Multi-objective Decision-Making of New Retailing Terminals Based on Particle Swarm Optimization and Genetic Algorithm

Abstract

This paper aims to solve the multi-objective decision-making for the optimal configuration of multi-type new retailing terminals. First, the distribution of consumer demand for convenience stores (CSs) and unmanned retail terminals (URTs) was investigated in different scenes. Then, the author set up an optimization model for the configuration of multi-type terminals, aiming to maximize the daily mean profit of the retailer, maximize consumer satisfaction in multiple dimensions, and minimize the number of terminals. To solve the model, the genetic algorithm (GA) and particle swarm optimization (PSO) were combined into a hybrid algorithm, based on elite recombination and directed optimization (directed elite search). The proposed model and algorithm were proved valid through empirical analysis. The results show that the retailer can achieve better profit and consumer satisfaction by deploying multiple types of retail terminals, and arranging different terminals at different scenes; moreover, the three optimization objectives can be achieved by improving the attraction of terminals, cautiously analyzing consumer rationality and properly setting the courier fee. The research findings lay theoretical basis for terminal configuration in new retailing and provide an important guide for new retailing operations.

A Novel Scheduling Algorithm for Common Rail Dual Automatic Guided Vehicles Particle Filtering Algorithm for Industrial Process Control

“…e dispatching of common rail dual AGVs is a typical TRSP in factories to quickly dispatch materials between tanks or automatically schedule express delivery in warehouses. Traditionally, this novel and specific problem is solved by simple heuristic algorithms, such as genetic algorithms (GA) and [7][8][9][10] particle swarm optimization (PSO) [11][12][13]. Due to the sheer scale of the problem, it is difficult for these algorithms to converge to the global optimal solution.…”