scholarly journals An Integrated Model of Material Supplier Selection and Order Allocation Using Fuzzy Extended AHP and Multiobjective Programming

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Zhi Li ◽  
W. K. Wong ◽  
C. K. Kwong

This paper presents a supplier selection and order allocation (SSOA) model to solve the problem of a multiperiod supplier selection and then order allocation in the environment of short product life cycle and frequent material purchasing, for example, fast fashion environment in apparel industry. At the first stage, with consideration of multiple decision criteria and the fuzziness of the data involved in deciding the preferences of multiple decision variables in supplier selection, the fuzzy extent analytic hierarchy process (FEAHP) is adopted. In the second stage, supplier ranks are inputted into an order allocation model that aims at minimizing the risk of material purchasing and minimizing the total material purchasing costs using a dynamic programming approach, subject to constraints on deterministic customer demand and deterministic supplier capacity. Numerical examples are presented, and computational results are reported.

Author(s):  
Tao Yu-Jwo ◽  
Lee Hsuan-Shih ◽  
Tu Chang-Shu

The airport ground handling services (AGHS) equipment supplier selection problem involves a safety guarantee on the part of the AGHS company that carries out the daily work. AGHS company can prevent aircraft damage and delays in airlines schedules, and ensure reliable and high-quality ground handling service. In our research, we developed an AGHS equipment supplier selection model based on the analytic hierarchy process and an AHP weighted fuzzy linear programming approach, and we solved the AGHS equipment supplier’s selection problem. The main objective of this article is to create an AHP and AHP-FLP decision model in order to help the AGHS company authorities select the best AGHS equipment supplier. The practical application in AGHS equipment supplier selection decisions can be interpreted as demonstrating that the proposed model provides knowledge and practical value for the AGHS industry.


2010 ◽  
Vol 52 (1-4) ◽  
pp. 365-376 ◽  
Author(s):  
Mohsen Jafari Songhori ◽  
Madjid Tavana ◽  
Ali Azadeh ◽  
Mohammad Hossien Khakbaz

2019 ◽  
Vol 127 ◽  
pp. 734-748 ◽  
Author(s):  
Elham Esmaeili-Najafabadi ◽  
Mohammad Saber Fallah Nezhad ◽  
Hamid Pourmohammadi ◽  
Mahboobeh Honarvar ◽  
Mohammad Ali Vahdatzad

Author(s):  
Delight Mhiribidi ◽  
Joel Nobert ◽  
Webster Gumindoga ◽  
Donald T. Rwasoka

Abstract. The management and allocation of water from multi-reservoir systems is complex and thus requires dynamic modelling systems to achieve optimality. A multi-reservoir system in the Southern Lowveld of Zimbabwe is used for irrigation of sugarcane estates that produce sugar for both local and export consumption. The system is burdened with water allocation problems, made worse by decommissioning of dams. Thus the aim of this research was to develop an operating policy model for the Lowveld multi-reservoir system.The Mann Kendall Trend and Wilcoxon Signed-Rank tests were used to assess the variability of historic monthly rainfall and dam inflows for the period 1899–2015. The WEAP model was set up to evaluate the water allocation system of the catchment and come-up with a reference scenario for the 2015/2016 hydrologic year. Stochastic Dynamic Programming approach was used for optimisation of the multi-reservoirs releases.Results showed no significant trend in the rainfall but a significantly decreasing trend in inflows (p < 0.05). The water allocation model (WEAP) showed significant deficits ( ∼  40 %) in irrigation water allocation in the reference scenario. The optimal rule curves for all the twelve months for each reservoir were obtained and considered to be a proper guideline for solving multi- reservoir management problems within the catchment. The rule curves are effective tools in guiding decision makers in the release of water without emptying the reservoirs but at the same time satisfying the demands based on the inflow, initial storage and end of month storage.


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