Vehicle Routing Problem with Deadline and Stochastic Service Times: Case of the Ice Cream Industry in Santiago City of Chile

The research evaluates the vehicular routing problem for distributing refrigerated products. The mathematical model corresponds to the vehicle routing problem with hard time windows and a stochastic service time (VRPTW-ST) model applied in Santiago de Chile. For model optimization, we used tabu search, chaotic search and general algebraic modeling. The model’s objective function is to minimize the total distance traveled and the number of vehicles using stochastic waiting restrictions at the customers’ facilities. The experiments were implemented in ten scenarios by modifying the number of customers. Experiments were established with several customers that can be solved using the general algebraic modeling technique in order to validate the tabu search and the chaotic search methods. The study considered two algorithms modified with Monte Carlo (tabu search and chaotic search). Additionally, two modified algorithms, TSv2 and CSv2, were proposed to reduce execution time. These algorithms were modified by delaying the Monte Carlo procedure until the first set of sub-optimal routes were found. The results validate the metaheuristic chaotic search to solve the VRPTW-ST. The chaotic search method obtained a superior performance than the tabu search method when solving a real problem in a large city. Finally, the experiments demonstrated a direct relationship between the percentage of customers with stochastic waiting time and the model resolution time.

Chaotic Search Based Equilibrium Optimizer for Dealing with Nonlinear Programming and Petrochemical Application

In this article, chaotic search based constrained equilibrium optimizer algorithm (CS-CEOA) is suggested by integrating a novel heuristic approach called equilibrium optimizer with a chaos theory-based local search algorithm for solving general non-linear programming. CS-CEOA is consists of two phases, the first one (phase I) aims to detect an approximate solution, avoiding being stuck in local minima. In phase II, the chaos-based search algorithm improves local search performance to obtain the best optimal solution. For every infeasible solution, repair function is implemented in a way such that, a new feasible solution is created on the line segment defined by a feasible reference point and the infeasible solution itself. Due to the fast globally converging of evolutionary algorithms and the chaotic search’s exhaustive search, CS-CEOA could locate the true optimal solution by applying an exhaustive local search for a limited area defined from Phase I. The efficiency of CS-CEOA is studied over multi-suites of benchmark problems including constrained, unconstrained, CEC’05 problems, and an application of blending four ingredients, three feed streams, one tank, and two products to create some certain products with specific chemical properties, also to satisfy the target costs. The results were compared with the standard evolutionary algorithms as PSO and GA, and many hybrid algorithms in the same simulation environment to approve its superiority of detecting the optimal solution over selected counterparts.

A City-Scale Optimization for Distribution of Goods with Stochastic Service

The research develops the vehicular routing problem for the distribution of refrigerated products in a multinational company. The mathematical model corresponds to the vehicle routing problem with hard time windows and stochastic service (VRPTW-ST) time model applied in the city of Santiago de Chile. For optimization of the model, optimization methods tabu search, chaotic search and general algebraic modeling were used. The results allow to validate the meta-heuristic chaotic search to solve the VRPTW-ST; chaotic search method obtains superior performance than tabu search method for solving a real problem in a large city.

Structural organization and cohesiveness of foraging groups in Camponotus sericeus (Fabricius) (Hymenoptera: Formicidae): locally regulated or self-organized?

Background In Camponotus sericeus (Fabricius), foraging ants are recruited mostly as individuals but occasionally as small groups that move in a single file. We studied the structure and organization of these small foraging groups and attempted to understand the process through which the cohesiveness of the moving file is maintained. Results The recruited group moves in a single file as if steered by a leader at the moving tip. Ants in the group were found to exhibit certain fidelity to their respective positions in the file, despite the occasional breakdown of the cohesiveness due to disturbance and or obstructions on their path. This fidelity decreases from both ends towards the middle part of the file. Accordingly, three segments could be recognized in the moving file: (a) the leading ant that almost always maintains its position and steers the group, (b) a short tail part with a few ants that always trail the file and, (c) the mid part that binds the group; ants in this segment always tend to follow the leader through a cascading chain of tactile communication. If the leader ant is removed, entire group loses its orientation and enters into a chaotic search state. But removing any other ant does not affect the cohesiveness; rather it’s position is occupied by the member preceding it and thus maintains the link in the group. Conclusions The cohesiveness of the moving group appeared to result from (a) regulation of the movement of the group by the leading ant, and, (b) an interactive process among the rest of the ants. Based on these two elements, a simple automated model of the group’s movement was developed that could effectively mimic the observed pattern. We also provide evidence to suggest that recruitment of groups occurs in the direction of, and in response to, the information received by the colony on, the resource rich patches.

A New Chaotic-Based Approach for Multi-Objective Optimization

Multi-objective optimization problems (MOPs) have been widely studied during the last decades. In this paper, we present a new approach based on Chaotic search to solve MOPs. Various Tchebychev scalarization strategies have been investigated. Moreover, a comparison with state of the art algorithms on different well known bound constrained benchmarks shows the efficiency and the effectiveness of the proposed Chaotic search approach.

An Optimal Solution for Smooth and Non-Smooth Cost Functions-Based Economic Dispatch Problem

A modified particle swarm optimization and incorporated chaotic search to solve economic dispatch problems for smooth and non-smooth cost functions, considering prohibited operating zones and valve-point effects is proposed in this paper. An inertia weight modification of particle swarm optimization is introduced to enhance algorithm performance and generate optimal solutions with stable solution accuracy and offers faster convergence characteristic. Moreover, an incorporation of chaotic search, called logistic map, is used to increase the global searching capability. To demonstrate the effectiveness and feasibility of the proposed algorithm compared to the several existing methods in the literature, five systems with different criteria are verified. The results show the excellent performance of the proposed method to solve economic dispatch problems.

On the Development of an Empirical Model for Carcinomic Treatment using Chaotic Hyper Heuristic Algorithm

A new dimension in the field of computational intelligence was introduced in the late nineties to comprehend a vivid combination of several multi-disciplinary areas. The coalescence biology along with data mining and statistical learning have given birth to Bioinformatics that provides various paradigms for studying the behaviour of unknown patterns at the micro level. In the present work, a recently developed human inspired optimization algorithm called search and rescue (SAR) optimization is employed with an improved version of parameters using Chaos theory. CSARO (Chaotic search and rescue optimization algorithm) unlike other existing algorithms has proven to be a better choice for optimising the gene selection mechanism as well as the control parameters of the learning model. This hyper heuristic algorithm obtained by the inclusion of chaos in SAR mainly aims at enhancement of its global search mobility and prevents from getting trapped in the local optimum. A comparative study with other existing techniques on seven benchmark datasets is performed. The performance of the algorithm is tested using evaluation metrics.