MULTILEVEL ANT COLONY OPTIMIZATION TO SOLVE CONSTRAINED FOREST TRANSPORTATION PLANNING PROBLEMS

In this dissertation, we focus on solving forest transportation planning related problems, including constraints that consider negative environmental impacts and multi-objective optimizations that provide forest managers and road planers alternatives for making informed decisions. Along this line of study, several multilevel techniques and mataheuristic algorithms have been developed and investigated. The forest transportation planning problem is a fixed-charge problem and known to be NP-hard. The general idea of utilizing multilevel approach is to solve the original problem of which the computational cost maybe prohibitive by using a set of increasingly smaller problems of which the computational cost is cheaper. The multilevel techniques are devised consisting of two parts. The first part is to recursively apply a graph coarsening heuristic to the original problem to produce a set of coarser level problems of which the sizes in terms of number of problem components such as edges and nodes are in decreasing order. The second part is to solve the set of the coarser level problems including the original problem bottom up, starting with the coarsest level. We propose that if coarser level problems inherit important properties (such as attribute value distribution) from their ancestor during the coarsening process, they can be treated as smaller versions of the original problem. Based on this hypothesis, the multilevel techniques use solutions obtained for the coarser level problems to solve the finer level problems. Mainly, we develop multilevel techniques to address three problems, namely a constrained fixed-charge problem, parameter configuration problem, and a multi-objective transportation optimization problem in this study. The performance of the multilevel techniques is compared with other commonly used approaches. The statistical analyses on the experimental results indicate that the multilevel approach can reduce computing time significantly without sacrificing the solution quality

Mixed integer linear programming approaches for solving the raw material allocation, routing and scheduling problems in the forest industry

Transportation planning in forest industry is a challenging activity since it involves complex decisions about raw material allocation, vehicle routing and scheduling of trucks arrivals to both harvest areas and the plants. In the Argentine context, specifically in the Argentinean Northeast (NEA) region, the forest industry plays essential role for the economic development and, among the included activities, the transportation is the key element considering the volumes that must be moved and the distances to be traveled. Therefore, enhancing efficiency in the transportation activity improves significantly the performance of this industry. In this work, a Mixed Integer Linear Programming (MILP) model is presented, where raw material allocation, vehicle routing and scheduling of trucks arrivals are simultaneously addressed. Since the resolution times of the proposed integrated MILP model are prohibitive for large instances, a hierarchical approach is also presented. The considered decomposition approach involves two stages: in the first phase, the raw material allocation and vehicle routing problems are solved through a MILP model, while in the second phase, fixing the route for each truck according to the results of the previous step, the scheduling of truck arrivals to both the harvest areas and the plants is solved through a new MILP model. The obtained results show that the proposed approach is very effective and could be easily applied in this industry.Fil: Bordon, Maximiliano Ramon. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Montagna, Jorge Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Corsano, Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin

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

The design and applications of the african buffalo algorithm for general optimization problems

Optimization, basically, is the economics of science. It is concerned with the need to maximize profit and minimize cost in terms of time and resources needed to execute a given project in any field of human endeavor. There have been several scientific investigations in the past several decades on discovering effective and efficient algorithms to providing solutions to the optimization needs of mankind leading to the development of deterministic algorithms that provide exact solutions to optimization problems. In the past five decades, however, the attention of scientists has shifted from the deterministic algorithms to the stochastic ones since the latter have proven to be more robust and efficient, even though they do not guarantee exact solutions. Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. A critical look at these ‘efficient’ stochastic algorithms reveals the need for improvements in the areas of effectiveness, the number of several parameters used, premature convergence, ability to search diverse landscapes and complex implementation strategies. The African Buffalo Optimization (ABO), which is inspired by the herd management, communication and successful grazing cultures of the African buffalos, is designed to attempt solutions to the observed shortcomings of the existing stochastic optimization algorithms. Through several experimental procedures, the ABO was used to successfully solve benchmark optimization problems in mono-modal and multimodal, constrained and unconstrained, separable and non-separable search landscapes with competitive outcomes. Moreover, the ABO algorithm was applied to solve over 100 out of the 118 benchmark symmetric and all the asymmetric travelling salesman’s problems available in TSPLIB95. Based on the successful experimentation with the novel algorithm, it is safe to conclude that the ABO is a worthy contribution to the scientific literature

An iterative solution approach for truck routing and scheduling in the forest industry

Log transportation in forest industry is a resource-intensive operation and represents a great challenge for logistic planners. Several trips must be generated in order to satisfy plants demand; in addition, trucks arrivals at each plant must be considered in order to avoid unproductive waiting times. In the Argentine context, specifically in the Argentinean Northeast (NEA) region, these activities represent the main sustenance of the regional economies, so enhancing efficiency in the transport operation would represent a considerable improvement for these economies. In this work, an iterative solution approach for the truck routing and scheduling problems is presented. The proposed strategy involves two stages which are iteratively solved: product allocation, trip composition and truck routing problems are first solved through a Mixed-Integer Linear Programming model (MILP), while in the second stage, fixing the route for each truck according to the results of the previous step, a MILP model for the scheduling of truck arrivals at plants is considered. If no feasible solution for the scheduling problem is obtained, then an integer cut is applied in order to exclude from the search space truck routes already explored in previous iterations. The solution approach is tested in a case study representative of the Argentine context and conclusions are detailed.Sociedad Argentina de Informática e Investigación Operativ

An iterative solution approach for truck routing and scheduling in the forest industry

Log transportation in forest industry is a resource-intensive operation and represents a great challenge for logistic planners. Several trips must be generated in order to satisfy plants demand; in addition, trucks arrivals at each plant must be considered in order to avoid unproductive waiting times. In the Argentine context, specifically in the Argentinean Northeast (NEA) region, these activities represent the main sustenance of the regional economies, so enhancing efficiency in the transport operation would represent a considerable improvement for these economies. In this work, an iterative solution approach for the truck routing and scheduling problems is presented. The proposed strategy involves two stages which are iteratively solved: product allocation, trip composition and truck routing problems are first solved through a Mixed-Integer Linear Programming model (MILP), while in the second stage, fixing the route for each truck according to the results of the previous step, a MILP model for the scheduling of truck arrivals at plants is considered. If no feasible solution for the scheduling problem is obtained, then an integer cut is applied in order to exclude from the search space truck routes already explored in previous iterations. The solution approach is tested in a case study representative of the Argentine context and conclusions are detailed.Sociedad Argentina de Informática e Investigación Operativ

An iterative solution approach for truck routing and scheduling in the forest industry

Log transportation in forest industry is a resource-intensive operation and represents a great challenge for logistic planners. Several trips must be generated in order to satisfy plants demand; in addition, trucks arrivals at each plant must be considered in order to avoid unproductive waiting times. In the Argentine context, specifically in the Argentinean Northeast (NEA) region, these activities represent the main sustenance of the regional economies, so enhancing efficiency in the transport operation would represent a considerable improvement for these economies. In this work, an iterative solution approach for the truck routing and scheduling problems is presented. The proposed strategy involves two stages which are iteratively solved: product allocation, trip composition and truck routing problems are first solved through a Mixed-Integer Linear Programming model (MILP), while in the second stage, fixing the route for each truck according to the results of the previous step, a MILP model for the scheduling of truck arrivals at plants is considered. If no feasible solution for the scheduling problem is obtained, then an integer cut is applied in order to exclude from the search space truck routes already explored in previous iterations. The solution approach is tested in a case study representative of the Argentine context and conclusions are detailed.Sociedad Argentina de Informática e Investigación Operativ

A comprehensive survey on cultural algorithms

Peer reviewedPostprin

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner