جستجوی مقالات مرتبط با کلیدواژه « مکان یابی تسهیلات » در نشریات گروه « صنایع »

In this paper, a mathematical model has been developed with the aim of locating branch warehouses of a pharmaceutical distribution company, taking into account the existing legal requirements regarding drug distribution. In this model, in addition to the structure of the distribution network, the optimal ow of contracted drugs at the level of the company's branches throughout the country is also determined. Due to the high complexity and high dimensions of the model in real conditions, a combined solution approach based on meta-heuristic methods has been proposed. To solve the model, at the beginning, the structure of the model was decomposed into a main problem and several sub-problems and then it was solved using a two-stage genetic algorithm. In the rst stage, it solves the main problem and in the second stage, it solves sub-problems. In order to apply the balance constraints related to the connection between stages in the supply chain (input or output ow of or from each stage to others), priority-based encoding was utilized in the second level. Since in solving the model with real dimensions, due to the high dimensions of the problem in addition to its complexity, solution time is very high, the p-medoid clustering method was incorporated to aggregate supply chain customers. Finally, the tuning of algorithms was used with the popular Taguchi method. In order to validate the model, a case study using real data from Elite Daru Distribution Company has been considered. Results of the case study indicate a 23% reduction in distribution costs in optimal design as compared to existing design. Studies have also shown that we face a 7% cost in the case of restrictions imposed by law compared to the case without it. Sensitivity analysis on the number of cluster centers showed that data integration would lead to an average cost change of less than 1%.

Design and analysis of distribution systems are among the key factors which have been of interest to logistics corporations in recent years. Two main elements in designing a distribution network are finding acceptable locations for facilities and effective routes. Simultaneous consideration of these two elements is called location routing problem. Nowadays, because of environment pollutions, making good decisions about declining the CO2 emission rate has become a critical issue. The main contributor in CO2 emissions are fleet vehicles. This paper aims to propose a new mathematical model for the location routing problem in order to reduce the distribution and hence the fuel costs which in turn lead to CO2 emission rate reduction. Driver satisfaction is also pursued by balancing the drivers' workloads. A mathematical model is proposed for the problem and then linearized and validated for small scale conditions. As the large scale problem has many complexities, a multipurpose optimization algorithm, namely the NSGA-II algorithm which is a well-known metaheuristic algorithm is applied. To obtain a better solution, facility locations and route allocations are considered simultaneously. The algorithm performance is evaluated by introducing 4 indicators and the numerical results are reported. The results show that the suggested algorithm has the required efficiency to produced high quality parato solutions which are uniformly distributed in the problem's solution space.

Supply chain management and distribution network design have attracted the attention of many researchers during recent years. This paper addresses a multi-product system of location problem in distribution network that incorporates inventory decisions for each product into capacitated facility location models. In the development of mathematical model, the data of real case study of fast moving consumer goods company is used. A mixed integer nonlinear mathematical programming model is proposed and due to difficulty of obtaining the optimal solution in real-scaled problems, a heuristic solution approach based on Lagrangian relaxation algorithm and sub-gradient method is presented. The proposed solution method is compared with the optimization software on randomly generated test problems with different size. Computational results show that the performance of proposed solution algorithm is very promising in terms of various indexes including 89.1% of DCs capacity, duality gap average and the worst case 1.14% and 3.12% respectively. The results of considered case study also reduced the number of distribution centers from 17 to 12 centers. Finally, some managerial insights are mentioned.

There are different factors, including economic development and population explosion, causing the rapid increase in municipal solid waste generation rates. In today's municipal solid waste management, a considerable amount of financial and human resources is spent on collection and transportation, and little attention has been paid to production, storage in place, recycling, and disposal. As proper collection and transportation are important issues in municipal solid waste system, creation of energy from waste as well as recycling and disposal are also significant, attention must be paid to the optimal usage of dirty gold (i.e. municipal solid wastes) for pollution reduction. Moreover, determining suitable locations for transfer stations and waste disposal facilities is a very complex problem, needing a comprehensive review and evaluation process accounting for the requirements of municipal, environmental, and governmental regulations. In this context, it is vital to develop an effective and efficient approach for designing and planning a waste management system. In order to realize this goal, a three-level network consisting of customers, transfer stations, and disposal facilities (recycling, composting, wasteincinerator, and landfill centers) was considered in the present paper. A bi-objective mixed-integer linear mathematical programming model was presented for location and allocation of municipal solid waste. Model goals were minimizing waste disposal cost and greenhouse gas emission. Other points taken into consideration were a variety of products derived from recycling waste, transferring them to sales areas, and the limitations of number and capacity for transfer stations and waste disposal facilities. Districts 1 to 8 and District 22 of Tehran were selected as a case study for measuring model performance. The results of the case study and sensitivity analysis demonstrated that the optimal solution requires the location of disposal facilities in different districts of Tehran, although these facilities incur considerable costs.

This paper presents a new mathematical model for designing a three-tier supply chain, including suppliers, medical centers, and demand points. In this mathematical model, conditions of uncertainty have been raised in different scenarios and medical centers have been disrupted in various scenarios. This disruption forces the decision makers to establish other service centers to support the existing centers. To illustrate the applicability of this mathematical model, a case study of medical service network design in Alborz state of Iran has been implemented and analyzed. The results obtained show the efficiency of the mathematical model in solving applied examples.

In recent decades, there is a remarkable increase in natural disasters because of population growth, climate change, and systems integrations, which have led to many causalities (death and injuries) around the world. Therefore, an integrated mathematical model is needed to simultaneously deal with all different issues before and after natural disasters. In this paper, we develop an integrated stochastic model for relief operations supply chain, which has two decisions types. First stage decisions include locating regional warehouses and determine pre-position amount of commodities in each warehouse. Second stage decision includes emergency network design, and determines each commodity flow in the network. The objective function is to minimum the total cost of the relief supply chain. Finally, in order to validate the model efficiency, a case-study of Tehran earthquake scenarios with real data of casualties is analyzed.

One of the most important issues of location is locating the facilities in two types of problem: facility location problems with limited capacity and non-limited capacity.The objective of these problems is to nd the best and most suitable location for facilities. In this paper, with studying various models for locating with in forward and reverse logistics systems and also reviewing recent papers in this area, we proposed an integrated model in facility location in which \forward" and \reverse" networks are considered simultaneously. In this model, we consider producers, intermediate centers, and remanufacturing centers, and warehouse centers simultaneously which are to be located in an integrated logistics system with limited capacities. To model this problem in this study, we propose a 0-1 mixed integer programming model in which forward and reverse ows and their mutual interactions are considered simultaneously. In this problem, customer demands are considered as stochastic, and demands for this model contain new products and returned products. To examine the eciency, the mixed 0-1 and integer programming models, various test problems, and numerical calculations are solved by GAMS 24.1.2 optimization software. To show the eectiveness of this model, we have considered the test problem extended to Bostel and Lu study in 2007 in which forward and reverse networks are consideredsimultaneouslywithrespecttotheremanufacturing centers. By comparing the numerical results obtained from the model, it was shown that the proposed model provides a more optimal solution. To satisfy customer demandsregardingreturnedproducts,wefocusedonthe remanufacturing centers. In the study, we also showed that considering the facilities simultaneously would directly aect reverse logistics network structure. Also, the cost of production and remanufacturing centers affect the total cost of logistic network, such that by increasing the percentage of remanufacturing due to lower production costs, the total cost of network is reduced. So, with appropriate decision making and correct information about choosing the recovery facility (remanufacturing centers), returned products can be as many as possible to remanufacture and repair the damage, and thus avoid extra production costs.

In the most of reliable location problems, the main objective is to minimize the total cost of establishment and shipping a single level network, taking into account a strategy to strengthen the reliability of the network. Therefore, in this paper more objective facts are considered; a model for designing a three-echelon supply chain network, taking into account reliable strategies across multiple distribution centers is presented. In this model, both reliable and unreliable distribution centers are considered. The strategy is in a way that if an unreliable distribution centers established in a candidate location, it should be fortified by the limited budget for unreliable distribution centers in critical situations or a reliable distribution center should be set as the backup to respond the customers in the case of failure. On the other hand, if a reliable distribution center were established in a candidate location, this center would be a backup itself. Considering the computational complexity of the problem and the presented model, a genetic algorithm is used. After setting the Taguchi design of experiments, the solutions in small, medium and large sizes were compared with results obtained from GAMS optimization software. The results show the percentage of algorithm gap, in all problems solved, is less than 3 percentage. This shows the effectiveness of the proposed algorithm.

Nowadays, fierce competition in global markets has forced companies to improve the design and management of supply chains, and provide competitive advantages. Decision integrity is one of the main factors which highly lead to a considerable reduction of supply chain costs, and higher costumer’s satisfaction. Distribution network design is based on three major problems: location allocation, vehicle routing and inventory control. Since the effective role of reducing distribution costs in the survival of the supply chain is clear to all, in this paper, these three problems will be incorporated into an integrated model under demand uncertainty. This approach leads to the significant reduction of distribution costs, higher customer satisfaction, and also providing an efficient supply chain. Also in this study, in addition to minimizing the total cost including fixed cost of establishing depots, transportation costs and inventory costs, the customers’ satisfaction will increase by reducing their waiting time. So, a bi-objective mixed integer non-linear model is presented by using chance constrained programming, where customer demands are assumed to have a normal distribution. Then, to solve the model, a hybrid algorithm based on simulated annealing and genetic algorithm is proposed, and is evaluated on a set of instances. The computational results illustrate the algorithm efficiency to solve a wide range of problems with different sizes.

In recent years, increased use of internet to conduct business electronically and globalization of business have forced producers of goods to re-examine the production and distribution of their products. Cellular manufacturing (CM) is an innovative manufacturing strategy, which is derived from a group technology (GT) concept. This approach can be used to improve both flexibility and efficiency in today's modern competitive manufacturing environments, such as flexible manufacturing systems (FMS). Some benefits of CM performance are reduction of setup time, reduction of work-in-process inventory, reduction of material handling cost, machine utilization improvement, and quality improvement. In a dynamic environment, we require the development of organizations and facilities to be more flexible. This work directed us to combine the dynamic model of cellular manufacturing system and supply chain, taking into account different issues such as the existence of multi plants, multi markets, several warehouse, different suppliers, multi periods, and reconfigurations. In this paper, an integrated dynamic cellular manufacturing system model is proposed, which takes into account both production planning and supply chain design. The objective of this model is to reduce costs such as inter-cell movement cost, intra-cell movement cost; to keep parts in stock, outsourced parts, establishment of warehouse, replacement of parts are shipped from the plant to the warehouse, from warehouse to markets, from plant to market, and so on. In addition, considering the candidate sites for the construction of warehouse, locating the warehouses outside the factory is discussed, which has not been considered in previous papers. Furthermore, with adding the phrase of machine splitting in the objective function and constraints, it is attempted to improve the dynamic cellular manufacturing system. This term prevents the excessive split of one type of machine in the several cells and optimizes the placement of existing machines. Finally, computational results are investigated and solved through GAMS software to show the validity and importance of the presented model.

A fundamental assumption in classical optimization is that all data are certain. However, many real-world problems contain uncertain parameters. The ignorance of these parameters affects the optimality and even feasibility of the solutions. That is why it is crucial to develop an optimization method to support real time fluctuating parameters. Robust optimization techniques have been developed for tackling the uncertainties which address data uncertainty while ensuring feasibility in different scenarios.Most of the robust approaches which assumed the uncertain data belong to a convex space and single deviation band that may be too limited in practice. The aim ofour work is to propose a new algorithm to consider real circumstances by applying histogram-base uncertainty. The suggested algorithm finds the robust counterpart of models with non-convex space built based on historical data as an uncertain space. The new algorithm changes the problem to multi-range robust model which assigns value from more than one uncertain range to the uncertain parameter. The extension of Bertsimas and Sim approach is used to find the robust counterpart model in which an uncertain parameter is allowed to take values from more than one uncertain band. Bertsimas and Sim approach deals with uncertainties in a tractable manner and does not add complexity to the deterministic problem. Moreover, the conservation level of the solution can be handled in their model. Consequently, the obtained robust model of the algorithm guarantees the optimality and the feasibility of solutions in real scenarios with predefined level of conservation.The suggested robust optimization approach is applied to the capacitated facility location problem with a histogram-base uncertainty for demand deviations, since considering probability-based uncertainty is very likely in real facility location problems. The experimental results show the benefits of using the proposed method.

Lean thinking is management strategy that is applicable to all organizations including health care organizations, and its main idea is to eliminate waste. Finding a suitable location for treatment facilities in the health chain plays an important role in the implementation of this idea in the organization, because on the one hand, proximity to suppliers of medical centers ensures rapid and low-cost supply of patient needs. On the other hand, locating health centers near the population areas leads to quick and affordable access to them. Thus locating health centers in a right position can largely reduce the waste of time and money. In this paper, a multi objective mathematical model was developed to locate and allocate Services provided at the clinics and hospitals. The model combines facility location model and DEA simultaneously to provide high quality services at reasonable costs to the clients. The proposed model has been solved using epsilon constraint method, besides locating provided services in Amol hospitals and assigning them to population areas has been done as a case study.

I‌n t‌h‌i‌s p‌a‌p‌e‌r, w‌e e‌n‌d‌e‌a‌v‌o‌r t‌o d‌e‌v‌e‌l‌o‌p a h‌y‌b‌r‌i‌d p‌r‌o‌b‌l‌e‌m o‌f l‌o‌c‌a‌t‌i‌o‌n, p‌r‌i‌c‌i‌n‌g a‌n‌d q‌u‌e‌u‌i‌n‌g i‌n a n‌e‌t‌w‌o‌r‌k w‌i‌t‌h M c‌u‌s‌t‌o‌m‌e‌r n‌o‌d‌e‌s a‌n‌d N p‌o‌t‌e‌n‌t‌i‌a‌l s‌e‌r‌v‌e‌r n‌o‌d‌e‌s. I‌n f‌a‌c‌t, w‌e p‌r‌o‌p‌o‌s‌e a b‌i-o‌b‌j‌e‌c‌t‌i‌v‌e m‌o‌d‌e‌l f‌o‌r t‌h‌e f‌a‌c‌i‌l‌i‌t‌y l‌o‌c‌a‌t‌i‌o‌n p‌r‌o‌b‌l‌e‌m s‌u‌b‌j‌e‌c‌t t‌o c‌o‌n‌g‌e‌s‌t‌i‌o‌n a‌n‌d a p‌r‌i‌c‌i‌n‌g p‌o‌l‌i‌c‌y. T‌h‌e m‌o‌d‌e‌l i‌s f‌o‌r‌m‌u‌l‌a‌t‌e‌d b‌y m‌e‌a‌n‌s o‌f a q‌u‌e‌u‌i‌n‌g f‌r‌a‌m‌e‌w‌o‌r‌k, i‌n w‌h‌i‌c‌h e‌a‌c‌h f‌a‌c‌i‌l‌i‌t‌y b‌e‌h‌a‌v‌e‌s a‌s a‌n M/M/m/k q‌u‌e‌u‌i‌n‌g s‌y‌s‌t‌e‌m, w‌h‌e‌r‌e m i‌s t‌h‌e n‌u‌m‌b‌e‌r o‌f s‌e‌r‌v‌e‌r‌s i‌n e‌a‌c‌h f‌a‌c‌i‌l‌i‌t‌y a‌n‌d k i‌s t‌h‌e q‌u‌e‌u‌i‌n‌g s‌y‌s‌t‌e‌m c‌a‌p‌a‌c‌i‌t‌y. W‌e c‌o‌n‌s‌i‌d‌e‌r t‌w‌o s‌i‌m‌u‌l‌t‌a‌n‌e‌o‌u‌s p‌e‌r‌s‌p‌e‌c‌t‌i‌v‌e‌s f‌o‌r t‌h‌i‌s p‌r‌o‌b‌l‌e‌m; (1) c‌u‌s‌t‌o‌m‌e‌r‌s (d‌e‌s‌i‌r‌e t‌o l‌i‌m‌i‌t t‌i‌m‌e‌s o‌f w‌a‌i‌t‌i‌n‌g f‌o‌r s‌e‌r‌v‌i‌c‌e) a‌n‌d (2) s‌e‌r‌v‌i‌c‌e p‌r‌o‌v‌i‌d‌e‌r (d‌e‌s‌i‌r‌e t‌o i‌n‌c‌r‌e‌a‌s‌e p‌r‌o‌f‌i‌t). O‌u‌r m‌a‌t‌h‌e‌m‌a‌t‌i‌c‌a‌l m‌o‌d‌e‌l c‌o‌n‌t‌a‌i‌n‌s t‌w‌o s‌i‌m‌u‌l‌t‌a‌n‌e‌o‌u‌s o‌b‌j‌e‌c‌t‌i‌v‌e‌s, i‌n‌c‌l‌u‌d‌i‌n‌g (I) m‌a‌x‌i‌m‌i‌z‌i‌n‌g p‌r‌o‌f‌i‌t a‌n‌d (I‌I) m‌i‌n‌i‌m‌i‌z‌i‌n‌g t‌h‌e a‌m‌o‌u‌n‌t o‌f w‌a‌i‌t‌i‌n‌g t‌i‌m‌e i‌n t‌h‌e w‌h‌o‌l‌e n‌e‌t‌w‌o‌r‌k. I‌n o‌u‌r m‌o‌d‌e‌l, w‌e a‌s‌s‌u‌m‌e t‌h‌a‌t d‌i‌f‌f‌e‌r‌e‌n‌t p‌r‌i‌c‌e‌s a‌r‌e p‌r‌o‌v‌i‌d‌e‌d a‌t d‌i‌f‌f‌e‌r‌e‌n‌t f‌a‌c‌i‌l‌i‌t‌i‌e‌s f‌o‌r s‌e‌r‌v‌i‌c‌e‌s. F‌u‌r‌t‌h‌e‌r‌m‌o‌r‌e, c‌a‌p‌a‌c‌i‌t‌y c‌o‌n‌s‌t‌r‌a‌i‌n‌t‌s a‌r‌e c‌o‌n‌s‌i‌d‌e‌r‌e‌d t‌o b‌r‌i‌n‌g t‌h‌e p‌r‌o‌b‌l‌e‌m e‌v‌e‌n c‌l‌o‌s‌e‌r t‌o r‌e‌a‌l‌i‌t‌y. T‌h‌i‌s a‌s‌s‌u‌m‌p‌t‌i‌o‌n i‌s r‌e‌f‌e‌r‌r‌e‌d t‌o a‌s ``m‌i‌l‌l p‌r‌i‌c‌i‌n‌g'', a‌n‌d g‌a‌s s‌t‌a‌t‌i‌o‌n‌s a‌n‌d p‌a‌r‌k‌i‌n‌g p‌l‌a‌c‌e‌s a‌r‌e e‌x‌a‌m‌p‌l‌e‌s o‌f m‌i‌l‌l p‌r‌i‌c‌i‌n‌g. T‌h‌e p‌r‌o‌p‌o‌s‌e‌d m‌o‌d‌e‌l b‌e‌l‌o‌n‌g‌s t‌o a c‌l‌a‌s‌s o‌f m‌i‌x‌e‌d i‌n‌t‌e‌g‌e‌r n‌o‌n‌l‌i‌n‌e‌a‌r p‌r‌o‌g‌r‌a‌m‌m‌i‌n‌g m‌o‌d‌e‌l‌s a‌n‌d t‌h‌e c‌l‌a‌s‌s o‌f N‌P-h‌a‌r‌d p‌r‌o‌b‌l‌e‌m‌s. T‌h‌e‌r‌e‌f‌o‌r‌e, w‌e p‌r‌e‌s‌e‌n‌t‌e‌d a m‌u‌l‌t‌i-o‌b‌j‌e‌c‌t‌i‌v‌e v‌i‌b‌r‌a‌t‌i‌o‌n d‌a‌m‌p‌i‌n‌g o‌p‌t‌i‌m‌i‌z‌a‌t‌i‌o‌n (M‌O‌V‌D‌O) a‌l‌g‌o‌r‌i‌t‌h‌m t‌o s‌o‌l‌v‌e t‌h‌e m‌a‌t‌h‌e‌m‌a‌t‌i‌c‌a‌l m‌o‌d‌e‌l. F‌i‌n‌a‌l‌l‌y, t‌h‌e p‌e‌r‌f‌o‌r‌m‌a‌n‌c‌e o‌f t‌h‌e p‌r‌o‌p‌o‌s‌e‌d a‌l‌g‌o‌r‌i‌t‌h‌m i‌s c‌o‌m‌p‌a‌r‌e‌d w‌i‌t‌h t‌h‌e l‌i‌t‌e‌r‌a‌t‌u‌r‌e a‌n‌d d‌i‌f‌f‌e‌r‌e‌n‌t t‌e‌s‌t p‌r‌o‌b‌l‌e‌m‌s a‌r‌e g‌e‌n‌e‌r‌a‌t‌e‌d a‌n‌d a‌n‌a‌l‌y‌z‌e‌d.

Increasing of the distribution efficiency is one of the most objectives of an integrated logistic system developed as a new management philosophy in the past few decades. The problem is examind in two parts: facilities location problem (FLP) for long policies and vehicle routing problem (VRP) to meet the customer demand. These two components can be solved separately; however, this solution may not be the optimum solution of the original problem. Hence, in this paper, facilities location and vehicle routing problems are considered simultaniously to visit the facilities that should be serviced. Due to the complexity of the integrated problem in large sizes, a hybrid imperialist competitive algorithm (ICA) is proposed. Furthermore, to show the efficiency of the proposed hybrid ICA, a number of test problems in small and large sizes are solved. Finally, the obtained results are evaluated with the results obtained by CPLEX. Finally, the conclusion is provided.

In this study the location routing problem with fuzzy parameters is taken into account, this problem involves determining the location of the depots and routing of the vehicles in order to serve the customers. In this study a location routing problem with fuzzy travel times and due dates is considered and two objective models are proposed. The considered objectives are minimizing the total costs of the network and minimizing the total weighted tardiness. The costs of the network include the fixed installation costs and the transportation costs. In order to solve this problem a mathematical model is proposed. However since this problem is categorized into NP-hard problem; the mathematical model cannot be solved efficiently. Therefore meta-heuristic algorithms are proposed to efficiently solve this problem.

In this article، a game in which terrorists and the state government are the players is considered. On the one hand، terrorists try to attack major cities and impose damages (loss in the state’s point of view)، on the other hand، the state tries to reduce damages through establishing the optimal number of facilities that are also optimally located. Furthermore، allocating of fixed facilities in order to decrease the destructions caused by terrorist attacks is studied. Simultaneous attacks to a number of cities are also examined. To achieve the optimal number of facilities، prediction cost and budget are considered to be the constraints. Finally، according to the police of Iran، an example is designed and solved using the model.

In this paper، a design problem for a two-echelon multi-commodity supply chain network with stochastic demands is taken into account. This network consists of production plants، warehouses and retailer (or final customers) in a single period. The problem considers strategic decisions (including location and capacities of production plants and warehouses) and operational decisions (including the transportation of commodities from the production plant to the warehouses and from warehouses to customers). Additionally، in this study، the demand is assumed to be stochastic and the given problem is modeled using a scenario-based stochastic programming approach. Specifically a two-stage stochastic programming model is presented to solve this problem. Furthermore، a Benders decomposition method is proposed in order to efficiently solve this problem. Finally، the conclusion is presented.