Identifying the success factors of knowledge management tools in research projects (Case study: A corporate university)

This research investigates the factors affecting the success of KM tools in the research projects of a corporate university. The research method is descriptive and the statistical population of the study consisted of all professors and knowledge workers of a university. 147 of them were selected through a targeted sampling method. Data collection was conducted through a questionnaire. To determine the validity of the questionnaire, content and formal validity were used and its reliability was calculated by using Cronbach's alpha with the value calculated of 0.83. Data were analyzed by using descriptive statistics, t-test and Friedman test. In this study, the factors of culture, information technology, strategy and goal, organizational infrastructure, employee motivation, leadership and management support, human resources management, education, financial resources, measurement, processes and activities, structure and communications in the knowledge management cycle of research projects of the university studied were identified as the effective factors in the KM cycle

Metaheuristic Algorithm for Solving Biobjective Possibility Planning Model of Location-Allocation in Disaster Relief Logistics

Thousands of victims and millions of affected people are hurt by natural disasters every year. Therefore, it is essential to prepare proper response programs that consider early activities of disaster management. In this paper, a multiobjective model for distribution centers which are located and allocated periodically to the damaged areas in order to distribute relief commodities is offered. The main objectives of this model are minimizing the total costs and maximizing the least rate of the satisfaction in the sense of being fair while distributing the items. The model simultaneously determines the location of relief distribution centers and the allocation of affected areas to relief distribution centers. Furthermore, an efficient solution approach based on genetic algorithm has been developed in order to solve the proposed mathematical model. The results of genetic algorithm are compared with the results provided by simulated annealing algorithm and LINGO software. The computational results show that the proposed genetic algorithm provides relatively good solutions in a reasonable time

A New Optimization via Invasive Weeds Algorithm for Dynamic Facility Layout Problem

Abstract-The dynamic facility layout problem (DFLP) is the problem of finding positions of departments o

Short lifetime product supply chain coordination and social benefit considering cannibalization effect and market segmentation

Short lifetime product retailers often face the challenge of cannibalization between new and old products, which can negatively impact their profitability. They attempt to influence consumers’ choices through price differentiation, resulting in internal competition regarding products’ age and price. The pricing decisions affect market demand, sales volume, and as a result, the whole supply chain (SC) profit. This paper coordinates inventory and pricing decisions in a short lifetime product supply chain (SLPSC), considering the cannibalization effect. The investigated SLPSC includes a supplier and a retailer operating in a segmented market. Firstly, the optimal decisions of the SLPSC members are obtained under decentralized and centralized decision-making structures. Then, a new coordination contract named wholesale price and double compensation (WPDC) is designed to motivate the SC members to shift from the decentralized structure to the centralized one. The findings indicate that the coordinated model creates more economic profitability for the whole SLPSC than the decentralized one. Furthermore, the proposed WPDC contract is more beneficial for the SLPSC from a social viewpoint, as it increases consumer surplus. The results also demonstrate that when consumers are more sensitive to the product’s freshness, a price differentiation policy is more profitable than the same pricing

An integrated framework for outsourcing using balanced score card and ELECTRE III

During the past few decades, many organizations have attempted to increase their productivity through outsourcing parts of their responsibilities. Outsourcing helps firms reduce their low value added activities and focus on their high value added activities. It also helps organization save their time and energy which leads to more efficient units. The idea of outsourcing is more important for project based organizations where the nature of works is different from a particular project to another one. This paper presents an integrated balanced score card system with an adaptation of ELECTRE III method to select suitable resources for outsourcing. The proposed model of the paper is implemented for a case study of subway system in Iran and the results are discussed

A Multi-period Multi-objective Location- routing Model for Relief Chain Management under Uncertainty

Natural disasters, accidents, and crises, that cause widespread destruction and inflict heavy casualties, accentuate the importance of a careful planning to deal with the aftermath and mitigate their impacts responsively. Thus, the logistics of disaster relief is one of the main activities in disaster management. In this paper, the response phase of the disaster management cycle is considered and a multi-objective model for location and routing of vehicles is presented. Uncertainties in transfer time, demands of regional warehouses in the damaged areas and inventories at supply centers in different periods are taken into account. Three objectives are considered in this model. Two objectives consist of minimizing total time required to reach the damaged areas and maximizing satisfaction of the damaged areas. The third objective, which is of secondary importance, attempts to minimize total costs, including startup costs, transfer costs, and shortage costs. In order to convert the proposed multi-objective formulation to a single objective one, Global Criterion approach is applied. Afterwards, the obtained single objective model is solved using an efficient genetic algorithm and simulated annealing. Finally, a case study in Southern Khorasan is conducted and the applicability of the proposed model is examined

A Robust Programming Approach to Bi-objective Optimization Model in the Disaster Relief Logistics Response Phase

Accidents and natural disasters and crises coming out of them indicate the importance of an integrated planning to reduce their effected. Therefore, disaster relief logistics is one of the main activities in disaster management. In this paper, we study the response phase of the disaster management cycle and a bi-objective model has been developed for relief chain logistic in uncertainty condition including uncertainty in traveling time an also amount of demand in damaged areas. The proposed mathematical model has two objective functions. The first one is to minimize the sum of arrival times to damaged area multiplying by amount of demand and the second objective function is to maximize the minimum ratio of satisfied demands in total period in order to fairness in the distribution of goods. In the proposed model, the problem has been considered periodically and in order to solve the mathematical model, Global Criterion method has been used and a case study has been done at South Khorasan

A hierarchical model for strategic and operational planning in blood transportation with drones.

Blood transportation is a critical aspect of the healthcare systems, ensuring whole blood and blood products are delivered to patients in a timely and efficient manner. However, transportation of blood and other medical supplies can be challenging, especially in urban areas with limited infrastructure and heavy traffic. Drones have become increasingly important in recent years as a means of delivering medical supplies, including blood, due to their ability to provide fast, reliable, and cost-effective transportation. This study proposes two mathematical programming models in the hierarchical structure to improve decision-making for strategic and operational planning in the blood supply chain network. The limited information available in strategic planning presents risks to the blood supply chain, making it imperative to address uncertainties. To tackle this challenge, a novel approach called Scenario-based Robust Bi-objective Optimization has been proposed. The first model employs this approach to efficiently handle demand uncertainty by simultaneously maximizing the covered demand and minimizing costs. The model is subsequently solved using the augmented ε-constraint method. The second model is a routing-scheduling operational model that aims to minimize the sum of operations time, taking into account time windows for blood collection centers and hospitals. The developed hierarchical model is implemented in a three-level supply chain of Tehran province under three crisis scenarios in different parts. The findings and analysis of this implementation suggest that it is beneficial to set up drone stations in cost-effective and central locations to avoid costly network design. Furthermore, utilizing the minimum number of feasible drones enhances operational time and results in cost savings and increased efficiency. Overall, this study highlights the potential of using drones for blood transportation in urban settings, which can have significant implications for improving the quality of healthcare delivery