Supplier selection and purchase problem with fixed cost and constrained order quantities under stochastic demand

2011 ◽  
Vol 129 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Ju-liang Zhang ◽  
Ming-yu Zhang
Keyword(s):  
Supplier Selection ◽  
Stochastic Demand ◽  
Fixed Cost ◽  
Order Quantities

Optimal multi-product supplier selection under stochastic demand with service level and budget constraints using learning vector quantization neural network

2019 ◽  
Vol 53 (5) ◽  
pp. 1709-1720
Author(s):  
Hajar HormozzadehGhalati ◽  
Alireza Abbasi ◽  
Abolghasem Sadeghi-Niaraki
Keyword(s):  
Neural Network ◽  
Vector Quantization ◽  
Supplier Selection ◽  
Stochastic Demand ◽  
Service Level ◽  
Learning Vector Quantization ◽  
Optimal Number ◽  
Optimal Order ◽  
Budget Constraints ◽  
Order Quantities

In today’s competitive marketplace demand, evaluation and selection of suppliers are pivotal for firms, and therefore decision makers need to select suppliers and the optimal order quantities when outsourcing. However, there is uncertainty and risk due to lack of precise data for supplier selection. Uncertainty can impose shortage or overstocks, because of stochastic demand, to firms; in this case, considering inventory control is essential. In this research, an appropriate spatial model is developed for a multi-product supplier selection model with service level and budget constraints. Learning Vector Quantization Neural Network is used to find the optimal number of decision variables with the goal of maximizing the expected profit of supply chains. By analyzing a practical example and conducting sensitivity analysis, we find that corporate profit will be maximized if the optimal integration of suppliers and the optimal order quantities from each supplier is determined. In addition, budget and service level should be considered in the process of finding the best result.

Fuzzy Multi-Choice Goal Programming for Supplier Selection

2012 ◽  
pp. 39-60 ◽  
Author(s):  
Ching-Ter Chang ◽  
Cheng-Yuan Ku ◽  
Hui-Ping Ho
Keyword(s):  
Supply Chain ◽  
Goal Programming ◽  
Supplier Selection ◽  
Liquid Crystal Display ◽  
Decision Aids ◽  
Decision Makers ◽  
Acrylic Sheet ◽  
Selection Decision ◽  
Order Quantities ◽  
Linguistic Quantifiers

Supplier selection decision is an important issue of purchasing management in supply chain management involving multiple objectives; however, it is difficult to solve because objectives are often conflicting in nature. This study integrates multi-choice goal programming (MCGP) and fuzzy approaches as decision aids to help decision makers to choose better suppliers by considering multiple aspiration levels and vague goal relations. According to the function of multiple aspirations provided by the fuzzy MCGP (FMCGP), decision makers can set fuzzy relations among multiple supplier goals with linguistic quantifiers according to their different strategies. Also, decision makers can define the membership function for each linguistic quantifier to describe their ambiguous selection preference in supplier selection. With the FMCGP method, decision makers can obtain the order quantities for suitable suppliers based on different organizations’ supply chain strategies. To demonstrate the usefulness of the proposed method, a real-world case of a Liquid Crystal Display (LCD) monitor and acrylic sheet manufacturer is presented.

Inventory lot-sizing with supplier selection under non-stationary stochastic demand

2015 ◽  
Vol 54 (8) ◽  
pp. 2459-2469 ◽  
Author(s):  
Arun Kr. Purohit ◽  
Devendra Choudhary ◽  
Ravi Shankar
Keyword(s):  
Supplier Selection ◽  
Lot Sizing ◽  
Stochastic Demand

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 The effects of different kinds of returns on an integrated chance-constrained stochastic mobile phone closed-loop supply chain configuration and supplier selection

2021 ◽  
Author(s):  
Shahrzad Ahmadi Kermanshah
Keyword(s):  
Supply Chain ◽  
Mobile Phone ◽  
Supplier Selection ◽  
Closed Loop ◽  
Stochastic Demand ◽  
Mixed Integer ◽  
Chance Constraint ◽  
Distance Method ◽  
Closed Loop Supply Chain ◽  
Multi Objective

One of the important concerns in the world is E-waste. Ending up e-waste in the landfill and inappropriate disposing of it are hazardous to the environment. The goal of this research is to design and optimize a multi-period, multi-product, multi-echelon, and multi-customer Closed-Loop Supply Chain (CLSC) network for a mobile phone network considering different types of product returns. Commercial, end of life, and end-of-use returns are well-known in practice. In this research, a multi-objective mixed-integer linear programming formulation with stochastic demand and return is proposed to maximize the total profit in the mobile phone CLSC network, alongside maximizing the weights of eligible suppliers which are estimated based on a fuzzy method for efficient supplier selection and order allocation. Chance-constraint programming is applied in order to deal with the stochastic demand and return. Moreover, distance method and εε-constraint technique are employed to solve the proposed multi-objective problem. The application of the proposed mathematical model is illustrated in Toronto, Canada using real maps.

scholarly journals Evaluation of Supplier Selection in Manufacturing Industries using Grey Relational Analysis- A Case Study

2014 ◽  
pp. 104-109
Author(s):  
Veera Bahadur Aravind Reddy K ◽  
Saras Chandra T Reddy ◽  
G. Rajyalakshmi
Keyword(s):  
Grey Relational Analysis ◽  
Supplier Selection ◽  
Selection Process ◽  
Evaluation Model ◽  
Evaluation Criteria ◽  
Objective Evaluation ◽  
Supply Contract ◽  
Relational Analysis ◽  
Order Quantities ◽  
Grey Relational

Selection of supplier is one of the most critical activities performed by the organizations because of its strategic importance. Over the years a number of quantitative approaches have been applied to supplier selection problems. The selection process is commonly based on their previous performance records, so the ranking determines which supplier will get their supply contract. However a survey on current evaluation methods shows that they are all less objective and lack accurate data processing. These evaluation criteria often conflict, however and it is frequently impossible to find a supplier that excels in all areas. In addition some of the criteria are quantitative and some are qualitative. Thus a methodology is needed that can capture both subjective and objective evaluation measures. In this paper, we presented AHP and Grey Relational Analysis to establish a complete and accurate evaluation model for selecting suppliers based on multiple criteria and places the order quantities among them for a spinning industry

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