Mehrdad Mohammadi

An M/M/c queue model for hub covering location problem

a b s t r a c t The hub location problem appears in a variety of applications including airline systems, cargo delivery systems, and telecommunication network design. Hub location problems deal with finding the location of hub facilities and the allocation of demand nodes to these located hub facilities. We consider a hub-and-spoke network problem with crowdedness or congestion in the system. The transportation time and the rate of arrived trucks to each hub are random variables. In addition, a hub cannot service all trucks simultaneously and it has some restrictions like capacity constraint and the service time limitations. Hubs, which are the most crowded parts of network, are modeled as M/M/c queuing systems. In the application of the proposed model for a cargo transportation system, the number of trucks follows Poisson probability distribution in the queuing system. In this paper at first a nonlinear mathematical programming is presented to find an optimal solution for the considered problem. A probabilistic constraint is included to ensure that the probability of b trucks in a queue is less than a threshold value θ for each hub. Then, we transfer the introduced nonlinear constraints of the mathematical programming model to the linear constraints. Due to the computational complexity of the resulted model, we propose an improved meta-heuristic based on Imperialist Competitive Algorithm and Genetic Algorithm to find near optimal solution of the problem. The performance of the solutions found by the proposed improved meta-heuristic is compared with those of pure GA and those of the mathematical programming model.

Mathematical modelling of a robust inspection process plan: Taguchi and Monte Carlo methods

This study develops a new optimisation framework for process inspection planning of a manufacturing system with multiple quality characteristics, in which the proposed framework is based on a mixed-integer mathematical programming (MILP) model. Due to the stochastic nature of production processes and since their production processes are sensitive to manufacturing variations; a proportion of products do not conform the design specifications. A common source of these variations is maladjustment of each operation that leads to a higher number of scraps. Therefore, uncertainty in maladjustment is taken into account in this study. A twofold decision is made on the subject that which quality characteristic needs what kind of inspection, and the time this inspection should be performed. To cope with the introduced uncertainty, two robust optimisation methods are developed based on Taguchi and Monte Carlo methods. Furthermore, a genetic algorithm is applied to the problem to obtain near-optimal solutions. To validate the proposed model and solution approach, several numerical experiments are done on a real industrial case. Finally, the conclusion is provided.

A game-based meta-heuristic for a fuzzy bi-objective reliable hub location problem

Abstract Nowadays, offering fast and reliable delivery service has become a vital issue associated with all shipment delivery systems. Due to unpredictable variability in travel times, configuration of transportation systems plays a key role in ensuring of meeting the delivery service requirement. This paper tries to investigate the effect of delivery service requirement on the configuration of the transportation system through a hub-and-spoke network. The primary goal of this paper is to study a bi-objective single allocation p -hub center-median problem (BS p HCMP) by taking into account the uncertainty in flows, costs, times and hub operations. The proposed problem is modeled through a bi-objective mixed-integer non-linear programming (BMINLP) formulation that simultaneously locates p hubs, allocates spokes to the located hubs, and assigns different transportation mode to the hub-to-hub links. Then, a fuzzy-queuing approach is used to model the uncertainties in the network. Additionally, an efficient and powerful evolutionary algorithm based on game theory and invasive weed optimization algorithm was developed to solve the proposed BS p HCMP model and obtain near optimal Pareto solutions. Several experiments besides a real transportation case show the applicability of the proposed model as well as the superiority of the proposed solution approaches compared to NSGA-II and PAES algorithms.

Design of a reliable multi-modal multi-commodity model for hazardous materials transportation under uncertainty

In this paper, we propose a new mathematical model for designing a reliable hazardous material (HAZMAT) transportation network (RHTND) on the basis of hub location topology under uncertainties, in which hub nodes may be disrupted by external events, as well as HAZMATs incidents. Hub locations and HAZMAT transportation routes using different transportation modes are simultaneously optimized to obtain minimum risk of incidents. A mixed integer nonlinear programing model is developed. To cope with the uncertainties in the model, we provide a solution framework based on an integration of the well-known chance-constrained programing with a possibilistic programing approach. Small size problems are solved to optimality. In order to solve large size instances, a meta-heuristic algorithm was applied and its performance is evaluated in comparison with a new lower bound approach through analysis of a real case-study of a HAZMAT transportation network.

Design of a fuzzy bi-objective reliable p-hub center problem

Nowadays in all shipment delivery systems, most of customers are looking for companies that offer fast and reliable delivery service as well as guarantee when deliveries will be made. With variability in travel time, the configuration of the hub-and-spoke network is vital to meet the delivery service requirement. For this purpose, a bi-objective stochastic p-hub center problem is considered to investigate the effect of delivery service requirement on the configuration of the hub- and-spoke network with fuzzy parameters, in which hub operational nodes may disrupt and fail to operate shipments. Since delivery time of the shipments is the sum of transit time over the links and spent time at the hubs, spent times with fuzzy arrival of shipments are calculated by utilizing a fuzzy M/M/1 queuing system. In order to solve large-sized instances of the presented mathematical model, two meta-heuristic algorithms, namely SA and self-adaptive DE (SADE) are developed and their performances are evaluated using a proposed lower bound approach. Computational experiments are provided to demonstrate the effectiveness of the proposed model and solution approaches. Finally, a real transportation case is studied.

Hierarchical Neural Regression Models for Customer Churn Prediction

As customers are the main assets of each industry, customer churn prediction is becoming a major task for companies to remain in competition with competitors. In the literature, the better applicability and efficiency of hierarchical data mining techniques has been reported. This paper considers three hierarchical models by combining four different data mining techniques for churn prediction, which are backpropagation artificial neural networks (ANN), self-organizing maps (SOM), alpha-cut fuzzy c-means (α-FCM), and Cox proportional hazards regression model. The hierarchical models are ANN

Hub Covering Location Problem under Capacity Constraints

The hub location problem appears in a variety of applications including airline systems, cargo delivery systems, and telecommunication network design. When we analyze hub location applications separately, we observe that each area has its own characteristics. In this paper, we study the single allocation hub covering problem under capacity constraints (or CSAHCLP - Capacitate Single Allocation Hub Covering Location Problem) over complete hub networks and propose a mixed-integer programming formulation to this end. The aim of our model is to find the location of hubs and allocate non-hub nodes to the located hub nodes so much that the travel cost between any hub-node pair is within a given cost bound and hubs are considered under capacity constraint. Unlike [1] we prepare new formulation with covering radius. In general this paper attempts to propose a new mixed-integer programming formulation and adapt the imperialist competitive algorithm to solve the hub covering location problem. Also unlike previous studies, we adapt new solution algorithm (Imperialist competitive algorithm) for solving our problem that has not used yet.

Optimization of a multi-modal tree hub location network with transportation energy consumption: A fuzzy approach

A tree hub location problem (THLP) is a recently introduced extension of the classical hub location problem with an incomplete graph. The aim of this problem is to design a network more economic and practical. This paper presents a new multi-objective model to design a multi-modal tree hub location network under uncertainty. For this purpose, a fuzzy approach is applied to cope with the inherent uncertainty of input data in the THLP. One important issue, which is recently introduced in transportation network, is the amount of fuel consumption effected on economic and environmental problems, In this model, the amount of fuel consumption used in a transportation sector are accounted and the effect of road and vehicle types on consumed fuel in the THLP are studied. This model allows having different transportation modes between hubs and a set of capacity levels for each potential hub so that only one of them can be chosen. The objectives of this model include the minimization of energy consumption and the minimization of transportation costs and fixed costs of locating hubs and hub links. To solve the model, a multi-objective imperialist competitive algorithm (MOICA) is proposed to obtain the Pareto-optimal solutions of the problem. Furthermore, the performance of this algorithm is compared with non-dominated sorting genetic algorithm (NSGA-II).

A review on optimisation of part quality inspection planning in a multi-stage manufacturing system

In multi-stage manufacturing systems, optimisation of part quality inspection planning (PQIP) problem means to determine the optimal time, place and extent of inspection activities for assessing the significant quality characteristics of products while maximising the system efficiency. An inspection activity is capable of detecting the produced defects partially and accordingly prevents further processing of them in downstream and more importantly avoids them to reach customers. In this paper, the existing researches on the optimisation of the part quality inspection are surveyed from the viewpoint of the considered production system characteristics; the applied modelling approaches and solution methodologies. This review found that although numerous works have been already done on the PQIP, the development of multi-objective optimisation frameworks considering real production constraints under parameters uncertainty is necessary. Also, by the Industry 4.0 trend, the creation of integrated models aiming to plan the inspection, maintenance and production activities simultaneously, seems to be an important potential future research direction.

Performance evaluation of single and multi-class production systems using an approximating queuing network

Performance evaluation, and in particular cycle time estimation, is critical to optimise production plans in high-tech manufacturing industries. This paper develops a new aggregation model based on queuing network, so-called queue-based aggregation (QAG) model, to estimate the cycle time in a production system. Multiple workstations in serial and job-shop configurations are aggregated into a single-step workstation. The parameters of the aggregated workstation are approximated based on the parameters of the original workstations. Numerical experiments indicate that the proposed QAG model is computationally efficient and yields fairly accurate results when compared to other aggregation approaches in the literature.