scholarly journals Algorithms for Picking and Distribution of Online Orders in New Retail Enterprises

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Weiya Zhong ◽  
Jia Cui
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Programming ◽  
Order Picking ◽  
Computational Experiments ◽  
Research Problems ◽  
Comparison Algorithms ◽  
Mathematical Programming Problems ◽  
Distribution Cost

This paper studies joint algorithms of order picking and distribution in new retail enterprises. The problem will consider many factors, such as the type of goods, picking time, batch capacity of distribution, distribution time, and distribution cost. First of all, the research problems are summarized as mathematical programming problems. Then, a genetic algorithm and comparison algorithms are proposed. Finally, the rationality of the model and the effectiveness of the algorithms are verified by computational experiments, and management enlightenments are revealed.

Computerized Selection of Optimum Machining Conditions for a Job Requiring Multiple Operations

1978 ◽  
Vol 100 (3) ◽  
pp. 356-362 ◽  
Author(s):  
S. S. Rao ◽  
S. K. Hati
Keyword(s):  
Nonlinear Programming ◽  
Mathematical Programming ◽  
Machine Tools ◽  
Machining Conditions ◽  
Coupling Constraints ◽  
Programming Techniques ◽  
Mathematical Programming Problems ◽  
The Individual ◽  
The Cost ◽  
Selection Of

The problem of determining the optimum machining conditions for a job requiring multiple operations has been investigated. Three objectives, namely, the minimization of the cost of production per piece, the maximization of the production rate and, the maximization of the profit are considered in this work. In addition to the usual constraints that arise from the individual machine tools, some coupling constraints have been included in the formulation. The problems are formulated as standard mathematical programming problems, and nonlinear programming techniques are used to solve the problems.

scholarly journals Research on Logistics Distribution Path Planning Based on Genetic Algorithm

2021 ◽  
Vol 2083 (3) ◽  
pp. 032013
Author(s):  
Shaokun Liu
Keyword(s):  
Genetic Algorithm ◽  
Simulation Experiment ◽  
Time Window ◽  
Planning Model ◽  
Genetic Operators ◽  
Intelligent Algorithm ◽  
Total Cost ◽  
Logistics Distribution ◽  
Survey Results ◽  
Distribution Cost

Abstract In this paper, SF express company Jinzhou Guta District Pinganli business point as an example, to investigate its distribution, statistical analysis of the survey results, summed up the problems in logistics and distribution. Through the systematic study of the problem, a planning model with time window and with the objective of minimizing the total cost of distribution is established. At the same time, an intelligent algorithm for distribution path optimization - Genetic Algorithm (GA) is designed. Genetic algorithm is used to design chromosome coding methods and genetic operators for solving the planning model with the objective of minimizing the total cost of distribution. Finally, the simulation experiment is carried out. MATLAB software is used to solve the distribution route and the total driving distance of vehicles, and the distribution route with the goal of minimizing the total distribution cost is obtained.

scholarly journals Advanced Method for Unit Commitment Problem Using Genetic Algorithm and Mathematical Programming

1999 ◽  
Vol 119 (3) ◽  
pp. 333-343 ◽  
Author(s):  
Toru Takata ◽  
Junichi Takahashi ◽  
Hiroomi Yokoi ◽  
Hiroshi Nakano ◽  
Mari Aoyagi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Programming ◽  
Unit Commitment ◽  
Unit Commitment Problem ◽  
Advanced Method ◽  
Commitment Problem

scholarly journals Efficient Scheduling of Plantation Company Workers using Genetic Algorithm

2020 ◽  
Vol 3 (2) ◽  
pp. 60
Author(s):  
Wayan Firdaus Mahmudy ◽  
Andreas Pardede ◽  
Agus Wahyu Widodo ◽  
Muh Arif Rahman
Keyword(s):  
Genetic Algorithm ◽  
Physical Health ◽  
Selection Method ◽  
Computational Experiments ◽  
Cut Point ◽  
Chromosome Representation ◽  
Plantation Company ◽  
Reciprocal Mutation ◽  
Best Parameters ◽  
Work Scheduling

Workers at large plantation companies have various activities. These activities include caring for plants, regularly applying fertilizers according to schedule, and crop harvesting activities. The density of worker activities must be balanced with efficient and fair work scheduling. A good schedule will minimize worker dissatisfaction while also maintaining their physical health. This study aims to optimize workers' schedules using a genetic algorithm. An efficient chromosome representation is designed to produce a good schedule in a reasonable amount of time. The mutation method is used in combination with reciprocal mutation and exchange mutation, while the type of crossover used is one cut point, and the selection method is elitism selection. A set of computational experiments is carried out to determine the best parameters’ value of the genetic algorithm. The final result is a better 30 days worker schedule compare to the previous schedule that was produced manually. 

scholarly journals Two meta-heuristics for solving the multi-vehicle multi-covering tour problem with a constraint on the number of vertices

2020 ◽  
pp. 14-14
Author(s):  
Manel Kammoun ◽  
Houda Derbel ◽  
Bassem Jarboui
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Variable Neighborhood Search ◽  
Search Algorithm ◽  
Descent Method ◽  
Computational Experiments ◽  
Neighborhood Search ◽  
Variable Neighborhood Descent ◽  
Covering Tour Problem ◽  
Neighborhood Search Algorithm

In this work we deal with a generalized variant of the multi-vehicle covering tour problem (m-CTP). The m-CTP consists of minimizing the total routing cost and satisfying the entire demand of all customers, without the restriction of visiting them all, so that each customer not included in any route is covered. In the m-CTP, only a subset of customers is visited to fulfill the total demand, but a restriction is put on the length of each route and the number of vertices that it contains. This paper tackles a generalized variant of the m-CTP, called the multi-vehicle multi-covering Tour Problem (mm-CTP), where a vertex must be covered several times instead of once. We study a particular case of the mm-CTP considering only the restriction on the number of vertices in each route and relaxing the constraint on the length (mm-CTP-p). A hybrid metaheuristic is developet by combining Genetic Algorithm (GA), Variable Neighborhood Descent method (VND), and a General Variable Neighborhood Search algorithm (GVNS) to solve the problem. Computational experiments show that our approaches are competitive with the Evolutionary Local Search (ELS) and Genetic Algorithm (GA), the methods proposed in the literature.

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