scholarly journals AUTOMOBILE COMPONENTS PROCUREMENT USING A DEA-TOPSIS-FMIP APPROACH WITH ALL-UNIT QUANTITY DISCOUNT AND FUZZY FACTORS

2020 ◽  
Vol 0 (0) ◽  
pp. 1-42
Author(s):  
Jiajia Chen ◽  
Zeshui Xu ◽  
Xunjie Gou ◽  
Dongbin Huang ◽  
Jianchuan Zhang
Keyword(s):  
Automobile Industry ◽  
Time Window ◽  
Mixed Integer ◽  
Two Stage ◽  
Quantity Discount ◽  
Comparative Performance ◽  
Solution Approach ◽  
Order Preference ◽  
Topsis Approach ◽  
Automobile Components

Components procurement is a crucial process in supply chain management of the automobile industry. The problem is further complicated by imprecise information and discount policies provided by suppliers. This paper aims to develop a computational approach for assisting automobile components procurement with all-unit quantity discount policy and fuzzy factors, from potential suppliers offering different product portfolios. We propose a two-stage approach consisting of a DEA-TOPSIS (data envelopment analysis procedures followed with a technique for order preference by similarity to an ideal solution) approach for screening suppliers, and subsequentially a fuzzy mixed integer programming (FMIP) model with multiple objectives for optimizing order allocations. The DEA-TOPSIS approach integrates suppliers’ comparative performance and diversity performance into an overall index that improves the ranking of potential suppliers, while the FMIP model features a soft time-window in delivery punctuality and an all-unit quantity discount function in cost. By applying it in a case of automobile components procurement, we show that this two-stage approach effectively supports decision makers in yielding procurement plans for various components offered by many potential suppliers. This paper contributes to integrating multi-attribute decision analysis approach in the form of DEA crossevaluation with TOPSIS and FMIP model for supporting components procurement decisions.

scholarly journals A Path-Based Selection Solution Approach for the Low Carbon Vehicle Routing Problem with a Time-Window Constraint

2020 ◽  
Vol 10 (4) ◽  
pp. 1489 ◽  
Author(s):  
Xianlong Ge ◽  
Xiaobo Ge ◽  
Weixin Wang
Keyword(s):  
Time Window ◽  
Programming Model ◽  
Model Simulation ◽  
Path Selection ◽  
Urban Traffic ◽  
Mixed Integer ◽  
Low Carbon ◽  
Gradual Improvement ◽  
Solution Approach ◽  
Urban Distribution

Due to the gradual improvement of urban traffic network construction and the increasing number of optional paths between any two points, how to optimize a vehicle travel path in a multi-path road network and then improve the efficiency of urban distribution has become a difficult problem for logistics companies. For this purpose, a mixed-integer mathematical programming model with a time window based on multiple paths for urban distribution in a multi-path environment is established and its exact solution solved using software CPLEX. Additionally, in order to test the application and feasibility of the model, simulation experiments were performed on the four parameters of time, distance, cost, and fuel consumption. Furthermore, using Jingdong (JD), the main urban area in Chongqing, as an example, the experimental results reveal that an algorithm that considers the path selection can significantly improve the efficiency of urban distribution in metropolitan areas with complex road structures.

scholarly journals A Hybrid IP/GA Approach to the Parallel Production Lines Scheduling Problem

2016 ◽  
Vol 2016 ◽  
pp. 1-11
Author(s):  
Huizhi Ren ◽  
Shenshen Sun
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Time Window ◽  
Valid Inequalities ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Production Lines ◽  
Solution Approach ◽  
Cp Decomposition

A special parallel production lines scheduling problem is studied in this paper. Considering the time window and technical constraints, a mixed integer linear programming (MILP) model is formulated for the problem. A few valid inequalities are deduced and a hybrid mixed integer linear programming/constraint programming (MILP/CP) decomposition strategy is introduced. Based on them, a hybrid integer programming/genetic algorithm (IP/GA) approach is proposed to solve the problem. At last, the numerical experiments demonstrate that the proposed solution approach is effective and efficient.

Enhancement of “Technique for Order Preference by Similarity to Ideal Solution” Approach for Evaluating the Web Sources to Select as External Source for Web Warehousing

2017 ◽  
Vol 6 (1) ◽  
pp. 1-16
Author(s):  
Hari Om Sharan Sinha
Keyword(s):  
Euclidean Distance ◽  
External Source ◽  
Main Concern ◽  
Ideal Solution ◽  
Multiple Features ◽  
Solution Approach ◽  
External Data ◽  
Order Preference ◽  
Topsis Approach ◽  
The Web

The main concern of this paper is to evaluate the web sources, which are to be selected as external data sources for web warehousing. In order to identify the web sources, they are evaluated on the ground of their multiple features. For it, Multi Criteria Decision Making (MCDM) approach has been used. Here, among all the MCDM approach, the focus is on “Technique for Order Preference by Similarity to Ideal Solution” (TOPSIS) approach and proposing an enhancement in this method. The conventional TOPSIS approach uses Euclidean Distance to measure the similarity. Here, Jeffrey Divergence has been proposed to measure the similarity instead of Euclidean Distance which includes all the symmetric distances during computation. The Euclidean Distance only measures unidirectional distance whereas the Jeffrey Divergence includes multidirectional distances. Unidirectional distance includes only distance in one dimension but multidirectional distances includes differences, so more relevant in web sources evaluation. Experimental analysis for both the variations of TOPSIS approach have been conducted and the result shows the enhancement in the selection of web sources.

scholarly journals M-Parameterized N-Soft Topology-Based TOPSIS Approach for Multi-Attribute Decision Making

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 748
Author(s):  
Muhammad Riaz ◽  
Ayesha Razzaq ◽  
Muhammad Aslam ◽  
Dragan Pamucar
Keyword(s):  
Decision Making ◽  
Soft Set ◽  
Optimal Decision ◽  
Soft Sets ◽  
Solution Approach ◽  
Soft Topology ◽  
Order Preference ◽  
Multi Attribute Decision Making ◽  
Topsis Approach ◽  
Topsis Technique

In this article, we presented the notion of M-parameterized N-soft set (MPNSS) to assign independent non-binary evaluations to both attributes and alternatives. The MPNSS is useful for making explicit the imprecise data which appears in ranking, rating, and grading positions. The proposed model is superior to existing concepts of soft set (SS), fuzzy soft sets (FSS), and N-soft sets (NSS). The concept of M-parameterized N-soft topology (MPNS topology) is defined on MPNSS by using extended union and restricted intersection of MPNS-power whole subsets. For these objectives, we define basic operations on MPNSSs and discuss various properties of MPNS topology. Additionally, some methods for multi-attribute decision making (MADM) techniques based on MPNSSs and MPNS topology are provided. Furthermore, the TOPSIS (technique for order preference by similarity to an ideal solution) approach under MPNSSs and MPNS topology is established. The symmetry of the optimal decision is illustrated by interesting applications of proposed models and new MADM techniques are demonstrated by certain numerical illustrations and well justified by comparison analysis with some existing techniques.

scholarly journals A Generalized Minimum Cost Flow Model for Multiple Emergency Flow Routing

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jianxun Cui ◽  
Shi An ◽  
Meng Zhao
Keyword(s):  
Flow Model ◽  
Time Window ◽  
Programming Model ◽  
Real Life ◽  
Minimum Cost ◽  
Emergency Evacuation ◽  
Mixed Integer ◽  
Fixed Cost ◽  
Minimum Cost Flow ◽  
Cost Flow

During real-life disasters, that is, earthquakes, floods, terrorist attacks, and other unexpected events, emergency evacuation and rescue are two primary operations that can save the lives and property of the affected population. It is unavoidable that evacuation flow and rescue flow will conflict with each other on the same spatial road network and within the same time window. Therefore, we propose a novel generalized minimum cost flow model to optimize the distribution pattern of these two types of flow on the same network by introducing the conflict cost. The travel time on each link is assumed to be subject to a bureau of public road (BPR) function rather than a fixed cost. Additionally, we integrate contraflow operations into this model to redesign the network shared by those two types of flow. A nonconvex mixed-integer nonlinear programming model with bilinear, fractional, and power components is constructed, and GAMS/BARON is used to solve this programming model. A case study is conducted in the downtown area of Harbin city in China to verify the efficiency of proposed model, and several helpful findings and managerial insights are also presented.

Corporate sustainability assessment through fuzzy TOPSIS

2018 ◽  
Author(s):  
Eleonora Bottani ◽  
Marta Rinaldi ◽  
Federico Solari
Keyword(s):  
Sustainability Assessment ◽  
Corporate Sustainability ◽  
Reference Value ◽  
Fuzzy Topsis ◽  
Analysis And Evaluation ◽  
Sustainability Performance ◽  
Order Preference ◽  
Pros And Cons ◽  
Topsis Approach ◽  
Corporate Level

"The aim of this paper is to propose a decisionmaking methodology that enables the analysis and evaluation of sustainability at the corporate level. The proposed methodology grounds on two tools, namely the technique for order preference by similarity to ideal solution (TOPSIS) approach and fuzzy logic. The integration of these tools offers an effective way to deal with two typical issues of sustainability assessment, i.e.: 1) the fact that the company’s performance should be frequently evaluated against qualitative key performance indicators; and 2) the fact that to be meaningful, the company’s sustainability performance needs to be compared to a reference value, e.g. a threshold or benchmark, to evaluating how the company is distant from a target. The proposed approach has been applied to a real firm, operating in the food machinery industry, for testing purpose. The main pros and cons of the approach are described."

A Novel Approach for Two-Stage UAV Path Planning in Pipeline Network Inspection

2020 ◽  
Author(s):  
Rui Qiu ◽  
Yongtu Liang
Keyword(s):  
Path Planning ◽  
Large Scale ◽  
Flight Path ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Two Stage ◽  
Calculation Time ◽  
Pipeline Network ◽  
Second Stage ◽  
Computational Performance

Abstract Currently, unmanned aerial vehicle (UAV) provides the possibility of comprehensive coverage and multi-dimensional visualization of pipeline monitoring. Encouraged by industry policy, research on UAV path planning in pipeline network inspection has emerged. The difficulties of this issue lie in strict operational requirements, variable flight missions, as well as unified optimization for UAV deployment and real-time path planning. Meanwhile, the intricate structure and large scale of the pipeline network further complicate this issue. At present, there is still room to improve the practicality and applicability of the mathematical model and solution strategy. Aiming at this problem, this paper proposes a novel two-stage optimization approach for UAV path planning in pipeline network inspection. The first stage is conventional pre-flight planning, where the requirement for optimality is higher than calculation time. Therefore, a mixed integer linear programming (MILP) model is established and solved by the commercial solver to obtain the optimal UAV number, take-off location and detailed flight path. The second stage is re-planning during the flight, taking into account frequent pipeline accidents (e.g. leaks and cracks). In this stage, the flight path must be timely rescheduled to identify specific hazardous locations. Thus, the requirement for calculation time is higher than optimality and the genetic algorithm is used for solution to satisfy the timeliness of decision-making. Finally, the proposed method is applied to the UAV inspection of a branched oil and gas transmission pipeline network with 36 nodes and the results are analyzed in detail in terms of computational performance. In the first stage, compared to manpower inspection, the total cost and time of UAV inspection is decreased by 54% and 56% respectively. In the second stage, it takes less than 1 minute to obtain a suboptimal solution, verifying the applicability and superiority of the method.

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