scholarly journals A new model for logistic and transportation of fashion goods in the presence of stochastic market demands considering restricted retailers capacity

2019 ◽  
Author(s):  
Aidin Delgoshaei ◽  
Hengameh Norozi ◽  
Abolfazl Mirzazadeh ◽  
Maryam Farhadi ◽  
Golnaz Hooshmand Pakdel ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Case Studies ◽  
Programming Model ◽  
Real Data ◽  
Objective Functions ◽  
Mathematical Programming Model ◽  
Multi Objective ◽  
Scheduling Method ◽  
Stochastic Market

In today’s world, using fashion goods is a vital of human. In this research, we focused on developing a scheduling method for distributing and selling fashion goods in a multi-market/multi-retailer supply chain while the product demands in markets are stochastic. For this purpose, a new multi-objective mathematical programming model is developed where maximizing the profit of selling fashion goods and minimizing delivering time and customer’s dissatisfaction are considered as objective functions. In continue due to the complexity of the problem, a number of metaheuristics are compared and a hybrid of Non-dominated Sorting Genetic Algorithm II (NSGAII) and simulated annealing is selected for solving the case studies. Then, in order to find the best values for input parameters of the algorithm, a Taguchi method is applied. In continue, a number of case studies are selected from literature review and solved by the algorithm. The outcomes are analyzed and it is found that using multi-objective models can find more realistic solutions. Then, the model is applied for a case study with real data from industry and outcomes showed that the proposed algorithm can be successfully applied in practice.

Bi-Objective Optimization of Service-Oriented Location-Pricing Model Using Electromagnetism-Like Mechanism Algorithm

2020 ◽  
Vol 19 (06) ◽  
pp. 1737-1769
Author(s):  
Alireza Alinezhad ◽  
Vahid Hajipour ◽  
Sanaz Hosseinzadeh
Keyword(s):  
Optimal Allocation ◽  
Programming Model ◽  
Waiting Times ◽  
Optimal Number ◽  
Objective Functions ◽  
Mathematical Programming Model ◽  
Multi Objective ◽  
Efficiency And Effectiveness ◽  
Service Oriented ◽  
Total Profit

This paper develops a multi-objective multi-layer location-pricing (MLLP) model with congested facilities in which the facilities act like a classic queuing system. The customers who arrive to this system receive service at all layers in a predetermined order to fulfill their demands. The goal is to determine (1) optimal number of the facilities required at each layer, (2) optimal allocation of customers to facilities, and (3) optimal price of providing service at each layer. The objective functions are to maximize the total profit of the system and to minimize the sum of travel and waiting times, simultaneously. The problem is formulated as a multi-objective nonlinear integer mathematical programming model. Since the problem is hard to be solved analytically, we present a multi-objective meta-heuristic algorithm (MHA) based on an electromagnetism-like mechanism (ELM) as a solution for multi-objective MLLP. This algorithm used an elitist mechanism to strengthen the structure of search engine in order to find better quality solutions. The results indicate the efficiency and effectiveness of the proposed algorithm in comparison with the traditional ELM.

scholarly journals A Method for Integration of Preferences to a Multi-Objective Evolutionary Algorithm Using Ordinal Multi-Criteria Classification

2021 ◽  
Vol 26 (2) ◽  
pp. 27
Author(s):  
Alejandro Castellanos-Alvarez ◽  
Laura Cruz-Reyes ◽  
Eduardo Fernandez ◽  
Nelson Rangel-Valdez ◽  
Claudia Gómez-Santillán ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Selective Pressure ◽  
Optimization Problems ◽  
Region Of Interest ◽  
Decision Makers ◽  
Multiple Objective ◽  
Objective Functions ◽  
Multi Objective ◽  
Imperfect Knowledge ◽  
Real World Problems

Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solutions. The imperfect knowledge can be present either in objective functions, restrictions, or decision-maker’s preferences. These optimization problems have been solved using various techniques such as multi-objective evolutionary algorithms (MOEAs). This paper proposes a new MOEA called NSGA-III-P (non-nominated sorting genetic algorithm III with preferences). The main characteristic of NSGA-III-P is an ordinal multi-criteria classification method for preference integration to guide the algorithm to the region of interest given by the decision-maker’s preferences. Besides, the use of interval analysis allows the expression of preferences with imprecision. The experiments contrasted several versions of the proposed method with the original NSGA-III to analyze different selective pressure induced by the DM’s preferences. In these experiments, the algorithms solved three-objectives instances of the DTLZ problem. The obtained results showed a better approximation to the region of interest for a DM when its preferences are considered.

Kinematic Multi-Objective Optimization of Circular-Toothed Gerotor Pumps by Genetic Algorithm

2017 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Genetic Algorithm ◽  
Design Method ◽  
Third Party ◽  
Low Flow ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Multi Objective ◽  
Pareto Curve

A gerotor gear generation algorithm has been developed that evaluates key performance objective functions to be minimized or maximized, and then an optimization algorithm is applied to determine the best design. Because of their popularity, circular-toothed gerotors are the focus of this study, and future work can extend this procedure to other gear forms. Parametric equations defining the circular-toothed gear set have been derived and implemented. Two objective functions were used in this kinematic optimization: maximize the ratio of displacement to pump radius, which is a measure of compactness, and minimize the kinematic flow ripple, which can have a negative effect on system dynamics and could be a major source of noise. Designs were constrained to ensure drivability, so the need for additional synchronization gearing is eliminated. The NSGA-II genetic algorithm was then applied to the gear generation algorithm in modeFRONTIER, a commercial software that integrates multi-objective optimization with third-party engineering software. A clear Pareto front was identified, and a multi-criteria decision-making genetic algorithm was used to select three optimal designs with varying priorities of compactness vs low flow variation. In addition, three pumps used in industry were scaled and evaluated with the gear generation algorithm for comparison. The scaled industry pumps were all close to the Pareto curve, but the optimized designs offer a slight kinematic advantage, which demonstrates the usefulness of the proposed gerotor design method.

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.

scholarly journals A study of production and harvesting planning for the chicken industry

2018 ◽  
Vol 64 (No. 7) ◽  
pp. 316-327 ◽  
Author(s):  
You Peng-Sheng ◽  
Hsieh Yi-Chih
Keyword(s):  
Sensitivity Analysis ◽  
Production Planning ◽  
Programming Model ◽  
Computational Results ◽  
Mathematical Programming Model ◽  
Planning Period ◽  
Hybrid Heuristic ◽  
Proposed Model ◽  
Parameter Variations

To order to raise chickens for meat, chicken farmers must select an appropriate breed and determine how many broilers to raise in each henhouse. This study proposes a mathematical programming model to develop a production planning and harvesting schedule for chicken farmers. The production planning comprises the number of batches of chickens to be raised in each henhouse, the number of chicks to be raised for each batch, what breed of chicken to raise, when to start raising and the duration of the raising period. The harvesting schedule focuses on when to harvest and how many broilers to harvest each time. Our aim was to develop proper production and harvesting schedules that enable chicken farmers to maximise profits over a planning period. The problem is a highly complicated one. We developed a hybrid heuristic approach to address the issue. The computational results have shown that the proposed model can help chicken farmers to deal with the problems of chicken-henhouse assignment, chicken raising and harvesting, and may thus contribute to increasing profits. A case study of a chicken farmer in Yunlin County (Taiwan) was carried out to illustrate the application of the proposed model. Sensitivity analysis was also conducted to explore the influence of parameter variations.

Broiler management using fuzzy multi-objective genetic algorithm: A case study

2020 ◽  
Vol 233 ◽  
pp. 103941
Author(s):  
Erfan Khosravani Moghadam ◽  
Mohammad Sharifi ◽  
Shahin Rafiee ◽  
Claus Aage Grøn Sørensen
Keyword(s):  
Genetic Algorithm ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

scholarly journals A fuzzy multi-objective linear programming with interval-typed triangular fuzzy numbers

2019 ◽  
Vol 17 (1) ◽  
pp. 607-626 ◽  
Author(s):  
Chunquan Li
Keyword(s):  
Linear Programming ◽  
Fuzzy Numbers ◽  
Programming Model ◽  
Optimal Solution ◽  
Objective Functions ◽  
Triangular Fuzzy Numbers ◽  
Matlab Toolbox ◽  
Multi Objective ◽  
Cut Set ◽  
The Stability

Abstract A multi-objective linear programming problem (ITF-MOLP) is presented in this paper, in which coefficients of both the objective functions and constraints are interval-typed triangular fuzzy numbers. An algorithm of the ITF-MOLP is provided by introducing the cut set of interval-typed triangular fuzzy numbers and the dominance possibility criterion. In particular, for a given level, the ITF-MOLP is converted to the maximization of the sum of membership degrees of each objective in ITF-MOLP, whose membership degrees are established based on the deviation from optimal solutions of individual objectives, and the constraints are transformed to normal inequalities by utilizing the dominance possibility criterion when compared with two interval-typed triangular fuzzy numbers. Then the equivalent linear programming model is obtained which could be solved by Matlab toolbox. Finally several examples are provided to illuminate the proposed method by comparing with the existing methods and sensitive analysis demonstrates the stability of the optimal solution.

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