Algorithms and Methods Inspired from Nature for Solving Supply Chain and Logistics Optimization Problems

2014 ◽  
Vol 4 (3) ◽  
pp. 26-51 ◽  
Author(s):  
Georgios Dounias ◽  
Vassilios Vassiliadis
Keyword(s):  
Supply Chain ◽  
Optimization Problems ◽  
Artificial Immune Systems ◽  
Supply Chain Optimization ◽  
Hybrid Schemes ◽  
Network Problems ◽  
Logistics Optimization ◽  
Team Orienteering ◽  
Nature Inspired Intelligence ◽  
High Degree

The current work surveys 245 papers and research reports related to algorithms and methods inspired from nature for solving supply chain and logistics optimization problems. Nature Inspired Intelligence (NII) is a challenging new subfield of artificial intelligence (AI) particularly capable of dealing with complex optimization problems. Related approaches are used either as stand-alone algorithms, or as hybrid schemes i.e. in combination to other AI techniques. Ant Colony Optimization (ACO), Particle Swarm Optimization, Artificial Bee Colonies, Artificial Immune Systems and DNA Computing are some of the most popular approaches belonging to nature inspired intelligence. On the other hand, supply chain management represents an interesting domain of OR applications, including a variety of hard optimization problems such as vehicle routing (VRP), travelling salesman (TSP), team orienteering, inventory, knapsack, supply network problems, etc. Nature inspired intelligent algorithms prove capable of identifying near optimal solutions for instances of those problems with high degree of complexity in a reasonable amount of time. Survey findings indicate that NII can cope successfully with almost any kind of supply chain optimization problem and tends to become a standard in related scientific literature during the last five years.

Algorithms and Methods Inspired from Nature for Solving Supply Chain and Logistics Optimization Problems

2015 ◽  
pp. 245-275 ◽  
Author(s):  
Georgios Dounias ◽  
Vassilios Vassiliadis
Keyword(s):  
Supply Chain ◽  
Optimization Problems ◽  
Artificial Immune Systems ◽  
Supply Chain Optimization ◽  
Hybrid Schemes ◽  
Network Problems ◽  
Logistics Optimization ◽  
Nature Inspired Intelligence ◽  
High Degree ◽  
Bee Colonies

The current work surveys 245 papers and research reports related to algorithms and methods inspired from nature for solving supply chain and logistics optimization problems. Nature Inspired Intelligence (NII) is a challenging new subfield of artificial intelligence (AI) particularly capable of dealing with complex optimization problems. Related approaches are used either as stand-alone algorithms, or as hybrid schemes i.e. in combination to other AI techniques. Ant Colony Optimization (ACO), Particle Swarm Optimization, Artificial Bee Colonies, Artificial Immune Systems and DNA Computing are some of the most popular approaches belonging to nature inspired intelligence. On the other hand, supply chain management represents an interesting domain of OR applications, including a variety of hard optimization problems such as vehicle routing (VRP), travelling salesman (TSP), team orienteering, inventory, knapsack, supply network problems, etc. Nature inspired intelligent algorithms prove capable of identifying near optimal solutions for instances of those problems with high degree of complexity in a reasonable amount of time. Survey findings indicate that NII can cope successfully with almost any kind of supply chain optimization problem and tends to become a standard in related scientific literature during the last five years.

scholarly journals Optimization of Consignment-Store-Based Supply Chain with Black Hole Algorithm

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Ágota Bányai ◽  
Tamás Bányai ◽  
Béla Illés
Keyword(s):  
Black Hole ◽  
Supply Chain ◽  
Optimization Problems ◽  
Optimization Method ◽  
Chain Model ◽  
Supply Chain Optimization ◽  
Proposed Model ◽  
Supply Chain Model ◽  
Supply Model ◽  
Black Hole Algorithm

The globalization of economy and market led to increased networking in the field of manufacturing and services. These manufacturing and service processes including supply chain became more and more complex. The supply chain includes in many cases consignment stores. The design and operation of these complex supply chain processes can be described as NP-hard optimization problems. These problems can be solved using sophisticated models and methods based on metaheuristic algorithms. This research proposes an integrated supply model based on consignment stores. After a careful literature review, this paper introduces a mathematical model to formulate the problem of consignment-store-based supply chain optimization. The integrated model includes facility location and assignment problems to be solved. Next, an enhanced black hole algorithm dealing with multiobjective supply chain model is presented. The sensitivity analysis of the heuristic black hole optimization method is also described to check the efficiency of new operators to increase the convergence of the algorithm. Numerical results with different datasets demonstrate how the proposed model supports the efficiency, flexibility, and reliability of the consignment-store-based supply chain.

A Nelder and Mead Methodology for Solving Small Fixed-Charge Transportation Problems

2011 ◽  
pp. 345-356
Author(s):  
G. Kannan ◽  
P. Senthil ◽  
P. Sasikumar ◽  
V. P. Vinay
Keyword(s):  
Supply Chain ◽  
Transportation Problem ◽  
Optimization Problems ◽  
Fixed Charge ◽  
Computational Time ◽  
Variable Cost ◽  
Fixed Cost ◽  
Supply Chain Optimization ◽  
Business World ◽  
Fixed Charge Transportation Problem

The term ‘supply chain management’ has become common in the business world, which can be understood from the positive results of research in the area, particularly in supply chain optimization. Transportation is a frontier in achieving the objectives of the supply chain. Thrust is also given to optimization problems in transportation. The fixed-charge transportation problem is an extension of the transportation problem that includes a fixed cost, along with a variable cost that is proportional to the amount shipped. This article approaches the problem with another meta-heuristics known as the Nelder and Mead methodology to save the computational time with little iteration and obtain better results with the help of a program in C++.

Nature-Inspired Intelligence in Supply Chain Management

2011 ◽  
pp. 1-31
Author(s):  
Vassilios Vassiliadis ◽  
Giorgos Dounias
Keyword(s):  
Artificial Intelligence ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Optimization Problems ◽  
Real Life ◽  
Ant Colonies ◽  
Chain Management ◽  
Complex Optimization ◽  
Nature Inspired Intelligence ◽  
Optimum Solution

Supply chain management is a vital process for the competitiveness and profitability of companies. Supply chain consists of a large and complex network of components such as suppliers, warehouses, customers etc. which are connected in almost every possible way. Companies’ main aim is to optimize the components of these complex networks to their benefit. This constitutes a challenging optimization problem and often, traditional mathematical approaches fail to overcome complexity and to converge to the optimum solution. More robust methods are required sometimes in order to yield to the optimal. The field of artificial intelligence offers a great variety of meta-heuristic techniques which specialize in solving such complex optimization problems, either accurately, or by obtaining a practically useful approximation, even if real time constraints are imposed. The aim of this chapter is to present a survey of the available literature, regarding the use of nature-inspired methodologies in supply chain management problems. Nature-inspired intelligence is a specific branch of artificial intelligence. Its unique characteristic is the algorithmic imitation of real life systems such as ant colonies, flock of birds etc. in order to solve complex problems.

scholarly journals A Hybrid Method for Modeling and Solving Supply Chain Optimization Problems with Soft and Logical Constraints

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Paweł Sitek ◽  
Krzysztof Bzdyra ◽  
Jarosław Wikarek
Keyword(s):  
Supply Chain ◽  
Hybrid Method ◽  
Optimization Problems ◽  
Programming Model ◽  
Combinatorial Problem ◽  
Search Space ◽  
Mixed Integer ◽  
Optimization Models ◽  
Supply Chain Optimization ◽  
Logical Constraints

This paper presents a hybrid method for modeling and solving supply chain optimization problems with soft, hard, and logical constraints. Ability to implement soft and logical constraints is a very important functionality for supply chain optimization models. Such constraints are particularly useful for modeling problems resulting from commercial agreements, contracts, competition, technology, safety, and environmental conditions. Two programming and solving environments, mathematical programming (MP) and constraint logic programming (CLP), were combined in the hybrid method. This integration, hybridization, and the adequate multidimensional transformation of the problem (as a presolving method) helped to substantially reduce the search space of combinatorial models for supply chain optimization problems. The operation research MP and declarative CLP, where constraints are modeled in different ways and different solving procedures are implemented, were linked together to use the strengths of both. This approach is particularly important for the decision and combinatorial optimization models with the objective function and constraints, there are many decision variables, and these are summed (common in manufacturing, supply chain management, project management, and logistic problems). TheECLiPSesystem with Eplex library was proposed to implement a hybrid method. Additionally, the proposed hybrid transformed model is compared with the MILP-Mixed Integer Linear Programming model on the same data instances. For illustrative models, its use allowed finding optimal solutions eight to one hundred times faster and reducing the size of the combinatorial problem to a significant extent.

Swarm intelligence approaches in supply chain management: potentials, challenges and future research directions

2019 ◽  
Vol 24 (1) ◽  
pp. 107-123 ◽  
Author(s):  
Gunjan Soni ◽  
Vipul Jain ◽  
Felix T.S. Chan ◽  
Ben Niu ◽  
Surya Prakash
Keyword(s):  
Linear Programming ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Network Design ◽  
Optimization Problems ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Supply Chain Optimization ◽  
Chain Management ◽  
Content Type

Purpose It is worth mentioning that in supply chain management (SCM), managerial decisions are often based on optimization of resources. Till the early 2000s, supply chain optimization problems were being addressed by conventional programming approaches such as Linear Programming, Mixed-Integer Linear Programming and Branch-and-Bound methods. However, the solution convergence in such approaches was slow. But with the advent of Swarm Intelligence (SI)-based algorithms like particle swarm optimization and ant colony optimization, a significant improvement in solution of these problems has been observed. The purpose of this paper is to present and analyze the application of SI algorithms in SCM. The analysis will eventually lead to development of a generalized SI implementation framework for optimization problems in SCM. Design/methodology/approach A structured state-of-the-art literature review is presented, which explores the applications of SI algorithms in SCM. It reviews 56 articles published in peer-reviewed journals since 1999 and uses several classification schemes which are critical in designing and solving a supply chain optimization problem using SI algorithms. Findings The paper revels growth of swarm-based algorithms and seems to be dominant among all nature-inspired algorithms. The SI algorithms have been used extensively in most of the realms of supply chain network design because of the flexibility in their design and rapid convergence. Large size problems, difficult to manage using exact algorithms could be efficiently handled using SI algorithms. A generalized framework for SI implementation in SCM is proposed which is beneficial to industry practitioners and researchers. Originality/value The paper proposes a generic formulation of optimization problems in distribution network design, vehicle routing, resource allocation, inventory management and supplier management areas of SCM which could be solved using SI algorithms. This review also provides a generic framework for SI implementation in supply chain network design and identifies promising emerging issues for further study in this area.

scholarly journals Genetic Algorithms for Scheduling and Optimization of Ore Train Networks

10.29007/fzrq ◽  
2018 ◽  
Author(s):  
Ghulam Mubashar Hassan ◽  
Mark Reynolds
Keyword(s):  
Genetic Algorithms ◽  
Supply Chain ◽  
Real World ◽  
Iron Ore ◽  
Optimization Problems ◽  
Cost Effective ◽  
Supply Chain Scheduling ◽  
Supply Chain Optimization ◽  
Cost Effective Approach ◽  
World Supply

Search and optimization problems are a major arena for the practical application of Artificial Intelligence. However, when supply chain optimization and scheduling is tackled, techniques based on linear or non-linear programming are often used in preference to Evolutionary Computation such as Genetic Algorithms (GAs). It is important to analyse whether GA are suitable for continuous real-world supply chain scheduling tasks which need regular updates.We analysed a practical situation involving iron ore train networks which is indeed one of significant economic importance. In addition, iron ore train networks have some interesting and distinctive characteristics so analysing this situation is an important step toward understanding the performance of GA in real-world supply chain scheduling. We compared the performance of GA with Nonlinear programming heuristics and existing industry scheduling approaches. The main result is that our comparison of techniques here produce an example in which GAs perform well and is a cost effective approach.

Case Study: Maximizing Business Performance by Optimizing Supply Chain Of LNG-C Distribution of 9 Gas Power Plants with A Capacity of 780 MW in Eastern Indonesia

2020 ◽  
Author(s):  
A. D. Wara
Keyword(s):  
Supply Chain ◽  
Business Performance ◽  
Power Plants ◽  
Total Power ◽  
First Year ◽  
Eastern Region ◽  
Transport Costs ◽  
Logistics Optimization ◽  
The Government ◽  
Milk Run

The Government of Indonesia plans to build 9 gas power plants in South Kalimantan, South Sulawesi and Southeast Nusa Tenggara with a total power capacity of 780 MW with an estimated actual gas demand of 46.56 MMSCFD which are planned to be supplied by the Bontang terminal, DS-LNG, Masela LNG, and Tangguh LNG. LNG-C logistics optimization is needed to get the best transportation scenario regarding the eastern region which consists of scattered islands and inadequate infrastructure. This study analyzes and evaluates the best-case scenarios by comparing the time and cost variables. The process of planning the supply chain starts from determining the upstream-downstream distribution scheme and then calculates the shipping distance which results in the determination of the quantity, capacity and shipping of the LNG-C. Based on the analysis and calculation of the logistics, it is concluded that there are 3 divisions of clusters of Kalimantan-Sulawesi, NTT and NTB having estimated needs in a row of 18.06, 18.8, and 9.7 MMSCFD with the Milk-Run transportation method. Logistics optimization results show that scenario 1 has an efficiency value of 87% with an LNG-C transport capacity of 0.35 MMSCF, a roundtrip cruise time of 8.6 days and the number of shipments is 36 / year. The detailed analysis of costs in scenario A is 1-2 USD / MMBTU for the milk and run transportation method, 1.49-1.73 USD / MBTU for LNG-C transport costs, and regasification costs which are 1.0-3.7 USD / MMBTU. Based on the above results it can be calculated that the price of gas in the first year of implementation was 13.4 USD / MMBTU, so the total value below this supply chain was Rp.8,812,876,800.00. Therefore, this idea was created as a solution for the initial steps for the utilization of the domestic natural gas distribution

Sign in / Sign up

Export Citation Format

Share Document