Resolving forward-reverse logistics multi-period model using evolutionary algorithms

© 2016 Elsevier Ltd In the changing competitive landscape and with growing environmental awareness, reverse logistics issues have become prominent in manufacturing organizations. As a result there is an increasing focus on green aspects of the supply chain to reduce environmental impacts and ensure environmental efficiency. This is largely driven by changes made in government rules and regulations with which organizations must comply in order to successfully operate in different regions of the world. Therefore, manufacturing organizations are striving hard to implement environmentally efficient supply chains while simultaneously maximizing their profit to compete in the market. To address the issue, this research studies a forward-reverse logistics model. This paper puts forward a model of a multi-period, multi-echelon, vehicle routing, forward-reverse logistics system. The network considered in the model assumes a fixed number of suppliers, facilities, distributors, customer zones, disassembly locations, re-distributors and second customer zones. The demand levels at customer zones are assumed to be deterministic. The objective of the paper is to maximize the total expected profit and also to obtain an efficient route for the vehicle corresponding to an optimal/near optimal solution. The proposed model is resolved using Artificial Immune System (AIS) and Particle Swarm Optimization (PSO) algorithms. The findings show that for the considered model, AIS works better than the PSO. This information is important for a manufacturing organization engaged in reverse logistics programs and in running units efficiently. This paper also contributes to the limited literature on reverse logistics that considers costs and profit as well as vehicle route management

From swarm intelligence to metaheuristics: nature-inspired optimization algorithms

Nature has provided rich models for computational problem solving, including optimizations based on the swarm intelligence exhibited by fireflies, bats, and ants. These models can stimulate computer scientists to think nontraditionally in creating tools to address application design challenges

From swarm intelligence to metaheuristics: nature-inspired optimization algorithms

Nature has provided rich models for computational problem solving, including optimizations based on the swarm intelligence exhibited by fireflies, bats, and ants. These models can stimulate computer scientists to think nontraditionally in creating tools to address application design challenges

Solutions to decision-making problems in management engineering using molecular computational algorithms and experimentations

制度:新 ; 報告番号:甲3368号 ; 学位の種類:博士(工学) ; 授与年月日:2011/5/23 ; 早大学位記番号:新568

Soft computing for hazardous waste routing in Malaysia: a review

Nowadays, a significant number of researchers are focusing on utilizing soft computing approaches to address the issue of scheduling in applications concerned with hazardous waste management. In Malaysia, there is thoughtless awareness of the management of hazardous waste, even though the production of wastes in hazardous domains at the industrial and domestic levels has been rising lately. According to previous research findings, the location routing problem (LRP) can be designated as one of the models closer to the actual situation, evaluating the most suitable and optimal location for establishing facilities and utilizing transportation for pick-up and distribution. Recent studies have focused on enhancing the LRP model, and its methodologies approach to solve the waste management problem in hazardous domains. In this paper, a comprehensive review of the better promising and practicable mathematical model of LRP and its methodology approach is discussed, as well as an analysis of the publishing pattern and the trend of research over the preceding five years and more, as retrieved from the web of science (WoS) database. In conclusion, this research is significant in ensuring the effectiveness of reliable mathematical model development and suitable methodologies in the future for solving hazardous waste management problems

SGA Model for Prediction in Cloud Environment

With virtual information, cloud computing has made applications available to users everywhere. Efficient asset workload forecasting could help the cloud achieve maximum resource utilisation. The effective utilization of resources and the reduction of datacentres power both depend heavily on load forecasting. The allocation of resources and task scheduling issues in clouds and virtualized systems are significantly impacted by CPU utilisation forecast. A resource manager uses utilisation projection to distribute workload between physical nodes, improving resource consumption effectiveness. When performing a virtual machine distribution job, a good estimation of CPU utilization enables the migration of one or more virtual servers, preventing the overflow of the real machineries. In a cloud system, scalability and flexibility are crucial characteristics. Predicting workload and demands would aid in optimal resource utilisation in a cloud setting. To improve allocation of resources and the effectiveness of the cloud service, workload assessment and future workload forecasting could be performed. The creation of an appropriate statistical method has begun. In this study, a simulation approach and a genetic algorithm were used to forecast workloads. In comparison to the earlier techniques, it is anticipated to produce results that are superior by having a lower error rate and higher forecasting reliability. The suggested method is examined utilizing statistics from the Bit brains datacentres. The study then analyses, summarises, and suggests future study paths in cloud environments

A Novel Metaheuristic Hybrid Parthenogenetic Algorithm for Job Shop Scheduling Problems: Applying Optimization Model

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3278372© Copyright 2023 The Authors. Metaheuristics are primarily developed to explore optimization techniques in many practice areas. Metaheuristics refer to computational procedures leading to finding optimal solutions to optimization problems. Due to the increasing number of optimization problems with large-scale data, there is an ongoing demand for metaheuristic algorithms and the development of new algorithms with more efficiencies and improved convergence speed implemented by a mathematical model. One of the most popular optimization problems is job shop scheduling problems. This paper develops a novel metaheuristic hybrid Parthenogenetic Algorithm (NMHPGA) to optimize flexible job shop scheduling problems for single-machine and multi-machine job shops and a furnace model. This method is based on the principles of genetic algorithms, underlying the combinations of different types of selections, proposed ethnic GA, and hybrid parthenogenetic algorithm. In this paper, a parthenogenetic algorithm combined with ethnic selection GA is tested; the parthenogenetic algorithm version includes parthenogenetic operators: swap, reverse, and insert. The ethnic selection uses different selection operators such as stochastic, roulette, sexual, and aging; then, top individuals are selected from each procedure and combined to generate an ethnic population. The ethnic selection procedure is tested with the PGA types on a furnace model, single-machine job shops, and multi-machines with tardiness, earliness, and due date penalties. A comparison of obtained results of the established algorithm with other selection procedures indicated that the NMHPGA is achieving better objective functions with faster convergence speed.10.13039/501100007914-Brunel University Londo

The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies

This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

Biologically inspired evolutionary temporal neural circuits

Biological neural networks have always motivated creation of new artificial neural networks, and in this case a new autonomous temporal neural network system. Among the more challenging problems of temporal neural networks are the design and incorporation of short and long-term memories as well as the choice of network topology and training mechanism. In general, delayed copies of network signals can form short-term memory (STM), providing a limited temporal history of events similar to FIR filters, whereas the synaptic connection strengths as well as delayed feedback loops (ER circuits) can constitute longer-term memories (LTM). This dissertation introduces a new general evolutionary temporal neural network framework (GETnet) through automatic design of arbitrary neural networks with STM and LTM. GETnet is a step towards realization of general intelligent systems that need minimum or no human intervention and can be applied to a broad range of problems. GETnet utilizes nonlinear moving average/autoregressive nodes and sub-circuits that are trained by enhanced gradient descent and evolutionary search in terms of architecture, synaptic delay, and synaptic weight spaces. The mixture of Lamarckian and Darwinian evolutionary mechanisms facilitates the Baldwin effect and speeds up the hybrid training. The ability to evolve arbitrary adaptive time-delay connections enables GETnet to find novel answers to many classification and system identification tasks expressed in the general form of desired multidimensional input and output signals. Simulations using Mackey-Glass chaotic time series and fingerprint perspiration-induced temporal variations are given to demonstrate the above stated capabilities of GETnet