A New Tradeoff Model for Fuzzy Supply Chain Network Design and Optimization

This article proposes a novel mixed integer linear programming model for solving a fuzzy supply chain network (SCN) design problem. This problem includes fuzzy parameters, choosing suppliers according to their quality of raw materials, and the supplier's engagement contracts. There is a tradeoff between raw material quality, and its purchasing and reprocessing costs. If a decision-maker (DM) wishes to work with a supplier that supplies a low-quality raw material, this raw material may be in need of reprocessing. To avoid the reprocessing costs, a supplier that provides a high-quality raw material can be chosen, but in this case the DM faces a high purchasing cost. An integrated fuzzy SCN system that consists of multiple suppliers, manufacturers, distribution centers, and retailers is considered in order to address problems under the aforementioned tradeoffs. Finally, concluding remarks and suggestions for future work are presented.     

Fuzzy Multi-Objective Optimization of a Green Supply Chain Network with Risk Management that Includes Environmental Hazards

Among the leading environmental risks, global climate alteration has become one of the most important controversial issues. Greenhouse gas emissions (CO₂, methane, etc.) and air pollution have motivated a need to develop and improve environmental management strategies. As a consequence, environmental sanctions are forcing commercial enterprises to re-consider and re-design supply chain processes in a green way. This article provides a multi-objective model to design a closed-loop supply chain (CLSC) network in a green framework. Our first and second objectives are to minimize all the transportation costs for the supply chain's forward and reverse logistics; the third objective is to minimize total CO₂ emissions; the fourth objective is to encourage customers to use recyclable materials as an environmental practice. To provide more realistic modeling by treating the uncertainty in decision-makers’ objectives, fuzzy modeling is used in this study. The model is explained and tested via fulfilling a numerical example. In scenario analyses, analytic hierarchy process (AHP), fuzzy AHP (F-AHP), and fuzzy TOPSIS (F-TOPSIS) approaches were applied and compared to evaluate different objectives to guide decision-makers.     

A robust fuzzy stochastic programming model for the design of a reliable green closed-loop supply chain network

Recently, governmental legislations, limitation of natural resources and adverse effects of End-of-Life products on ecological system have spurred researchers to design closed-loop supply chains (CLSCs). Accordingly, designing green supply chains (SCs) that manage greenhouse gas emissions and prevent air pollution can be helpful for companies to heighten profitability and customer loyalty, besides, uncertainty of parameters and disruption strikes could adversely affect performance of SCs and lower quality of output decisions. Effective planning prevents great losses and increases reliability of manager's decisions against uncertainties. Therefore, this paper is proceeding to design a reliable bi-objective green CLSC that minimizes total costs of network aside with minimizing harmful gas emissions. The proposed model is capable of controlling adverse effects of disruptions via applying scenario-based stochastic programming approach. Also, an effective hybrid robust fuzzy stochastic programming method is extended to effectively control uncertainty of parameters and risk-aversion level of output decisions. Extended model analyzing is based on lead-acid battery SC case study that output results approve applicability and effectiveness of model.     

A mathematical model to design a lignocellulosic biofuel supply chain system with a case study based on a region in Central Texas

This study formulates a model to maximize the profit of a lignocellulosic biofuel supply chain ranging from feedstock suppliers to biofuel customers. The model deals with a time-staged, multi-commodity, production/distribution system, prescribing facility locations and capacities, technologies, and material flows. A case study based on a region in Central Texas demonstrates application of the proposed model to design the most profitable biofuel supply chain under each of several scenarios. A sensitivity analysis identifies that ethanol (ETOH) price is the most significant factor in the economic viability of a lignocellulosic biofuel supply chain.     

Agent-Oriented Supply-Chain Management

The supply chain is a worldwide network of suppliers, factories, warehouses, distribution centers, and retailers through which raw materials are acquired, transformed, and delivered to customers. In recent years, a new software architecture for managing the supply chain at the tactical and operational levels has emerged. It views the supply chain as composed of a set of intelligent software agents, each responsible for one or more activities in the supply chain and each interacting with other agents in the planning and execution of their responsibilities. This paper investigates issues and presents solutions for the construction of such an agent-oriented software architecture. The approach relies on the use of an agent building shell, providing generic, reusable, and guaranteed components and services for communicative-act-based communication, conversational coordination, role-based organization modeling, and others. Using these components, we show two nontrivial agent-based supply-chain architectures able to support complex cooperative work and the management of perturbation caused by stochastic events in the supply chain.     

Application of fuzzy VIKOR for evaluation of green supply chain management practices

Environmentally sustainable activities have received an increasing interest among the firms to improve their practices in the supply chain. Although environmental regulations force firms consider these issues, but, green issues are new, evolving every day, and requires a continuous study in the field to gain a complete understanding of the problems. In this study, we illustrate the case of a laptop manufacturer in Malaysia that pursues to evaluate green supply chain management (GSCM) indicators among its practitioners. This paper develops a quantitative evaluation model to measure the uncertainty of GSCM activities and applies an approach based on Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method which is an extension of intuitionistic fuzzy environment aiming to solve the green multi-criteria decision making (GMCDM) problem. The triangular fuzzy numbers (TFNs) were used to handle imprecise numerical quantities. Then, a hierarchical multiple criteria decision making (MCDM) model was proposed based on fuzzy sets theory and VIKOR method to deal with the problem. The results show the alternative ranks of the four evaluated companies which was based on their performance in GSCM initiatives. The results also indicated that the main criteria of the research ranked as follows respectively: eco-design, green production, green purchasing, green recycling, green transportation and green warehousing. Finally, a comparative analysis of results by fuzzy VIKOR is presented. Additionally the scope for future studies is provided at the end of the paper.     

A fuzzy multi-objective optimization model for sustainable reverse logistics network design

Decreasing the environmental impact, increasing the degree of social responsibility, and considering the economic motivations of organizations are three significant features in designing a reverse logistics network under sustainability respects. Developing a model, which can simultaneously consider these environmental, social, and economic aspects and their indicators, is an important problem for both researchers and practitioners. In this paper, we try to address this comprehensive approach by using indicators for measurement of aforementioned aspects and by applying fuzzy mathematical programming to design a multi-echelon multi-period multi-objective model for a sustainable reverse logistics network. To reflect all aspects of sustainability, we try to minimize the present value of costs, as well as environmental impacts, and optimize the social responsibility as objective functions of the model. In order to deal with uncertain parameters, fuzzy mathematical programming is used, and to obtain solutions on Pareto front, a customized multi-objective particle swarm optimization (MOPSO) algorithm is applied. The validity of the proposed solution procedure has been analyzed in small and large size test problems based on four comparison metrics and computational time using analysis of variance. Finally, in order to indicate the applicability of the suggested model and the practicality of the proposed solution procedure, the model has been implemented in a medical syringe recycling system. The results reveal that the suggested MOPSO algorithm overtakes epsilon-constraint method from the aspects of quality of the solutions as well as computational time. Proper use of the proposed process could help managers efficiently manage the flow of recycled products with regard to environmental and social considerations, and the process offers a sustainable competitive advantage to corporations.     

Sustainable development of global supply chains—part 1: sustainability optimization framework

In global industry supply chains, environmental sustainability optimization addresses the overall consumption of resources and energy, the reduction of carbon emissions and generated waste to name a few. In this paper, we propose a holistic sustainability optimization framework for strategic network design of industry supply chains under consideration of economic, social as well as ecologic objectives. The framework is flexible to incorporate multiple sustainability indicators, alternative sustainability optimization strategies as well as a variety of internal and external industry-specific factors which impact the sustainability of the entire industry supply chain in the long-term. The core of the framework is an end-to-end closed-loop value chain model consisting of process, transport and product-in-use modules. For the first time, the product-in-use impact (“use” vs. “make”) is integrated in one network design approach. In addition, the model fully closes the loop from sourcing of raw materials via manufacturing towards reverse value chain steps such as disposal and recycling. Finally, we propose the minimize-time-to-sustainability approach as new optimization strategy for long-term network design problems focusing on minimizing the time, industry supply chain structures need to transform into sustainability steady states for all defined sustainability indicators such as CO₂e emissions, costs or social indicators based on defined target values. In part 2 of this paper the application of the optimization framework to the European automotive industry is shown.     

Transportation Optimization with Fuzzy Trapezoidal Numbers Based on Possibility Theory

In this paper, a parametric method is introduced to solve fuzzy transportation problem. Considering that parameters of transportation problem have uncertainties, this paper develops a generalized fuzzy transportation problem with fuzzy supply, demand and cost. For simplicity, these parameters are assumed to be fuzzy trapezoidal numbers. Based on possibility theory and consistent with decision-makers'' subjectiveness and practical requirements, the fuzzy transportation problem is transformed to a crisp linear transportation problem by defuzzifying fuzzy constraints and objectives with application of fractile and modality approach. Finally, a numerical example is provided to exemplify the application of fuzzy transportation programming and to verify the validity of the proposed methods.     

A method for solution of the multi-objective inverse problems under uncertainty

We describe a method to solve multi-objective inverse problems under uncertainty. The method was tested on non-linear models of dynamic series and population dynamics, as well as on the spatiotemporal model of gene expression in terms of non-linear differential equations. We consider how to identify model parameters when experimental data contain additive noise and measurements are performed in discrete time points. We formulate the multi-objective problem of optimization under uncertainty. In addition to a criterion of least squares difference we applied a criterion which is based on the integral of trajectories of the system spatiotemporal dynamics, as well as a heuristic criterion CHAOS based on the decision tree method. The optimization problem is formulated using a fuzzy statement and is constrained by penalty functions based on the normalized membership functions of a fuzzy set of model solutions. This allows us to reconstruct the expression pattern of hairy gene in Drosophila even-skipped mutants that is in good agreement with experimental data. The reproducibility of obtained results is confirmed by solution of inverse problems using different global optimization methods with heuristic strategies.     

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).     

A network approach for modeling and design of agile supply chains using a flexibility construct

Agility can be viewed as a need to encourage the enterprise-wide integration of flexible and core competent resources so as to offer value-added product and services in a volatile competitive environment. Since flexibility is considered a property that provides change capabilities of different enterprise-wide resources and processes in time and cost dimensions, supply chain flexibility can be considered a composite state to enterprise-wide resources to meet agility needs. Enterprise modeling frameworks depicting these composite flexibility states are difficult to model because of the complex and tacit interrelationship among system parameters and also because agility thrives on many business objectives. In view of this, the modeling framework presented in this paper is based on analytical network process (ANP) since this methodology can accommodate the complex and tacit interrelationship among factors affecting enterprise agility. The modeling framework forms a three-level network with the goal of attaining agility from the perspective of market, product, and customer as the actors. The goal depends on substrategies that address the characteristics of the three actors. Each of these substrategies further depends on manufacturing, logistic, sourcing, and information technology (IT) flexibility elements of the enterprise supply chain (SC). The research highlights that, under different environmental conditions, enterprises require synergy among appropriate supply chain flexibilities for practising agility. In the present research, the ANP modeling software tool Super Decisions? has been used for relative prioritization of the supply chain flexibilities. We demonstrate through sensitivity analysis that dynamic conditions do require adjustments in the enterprise-wide flexibility spectrum.     

An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation

This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality.     

The Worst-Case Weighted Multi-Objective Game with an Application to Supply Chain Competitions

In this paper, we propose a worst-case weighted approach to the multi-objective n-person non-zero sum game model where each player has more than one competing objective. Our “worst-case weighted multi-objective game” model supposes that each player has a set of weights to its objectives and wishes to minimize its maximum weighted sum objectives where the maximization is with respect to the set of weights. This new model gives rise to a new Pareto Nash equilibrium concept, which we call “robust-weighted Nash equilibrium”. We prove that the robust-weighted Nash equilibria are guaranteed to exist even when the weight sets are unbounded. For the worst-case weighted multi-objective game with the weight sets of players all given as polytope, we show that a robust-weighted Nash equilibrium can be obtained by solving a mathematical program with equilibrium constraints (MPEC). For an application, we illustrate the usefulness of the worst-case weighted multi-objective game to a supply chain risk management problem under demand uncertainty. By the comparison with the existed weighted approach, we show that our method is more robust and can be more efficiently used for the real-world applications.     

Collaborative fractal-based supply chain management based on a trust model for the automotive industry

In the supply chain of the automotive industry, where the procurement ratio of parts from partners is very high, the trustworthiness of partners should be considered during supply chain optimization. In this paper, to deal with the trust issue related to collaboration and reduce the computational load in production planning, we develop a collaborative fractal-based supply chain management framework for the automotive industry. In our framework, the relationships between the participants of a supply chain are modeled as a fractal. Each fractal has a goal model and generates a production plan of its participants based on the goal model. The goal model of each fractal is developed from an operational perspective to consider the trust value of participants during production planning. A fuzzy trust evaluation model is used to evaluate the trust value in terms of numerical value. To validate the developed framework in the automotive industry, simulations are conducted. The results of the simulations indicate that our framework can be useful in generating precise production plans.     

An extended multi-objective mixed integer programming for water resources management through possibility theory

In this study, we develop an extended multi-objective mixed integer programming (EMOMIP) approach for water resources management under uncertainty, in which the parameters are fuzzy random variables while the decision variables are interval variables. Furthermore, some alternatives are considered to retrieve the difference between the quantities of promised water-allocation targets and the actual allocated water. Then, the proposed EMOMIP for the problem is solved by a new method using fuzzy random chance-constrained programming based on the idea of possibility theory. This method can satisfy both optimistic and pessimistic decision makers simultaneously. Finally, a real example is given to explain the proposed method.     

Uncovering and Testing the Fuzzy Clusters Based on Lumped Markov Chain in Complex Network

Identifying clusters, namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. By means of a lumped Markov chain model of a random walker, we propose two novel ways of inferring the lumped markov transition matrix. Furthermore, some useful results are proposed based on the analysis of the properties of the lumped Markov process. To find the best partition of complex networks, a novel framework including two algorithms for network partition based on the optimal lumped Markovian dynamics is derived to solve this problem. The algorithms are constructed to minimize the objective function under this framework. It is demonstrated by the simulation experiments that our algorithms can efficiently determine the probabilities with which a node belongs to different clusters during the learning process and naturally supports the fuzzy partition. Moreover, they are successfully applied to real-world network, including the social interactions between members of a karate club.     

Environmental and economic issues arising from the pooling of SMEs’ supply chains: case study of the food industry in western France

Supply chains pooling is an emergent strategy for improving logistical performance. The pooling concept consists in transferring the effort of coordination for consolidating independent operators’ flows towards an ad hoc pooled system. This organisation results from a design of a pooled logistics network by merging different supply chains to share transport and logistics resources in order to improve logistics performance. In this case study, the pooling concept is applied to a collection of small and medium-sized western France food suppliers serving the same retail chain. In order to demonstrate the efficiency of the pooling, the existing transport organisation was compared to various pooling scenarios. The methodology consisted in accessing a current situation through a survey of the flow of goods at one of the main distribution centre of the studied supply network, then comparing this situation with three other pooling scenarios. Using supply network optimisation models, these scenarios were assessed considering cost and CO₂ emission levels. This study demonstrates the interest of transport pooling in the case independent shipping networks of Small and Medium Enterprises compared to the partially know existing strategies adopted by logistics service providers for less than truckload shipments. Moreover, it suggests that there is no dominant supply organisation and that transport pooling is a new stimulus for network design. These results also bring new research perspectives for generalisation of pooling and gain sharing within large coalitions.     

Variable selection in nonlinear modeling based on RBF networks and evolutionary computation

In this paper a novel variable selection method based on Radial Basis Function (RBF) neural networks and genetic algorithms is presented. The fuzzy means algorithm is utilized as the training method for the RBF networks, due to its inherent speed, the deterministic approach of selecting the hidden node centers and the fact that it involves only a single tuning parameter. The trade-off between the accuracy and parsimony of the produced model is handled by using Final Prediction Error criterion, based on the RBF training and validation errors, as a fitness function of the proposed genetic algorithm. The tuning parameter required by the fuzzy means algorithm is treated as a free variable by the genetic algorithm. The proposed method was tested in benchmark data sets stemming from the scientific communities of time-series prediction and medicinal chemistry and produced promising results.     

A multiscale simulation framework for the manufacturing facility and supply chain of autologous cell therapies

Background aimsAutologous cell therapy (AuCT) is an emerging therapeutic treatment that is undergoing transformation from laboratory- to industry-scale manufacturing with recent regulatory approvals. Various challenges facing the complex AuCT manufacturing and supply chain process hinder the scale out and broader application of this highly potent treatment.MethodsWe present a multiscale logistics simulation framework, AuCT-Sim, that integrates novel supply chain system modeling algorithms, methods, and tools. AuCT-Sim includes a single facility model and a system-wide network model. Unique challenges of the AuCT industry are analyzed and addressed in AuCT-Sim. Decision-supporting tools can be developed based on this framework to explore “what-if” manufacturing and supply chain scenarios of importance to various cell therapy stakeholder groups.ResultsTwo case studies demonstrate the decision-supporting capability of AuCT-Sim where one investigates the optimal reagent base stocking level, and the other one simulates a reagent supply disruption event. These case studies serve as guidelines for designing computational experiments with AuCT-Sim to solve specific problems in AuCT manufacturing and supply chain.DiscussionThis simulation framework will be useful in understanding the impact of possible manufacturing and supply chain strategies, policies, regulations, and standards informing strategies to increase patient access to AuCT.