A Fuzzy Programming Model of Supplier Selection

2012 ◽  
Vol 468-471 ◽  
pp. 668-673 ◽  
Author(s):  
Hua Jiang ◽  
Zhi Gang Lu
Keyword(s):  
Supplier Selection ◽  
Programming Model ◽  
Fuzzy Programming ◽  
Chance Constrained Programming ◽  
Hybrid Intelligent Algorithm ◽  
Fuzzy Variables ◽  
Fuzzy Expected Value ◽  
Order Quantities ◽  
Constrained Programming ◽  
Fuzzy Programming Model

An integrated supplier selection problem under fuzzy environment is studied in this paper. Firstly, the linear weight method is used to calculate the scores of suppliers according to their different attributes, such as: quality, service, warranty, delivery, reputation and position, which are assumed as fuzzy variables. Secondly, a fuzzy expected value programming model and a fuzzy chance-constrained programming model are proposed to select the best combination of the suppliers and determine the order quantities. A hybrid intelligent algorithm, based on fuzzy simulation, genetic algorithm and neural network, is used to solve the two models. Finally, a numerical example is given to illustrate the effectiveness of the proposed models.

scholarly journals Optimization for Service Routes of Pallet Service Center Based on the Pallet Pool Mode

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Kang Zhou ◽  
Shiwei He ◽  
Rui Song
Keyword(s):  
Shortest Path ◽  
Programming Model ◽  
Time Windows ◽  
Method Of Lines ◽  
Chance Constrained Programming ◽  
Service Center ◽  
Chinese Postman Problem ◽  
Routing Problem ◽  
Hybrid Intelligent Algorithm ◽  
Constrained Programming

Service routes optimization (SRO) of pallet service center should meet customers’ demand firstly and then, through the reasonable method of lines organization, realize the shortest path of vehicle driving. The routes optimization of pallet service center is similar to the distribution problems of vehicle routing problem (VRP) and Chinese postman problem (CPP), but it has its own characteristics. Based on the relevant research results, the conditions of determining the number of vehicles, the one way of the route, the constraints of loading, and time windows are fully considered, and a chance constrained programming model with stochastic constraints is constructed taking the shortest path of all vehicles for a delivering (recycling) operation as an objective. For the characteristics of the model, a hybrid intelligent algorithm including stochastic simulation, neural network, and immune clonal algorithm is designed to solve the model. Finally, the validity and rationality of the optimization model and algorithm are verified by the case.

Methodology for Solving Multi-Objective Quadratic Programming Problems in a Fuzzy Stochastic Environment

2019 ◽  
pp. 177-217
Keyword(s):  
Quadratic Programming ◽  
Deterministic Model ◽  
Programming Model ◽  
Probability Distributions ◽  
Fuzzy Programming ◽  
Chance Constraints ◽  
Membership Functions ◽  
Quadratic Programming Problems ◽  
Constrained Programming ◽  
Fuzzy Programming Model

This chapter presents two methodologies for solving quadratic programming problems with multiple objectives under fuzzy stochastic environments. The right side parameters of the chance constraints of both the models are chosen as fuzzy random variables (FRVs) following different probability distributions. Like the previous chapters, chance constrained programming (CCP) methodology is employed to the fuzzy chance constraints to develop fuzzy programming model. In the first model, cut of fuzzy sets and fuzzy partial order relations are incorporated to the fuzzy programming model to develop an equivalent deterministic model. For the second model, defuzzification method of fuzzy numbers (FNs), which are presented in Chapter 2, are taken into consideration to generate equivalent quadratic programming model in a crisp environment. As the objective functions are quadratic in nature, it is easy to understand that the membership functions obtained through methodological development process are also quadratic in nature. To linearize the quadratic membership functions, linearization techniques are employed in this chapter. Finally, for achieving the maximum degree of each of the membership goals of the objectives, a fuzzy goal programming (FGP) approach is developed for the linearized membership goals and solved by minimizing under-deviational variables and satisfying modified system constraints in fuzzy stochastic decision-making environments. To illustrate the acceptability of the developed methodology presented in this chapter, some numerical examples are included.

Fixed Charged Solid Transportation Problem With Budget Constraints in Type-2 Fuzzy Variables

2018 ◽  
pp. 35-71 ◽  
Author(s):  
Dhiman Dutta ◽  
Mausumi Sen
Keyword(s):  
Programming Model ◽  
Credibility Measure ◽  
Chance Constrained Programming ◽  
Budget Constraints ◽  
Fuzzy Variables ◽  
Gradient Technique ◽  
Objective Function Values ◽  
Generalized Reduced Gradient ◽  
Constrained Programming

A multi-objective fixed charged solid transportation model with criterion e.g. transportation penalty, amounts, demands, carriages and budget constraints as type-2 triangular fuzzy variables with condition on few components and carriages is proposed here. With the critical value based reductions of corresponding type-2 fuzzy variables, a nearest interval approximation model and a chance constrained programming model applying generalized credibility measure for the constraints is proposed for this particular problem. The credibility measure is also applied to the objective functions of the chance constrained programming model. The model is then transformed into the corresponding crisp deterministic form by these two methods. A numerical example is provided to explain the model with hypothetical data and is then worked out by applying a gradient based optimization - Generalized Reduced Gradient technique (applying LINGO 16). The corresponding objective function values are compared numerically by two approaches after transforming it to crisp form by these two methods.

scholarly journals Study on the Stochastic Chance-Constrained Fuzzy Programming Model and Algorithm for Wagon Flow Scheduling in Railway Bureau

2012 ◽  
Vol 2012 ◽  
pp. 1-13
Author(s):  
Bin Liu
Keyword(s):  
Programming Model ◽  
Operation Time ◽  
Fuzzy Programming ◽  
Uncertain Information ◽  
Intelligent Algorithm ◽  
Hybrid Intelligent Algorithm ◽  
Size Limitation ◽  
Train Operation ◽  
Fuzzy Programming Model ◽  
Actual Decision

The wagon flow scheduling plays a very important role in transportation activities in railway bureau. However, it is difficult to implement in the actual decision-making process of wagon flow scheduling that compiled under certain environment, because of the interferences of uncertain information, such as train arrival time, train classify time, train assemble time, and flexible train-size limitation. Based on existing research results, considering the stochasticity of all kinds of train operation time and fuzziness of train-size limitation of the departure train, aimed at maximizing the satisfaction of departure train-size limitation and minimizing the wagon residence time at railway station, a stochastic chance-constrained fuzzy multiobjective model for flexible wagon flow scheduling problem is established in this paper. Moreover, a hybrid intelligent algorithm based on ant colony optimization (ACO) and genetic algorithm (GA) is also provided to solve this model. Finally, the rationality and effectiveness of the model and algorithm are verified through a numerical example, and the results prove that the accuracy of the train work plan could be improved by the model and algorithm; consequently, it has a good robustness and operability.

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