The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Aivars Muravjovs - One of the best experts on this subject based on the ideXlab platform.
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ECMS - Comparative Assessment Of ExtendSim And AnyLogic Efficiency For Inventory Control Systems Simulation.
ECMS 2012 Proceedings edited by: K. G. Troitzsch M. Moehring U. Lotzmann, 2012Co-Authors: Eugene Kopytov, Aivars MuravjovsAbstract:The given research fulfils the evaluation of the efficiency of application of two universal simulation packages ExtendSim 8 and AnyLogic 6.7 for Inventory Control System simulation. In the study twenty eight evaluating criteria have been developed which are distributed in five groups: general, programming aspects, visualization, simulation and user support. As an object of the simulation the Inventory Control model with reorder point strategy and stochastic demand has been chosen.
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simulation of Inventory Control System for supply chain producer wholesaler client in extendsim environment
ECMS, 2011Co-Authors: Eugene Kopytov, Aivars MuravjovsAbstract:There is considered a two-level single-product Inventory Control System which Controls correspondingly the wholesale’s warehouse and the customers’ warehouses. For delivering the product there has been organized a supply chain “producer – wholesaler – customer”. It is assumed that the customers and the wholesaler shape their stocks having in mind the minimization of the total costs of the product ordering, holding and losses from deficit per time unit. The customers’ demands for the product and the time of delivering the cargo from the producer to the wholesaler and from the wholesaler to each of the customers are random values with known laws of distribution. The wholesaler orders the goods at the fixed, equally distant, moments of time. The order of goods is performed by the customer at a random moment of time, when the remains of the goods in his warehouse have reduced up to a fixed level called the reorder point. In the given paper there is suggested a simulation model of the above Inventory Control. The ExtendSim 8 package has been used as the means of simulation. The numerical examples of the problem solving are presented.
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ECMS - Simulation Of Inventory Control System For Supply Chain "Producer - Wholesaler - Client" In ExtendSim Environment.
2011Co-Authors: Eugene Kopytov, Aivars MuravjovsAbstract:There is considered a two-level single-product Inventory Control System which Controls correspondingly the wholesale’s warehouse and the customers’ warehouses. For delivering the product there has been organized a supply chain “producer – wholesaler – customer”. It is assumed that the customers and the wholesaler shape their stocks having in mind the minimization of the total costs of the product ordering, holding and losses from deficit per time unit. The customers’ demands for the product and the time of delivering the cargo from the producer to the wholesaler and from the wholesaler to each of the customers are random values with known laws of distribution. The wholesaler orders the goods at the fixed, equally distant, moments of time. The order of goods is performed by the customer at a random moment of time, when the remains of the goods in his warehouse have reduced up to a fixed level called the reorder point. In the given paper there is suggested a simulation model of the above Inventory Control. The ExtendSim 8 package has been used as the means of simulation. The numerical examples of the problem solving are presented.
Eugene Kopytov - One of the best experts on this subject based on the ideXlab platform.
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ECMS - Comparative Assessment Of ExtendSim And AnyLogic Efficiency For Inventory Control Systems Simulation.
ECMS 2012 Proceedings edited by: K. G. Troitzsch M. Moehring U. Lotzmann, 2012Co-Authors: Eugene Kopytov, Aivars MuravjovsAbstract:The given research fulfils the evaluation of the efficiency of application of two universal simulation packages ExtendSim 8 and AnyLogic 6.7 for Inventory Control System simulation. In the study twenty eight evaluating criteria have been developed which are distributed in five groups: general, programming aspects, visualization, simulation and user support. As an object of the simulation the Inventory Control model with reorder point strategy and stochastic demand has been chosen.
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simulation of Inventory Control System for supply chain producer wholesaler client in extendsim environment
ECMS, 2011Co-Authors: Eugene Kopytov, Aivars MuravjovsAbstract:There is considered a two-level single-product Inventory Control System which Controls correspondingly the wholesale’s warehouse and the customers’ warehouses. For delivering the product there has been organized a supply chain “producer – wholesaler – customer”. It is assumed that the customers and the wholesaler shape their stocks having in mind the minimization of the total costs of the product ordering, holding and losses from deficit per time unit. The customers’ demands for the product and the time of delivering the cargo from the producer to the wholesaler and from the wholesaler to each of the customers are random values with known laws of distribution. The wholesaler orders the goods at the fixed, equally distant, moments of time. The order of goods is performed by the customer at a random moment of time, when the remains of the goods in his warehouse have reduced up to a fixed level called the reorder point. In the given paper there is suggested a simulation model of the above Inventory Control. The ExtendSim 8 package has been used as the means of simulation. The numerical examples of the problem solving are presented.
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ECMS - Simulation Of Inventory Control System For Supply Chain "Producer - Wholesaler - Client" In ExtendSim Environment.
2011Co-Authors: Eugene Kopytov, Aivars MuravjovsAbstract:There is considered a two-level single-product Inventory Control System which Controls correspondingly the wholesale’s warehouse and the customers’ warehouses. For delivering the product there has been organized a supply chain “producer – wholesaler – customer”. It is assumed that the customers and the wholesaler shape their stocks having in mind the minimization of the total costs of the product ordering, holding and losses from deficit per time unit. The customers’ demands for the product and the time of delivering the cargo from the producer to the wholesaler and from the wholesaler to each of the customers are random values with known laws of distribution. The wholesaler orders the goods at the fixed, equally distant, moments of time. The order of goods is performed by the customer at a random moment of time, when the remains of the goods in his warehouse have reduced up to a fixed level called the reorder point. In the given paper there is suggested a simulation model of the above Inventory Control. The ExtendSim 8 package has been used as the means of simulation. The numerical examples of the problem solving are presented.
Dingwei Wang - One of the best experts on this subject based on the ideXlab platform.
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Simulation-Based Optimization on Control Strategies of Three-Echelon Inventory in Hybrid Supply Chain With Order Uncertainty
IEEE Access, 2018Co-Authors: Wendan Zhao, Dingwei WangAbstract:Multi-echelon Inventory Control is the main form of the management of supply chain Inventory, and it has been paid extensive attention by scholars because of its importance in supply chain management. Process industries, represented by metallurgy, petrochemical and pharmaceutical industry, have typical characteristics such as high investment, high costs, and high resource utilization, they play a key role in industrialization. In this paper, the simulation model of a multiproduct three-echelon Inventory Control System is established for the hybrid supply chain of the process industry. Based on the idea of Control engineering, a feedback Control law is designed for Inventory Control. Several mixed Inventory Control strategies were applied to the model. The proportional plus integral Control algorithm is used to modify the Inventory Control strategies. Based on this simulation model, a simulation-based optimization method is applied to the three-echelon Inventory Control System. The results show that the reciprocal of total entropy ratio and customer satisfaction are optimized by the mixed Inventory Control strategies when the hybrid supply chain is affected by order uncertainty.
Seyed Taghi Akhavan Niaki - One of the best experts on this subject based on the ideXlab platform.
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replenish up to multi chance constraint Inventory Control System under fuzzy random lost sale and backordered quantities
Knowledge Based Systems, 2013Co-Authors: Ata Allah Taleizadeh, Seyed Taghi Akhavan Niaki, Ramak Ghavamizadeh MeibodiAbstract:In this paper, a multiproduct multi-chance constraint stochastic Inventory Control problem is considered, in which the time-periods between two replenishments are assumed independent and identically distributed random variables. For the problem at hand, the decision variables are of integer-type, the service-level is a chance constraint for each product, and the space limitation is another constraint of the problem. Furthermore, shortages are allowed in the forms of fuzzy random quantities of lost sale that are backordered. The developed mathematical formulation of the problem is shown to be a fuzzy random integer-nonlinear programming model. The aim is to determine the maximum level of Inventory for each product such that the total profit under budget and service level constraints is maximized. In order to solve the model, a hybrid method of fuzzy simulation, stochastic simulation, and particle swarm optimization approach (Hybrid FS-SS-PSO) is used. At the end, several numerical illustrations are given to demonstrate the applicability of the proposed methodology and to compare its performances with the ones of another hybrid algorithm as a combination of fuzzy simulation, stochastic simulation, and genetic algorithm (FS-SS-GA). The results of the numerical illustrations show that FS-SS-PSO performs better than FS-SS-GA in terms of both objective functions and CPU time.
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optimizing multi item multi period Inventory Control System with discounted cash flow and inflation two calibrated meta heuristic algorithms
Applied Mathematical Modelling, 2013Co-Authors: Seyed Mohsen Mousavi, Seyed Taghi Akhavan Niaki, Vahid Hajipour, Najmeh AlikarAbstract:Abstract A mixed binary integer mathematical programming model is developed in this paper for ordering items in multi-item multi-period Inventory Control Systems, in which unit and incremental quantity discounts as well as interest and inflation factors are considered. Although the demand rates are assumed deterministic, they may vary in different periods. The situation considered for the problem at hand is similar to a seasonal Inventory Control model in which orders and sales happen in a given season. To make the model more realistic, three types of constraints including storage space, budget, and order quantity are simultaneously considered. The goal is to find optimal order quantities of the products so that the net present value of total System cost over a finite planning horizon is minimized. Since the model is NP-hard, a genetic algorithm (GA) is presented to solve the proposed mathematical problem. Further, since no benchmarks can be found in the literature to assess the performance of the proposed algorithm, a branch and bound and a simulated annealing (SA) algorithm are employed to solve the problem as well. In addition, to make the algorithms more effective, the Taguchi method is utilized to tune different parameters of GA and SA algorithms. At the end, some numerical examples are generated to analyze and to statistically and graphically compare the performances of the proposed solving algorithms.
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a genetic algorithm for vendor managed Inventory Control System of multi product multi constraint economic order quantity model
Expert Systems With Applications, 2011Co-Authors: Seyed Hamid Reza Pasandideh, Seyed Taghi Akhavan Niaki, Ali Roozbeh NiaAbstract:In this research, an economic order quantity (EOQ) model is first developed for a two-level supply chain System consisting of several products, one supplier and one-retailer, in which shortages are backordered, the supplier's warehouse has limited capacity and there is an upper bound on the number of orders. In this System, the supplier utilizes the retailer's information in decision making on the replenishments and supplies orders to the retailer according to the well known (R,Q) policy. Since the model of the problem is of a non-linear integer-programming type, a genetic algorithm is then proposed to find the order quantities and the maximum backorder levels such that the total Inventory cost of the supply chain is minimized. At the end, a numerical example is given to demonstrate the applicability of the proposed methodology and to evaluate and compare its performances to the ones of a penalty policy approach that is taken to evaluate the fitness function of the genetic algorithm.
Ali Roozbeh Nia - One of the best experts on this subject based on the ideXlab platform.
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a genetic algorithm for vendor managed Inventory Control System of multi product multi constraint economic order quantity model
Expert Systems With Applications, 2011Co-Authors: Seyed Hamid Reza Pasandideh, Seyed Taghi Akhavan Niaki, Ali Roozbeh NiaAbstract:In this research, an economic order quantity (EOQ) model is first developed for a two-level supply chain System consisting of several products, one supplier and one-retailer, in which shortages are backordered, the supplier's warehouse has limited capacity and there is an upper bound on the number of orders. In this System, the supplier utilizes the retailer's information in decision making on the replenishments and supplies orders to the retailer according to the well known (R,Q) policy. Since the model of the problem is of a non-linear integer-programming type, a genetic algorithm is then proposed to find the order quantities and the maximum backorder levels such that the total Inventory cost of the supply chain is minimized. At the end, a numerical example is given to demonstrate the applicability of the proposed methodology and to evaluate and compare its performances to the ones of a penalty policy approach that is taken to evaluate the fitness function of the genetic algorithm.
