Hybrid NSGA-II Research Articles

Collaboration and transportation resource sharing in multiple centers vehicle routing optimization with delivery and pickup

The adoption of collaboration strategies among logistics facilities and the formation of one or multiple coalitions constitute a sustainable approach to vehicle routing network optimization. This paper introduces a collaborative multiple centers vehicle routing problem with simultaneous delivery and pickup (CMCVRPSDP) to minimize operating cost and the total number of vehicles in the network. Distribution and pickup centers are allowed to share vehicles and customers in order to increase the entire network's efficiency and maximize profit. To provide the coalition coordinators with good routing solutions, we propose a hybrid heuristic algorithm which properly combines k-means and Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Based on clustering solutions, the proposed Hybrid NSGA-II (HNSGA-II) first generates a real coded population to bind our mathematical model constraints and to obtain a large number of feasible solutions which converge to optimality. Chromosomes are divided for genetic operations with partial mapped crossover and swap mutation algorithms, before their recombination to ensure the quality of our results. Comparisons with the traditional NSGA-II and the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm indicate better performances of HNSGA-II in terms of objective function values. We also apply Cost Gap Allocation method (CGA) and the strictly monotonic path selection principle to examine profit allocation schemes. Numerical analyses on part of Chongqing city's logistics network show the superiority of HNSGA-II over MOPSO and NSGA-II on the practical case study, as well that of CGA over the Minimum Costs-Remaining Savings (MCRS), Shapley and Game Quadratic Programming (GQP) methods. In addition, the proposed profit allocation approach has supported the establishment of a grand coalition instead of two sub-coalitions. CMCVRPSDP optimization reduces long-haul transportation, improves the vehicle loading rate and facilitates sustainable development. Through the rational allocation of profits, the proposed solution methodology assures the stability and fairness among coalition members. The implementation is also important to design sustainable urban transportation networks.

Optimal controller placement in large scale software defined networks based on modified NSGA-II

Software Defined Network (SDN) is an emerging approach to overcome challenges of traditional networks. One particularly important issue in SDN architectures is that of controller placement problem (CPP), i.e., deploying a desired number of controllers within a network while some possibly conflicting requirements have to be fulfilled. A single optimal placement may not be possible and decision makers need to seek for an appropriate trade-off among the metrics. Although an exhaustive evaluation of all possible placements can be practically performed well for small and medium-sized networks, regarding realistic time and resource restrictions, heuristic approaches are required for large-scale networks. Hence, a heuristic called Multi-Start Hybrid NSGA-II (MHNSGA-II) is introduced which yields faster computation times and needs reasonable memory to perform. The obtained results on several topologies extracted from Internet Topology Zoo showed the efficiency of the proposed approach.

Mathematical Modeling and a Hybrid NSGA-II Algorithm for Process Planning Problem Considering Machining Cost and Carbon Emission

Process planning is an important function in a manufacturing system; it specifies the manufacturing requirements and details for the shop floor to convert a part from raw material to the finished form. However, considering only economical criterion with technological constraints is not enough in sustainable manufacturing practice; formerly, criteria about low carbon emission awareness have seldom been taken into account in process planning optimization. In this paper, a mathematical model that considers both machining costs reduction as well as carbon emission reduction is established for the process planning problem. However, due to various flexibilities together with complex precedence constraints between operations, the process planning problem is a non-deterministic polynomial-time (NP) hard problem. Aiming at the distinctive feature of the multi-objectives process planning optimization, we then developed a hybrid non-dominated sorting genetic algorithm (NSGA)-II to tackle this problem. A local search method that considers both the total cost criterion and the carbon emission criterion are introduced into the proposed algorithm to avoid being trapped into local optima. Moreover, the technique for order preference by similarity to an ideal solution (TOPSIS) method is also adopted to determine the best solution from the Pareto front. Experiments have been conducted using Kim’s benchmark. Computational results show that process plan schemes with low carbon emission can be captured, and, more importantly, the proposed hybrid NSGA-II algorithm can obtain more promising optimal Pareto front than the plain NSGA-II algorithm. Meanwhile, according to the computational results of Kim’s benchmark, we find that both of the total machining cost and carbon emission are roughly proportional to the number of operations, and a process plan with less operation may be more satisfactory. This study will draw references for the further research on green manufacturing in the process planning level.

A Hybrid NSGA-II Algorithm for Multiobjective Quadratic Assignment Problems

3rd International Conference on Computational and Experimental Science and Engineering (ICCESEN) -- OCT 19-24, 2016 -- Antalya, TURKEY

Shear wave travel time estimation from petrophysical logs using ANFIS-PSO algorithm: A case study from Ab-Teymour Oilfield

Among petrophysical logs, shear wave velocity is known to provide more accurate results in terms of mechanical rock properties. However, due to higher costs associated with acquiring shear waves, it is not a common practice to acquire shear wave at all wells. As such, many efforts have been made to estimate the log from other petrophysical logs. In the present research, shear wave travel time (slowness) is estimated from other petrophysical logs acquired at a well drilled into Ab-Teymour Oilfield, southwestern Iran. For this purpose, first, outliers were identified, using Tukey's method, and omitted from the dataset. Then, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Layer Perceptron (MLP) methodologies were utilized to select effective input logs. Continuing with the research, two hybrid algorithms, namely Adaptive Neuro-Fuzzy System combined with Particle Swarm Optimization (ANFIS-PSO) and Adaptive Neuro-Fuzzy System combined with Genetic Algorithm (ANFIS-GA), were used to estimate shear wave from the selected logs. The results of hybrid NSGA-II and MLP on pre-processed data showed that, with increasing the number of inputs, the model's MSE decreases. It should be noted that the decreasing rate has no significant differences if the input parameters exceed four. Accordingly, using the hybrid algorithms on 4 selected logs (i.e. compressional wave, density, porosity, and depth logs), shear wave travel time (DTSM) was estimated. The estimation results indicated superior performance of the ANFIS-PSO model over that of ANFIS-GA. Furthermore, low difference between corresponding estimation errors to training and testing phases of ANFIS-PSO indicated that, the model can provide adequate reliability and accuracy when it comes to shear wave estimation. A comparison between the raw data-based and pre-processed data-based ANFIS-PSO models highlighted the efficiency of the pre-processing stage in enhancing the model results. In order to undertake a performance evaluation of the proposed model, the proposed method was compared to empirical correlations and multivariate regression analysis (MVRA). The results indicated significant superiority of the model over other models. The proposed method in the present paper can provide even more reliable and accurate outputs in terms of shear wave across the field understudy provided larger sources of data can be incorporated into the method, so as to save the costs required to acquire shear data.

Solving a bi-objective location routing problem by a NSGA-II combined with clustering approach: application in waste collection problem

It is observed that the separated design of location for depots and routing for servicing customers often reach a suboptimal solution. So, solving location and routing problem simultaneously could achieve better results. In this paper, waste collection problem is considered with regard to economic and societal objective functions. A non-dominated sorting genetic algorithm (NSGA-II) is used to locate depots and treatment facilities and design the routes starting from depots to serve customers. A new mathematical model is proposed and two objective functions including economic objective (opening cost of depots and treatment facility and transportation cost) and societal objective; that is, negative impact of treatment facilities which are close to towns are addressed in this study. A straightforward order based solution representation is applied for coding solutions of the problem and clustering approach is used to generate appropriate initial solutions. Moreover, three multi-objective decomposition methods including weighted sum, goal programming, and goal attainment are applied to validate the performance of the proposed algorithm. Number of test problems are conducted and the results obtained by algorithms are compared with respect to some comparison metrics. Finally, the experimental results show that the proposed hybrid NSGA-II outperforms all decomposition methods, but the computational times for decomposition methods are less than NSGA-II.

Carbon-capped Distribution Planning: A JIT Perspective

Products distribution and transportation is one of the largest sources of CO2 emission in supply chains. To date, a number of researchers have argued that intensive transportation activities through popular distribution strategies such as Just-In-Time (JIT) could significantly increase carbon emissions within logistics chains. However, a systematic understanding of how JIT distribution affects carbon emissions is still lacking in current literature. In this study, we develop a bi-objective optimization model for a carbon-capped JIT distribution of multiple products in a multi-period and multi-echelon distribution network. The aims are to jointly minimize total logistics cost and to minimize the maximum carbon quota allowed per period (carbon cap). The considered problem is investigated under three different carbon emission constraints namely periodic, cumulative and global. Since the studied problem is NP-Hard, a non-dominated sorting genetic algorithm-II (NSGA-II) is developed and its parameters are tuned by Taguchi method. For further quality improvement of the developed solution approach, a novel local search approach called modified firefly algorithm incorporates NSGA-II. Different sizes of the problem are considered to compare the performances of the proposed hybrid NSGA-II and the classical one. Finally, the results are presented along with some policy and managerial insights. For policy makers, the findings show the impact of varying the carbon emission cap on total cost and total emissions under JIT distribution concept. From managerial perspectives, we analyze the relationships between average inventory holding and backlog level per period which can assist mangers to identify critical decisions for JIT distribution of products in carbon-capped environment.

A hybrid NSGA-II and VNS for solving a bi-objective no-wait flexible flowshop scheduling problem

We address the no-wait k-stage flexible flowshop scheduling problem where there are m identical machines at each stage. The objectives are to schedule the available n jobs so that makespan and mean tardiness of n jobs are minimized. Sequence-dependent setup times are treated in this problem as one of the prominent practical assumptions. This problem is NP-hard, and therefore we present a new multiobjective approach for solving the mentioned problem. The proposed meta-heuristic is evaluated based on randomly generated data in comparison with two well-known multiobjective algorithm including NSGA-II and SPEA-II. Due to sensitivity of our proposed algorithm to parameter values, a new approach for tackling of this issue was designed. Our proposed method includes Taguchi method (TM) and multiobjective decision making (MODM). We have chosen six measures into two groups. Qualitative metrics including number of Pareto solutions (NPS), diversity metric (DM) as well as the spread of non-dominance solution (SNS) and quantitative metrics including the rate of achievement to two objectives simultaneously (RAS), mean ideal distance (MID) and quality metric (QM) to evaluate the performance of our proposed algorithms. Computational experiments and comparisons show that the proposed NSGA-II + VNS algorithm generates better or competitive results than the existing NSGA-II and SPEA-II for the no-wait flexible flow shop scheduling problem with sequence-dependent setup times to simultaneous minimizing the makespan and mean tardiness criterion.

A hybrid multi-objective evolutionary algorithm for wind-turbine blade optimization

A concurrent-hybrid non-dominated sorting genetic algorithm (hybrid NSGA-II) has been developed and applied to the simultaneous optimization of the annual energy production, flapwise root-bending moment and mass of the NREL 5 MW wind-turbine blade. By hybridizing a multi-objective evolutionary algorithm (MOEA) with gradient-based local search, it is believed that the optimal set of blade designs could be achieved in lower computational cost than for a conventional MOEA. To measure the convergence between the hybrid and non-hybrid NSGA-II on a wind-turbine blade optimization problem, a computationally intensive case was performed using the non-hybrid NSGA-II. From this particular case, a three-dimensional surface representing the optimal trade-off between the annual energy production, flapwise root-bending moment and blade mass was achieved. The inclusion of local gradients in the blade optimization, however, shows no improvement in the convergence for this three-objective problem.

Hybrid NSGA-II algorithm on robust multi-objective inventory management problem

Risk management and inventory cost control are key issues in supply chain management. Based on an ( r, Q) strategy, this paper formulates a multi-layer multi-period stochastic inventory problem as a robust multi-objective model. The novelty lies in the consideration of both perturbed variables and stochastic demand in the model. The goal is to minimize the expected cost and the risk measured by conditional value at risk (CVaR). To solve this model, we propose a hybrid Non-Dominated Genetic Algorithm-II (NSGA-II) where a polynomial time algorithm is designed to obtain the optimal CVaR for a given ( r, Q). Moreover, a local search method is tailored for the NSGA-II to improve solutions. This hybrid algorithm can significantly increase the number of optimal solutions and decrease the inventory cost. Numerical results validate the effectiveness of our robust multi-objective model and the hybrid algorithm.

Application and comparison of hybrid evolutionary multiobjective optimization algorithms for solving task scheduling problem on heterogeneous systems

Task scheduling problem in heterogeneous systems (TSPHS) is a multiobjective optimization problem (MOP). Multiobjective evolutionary algorithms (MOEA) are well suited for solving multiobjective task scheduling problem. In this paper, the two conflicting objectives namely, makespan and reliability are considered. The performance of MOEAs can be improved by hybridization with local search. Hybridization of MOEAs improves the convergence speed to Pareto front. Simple neighborhood search (SNS) algorithm is used as the local search algorithm. The weighted-sum based approach for solving the MOP with its hybrid version is compared. Then the two MOEAs: SPEA2 and NSGA-II are compared with each other in the pure and hybrid version for random task graphs and also for a real-time numerical application graph. The simulations confirm that Hybrid NSGA-II is best suited for solving the task scheduling problem.