Production planning in industrial townships modeled as hub location-allocation problems considering congestion in manufacturing plants

In this paper, we aim to develop optimal production plans in industrial townships modeled as hub location-allocation problems (HLAP) taking congestion into account. In the proposed model, hub nodes are considered as industrial townships where manufacturing plants and a central distribution warehouse are located, and two objectives are targeted. The first is to minimize the total costs, which includes the cost of hub deployment, factories and warehouses, transportation, and so forth. The second is to minimize the total elapsed time of products in manufacturing plants and warehouses modeled as queues. Due to the ambiguity in estimating the model's parameters, they are considered as fuzzy parameters to make model closer to reality. The fuzzy model is then converted into an equivalent crisp model by combining the expected value (EV) and the fuzzy chance constrained programming (FCCP) approaches. Subsequently, the bi-objective crisp model is converted into a single aggregated objective model. In order to validate the proposed model, six numerical examples are solved, and the sensitivity of the proposed model with regard to changes in model's parameters is investigated

A bi˗objective hub location-allocation model considering congestion

In this paper, a new hub location-allocation model is developed considering congestion and production scheduling. This model assumes that manufacturing and distributing goods, including raw materials and semi-finished or finished goods, take place in hubs only (such as industrial township). The main objective of this study is to minimize the total costs and to minimize the sum of waiting times for processing goods in factories and warehouses. In order to solve the bi-objective model, goal attainment and LP metric techniques are combined to develop a more effective multi-objective technique. Due to the exponential complexity of the proposed approach as well as the nonlinearity of the mathematical model, a number of small and medium-sized problems are solved to demonstrate the effectiveness of the solution methodology

Expected Duration of Dynamic Markov PERT Networks

Abstract : In this paper , we apply the stochastic dynamic programming to approximate the mean project completion time in dynamic Markov PERT networks. It is assumed that the activity durations are independent random variables with exponential distributions, but some social and economical problems influence the mean of activity durations. It is also assumed that the social problems evolve in accordance with the independent semi-Markov processes over the planning horizon. By using the stochastic dynamic programming, we find a dynamic path with maximum expected length from the source node to the sink node of the stochastic dynamic network. The expected value of such path can be considered as an approximation for the mean project completion time in the original dynamic PERT network

Robust and fuzzy goal programming optimization approaches for a novel multi-objective hub location-allocation problem:a supply chain overview

In this paper, a novel multi-objective mathematical model is developed to solve a capacitated single-allocation hub location problem with a supply chain overview. Three mathematical models with various objective functions are developed. The objective functions are to minimize: (a) total transportation and installation costs, (b) weighted sum of service times in the hubs to produce and transfer commodities and the tardiness and earliness times of the flows including raw materials and finished goods, and (c) total greenhouse gas emitted by transportation modes and plants located in the hubs. To come closer to reality, some of the parameters of the proposed mathematical model are regarded as uncertain parameters, and a robust approach is used to solve the given problem. Furthermore, two methods, namely fuzzy multi-objective goal programming (FMOGP) and the Torabi and Hassini's (TH) method are used to solve the multi-objective mathematical model. Finally, the concluding part presents the comparison of the obtained results

R. Tavakkoli-Moghaddam a, M.B. Aryanezhad b,

a dynamic cell formatio

Genetic algorithm-based clustering ensemble: determination number of clusters

Genetic algorithms (GAs) have been used in the clustering subject. Also, a clustering ensemble as one acceptable clustering method combines the results of multiple clustering methods on a given dataset and creates final clustering on the dataset. In this paper, genetic algorithm base on clustering ensemble (GACE) is introduced for finding optimal clusters. The most important property of our method is the ability to extract the number of clusters. With this ability, the need for data examination is removed, and then solving related problems will not be time consuming. GACE is applied to eight series of databases. Experimental results were compared with other four clustering methods. Data envelopment analysis (DEA) is used to compare methods. The results of DEA indicate that GACE is the best method. The four methods are co-association function and average link (CAL), co-association function and K-means (CK), hypergraph-partitioning algorithm (HGPA) and cluster-based similarity partitioning (CSPA)