International Journal of Industrial Engineering & Production Research

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Location-allocation of facilities in service systems is an essential factor of their performance. One of the considerable situations which less addressed in the relevant literature is to balance service among customers in addition to minimize location-allocation costs. This is an important issue, especially in the public sector. Reviewing the recent researches in this field shows that most of them allocated demand customer to the closest facility. While, using probability rules to predict customer behavior when they select the desired facility is more appropriate. In this research, equitable facility location problem based on the gravity rule was investigated. The objective function has been defined as a combination of balancing and cost minimization, keeping in mind some system constraints. To estimate demand volume among facilities, utility function(attraction function) added to model as one constraint. The research problem is modeled as one mixed integer linear programming. Due to the model complexity, two heuristic and genetic algorithms have been developed and compared by exact solutions of small dimension problems. The results of numerical examples show the heuristic approach effectiveness with good-quality solutions in reasonable run time.

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This paper addresses a reliable facility location problem with considering facility capacity constraints. In reliable facility location problem some facilities may become unavailable from time to time. If a facility fails, its clients should refer to other facilities by paying the cost of retransfer to these facilities. Hence, the fail of facilities leads to disruptions in facility location decisions and this problem is an attempt to reducing the impact of these disruptions. In order to formulate the problem, a new mixed-integer nonlinear programming (MINLP) model with the objective of minimizing total investment and operational costs is presented. Due to complexity of MINLP model, two different heuristic procedures based on mathematical model are developed. Finally, the performance of the proposed heuristic methods is evaluated through executive numerical example. The numerical results show that the proposed heuristic methods are efficient and provide suitable solutions.

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In this paper, a stochastic cell formation problem is studied using queuing theory framework and considering reliability. Since cell formation problem is NP-Hard, two algorithms based on genetic and modified particle swarm optimization (MPSO) algorithms are developed to solve the problem. For generating initial solutions in these algorithms, a new heuristic method is developed, which always creates feasible solutions. Moreover, full factorial and Taguchi methods are implemented to set crucial parameters in the solutions procedures. Deterministic method of branch and bound (B&B) algorithm is used to evaluate the results of modified particle swarm optimization algorithm and the genetic algorithm. The results indicate that proposed algorithms have better performance in quality of the metaheurstic algorithms final answer and solving time compared with the method of Lingo software’s B&B algorithm. The solution of two metaheurstic algorithms is compared by t test. Ultimately, the results of numerical examples indicate that considering reliability has significant effect on block structures of machine-part matrixes.

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Hubs are critical elements of transportation networks. Location of hubs and allocation of demands to them are of high importance in the network design. The most important purpose of these models is to minimize the cost, but path reliability is also another important factor which can influence the location of hubs. In this paper, we propose a P-center hub location model with full interconnection among hubs while there are different paths between origins and destinations. The purpose of the model is to determine the reliable path with lower cost. Unlike the prior studies, the number of hubs in the path is not limited to two hubs. The presented model in this paper is bi-objective and includes cost and reliability to determine the best locations for hubs, allocation of the demands to hubs and the best path. In order to illustrate our model, a numerical example is presented and solved using the Cuckoo Optimization Algorithm.

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Today, many companies after achieving improvements in manufacturing operations are focused on the improvement of distribution systems and have long been a strong tendency to optimize the distribution network in order to reduce logistics costs that the debate has become challenging. Improve the flow of materials, an activity considered essential to increase customer satisfaction. In this study, we benefit cross docking method for effective control of cargo flow to reduce inventory and improve customer satisfaction. Also every supply chain is faced with risks that threaten its ability to work effectively. Many of these risks are not in control but can cause great disruption and costs for the supply chain process. In this study we are looking for a model to collect and deliver the demands for the limited capacity vehicle in terms of disruption risk finally presented a compromised planning process. In fact, we propose a framework which can consider all the problems on the crisis situation for decision-making in these conditions, by preparing a mathematical model and software gams for the following situation in a case study. In the first step, the results presented in mode of a two-level planning then the problem expressed in form of a multi-objective optimization model and the results was explained.

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Development of every society is incumbent upon energy sector’s technological and economic effectiveness. The electricity industry is a growing and needs to have a better performance to effectively cover the demand. The industry requires a balance between cost and efficiency through careful design and planning. In this paper, a two-stage stochastic programming model is presented for the design of electricity supply chain networks. The proposed network consists of power stations, transmission lines, substations, and demand points. While minimizing costs and maximizing effectiveness of the grid, this paper seeks to determine time and location of establishing new facilities as well as capacity planning for facilities. We use chance constraint method to satisfy the uncertain demand with high probability. The proposed model is validated by a case study on Southern Khorasan Province’s power grid network, the computational results show that the reliability rate is a crucial factor which greatly effects costs and demand coverage. 

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One of the major risks that can affect supply chain design and management is the risk of facility disruption due to natural hazards, economic crises, terrorist attacks, etc. Static resiliency of the network is one of the features that is considered when designing networks to manage disruptions, which increases the network reliability. This feature refers to the ability of the network to maintain its operation and connection in the lack of some members of the chain. Facility hardening is one of the strategies used for this purpose. In this paper, different reliable capacitated fixed-charge location allocation models are developed for hedging network from failure. In these proposed models, hardening, resilience, and hardening and resilience abilities are considered respectively. These problems are formulated as a nonlinear programming models and their equivalent linear form are presented. The sensitivity analysis confirms that the proposed models construct more effective and reliable network comparing to the previous networks. A Lagrangian decomposition algorithm (LDA) is developed to solve the linear models. Computational results show that the LDA is efficient in computational time and quality of generated solutions for instances with different sizes. Moreover, the superiority of the proposed model is confirmed comparing to the classical model.

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Tourists are getting more aware of the environment. To determine the effectiveness of eco-tourism infrastructure management, the motivation and segmentation of demand for eco-tourism have been analysed using functional theory as a guide. The empirical analysis was conducted in the Dnipro, Zaporizhzhia, and Kherson regions. 382 surveys were obtained by random sampling. To make the data analysis, factor analysis and non-hierarchical segmentation were performed. The results indicate that there are several eco-tourism motivational aspects including self-development, interpersonal relationships, defence functions, building personal relationships, reward, and appreciation from nature. Three different segments of eco-tourists were also identified based on their motives related to nature, reward, and escape. Characteristics of different segments were also specified. This study will help government agencies and private companies improve their travel content and develop more effective marketing plans.
The research has shown that in most cases, the success of any project is in cooperation between NGOs, locals, authorities, and the private sector. The optimal level of local participation is determined by the specifics and scale of each project, which may focus on individual villages or several communities that experience any impact of tourism.
The economic essence of the concept of tourism motivation has been improved, which is defined as a set of needs that affect a person in the process of participation in tourism activities and are a central factor in the decision-making process. Studying the most important motivations of eco-tourists in the region, three groups of motives have been identified: cultural and educational activities, proximity to nature, health and rehabilitation measures.

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The paper suggests methodological approaches to the complex evaluation of the efficiency of decentralization reform, which can be used for monitoring and adjusting decisions at any of its implementation stages. The suggested methodological approaches contribute to narrowing the results of the analysis of diverse phenomena and processes down to a single unified system of standardized parameters, thereby developing a single monitoring model and simplifying the decision-making process. The fact that the decentralization process has primarily 3 interrelated and interdependent directions is taken into account in forming the methodological approaches to evaluation of the ongoing reform efficiency. The indicators of analysis of the reform implementation efficiency across the organizational-administrative, budgetary, and socio-economic components are systematized in order to be used both for determining the average reform implementation paces in the country and comparing the respective structural changes. The set of mathematical tools to evaluate them is suggested. The efficiency of the decentralization reform implementation is comprehensively analyzed across Ukrainian regions. The major accomplishments and bottlenecks to be addressed at the current stage are determined. The comparative analysis of the efficiency of the decentralization reform implementation across Ukrainian regions has shown that, currently, there isn’t any consistent relationship between the organizational, budgetary, and socio-economic results of the reform in the regions. The abovementioned processes remain to be unbalanced due to slow reform implementation in some regions, available unresolved controversies, some duplicated managerial functions, and lacking sufficient level of residents’ confidence in authorities.

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