A Public Bicycle Sharing System Considering Renting and Middle Stations

Recently, public bicycle sharing system (PBSS) has become one of the most favorite urban transportation systems that can help governments to decrease environmental problems such as pollution and traffic. This paper studies a sharing system that includes two types of stations. The first category contains stations that users can rent or return back bicycles and each bicycle can be rented by any new user who arrives to the stations. The second group is the stations which are near shopping centers, historical and other places that users and tourists can stop and visit them. These stations are used only for parking the rented bicycles for a period of time and after that, the users must ride their bicycles and turn them back to their destination stations. After discussing the network of the model under the closed Jackson network, the Mean Value Analysis (MVA) method will be used to calculate the mean queue of each station and analyzing the proposed model

Optimizing Employment and learning system using big data and knowledge management based on deduction graph

In recent years, big data has usefully been deployed by organizations with the aim of getting a better prediction for the future. Moreover, knowledge management systems are being used by organizations to identify and create knowledge. Here, the output from analysis of big data and a knowledge management system are used to develop a new model with the goal of minimizing the cost of implementing new recognized processes including staff training, transferring and employment costs. Strategies are proposed from big data analysis and new processes are defined accordingly. The company requires various skills to execute the proposed processes. Organization\u2019s current experts and their skills are known through a pre-established knowledge management system. After a gap analysis, managers can make decisions about the expert arrangement, training programs and employment to bridge the gap and accomplish their goals. Finally, deduction graph is used to analyze the model

A Bi-Objective Programming Model for Reliable Supply Chain Network Design Under Facility Disruption

Supply chain networks generally are composed of four main entity types: supplier, production centers, distribution centers and demand zones that consist of facilities whose activities involve the transformation of raw material into finished products that are later delivered from the suppliers to the end customers. Supply chain network design as the most important strategic decision in supply chain management plays an important role in the overall environmental and economic performance of the supply chain. The nature and complexity of today’s supply chains network make them vulnerable to various risks. One of the most important risks is disruption risk. Disruptions are costly and can be caused by internal or external sources to the supply chain, thus it is crucial that managers take appropriate measures of responses to reduce its negative effects. A recovery time of disrupted facilities and return it to the normal condition can be an important factor for members of the supply chain. In this paper, a bi-objective model is developed for reliable supply chain network design under facility disruption. To solve this model, we have applied two approaches, i.e., ε constraint method as an exact method and non- dominated sorting genetic algorithm (NSGAII) as a meta-heuristic method

Clustering Organizational Learning Capability Indices for Knowledge Sharing in Different Segments of the Firm

Since maximization of learning and organizational learning capabilities is the most important element for the success of knowledge management in each organization, this paper focuses on the dimensions of organizational learning capability. We suggest a mathematical clustering structure of dimensions according to their effect on the learning capability for different parts of the organization in order to obtain the highest level of learning capability in an organization. The proposed mathematical clustering aims to relate the needs of different sections of a firm to the corresponding learning capabilities

An integrated model of cellular manufacturing and supplier selection considering product quality

Today’s business environment has forced manufacturers and plants to produce high-quality products at low cost and the shortest possible delivery time. To cope with this challenge, manufacturing organizations need to optimize the manufacturing and other functions that are in logical association with each other. Therefore, manufacturing system design and supplier selection process are linked together as two major and interrelated decisions involved in viability of production firm. As a matter of fact, production and purchasing functions interact in the form of an organization’s overall operation and jointly determine corporate success. In this research, we tried to show the relationship between designing cellular manufacturing system (CMS) and supplier selection process by providing product quality considerations as well as the imprecise nature of some input parameters including parts demands and defects rates. A unified fuzzy mixed integer linear programming model is developed to make the interrelated cell formation and supplier selection decisions simultaneously and to obtain the advantages of this integrated approach with product quality and consequently reduction of total cost. Computational results also display the efficiency of proposed mathematical model for simultaneous consideration of cellular manufacturing design and supplier selection as compared to when these two decisions separately taken into account

Designing an integrated production/distribution and inventory planning model of fixed-life perishable products

This paper aims to investigate the integrated production/distribution and inventory planning for perishable products with fixed life time in the constant condition of storage throughout a two-echelon supply chain by integrating producers and distributors. This problem arises from real environment in which multi-plant with multi-function lines produce multi-perishable products with fixed life time into a lot sizing to be shipped with multi-vehicle to multi-distribution-center to minimize multi-objective such as setup costs between products, holding costs, shortage costs, spoilage costs, transportation costs and production costs. There are many investigations which have been devoted on production/distribution planning area with different assumption. However, this research aims to extend this planning by integrating an inventory system with it in which for each distribution center, net inventory, shortage, FIFO system and spoilage of items are calculated. A mixed integer non-linear programming model (MINLP) is developed for the considered problem. Furthermore, a genetic algorithm (GA) and a simulated annealing (SA) algorithm are proposed to solve the model for real size applications. Also, Taguchi method is applied to optimize parameters of the algorithms. Computational characteristics of the proposed algorithms are examined and tested using t-tests at the 95% confidence level to identify the most effective meta-heuristic algorithm in term of relative percentage deviation (RPD). Finally, Computational results show that the GA outperforms SA although the computation time of SA is smaller than the GA