Babak Abbasi

An efficient tabu search algorithm for flexible flow shop sequence-dependent group scheduling problems

In this paper, the flexible flow shop sequence-dependent group scheduling problem (FFSDGS) with minimisation of makespan as the criterion (FFm  | fmls, Splk  | C max) is investigated. For the first time a mathematical model for the proposed research problem is developed. Since the problem is shown to be NP-hard, six metaheuristic algorithms based on tabu search (TS) are developed to efficiently solve the problem. The proposed metaheuristics are different to the only available metaheuristic algorithm in the literature based on TS. By applying randomised complete block design and using available test problems in the literature, the best of the proposed TS algorithms in this research is identified. The performance of the best developed metaheuristic algorithm is then compared with the existing algorithm in the literature by solving the test problems, also available in the literature, ranging in size from small, medium, to large. A comparison based on paired t-test at 95% confidence interval, shows that the best proposed algorithm in this research has a better performance than the existing algorithm in the literature with an average percentage deviation of around 1.0% for medium and large size problems.

Improving response surface methodology by using artificial neural network and simulated annealing

Highlights? We apply neural networks to improve response surface methodology. ? We use simulated annealing algorithm to optimize the RS function obtained by ANN. ? We examine the performance of using neural network in RSM by using three test problems. Response surface methodology (RSM) explores the relationships between several explanatory variables and one or more response variables. The main idea of RSM is to use a set of designed experiments to obtain an optimal response. RSM tries to simplify the original problem through some polynomial estimation over small sections of the feasible area, elaborating on optimum provision through a well known optimization technique, say Gradient Method.As the real world problems are usually very complicated, polynomial estimation may not perform well in providing a good representation of the objective function. Also, the main problem of the Gradient Method, getting trapped in local minimum (maximum), makes RSM at a disadvantage, while defining sub-sections of the feasible area is also a problem faced by analyst.In this article, neural networks are used as a means to improve the estimation in the RSM context. This approach leads to reducing the calculations. Furthermore, it is proposed to use simulated annealing in maximizing the estimated objective function in reaching a suitable point. Three examples of different complexities are solved to shed light on the merits of the proposed method. The comparison results indicate that the proposed algorithm outperforms the classical method.

Spatial analyses of wildlife contact networks

Datasets from which wildlife contact networks of epidemiological importance can be inferred are becoming increasingly common. A largely unexplored facet of these data is finding evidence of spatial constraints on who has contact with whom, despite theoretical epidemiologists having long realized spatial constraints can play a critical role in infectious disease dynamics. A graph dissimilarity measure is proposed to quantify how close an observed contact network is to being purely spatial whereby its edges are completely determined by the spatial arrangement of its nodes. Statistical techniques are also used to fit a series of mechanistic models for contact rates between individuals to the binary edge data representing presence or absence of observed contact. These are the basis for a second measure that quantifies the extent to which contacts are being mediated by distance. We apply these methods to a set of 128 contact networks of field voles (Microtus agrestis) inferred from mark–recapture data collected over 7 years and from four sites. Large fluctuations in vole abundance allow us to demonstrate that the networks become increasingly similar to spatial proximity graphs as vole density increases. The average number of contacts, , was (i) positively correlated with vole density across the range of observed densities and (ii) for two of the four sites a saturating function of density. The implications for pathogen persistence in wildlife may be that persistence is relatively unaffected by fluctuations in host density because at low density is low but hosts move more freely, and at high density is high but transmission is hampered by local build-up of infected or recovered animals.

Economic Sharing of Basin Water Resources between Competing Stakeholders

This paper describes an application of linear programming (LP) methods for optimal allocation of water among competing stakeholders that would achieve the highest economic return from water use in the agricultural section of the Sefidrud Basin, northern Iran. In a network presentation of the basin, the nodes stand for the supply and demand points and arcs represent reaches. The constraints of the LP model are the network structure of the basin (flows, stream geography and channel capacity), the available surface and ground water in each node, the environmental demand in different reaches, upper and lower bands of supply in each node and water balances. Optimal policies are derived for current and future demand. The optimal policies indicate that, at present, the basin water resources satisfy the demands of all stakeholders. Although, the results show that there is no conflict for supplying stakeholders’ current demands, they indicate that the current proportion of surface water used is not optimal compared with the proportion of ground water used. The results also indicate that some future demands of provinces with lower marginal value of water are unsatisfied and that this could cause conflict between stakeholders. Since in some nodes the optimal solutions suggest using surface water even where they have available ground water, they are categorized as having a higher possibility to construct dams in the basin.

Bootstrap control charts in monitoring value at risk in insurance

A risk measure is a mapping from the random variables representing the risks to a number. It is estimated using historical data and utilized in making decisions such as allocating capital to each business line or deposit insurance pricing. Once a risk measure is obtained, an efficient monitoring system is required to quickly detect any drifts in the risk measure. This paper investigates the problem of detecting a shift in value at risk as the most widely used risk measure in insurance companies. The probabilistic C control chart and the parametric bootstrap method are employed to establish a risk monitoring scheme in insurance companies. Since the number of claims in a period is a random variable, the proposed method is a variable sample size scheme. Monte Carlo simulations for Weibull, Burr XII, Birnbaum-Saunders and Pareto distributions are carried out to investigate the behavior and performance of the proposed scheme. In addition, a real example from an insurance company is presented to demonstrate the applicability of the proposed method.

Quantitative analysis of rate-driven and due date-driven construction: production efficiency, supervision, and controllability in residential projects

AbstractConcerns about production efficiency, quality, and affordability in the residential construction indicate there may be benefits in adopting alternative production control strategies to those traditionally used. Reducing adverse effects of exogenous variability in demand and endogenous variability in process are the ultimate goals of production control strategies. For residential construction this means controlling the number of houses under construction and controlling the start rate of new house constructions. The aim of this investigation is to compare and contrast the outcomes of these two production management strategies. Production data of two volume house builders in Victoria and Queensland, Australia, were collected. Tangible performance metrics from the builders were analyzed and compared using the principles of queuing theory. Then numerous simulation experiments were designed and run to analyze different what-if scenarios in the building environment. A special purpose simulation template...

A Multi-objective Approach for Transboundary River Water Allocation

The allocation of water to the stakeholders of a large basin involves conflicting objectives, since increasing the allocated water to one stakeholder leads to a reduction in water allocated to other stakeholders. The consideration of conflicting objectives is inevitable when the basin is a transboundary basin, where a river crosses at least one political border, either a border within a nation or an international boundary. This paper proposes a multi-objective optimization model for sharing water among stakeholders of a transboundary river, assuming that the stakeholders cooperate. Here, the cooperation implies a balanced water allocation to stakeholders since shortage in each stakeholder have negative impacts on others. Each objective function of the multi-objective model represents the water profit of a stakeholder; which has to be maximized. To reach a cooperative solution, a new method for transforming the multi-objective formulation to a three-step single objective formulation is proposed. The solution guarantees each stakeholders profit which is larger than a percentage of its highest possible profit obtained in the case when the percentage of profit is equal for all stakeholders. The proposed model formulation was applied to the Sefidrud River where eight provinces are the stakeholders competing for water resources of this basin.

Three new multivariate process capability indices

Different multivariate process capability indices are developed by researchers to evaluate process capability when vectors of quality characteristics are considered in a study. This article presents three indices referred to as NCpM, MCpM, and NMC PM in order to evaluate process capability in multivariate environment. The performance of the proposed indices is investigated numerically. Simulation results indicate that the proposed indices have descended estimation error and improved performance compared to the existing ones. These results can be important to researchers and practitioners who are interested in evaluating process capability in multivariate domain.

Integration of resource investment problem with quantity discount problem in material ordering for minimizing resource costs of projects

Minimizing the costs in a project is highly tied with the way the required resources are provided. The resource investment problem deals with how to employ the renewable resources such that the related costs are minimized. Furthermore, the material ordering problem alludes to outlining a proper plan for supplying the nonrenewable resources (materials) to minimize the associated costs. The present paper studies the integration of the resource investment problem with the quantity discount problem in material ordering to thoroughly investigate the resource costs of projects in a single circumstance. The integrated model is presented and mathematically formulated. Three hybrid procedures are proposed for the model, each of which includes a genetic algorithm combined with a dynamic programming, a simulated annealing or a particle swarm optimization algorithm. The mathematical formulations of some small instances are solved to be the subject of an exact comparison with hybrid procedures. The proposed procedures are tested on a set of 810 benchmarks known in the literature. The computational experiments reported by algorithms validate the efficiency of the hybrid genetic algorithm and dynamic programming for the model more than other hybrid approaches.

Estimating the four parameters of the Burr III distribution using a hybrid method of variable neighborhood search and iterated local search algorithms

Abstract The Burr III distribution properly approximates many familiar distributions such as Normal, Lognormal, Gamma, Weibull, and Exponential distributions. It plays an important role in reliability engineering, statistical quality control, and risk analysis models. The Burr III distribution has four parameters known as location, scale, and two shape parameters. The estimation process of these parameters is controversial. Although the maximum likelihood estimation (MLE) is understood as a straightforward method in parameters estimation, using MLE to estimate the Burr III parameters leads to maximize a complicated function with four unknown variables, where using a conventional optimization such as the gradient method is difficult. In this paper to circumvent the difficulty of maximizing the Burr III likelihood function, a meta-heuristics hybrid approach is proposed which composes of a variable neighborhood search (VNS) along with an iterated local search (ILS) algorithm. In the proposed algorithm, different heuristic local search methods are investigated to promote the ILS algorithm performance. Furthermore, the Taguchi technique is employed to tune the parameters. The results of some numerical examples and a simulation study indicate satisfactory performance of the proposed algorithm.