Applying nonlinear MODM model to supply chain management with quantity discount policy under complex fuzzy environment

Purpose: The aim of this paper is to deal with the supply chain management (SCM) with quantity discount policy under the complex fuzzy environment, which is characterized as the bi-fuzzy variables. By taking into account the strategy and the process of decision making, a bi-fuzzy nonlinear multiple objective decision making (MODM) model is presented to solve the proposed problem. Design/methodology/approach: The bi-fuzzy variables in the MODM model are transformed into the trapezoidal fuzzy variables by the DMs's degree of optimism ?1 and ?2, which are de-fuzzified by the expected value index subsequently. For solving the complex nonlinear model, a multi-objective adaptive particle swarm optimization algorithm (MO-APSO) is designed as the solution method. Findings: The proposed model and algorithm are applied to a typical example of SCM problem to illustrate the effectiveness. Based on the sensitivity analysis of the results, the bi-fuzzy nonlinear MODM SCM model is proved to be sensitive to the possibility level ?1. Practical implications: The study focuses on the SCM under complex fuzzy environment in SCM, which has a great practical significance. Therefore, the bi-fuzzy MODM model and MO-APSO can be further applied in SCM problem with quantity discount policy. Originality/value: The bi-fuzzy variable is employed in the nonlinear MODM model of SCM to characterize the hybrid uncertain environment, and this work is original. In addition, the hybrid crisp approach is proposed to transferred to model to an equivalent crisp one by the DMs's degree of optimism and the expected value index. Since the MODM model consider the bi-fuzzy environment and quantity discount policy, so this paper has a great practical significance.Peer Reviewe

Hazmats Transportation Network Design Model with Emergency Response under Complex Fuzzy Environment

A bilevel optimization model for a hazardous materials transportation network design is presented which considers an emergency response teams location problem. On the upper level, the authority designs the transportation network to minimize total transportation risk. On the lower level, the carriers first choose their routes so that the total transportation cost is minimized. Then, the emergency response department locates their emergency service units so as to maximize the total weighted arc length covered. In contrast to prior studies, the uncertainty associated with transportation risk has been explicitly considered in the objective function of our mathematical model. Specifically, our research uses a complex fuzzy variable to model transportation risk. An improved artificial bee colony algorithm with priority-based encoding is also applied to search for the optimal solution to the bilevel model. Finally, the efficiency of the proposed model and algorithm is evaluated using a practical case and various computing attributes

Two-stage based dynamic earth-rock transportation assignment problem under fuzzy random environment to earth-rock dam construction

This paper discusses a two-stage based dynamic transportation assignment problem (TS-based DTAP) under a fuzzy random environment in an earth-rock transportation system. This problem is a multi-objective dynamic programming optimization process for minimizing total operational cost, transportation duration and total waste. Triangular fuzzy random numbers are used for the uncertain parameters, and a hybrid crisp approach and an expected value operator are introduced to deal with these uncertainties. A dynamic programming based contraction particle swarm optimization is developed to solve the proposed expected value model for TS-based DTAP. Then, the earth-rock dam construction at Pubugou Hydropower project is used as a practical application to verify the proposed approach. Results and analysis are presented to highlight the performance of the proposed TS-based DTAP model and the optimization method, which proves to be effective and relatively efficient compared to the models under other environments and a standard PSO algorithm

Retrofitting Transportation Network Using a Fuzzy Random Multiobjective Bilevel Model to Hedge against Seismic Risk

This paper focuses on the problem of hedging against seismic risk through the retrofit of transportation systems in large-scale construction projects (LSCP). A fuzzy random multiobjective bilevel programming model is formulated with the objectives of the retrofit costs and the benefits on two separate levels. After establishing the model, a fuzzy random variable transformation approach and fuzzy variable approximation decomposition are used to deal with the uncertainty. An approximation decomposition-based multi-objective AGLNPSO is developed to solve the model. The results of a case study validate the efficiency of the proposed approach

The Economic Role of Rating Behavior in Third-Party Application Market

This paper explores the fundamental influence of consumer rating behavior on an emerging third-party software application market, mobile app market. In app market, consumers’ ex ante belief on app utility essentially is determined by the app rating while at the same time the app rating itself is derived from the ex post utility obtained by purchased customers. We develop an analytical model which explicitly characterizes this bidirectional rating-utility conversion based on a newly introduced concept “reservation rating”. After building this conversion process into the utility function, we examine the market equilibrium and show how change in consumer rating attitude, such as being severer in offering ratings or being less critical in accepting ratings, would affect the developers’ optimal choices of app price and app quality level as well as the platform owner’s optimal revenue sharing policy

Multidemand Multisource Order Quantity Allocation with Multiple Transportation Alternatives

This paper focuses on a multidemand multisource order quantity allocation problem with multiple transportation alternatives. To solve this problem, a bilevel multiobjective programming model under a mixed uncertain environment is proposed. Two levels of decision makers are considered in the model. On the upper level, the purchaser aims to allocate order quantity to multiple suppliers for each demand node with the consideration of three objectives: total purchase cost minimization, total delay risk minimization, and total defect risk minimization. On the lower level, each supplier attempts to optimize the transportation alternatives with total transportation and penalty costs minimization as the objective. In contrast to prior studies, considering the information asymmetry in the bilevel decision, random and fuzzy random variables are used to model uncertain parameters of the construction company and the suppliers. To solve the bilevel model, a solution method based on Kuhn-Tucker conditions, sectional genetic algorithm, and fuzzy random simulation is proposed. Finally, the applicability of the proposed model and algorithm is evaluated through a practical case from a large scale construction project. The results show that the proposed model and algorithm are efficient in dealing with practical order quantity allocation problems

A fuzzy multi-objective model and application for the discrete dynamic temporary facilities location planning problem

The location of temporary facilities on construction sites is essential to the enhancement of productivity and safety, but it is complex due to the unique issues associated with construction. To positively contribute to the dynamic construction site layout planning field, this paper proposes a new fuzzy multi-objective decision making model. The proposed hybrid optimization model utilizes fuzzy numbers and logic to represent the closeness relationship between temporary facilities. The two main phases presented represent a specific advance in knowledge in through: (1) an opti­mization model that considers both uncertainty and dynamic elements; (2) the application of this optimization to a spe­cial project. A multi-objective simulated annealing-based genetic algorithm (MOSA-based GA) is proposed to solve the model and the case of Jinping-II hydroelectric station is studied to evaluate the model’s performance. The computational study was carried out to demonstrate the practicality and efficiency of the proposed optimization method. The study is applicable and useful to the profession

A Dynamical Innovation Diffusion Model with Fuzzy Coefficient and Its Application to Local Telephone Diffusion in China

This paper studies the innovation diffusion problem with the affection of urbanization, proposing a dynamical innovation diffusion model with fuzzy coefficient, and uses the shifting rate of people from rural areas stepping into urban areas to show the process of urbanization. The numerical simulation shows the diffusion process for telephones in China with Genetic Algorithms and this model is effective to show the process of innovation diffusion with the condition of urbanization process

Protein Kinase Activation Increases Insulin Secretion by Sensitizing the Secretory Machinery to Ca2+

Glucose and other secretagogues are thought to activate a variety of protein kinases. This study was designed to unravel the sites of action of protein kinase A (PKA) and protein kinase C (PKC) in modulating insulin secretion. By using high time resolution measurements of membrane capacitance and flash photolysis of caged Ca2+, we characterize three kinetically different pools of vesicles in rat pancreatic β-cells, namely, a highly calcium-sensitive pool (HCSP), a readily releasable pool (RRP), and a reserve pool. The size of the HCSP is ∼20 fF under resting conditions, but is dramatically increased by application of either phorbol esters or forskolin. Phorbol esters and forskolin also increase the size of RRP to a lesser extent. The augmenting effect of phorbol esters or forskolin is blocked by various PKC or PKA inhibitors, indicating the involvement of these kinases. The effects of PKC and PKA on the size of the HCSP are not additive, suggesting a convergent mechanism. Using a protocol where membrane depolarization is combined with photorelease of Ca2+, we find that the HCSP is a distinct population of vesicles from those colocalized with Ca2+ channels. We propose that PKA and PKC promote insulin secretion by increasing the number of vesicles that are highly sensitive to Ca2+