A mathematical programming model for AGVS planning and control in manufacturing systems

1996 ◽  
Vol 30 (4) ◽  
pp. 647-658 ◽  
Author(s):  
Mingyuan Chen
Keyword(s):  
Mathematical Programming ◽  
Manufacturing Systems ◽  
Programming Model ◽  
Mathematical Programming Model ◽  
Planning And Control ◽  
And Control

scholarly journals Green Production Planning and Control for the Textile Industry by Using Mathematical Programming and Industry 4.0 Techniques

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2072 ◽  
Author(s):  
Wen-Hsien Tsai
Keyword(s):  
Mathematical Programming ◽  
Textile Industry ◽  
Industry 4.0 ◽  
Programming Model ◽  
Environmental Issues ◽  
Mathematical Programming Model ◽  
Waste Reuse ◽  
Planning And Control ◽  
Green Production ◽  
And Control

The textile industry is one of the world’s major sources of industrial pollution, and related environmental issues are becoming an ever greater concern. This paper considers the environmental issues of carbon emissions, energy recycling, and waste reuse, and uses a mathematical programming model with Activity-Based Costing (ABC) and the Theory of Constraints (TOC) to achieve profit maximization. This paper discusses the combination of mathematical programming and Industry 4.0 techniques to achieve the purpose of green production planning and control for the textile industry in the new era. The mathematical programming model is used to determine the optimal product mix under various production constraints, while Industry 4.0 techniques are used to control the production progress to achieve the planning targets. With the help of an Industry 4.0 real-time sensor and detection system, it can achieve the purposes of recycling waste, reducing carbon emission, saving energy and cost, and finally achieving a maximization of profit. The main contributions of this research are using mathematical programming approach to formulate the decision model with ABC cost data and TOC constraints for the textile companies and clarifying the relation between mathematical programming models and Industry 4.0 techniques. Managers in the textile companies can apply this decision model to achieve the optimal product-mix under various constraints and to evaluate the effect on profit of carbon emissions, energy recycling, waste reuse, and material quantity discount.

scholarly journals A Framework of Production Planning and Control with Carbon Tax under Industry 4.0

2018 ◽  
Vol 10 (9) ◽  
pp. 3221 ◽  
Author(s):  
Wen-Hsien Tsai ◽  
Yin-Hwa Lu
Keyword(s):  
Production Planning ◽  
Industry 4.0 ◽  
Programming Model ◽  
Carbon Tax ◽  
Optimal Solution ◽  
Production Planning And Control ◽  
Mathematical Programming Model ◽  
Tire Industry ◽  
Planning And Control ◽  
And Control

In recent years, the international community has placed great emphasis on environmental protection issues. The United Nations has also successively enacted relevant laws and regulations to restrain international greenhouse gas emissions and some countries implemented carbon tax levies to reduce air pollution. The tire industry is a manufacturing industry with high pollution and high carbon emissions; therefore, the purpose of this paper is to propose a framework of production planning and control with carbon tax under Industry 4.0 and use the tire industry as the illustrative example. In this framework, the mathematical programming model, with Activity-Based Costing (ABC) and Theory of Constraints (TOC) for production planning, is used to achieve the optimal solution under various production and sale constraints in order to find the optimal product-mix maximizing the profit. On the other hand, Industry 4.0 utilizes new technologies such as 3D printing, robot and automated guided vehicle (AGV) and links all the components in the manufacturing systems by using various sensor systems, Cyber-Physical Systems (CPS) and Internet of Things (IoT) to collect and monitor the activity data of all the components in real-time, to give intelligent responses to various problems that may arise in the factory by the real-time analysis results of cloud computing and big data and to attain the various benefits of Industry 4.0 implementation. The parameters of the mathematical programming model will be updated periodically from the new big data set. In this paper, an illustrative example is used is used to demonstrate the application of the model. From the optimal solution and sensitivity analyses on increasing the raw material’s prices and carbon taxes will affect the profits. This framework can provide a general approach to help companies execute production management in the way of more efficiency, less cost, lower carbon emission and higher quality across the value chain for the tire industry and other industries.

scholarly journals A Mathematical Programming Model to Determine Objective Weights for the Interval Extension of TOPSIS

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Hai Shen ◽  
Lingyu Hu ◽  
Kin Keung Lai
Keyword(s):  
Mathematical Programming ◽  
Input Data ◽  
Programming Model ◽  
Multiple Criteria Decision Making ◽  
Decision Makers ◽  
Mathematical Programming Model ◽  
Topsis Method ◽  
Data Set ◽  
Interval Extension ◽  
Interval Valued

Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method has been extended in previous literature to consider the situation with interval input data. However, the weights associated with criteria are still subjectively assigned by decision makers. This paper develops a mathematical programming model to determine objective weights for the implementation of interval extension of TOPSIS. Our method not only takes into account the optimization of interval-valued Multiple Criteria Decision Making (MCDM) problems, but also determines the weights only based upon the data set itself. An illustrative example is performed to compare our results with that of existing literature.

CREDIBILITY-BASED FUZZY MATHEMATICAL PROGRAMMING MODEL FOR PORTFOLIO SELECTION UNDER UNCERTAINTY

2014 ◽  
Vol 13 (01) ◽  
pp. 101-135 ◽  
Author(s):  
MUKESH KUMAR MEHLAWAT ◽  
PANKAJ GUPTA
Keyword(s):  
Mathematical Programming ◽  
Portfolio Selection ◽  
Programming Model ◽  
Fuzzy Optimization ◽  
Chance Constrained Programming ◽  
Fuzzy Mathematical Programming ◽  
Mathematical Programming Model ◽  
Two Phase ◽  
Solution Approach ◽  
Practical Applications

In this paper, we develop a hybrid bi-objective credibility-based fuzzy mathematical programming model for portfolio selection under fuzzy environment. To deal with imprecise parameters, we use a hybrid credibility-based approach that combines the expected value and chance constrained programming techniques. The model simultaneously maximizes the portfolio return and minimizes the portfolio risk. We also consider an additional important criterion, namely, portfolio liquidity as a constraint in the model to make it better suited for practical applications. The proposed fuzzy optimization model is solved using a two-phase approach. An empirical study is included to demonstrate applicability of the proposed model and the solution approach in real-world applications of portfolio selection.

scholarly journals Mathematical programming models for agri-environmental policy analysis: A case study from the White Carpathians

2012 ◽  
Vol 52 (No. 2) ◽  
pp. 51-66 ◽  
Author(s):  
P. Havlík ◽  
F. Jacquet ◽  
Boisson J-M ◽  
S. Hejduk ◽  
P. Veselý
Keyword(s):  
Mathematical Programming ◽  
Programming Model ◽  
Mathematical Programming Model ◽  
The Czech Republic ◽  
Environmental Goods ◽  
Environmental Good ◽  
Environmental Policy Analysis ◽  
Grassland Biodiversity ◽  
White Carpathians

BEGRAB_PRO.1 – a mathematical programming model for BEef and GRAssland Biodiversity PRoduction Optimisation – elaborated for analysis of organic suckler cow farms in the Protected Landscape Area White Carpathians, the Czech Republic, is presented and applied to the analysis of jointness between several environmental goods. In this way, the paper complements recent studies on jointness between commodities and non-commodities. If these goods are joint in production, agri-environmental payments must be carefully designed because they do not influence only production of the environmental good they are intended for but also the production of other environmental goods. If jointness is negative, any increase in the payment for an environmental good leads to a decrease in production of other environmental goods.

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