡,这是一个非确定性多项式（NP）问题。借鉴智能群体算法的优点,提出基于改进的粒子群优化（PSO）算法构建混合式群体智能调度策略——动态随机扰动的PSO策略（DRDPSO）。首先,将PSO的惯性权重常数修改为变量,实现对求解过程收敛速度的合理控制;其次,缩小每次迭代的搜索范围,在保留候选最优集合的前提下减少无效搜索;然后,引入选择操作,筛选出优质个体并传递到下一代;最后,设计随机扰动,提高候选解的多样性,在一定程度上避免了局部最优陷阱。在CloudSim平台上进行了两类仿真测试,结果表明,处理同构任务时,在大部分情况下DRDPSO的指标都优于模拟退火遗传算法（SAGA）和遗传算法（GA）+PSO算法,总执行时间比SAGA减少13.7%~37.0%,比GA+PSO减少13.6%~31.6%;其资源耗费比SAGA减少9.8%~17.1%,比GA+PSO减少0.6%~31.1%;其迭代次数比SAGA减少15.7%~60.2%,比GA+PSO减少1.4%~54.7%;其负载均衡度比SAGA减小8.1%~18.5%,比GA+PSO减少2.7%~15.3%,且波动幅度最小。处理异构任务时,三种算法表现出相似的规律：CPU型任务的总执行时间最多,混合型任务次之,IO型任务最少,DRDPSO的综合指标最好,较为适合处理多种类型的异构任务,而GA+PSO算法适合快速求解混合型任务,SAGA则适合快速求解IO型任务。所提DRDPSO在处理较大规模的同构和异构任务时,能够较为明显地缩短总的任务执行时间,不同程度地提高资源利用率,并适当兼顾计算节点的负载均衡。     

融合分区和局部搜索的多模态多目标优化

为解决多模态多目标优化中种群多样性维持难和所得等价解数量不足问题,基于分区搜索和局部搜索,本研究提出一种融合分区和局部搜索的多模态多目标粒子群算法(multimodal multi-objective particle swarm optimization combing zoning search and local search,ZLS-SMPSO-MM)。在所提算法中,整个搜索空间被分割成多个子空间以维持种群多样性和降低搜索难度;然后,使用已有的自组织多模态多目标粒子群算法在每个子空间搜索等价解和挖掘邻域信息,并利用局部搜索能力较强的协方差矩阵自适应算法对有潜力的区域进行精细搜索。通过14个多模态多目标优化问题测试,并与其他5种知名算法进行比较;实验结果表明ZLS-SMPSO-MM在决策空间能够找到更多的等价解,且整体性能要好于所比较算法。     

Cryptanalysis of Vigenere cipher using Cuckoo Search

Evolutionary techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cuckoo Search (CS) are promising nature-inspired meta-heuristic optimization algorithms. Cuckoo Search combined with Lévy flights behavior and Markov chain random walk can search global optimal solution very quickly. The aim of this paper is to investigate the applicability of Cuckoo Search algorithm in cryptanalysis of Vigenere cipher. It is shown that optimal solutions obtained by CS are better than the best solutions obtained by GA or PSO for the analysis of the Vigenere cipher. The results show that a Cuckoo Search based attack is very effective on the Vigenere cryptosystem.     

A novel dynamic genetic algorithm-based method for vehicle scheduling in cross docking systems with frequent unloading operation

An important factor for efficiently managing the supply chain is to efficiently control the physical flow of the supply chain. For this purpose, many companies try to use efficient methods to increase customer satisfaction and reduce costs. Cross docking is a good method to reduce the warehouse space requirements, inventory management costs, and turnaround times for customer orders. This paper proposes a novel dynamic genetic algorithm-based method for scheduling vehicles in cross docking systems such that the total operation time is minimized. In this paper, it is assumed that a temporary storage is placed at the shipping dock and inbound vehicles are allowed to repeatedly enter and leave the dock to unload their products. In the proposed method of this paper two different kinds of chromosome for inbound and outbound trucks are proposed. In addition, some algorithms are proposed including initialization, operational time calculation, crossover and mutation for inbound and outbound trucks, independently. Moreover a dynamic approach is proposed for performing crossover and mutation operation in genetic algorithm. In order to evaluate the performance of the proposed algorithm of this paper, various examples are provided and analyzed. The computational results reveal that the proposed algorithm of this paper performs better than two well-known works of literature in providing solutions with shorter operation time.     

Customer satisfaction‐aware scheduling for utility maximization on geo‐distributed data centers

With the increasingly growing amount of service requests from the world‐wide customers, the cloud systems are capable of providing services while meeting the customers' satisfaction. Recently, to achieve the better reliability and performance, the cloud systems have been largely depending on the geographically distributed data centers. Nevertheless, the dollar cost of service placement by service providers (SP) differ from the multiple regions. Accordingly, it is crucial to design a request dispatching and resource allocation algorithm to maximize net profit. The existing algorithms are either built upon energy‐efficient schemes alone, or multi‐type requests and customer satisfaction oblivious. They cannot be applied to multi‐type requests and customer satisfaction‐aware algorithm design with the objective of maximizing net profit. This paper proposes an ant‐colony optimization‐based algorithm for maximizing SP's net profit (AMP) on geographically distributed data centers with the consideration of customer satisfaction. First, using model of customer satisfaction, we formulate the utility (or net profit) maximization issue as an optimization problem under the constraints of customer satisfaction and data centers. Second, we analyze the complexity of the optimal requests dispatchment problem and rigidly prove that it is an NP‐complete problem. Third, to evaluate the proposed algorithm, we have conducted the comprehensive simulation and compared with the other state‐of‐the‐art algorithms. Also, we extend our work to consider the data center's power usage effectiveness. It has been shown that AMP maximizes SP net profit by dispatching service requests to the proper data centers and generating the appropriate amount of virtual machines to meet customer satisfaction. Moreover, we also demonstrate the effectiveness of our approach when it accommodates the impacts of dynamically arrived heavy workload, various evaporation rate and consideration of power usage effectiveness. Copyright © 2014 John Wiley & Sons, Ltd.     

A COMPARATIVE STUDY ON PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHMS FOR TRAVELING SALESMAN PROBLEMS

This article deals with a performance evaluation of particle swarm optimization (PSO) and genetic algorithms (GA) for traveling salesman problem (TSP). This problem is known to be NP-hard, and consists of the solution containing N! permutations. The objective of the study is to compare the ability to solve the large-scale and other benchmark problems for both algorithms. All simulation has been performed using a software program developed in the Delphi environment. As yet, overall results show that genetic algorithms generally can find better solutions compared to the PSO algorithm, but in terms of average generation it is not good enough.     

易腐生鲜货品车辆路径问题的改进混合蝙蝠算法

针对配送易腐生鲜货品的车辆其配送路径的选择不仅受货品类型、制冷环境变化、车辆容量限制、交货时间等多种因素的影响,而且需要达到一定的目标（如：费用最少、客户满意度最高）,构建了易腐生鲜货品车辆路径问题（VRP）的多目标模型,并提出了求解该模型的改进混合蝙蝠算法。首先,采用时间窗模糊化处理方法定义客户满意度函数,细分易腐生鲜货品类型并定义制冷成本,建立了最优路径选择的多目标模型;然后,在分析蝙蝠算法求解离散问题易陷入局部最优、过早收敛等问题的基础上,精简经典蝙蝠算法的速度更新公式,并对混合蝙蝠算法的单多点变异设定选择机制,提高算法性能;最后,对改进混合蝙蝠算法进行性能测试。实验结果表明,与基本蝙蝠算法和已有混合蝙蝠算法相比,所提算法在求解VRP时能够提高客户满意度1.6%~4.2%,且减小平均总成本0.68%~2.91%。该算法具有计算效率高、计算性能好和较高的稳定性等优势。     

On a cuckoo search optimization approach towards feedback system identification

This paper presents a cuckoo search algorithm (CSA) based adaptive infinite impulse response (IIR) system identification scheme. The proposed scheme prevents the local minima problem encountered in conventional IIR modeling mechanisms. The performance of the new method has been compared with that obtained by other evolutionary computing algorithms like genetic algorithm (GA) and particle swarm optimization (PSO). The superior system identification capability of the proposed scheme is evident from the results obtained through an exhaustive simulation study.