Multi-facilities Location and Allocation Problem of Three-Echelon Supply Chain Based on an Improved Genetic Algorithm

2016 ◽  
Author(s):  
Zhishuo Liu ◽  
Han Li ◽  
Pengfei Gao
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Allocation Problem ◽  
Improved Genetic Algorithm ◽  
Facilities Location

scholarly journals A Comparison between Memetic Algorithm and Genetic Algorithm for an Integrated Logistics Network with Flexible Delivery Path

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Elham Behmanesh ◽  
Jürgen Pannek
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Real World ◽  
Memetic Algorithm ◽  
Allocation Problem ◽  
Strategic Decision ◽  
Neighborhood Search ◽  
Test Problems ◽  
Chain Model ◽  
Term Operation

AbstractThe distribution/allocation problem is known as one of the most comprehensive strategic decision. In real-world cases, it is impossible to solve a distribution/allocation problem in traditional ways with acceptable time. Hence researchers develop efficient non-traditional techniques for the large-term operation of the whole supply chain. These techniques provide near optimal solutions particularly for large-scale test problems. This paper presents an integrated supply chain model which is flexible in the delivery path. As the solution methodology, we apply a memetic algorithm with a neighborhood search mechanism and novelty in population presentation method called “extended random path direct encoding method.” To illustrate the performance of the proposed memetic algorithm, LINGO optimization software serves as comparison basis for small size problems. In large-size cases that we are dealing with in real world, a classical genetic algorithm as the second metaheuristic algorithm is considered to compare the results and show the efficiency of the memetic algorithm.

Genetic Algorithm and Particle Swarm Optimization for Solving Balanced Allocation Problem of Third Party Logistics Providers

2011 ◽  
Vol 4 (1) ◽  
pp. 24-44 ◽  
Author(s):  
R. Rajesh ◽  
S. Pugazhendhi ◽  
K. Ganesh
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Service Providers ◽  
Particle Swarm ◽  
Business Environment ◽  
Allocation Problem ◽  
Third Party ◽  
Third Party Logistics ◽  
Balanced Allocation ◽  
Number Of Customers

Third party logistics (3PL) service providers play a growing responsibility in the management of supply chain. The global and competitive business environment of 3PLs has recognized the significance of a speedy and proficient service towards the customers in the past few decades. Particularly in warehousing, distribution, and transportation services, a number of customers anticipate 3PLs to improve lead times, fill rates, inventory levels, etc. Therefore, the 3PLs are under demands to convene a range of service necessities of customers in an active and uncertain business environment. As a consequence of the dynamic environment in which supply chain must operate, 3PLs should sustain an effective distribution system of high performance and must make a sequence of inter-related decisions over time for their distribution networks. Warehouses play an important role in sustaining the continual flow of goods and materials between the manufacturer and customers. The performance of the 3PL supply chain network can be effortlessly enhanced by a balanced allocation of customers to warehouses. In this paper, the authors develop a genetic algorithm and a particle-swarm-optimisation algorithm for solving the balanced allocation problem and the results are encouraging.

Genetic Algorithm and Particle Swarm Optimization for Solving Balanced Allocation Problem of Third Party Logistics Providers

2013 ◽  
pp. 184-203 ◽  
Author(s):  
R. Rajesh ◽  
S. Pugazhendhi ◽  
K. Ganesh
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Particle Swarm ◽  
Distribution Networks ◽  
Business Environment ◽  
Allocation Problem ◽  
Third Party ◽  
Third Party Logistics ◽  
Transportation Services ◽  
Balanced Allocation

Third party logistics (3PL) service providers play a growing responsibility in the management of supply chain. The global and competitive business environment of 3PLs has recognized the significance of a speedy and proficient service towards the customers in the past few decades. Particularly in warehousing, distribution, and transportation services, a number of customers anticipate 3PLs to improve lead times, fill rates, inventory levels, etc. Therefore, the 3PLs are under demands to convene a range of service necessities of customers in an active and uncertain business environment. As a consequence of the dynamic environment in which supply chain must operate, 3PLs should sustain an effective distribution system of high performance and must make a sequence of inter-related decisions over time for their distribution networks. Warehouses play an important role in sustaining the continual flow of goods and materials between the manufacturer and customers. The performance of the 3PL supply chain network can be effortlessly enhanced by a balanced allocation of customers to warehouses. In this paper, the authors develop a genetic algorithm and a particle-swarm-optimisation algorithm for solving the balanced allocation problem and the results are encouraging.

scholarly journals Multiobjective Sustainable Order Allocation Problem Optimization with Improved Genetic Algorithm Using Priority Encoding

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Zhiqiang Fan ◽  
Shanshan Li ◽  
Zhijun Gao
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Technique ◽  
Allocation Problem ◽  
Improved Genetic Algorithm ◽  
Order Allocation ◽  
Machine Material ◽  
Allocation Process ◽  
Efficiency And Effectiveness ◽  
Random Fluctuations

Recently, incorporating carbon emissions into order allocation decisions has attracted considerable attention among scholars and industrialists. Moreover, affected by the random fluctuations of the man, machine, material, method, and environment (4M1E), the production process is usually imperfect with defective products. Reducing product defective rates can effectively improve the quality of the order allocation process. Therefore, considering product defective rate and carbon emission, a multiobjective integer nonlinear programming (INLP) formulation is presented to address this multiproduct, multiperiod, and multi-OEM order allocation problem. Furthermore, exploring the existing literatures, an improved genetic algorithm using priority encoding (IGAUPE) is put forward as a novel optimization technique. Finally, numerical experiments are conducted to validate the correctness of the proposed INLP model as well as the effectiveness of the proposed algorithm. Compared with the genetic algorithm using binary encoding (GAUBE), genetic algorithm using two-layer encoding (GAUTE), and LINGO software, the experiment results show that IGAUPE can improve the efficiency and effectiveness within the predetermined time limit when solving large-scale instances.

A Hybrid Genetic Algorithm for Production Capacity Allocation Problem with Multiple Regional Demands in Supply Chain

2014 ◽  
Vol 945-949 ◽  
pp. 3107-3111
Author(s):  
Zhen Wang ◽  
Lei Huang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Supply Chain ◽  
Hybrid Genetic Algorithm ◽  
Production Capacity ◽  
Experimental Tests ◽  
Capacity Allocation ◽  
Allocation Problem ◽  
Proposed Model

Concentrating on the supplier with limited production capacity in supply chain, this paper established a mathematical model for production capacity allocation problem with consideration of multiple regional demands. The genetic algorithm is employed as solution mainframe in which a heuristics rule is developed to initiate the population and an elite pool is adopted to store those solutions with outstanding fitness values. The experimental tests show that the proposed model and algorithm are feasible and effective.

A New Genetic Algorithm for Continuous Berth and Quay Crane Allocation Problem

2011 ◽  
Vol 97-98 ◽  
pp. 619-622 ◽  
Author(s):  
Na Li ◽  
Zhi Hong Jin ◽  
Erick Massami
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Container Terminal ◽  
Allocation Problem ◽  
Berth Allocation ◽  
Improved Genetic Algorithm ◽  
Berth Allocation Problem ◽  
The Real ◽  
Quay Cranes ◽  
Quay Crane Assignment

The combined optimization of continuous berth allocation problem and quay crane assignment problem are solved. Considering the real constraints of container terminal, an improved genetic algorithm is proposed. The chromosome is composed of berthing time, berthing location and number of quay cranes. While in the following, specific quay cranes are fixed to assign to ships. Through comparisons with the former two literatures, the results are improved averagely by 33.78% and 28.57% respectively by the proposed genetic algorithm, which shows its effectiveness.

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