Two Efficient Meta-Heuristic Algorithms for the Robust Inventory Routing Problem with Backhaul

The inventory routing problem (IRP) involves the integration and coordination of two components of the logistics value chain: inventory management and vehicle routing. Therefore, consideration of this issue can be effective in decision making of the organization and will lead to lower costs or other goals. Our objective in this article is to examine a new inventory-routing model and solve it with meta-heuristic methods. For more flexibility of the model, and approaching the real world, the model of this article is considered multi-period and multi-product. Also, two objective functions, including minimizing system costs and transportation risk, are included in this model. Given that the main parameter of the model, that is, demand, is uncertain, we have used a robust optimization approach to solve it, and since this model is in the classification of NP-Hard problems, we have used two meta-heuristic algorithms consisting of non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective imperialist competitive algorithm (MOICA). By examining the model in two deterministic and robust conditions, according to two criteria, the mean values of the objective function and its standard deviation, it has been determined that in almost all cases, the robust optimization model produces better solutions. Also, between the two meta-heuristics method, the NSGA-II algorithm has shown better quality according to the mentioned criteria. Obviously, taking into account the different features of a model increases its efficiency. But this, obviously, makes the model even more complex. However, this complexity of models can work like a real system. Our attention in this article has been to this subject. To analyze such models, exact methods do not have the required performance and paying attention to heuristic and meta-heuristic methods is very effective. In this paper, a robust optimization and meta-heurictic approaches focus on these goals

Optimization of Location-Routing for the Waste Household Appliances Recycling Logistics under the Uncertain Condition

Waste household appliances and electronic products usually contain harmful substances which need scientific and reasonable collection, classification, processing, recovery and disposal to achieve sustainable and effective recycling and utilization. In recent years, due to the poor management of waste household appliances recycling logistics system, safety accidents occur frequently, which seriously harm the health and life safety of the society. This paper studies the risk management of recycling waste household appliances under uncertain conditions and establishes a risk measurement model under fuzzy population density. Considering the multi-stage and classification diversity of waste household appliances recycling logistics, the multi-objective location routing model and location - routing model are established respectively. Based on the model complexity analysis, the solution method of multi-objective model is designed. Finally, the validity of the model and algorithm is verified by examples and tests

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Revisiting the Evolution and Application of Assignment Problem: A Brief Overview

The assignment problem (AP) is incredibly challenging that can model many real-life problems. This paper provides a limited review of the recent developments that have appeared in the literature, meaning of assignment problem as well as solving techniques and will provide a review on   a lot of research studies on different types of assignment problem taking place in present day real life situation in order to capture the variations in different types of assignment techniques. Keywords: Assignment problem, Quadratic Assignment, Vehicle Routing, Exact Algorithm, Bound, Heuristic etc