A Fuzzy Logic Approach to Prove Bullwhip Effect in Supply Chains

The bullwhip effect in nowadays Supply Chains has become a major source of problems and has attracted supply chain scientists attentions. This paper explores the concept of bullwhip effect in supply chains throughout a completely new approach. Assuming all demands are fuzzy in supply chain, fuzzy If-Then rules are used to show the bullwhip effect. Application of fuzzy logic is due to the fuzzy nature of supply chain problems. The new approach can be the source of inspiration for new solutions to the bullwhip effect in supply chains base on fuzzy logic and fuzzy If-Then rules. Fuzzy time series are widely used in this paper. First for data generation, we apply a modified version of Hwang fuzzy time series with a neural network for defuzzification and finally to show the bullwhip effect, we use Lee fuzzy time series which is based on Fuzzy If-Then rules, Genetic Algorithm and Simulated Annealing

A Fuzzy Logic Approach to Prove Bullwhip Effect in Supply Chains

The bullwhip effect in nowadays Supply Chains has become a major source of problems and has attracted supply chain scientists attentions. This paper explores the concept of bullwhip effect in supply chains throughout a completely new approach. Assuming all demands are fuzzy in supply chain, fuzzy If-Then rules are used to show the bullwhip effect. Application of fuzzy logic is due to the fuzzy nature of supply chain problems. The new approach can be the source of inspiration for new solutions to the bullwhip effect in supply chains base on fuzzy logic and fuzzy If-Then rules. Fuzzy time series are widely used in this paper. First for data generation, we apply a modified version of Hwang fuzzy time series with a neural network for defuzzification and finally to show the bullwhip effect, we use Lee fuzzy time series which is based on Fuzzy If-Then rules, Genetic Algorithm and Simulated Annealing

Order Management System Proposal Using Inventory Balance Equation with Non-continuous Replenishment

The purpose of this paper is to present an inventory balance model including an order-up-to replenishment policy with partial backlogging. Pictured in the model is a situation, where goods are not replenished continuously, but only at predetermined intervals. The model is described by ordinary differential equations with delayed argument because of the assumption of a time lag between ordering and delivery. A computer simulation which helps to demonstrate and verify model behaviour is utilized for the numerical solution of the model. Sales data of a real company are used as the input data. Due to the comparison of the designed model outputs against the real state in the company, it was verified that it is possible to achieve a substantial reduction in warehousing costs without a disproportionate increase in the risk of inventory shortage. The authors note that modern methods of functional analysis can be successfully applied in solving an inventory balance model

Applying Goldratt's theory of constraints to reduce the Bullwhip Effect through agent-based modeling

In the current environment, Supply Chain Management (SCM) is a major concern for businesses. The Bullwhip Effect is a proven cause of significant inefficiencies in SCM. This paper applies Goldratt’s Theory of Constraints (TOC) to reduce it. KAOS methodology has been used to devise the conceptual model for a multi-agent system, which is used to experiment with the well known ‘Beer Game’ supply chain exercise. Our work brings evidence that TOC, with its bottleneck management strategy through the Drum-Buffer-Rope (DBR) methodology, induces significant improvements. Opposed to traditional management policies, linked to the mass production paradigm, TOC systemic approach generates large operational and financial advantages for each node in the supply chain, without any undesirable collateral effect

A multiagent simulator for supporting logistic decisions of unloading petroleum ships in habors

This work presents and evaluates the performance of a simulation model based on multiagent system technology in order to support logistic decisions in a harbor from oil supply chain. The main decisions are concerned to pier allocation, oil discharge, storage tanks management and refinery supply by a pipeline. The real elements as ships, piers, pipelines, and refineries are modeled as agents, and they negotiate by auctions to move oil in this system. The simulation results are compared with results obtained with an optimization mathematical model based on mixed integer linear programming (MILP). Both models are able to find optimal solutions or close to the optimal solution depending on the problem size. In problems with several elements, the multiagent model can find solutions in seconds, while the MILP model presents very high computational time to find the optimal solution. In some situations, the MILP model results in out of memory error. Test scenarios demonstrate the usefulness of the multiagent based simulator in supporting decision taken concerning the logistic in harbors. O objetivo deste artigo é apresentar e avaliar o desempenho de um modelo de simulação baseado em sistemas multiagentes para auxiliar a tomada de decisão na alocação de petróleo em complexos portuários. Os diversos elementos do problema são modelados como agentes e negociam por meio de leilões a alocação dos inventários de óleo. Os resultados obtidos são comparados com resultados gerados por modelos de otimização matemática, estes baseados em programação linear inteira mista. Esses modelos são capazes de encontrar soluções ótimas ou próximas da ótima dependendo do tamanho da instância testada. Em problemas com muitos navios e tanques, o modelo baseado em sistema multiagente encontrou soluções em segundos, enquanto os modelos baseados em otimização matemática apresentaram problemas de tempo computacional e falta de memória, não encontrando a solução ótima. Os diversos exemplos aqui apresentados evidenciam a necessidade do modelo de simulação baseado em multiagentes no auxílio a tomada de decisões logísticas de porto Document type: Articl

Estimation of bullwhip effect in supply chain management

A supply chain consists of all parties involved, directly or indirectly, in fulfilling a customer request or demand. The supply chain not only includes the manufacturers and suppliers,but also transporters, warehouses, retailers, and finally the end consumers themselves. The objective of every supply chain is to maximize the overall value generated. The value a supply chain generates is the difference between what the final product is worth to the customer and the effort the supply chain expends in filling the customer’s request. An important phenomenon in Supply Chain Management is known as bullwhip effect (BWE), which suggests that the demand variability increases as one moves up a supply chain. Bullwhip effect is an undesirable phenomenon in the supply chain which exacerbates the supply chain performance. The impact of BWE is to increase manufacturing cost, inventory cost, replenishment lead time, transportation cost, labor cost for shipping and receiving, cost for building surplus capacity and holding surplus inventories, and to decrease level of product availability and relationship across the supply chain. Various factors can cause bullwhip effect, one of which is customer demand forecasting. In this study, impact of forecasting methods on the bullwhip effect and mean square error has been considered. The preceding study highlights the effect of forecasting technique, order processing cost and demand pattern on BWE and mean square error (MSE). The BWE and MSE have been evaluated using MATLAB coding. The results were analyzed using ANOVA and Fuzzy Logic,and finally the optimal parameters for minimum values of BWE and MSE have been determined

Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems

Literature on the modeling and simulation of complex adaptive systems (cas) has primarily advanced vertically in different scientific domains with scientists developing a variety of domain-specific approaches and applications. However, while cas researchers are inherently interested in an interdisciplinary comparison of models, to the best of our knowledge, there is currently no single unified framework for facilitating the development, comparison, communication and validation of models across different scientific domains. In this thesis, we propose first steps towards such a unified framework using a combination of agent-based and complex network-based modeling approaches and guidelines formulated in the form of a set of four levels of usage, which allow multidisciplinary researchers to adopt a suitable framework level on the basis of available data types, their research study objectives and expected outcomes, thus allowing them to better plan and conduct their respective research case studies. Firstly, the complex network modeling level of the proposed framework entails the development of appropriate complex network models for the case where interaction data of cas components is available, with the aim of detecting emergent patterns in the cas under study. The exploratory agent-based modeling level of the proposed framework allows for the development of proof-of-concept models for the cas system, primarily for purposes of exploring feasibility of further research. Descriptive agent-based modeling level of the proposed framework allows for the use of a formal step-by-step approach for developing agent-based models coupled with a quantitative complex network and pseudocode-based specification of the model, which will, in turn, facilitate interdisciplinary cas model comparison and knowledge transfer. Finally, the validated agent-based modeling level of the proposed framework is concerned with the building of in-simulation verification and validation of agent-based models using a proposed Virtual Overlay Multiagent System approach for use in a systematic team-oriented approach to developing models. The proposed framework is evaluated and validated using seven detailed case study examples selected from various scientific domains including ecology, social sciences and a range of complex adaptive communication networks. The successful case studies demonstrate the potential of the framework in appealing to multidisciplinary researchers as a methodological approach to the modeling and simulation of cas by facilitating effective communication and knowledge transfer across scientific disciplines without the requirement of extensive learning curves