Supply Chain Production-distribution Cost Optimization under Grey Fuzzy Uncertainty

Most supply chain programming problems are restricted to the deterministic situations or stochastic environmcnts. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost. The programming parameters of the material suppliers, manufacturer, distribution centers, and the customers are integrated into the presented model. On the basis of the chance measure and the credibility of grey fuzzy variable, the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved Particle Swarm Optimization （PSO） algorithm based on the Differential Evolution （DE） algorithm can optimize the uncertain programming problems. A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.

Distribution Inventory Cost Optimization Under Grey and Fuzzy Uncertainty

The grey fuzzy variable was defined for the two fold uncertain parameters combining grey and fuzziness factors. On the basis of the credibility and chance measure of grey fuzzy variables, the distribution center inventory uncertain programming model was presented. The grey fuzzy simulation technology can generate input-output data for the uncertain functions. The neural network trained from the inputoutput data can approximate the uncertain functions. The designed hybrid intelligent algorithm by embedding the trained neural network into genetic algorithm can optimize the general grey fuzzy programming problems. Finally, one numerical example is provided to illustrate the effectiveness of the model and the hybrid intelligent algorithm.

Bipolar Interval-Valued Neutrosophic Optimization Model of Integrated Healthcare System

Bipolar Interval-valued neutrosophic set is another generalization of fuzzy set,neutrosophic set,bipolar fuzzy set and bipolar neutrosophic set and thus when applied to the optimization problem handles uncertain data more efficiently and flexibly.Current work is an effort to design a flexible optimization model in the backdrop of interval-valued bipolar neutrosophic sets.Bipolar interval-valued neutrosophic membership grades are picked so that they indicate the restriction of the plausible infringement of the inequalities given in the problem.To prove the adequacy and effectiveness of the method a unified system of sustainable medical healthcare supply chain model with an uncertain figure of product complaints is used.Time,quality and cost are considered as satisfaction level to choose best supplier for medicine procurement.The proposed model ensures 99%satisfaction for cost reduction,63%satisfaction for the quality of product and 64%satisfaction for total time taken in medicine supply chain.

A new FPGA with 4/5-input LUT and optimized carry chain

A new LUT and carry structure embedded in the configurable logic block of an FPGA is proposed. The LUT is designed to support both 4-input and 5-input structures, which can be configured by users according to their needs without increasing interconnect resources. We also develop a new carry chain structure with an optimized critical path. Finally a newly designed configurable scan-chain is inserted. The circuit is fabricated in 0.13μm 1P8M 1.2/2.5/3.3 V logic CMOS process. The test results show a correct function of 4/5-input LUT and scan- chain, and a speedup in carry performance of nearly 3 times over current architecture in the same technology at the cost of an increase in total area of about 72.5%. Our results also show that the logic utilization of this work is better than that of a Virtex lI/Virtex 4/Virtex 5/Virtex 6/Virtex 7 FPGA when implemented using only 4-LUT and better than that of a Virtex lI/Virtex 4 FPGA when implemented using only 5-LUT.

Theoretical search for solutions to minimize negative influence of segregation in mixing of particulate solids

The objective of this study is to examine several optimization problems in the batch mixing of segregating particulate solids that can be set up and solved using Markov chain models. To improve the adequacy of such models and exclude some physical contradictions that arise in the linear form, a non-linear Markov chain model for the mixing of segregating components is proposed. Optimal solutions are obtained by controlling the particle flow outside the mixing operating volume while the components are being loaded, modifying particle circulation inside the mixing zone during the process, and by structuring the load in the mixing zone. Solutions are found that not only reduce the negative influence of segregation, but also exclude it altogether. The gain resulting from optimization grows with the rate of segregation. The optimal solutions presented here can be used to improve the design of mixers.

Perspectives in multilevel decision-making in the process industry

Decisions in supply chains are hierarchically organized. Strategic decisions involve the long-term planning of the structure of the supply chain network.Tactical decisions are mid-term plans to allocate the production and distribution of materials, while operational decisions are related to the daily planning of the execution of manufacturing operations. These planning processes are conducted independently with minimal exchange of information between them. Achieving a better coordination between these processes allows companies to capture benefits that are currently out of their reach and improve the communication among their functional areas. We propose a network representation for the multilevel decision structure and analyze the components that are involved in finding integrated solutions that maximize the sum of the benefits of all nodes of the decision network.Although such task is very challenging, significant research progress has been made in each component of this structure. An overview of strategic models, mid-term planning models, and scheduling models is presented to address the solution of each node in the decision network.Coordination mechanisms for converging the integrated solutions are also analyzed, including solving large-scale models, multiobjective optimization, bi-level programming, and decomposition. We conclude by summarizing the challenges that hinder the full integration of multilevel decision making in supply chain management.

SIMPLE COMPUTING OF THE CUSTOMER LIFETIME VALUE:A FIXED LOCAL-OPTIMAL POLICY APPROACH

In this paper,we present a new method for finding a fixed local-optimal policy for computing the customer lifetime value.The method is developed for a class of ergodic controllable finite Markov chains.We propose an approach based on a non-converging state-value function that fluctuates（increases and decreases） between states of the dynamic process.We prove that it is possible to represent that function in a recursive format using a one-step-ahead fixed-optimal policy.Then,we provide an analytical formula for the numerical realization of the fixed local-optimal strategy.We also present a second approach based on linear programming,to solve the same problem,that implement the c-variable method for making the problem computationally tractable.At the end,we show that these two approaches are related：after a finite number of iterations our proposed approach converges to same result as the linear programming method.We also present a non-traditional approach for ergodicity verification.The validity of the proposed methods is successfully demonstrated theoretically and,by simulated credit-card marketing experiments computing the customer lifetime value for both an optimization and a game theory approach.