Supply chain management
Mona Beiranvand; Sayyed mohammad reza Davoodi
Abstract
Today, one of the topics in supply chain management is "multiple sales channels" and "pricing". In this research, a food producer (west Sahar Dasht Company) has been selected, and several retailers and wholesalers have been considered as the company's customers. This research dynamically solves the model ...
Read More
Today, one of the topics in supply chain management is "multiple sales channels" and "pricing". In this research, a food producer (west Sahar Dasht Company) has been selected, and several retailers and wholesalers have been considered as the company's customers. This research dynamically solves the model through the game theory method. To obtain the equilibrium point and Stockelberg, the lower level optimal values (retailers and suppliers) are calculated based on the higher-level values (manufacturer), which turns the multi-level model into a single-level model to calculate the higher level optimal values. By presenting a case study and analyzing the sensitivity of the parameters, it was shown that some changes in the parameters have a significant effect on the problem variables, and its equilibrium model is better. Because game theory is proposed to solve problems on a small scale, and because the present problem is so complex, genetic algorithm meta-heuristic and particle aggregation optimization have been used to solve medium and large problems. To validate their results, they are compared with the results obtained from the mathematical model. Finally, comparing the performance of the two meta-heuristic algorithms through statistical analysis has shown that the particle aggregation optimization algorithm performs better than the genetic algorithm.
Transportation
Mohamad Ebrahim Tayebi Araghi; Fariborz Jolai; Reza Tavakkoli-Moghaddam; Mohammad Molana
Abstract
The Location Routing Problem (LRP), Automatic Guided Vehicle (AGV), and Uncertainty Planner Facility (UPF) in Facility Location Problems (FLP) have been critical. This research proposed the role of LRP in Intelligence AGV Location–Routing Problem (IALRP) and energy-consuming impact in CMS. The ...
Read More
The Location Routing Problem (LRP), Automatic Guided Vehicle (AGV), and Uncertainty Planner Facility (UPF) in Facility Location Problems (FLP) have been critical. This research proposed the role of LRP in Intelligence AGV Location–Routing Problem (IALRP) and energy-consuming impact in CMS. The goal of problem minimization dispatching opening cost and the cost of AGV trucking. We set up multi-objective programming. To solve the model, we utilized and investigate the Imperialist Competitor Algorithm (ICA) with Variable Neighborhood Search (VNS). It is shown that the ICAVNS algorithm is high quality effects for the integrated LRP in AGVs and comparison, with the last researches, the sensitivity analysis, and numerical examples imply the validity and good convexity of the purposed model according to the cost minimization.
Heuristics and Metaheuristics Algorithms
Hojatollah Rajabi Moshtaghi; Abbas Toloie Eshlaghy; Mohammad Reza Motadel
Abstract
Conventional and classical optimization methods are not efficient enough to deal with complicated, NP-hard, high-dimensional, non-linear, and hybrid problems. In recent years, the application of meta-heuristic algorithms for such problems increased dramatically and it is widely used in various fields. ...
Read More
Conventional and classical optimization methods are not efficient enough to deal with complicated, NP-hard, high-dimensional, non-linear, and hybrid problems. In recent years, the application of meta-heuristic algorithms for such problems increased dramatically and it is widely used in various fields. These algorithms, in contrast to exact optimization methods, find the solutions which are very close to the global optimum solution as possible, in such a way that this solution satisfies the threshold constraint with an acceptable level. Most of the meta-heuristic algorithms are inspired by natural phenomena. In this research, a comprehensive review on meta-heuristic algorithms is presented to introduce a large number of them (i.e. about 110 algorithms). Moreover, this research provides a brief explanation along with the source of their inspiration for each algorithm. Also, these algorithms are categorized based on the type of algorithms (e.g. swarm-based, evolutionary, physics-based, and human-based), nature-inspired vs non-nature-inspired based, population-based vs single-solution based. Finally, we present a novel classification of meta-heuristic algorithms based on the country of origin.
Supply chain management
Javid Ghahremani-Nahr; Hamed Nozari; Seyyed Esmaeil Najafi
Abstract
The mathematical model of a multi-product multi-period multi-echelon closed-loop supply chain network design under uncertainty is designed in this paper. The designed network consists of raw material suppliers, plants, warehouses, distribution centers, and customer zones in forward chain and collection ...
Read More
The mathematical model of a multi-product multi-period multi-echelon closed-loop supply chain network design under uncertainty is designed in this paper. The designed network consists of raw material suppliers, plants, warehouses, distribution centers, and customer zones in forward chain and collection centers, repair centers, recovery/decomposition center, and disposal center in the reverse chain. The goal of the model is to determine the quantities of products and raw material transported between the supply chain entities in each period by considering different transportation mode, the number and locations of the potential facilities, the shortage of products in each period, and the inventory of products in warehouses and plants with considering discount and uncertainty parameters. The robust possibilistic optimization approach was used to control the uncertainty parameter. At the end to solve the proposed model, five meta-heuristic algorithms include genetic algorithm, bee colony algorithm, simulated annealing, imperial competitive algorithm, and particle swarm optimization are utilized. Finally, some numerical illustrations are provided to compare the proposed algorithms. The results show the genetic algorithm is an efficient algorithm for solving the designed model in this paper.
