Intelligent Optimization

The physical limitations of CMOS technology triggered several research for finding an alternative technology. QCA is one of the emerging nanotechnologies which is gaining attention as a substitute of CMOS. The main potential of QCA is its ultra low power consumption, less area overhead, and high speed. Majority and inverter gates are the basic gates in QCA, which together works as a universal logic gate to implement any QCA circuit. This paper proposes an efficient methodology for optimal QCA circuit synthesis of arbitrary multi-output boolean functions. A multi-objective genetic algorithm based approach is used to reduce worst case delay and gate count of a QCA circuit. Different importance is given to worst case delay, no. of majority and no. of inverter gates. Several efficient techniques are used in order to achieve the optimal result and reduce the computational complexity furthermore additional methodologies are used to eliminate redundancies from the final solution. Comparison of the obtained results with the existing best techniques indicates, the proposed technique outperforms in terms of worst case delay and gate count.

Trained Reward-based Action Classification Engine (TRACE) is a general process for capturing operator outcome data during metaheuristic search, training classifiers to predict whether an operator will yield an improved solution, and deploying those models to guide neighborhood selection during future search runs. This study introduces TRACE-VNS, a modular extension of General Variable Neighborhood Search (GVNS) applied to the Capacitated Vehicle Routing Problem (CVRP), where neighborhood selection is driven by these offline-trained models. Classifiers are trained on features extracted from GVNS traces, including action history, graph metrics, temporal state, and Upper Confidence Bound (UCB) indicators. Twelve classifiers, including tree ensembles, neural networks, and kernel-based models, are benchmarked using the Precision-Recall Area Under the Curve (PR-AUC) to evaluate predictive quality. Empirical results show that TRACE-VNS improves convergence speed and final solution quality over conventional GVNS across 84 CVRP instances. A detailed feature importance analysis identifies strong contributors, offering insights into the effective selection of operators. TRACE requires no runtime exploration or feedback loops and can generalize to other metaheuristics through minimal structural adaptation.

This study presents the design and integration of novel adaptive components within the Double-Adaptive General Variable Neighborhood Search (DA-GVNS) algorithm, aimed at improving its overall efficiency. These adaptations utilize iteration-based data to refine the search process, with enhancements such as an adaptive reordering mechanism in the refinement phase and a knowledge-guided approach to adjust the search strategy. Additionally, an adaptive mechanism for dynamically controlling the shaking intensity was introduced. The proposed knowledge-guided adaptations demonstrated superior performance over the original DA-GVNS framework, with the most effective scheme selected for further evaluation. Initially, the symmetric Traveling Salesman Problem (TSP) was used as a benchmark to quantify the impact of these mechanisms, showing significant improvements through rigorous statistical analysis. A comparative study was then conducted against six advanced heuristics from the literature. Finally, the most promising knowledge-guided GVNS (KG-GVNS) was tested against the original DA-GVNS on selected instances of the Quadratic Assignment Problem (QAP), where detailed statistical analysis highlighted its competitive advantage and robustness in addressing complex combinatorial optimization problems.

Increasing environmental awareness in today's society and stricter environmental regulations have forced manufacturing firms to take necessary actions for the recovery of end-of-life (EOL) products through different options (e.g., recycling, remanufacturing,). Disassembly is regarded as a critical operation in EOL treatment of used products since all product recovery options require the disassembly of EOL products at certain levels. This critical operation is generally carried out by forming disassembly lines in product recovery facilities. Miscellaneous methodologies based on heuristics, metaheuristics and mathematical programming have been proposed for the balancing of disassembly lines. Majority of those methodologies assume that disassembly line parameters are deterministic by ignoring the fact that a disassembly line involves great deal of uncertainty mainly due to uncertain conditions of arriving EOL products. Considering this high level of uncertainty, simulation modeling can be an effective tool for the modeling of disassembly lines. In this study, a simulation-based disassembly line balancing methodology is proposed for the explicit consideration of stochastic parameters. First, simulation model of a disassembly line is constructed. Since the disassembly line balancing problem has a combinatorial nature, two commonly used metaheuristics (i.e., genetic algorithms (GAs) and simulated annealing (SA)) are integrated with the simulation model in order to balance the disassembly line. The disassembly sequence and task assignments proposed by GA are compared with the sequence and task assignments proposed by SA. This comparison indicates that GA outperforms SA in four of eight performance measures while both algorithms have the same value for line efficiency measure.

In this study, effective and fast removal efficiency of carob powder (as an absorbent material from liquid phase) was investigated by focusing on the dangerous paint methylene blue mixture. The surface texture developed adsorbent was revealed to be porous by characterizing done thanks to scanning through electron microscope. Experiment parameters of pH, ultrasonic frequency, particle size, contact time, temperature and initial concentration of dissolved methylene blue dye were investigated accordingly. Thereafter, Box-Behnken design experiment was applied for adsorption experiments. Regression analysis findings demonstrated that the experimental data is good for to the non-linear model with correlation coefficients of correction value at 0.8899 and 0.9830. The maximum adsorption value was determined as around 256.44 mg/g thanks to the Electro-Search Algorithm, a recent Artificial Intelligence based intelligent optimization technique. Additionally, some alternative intelligent optimization algorithms were also used for determining optimum values. According to the results of the study, the carob bean can be used as an alternative adsorbent and the found optimum values can be employed for that purpose.

This work introduces a multi-period, multi-commodity, inventory-routing problem with strategic fleet scheduling decisions, under the consideration of speed limits, as well as strict European Union regulations on truck drivers' working and driving time. To address the new problem, a mixed integer linear programming model was developed. Several artificial but realistic problem instances were randomly generated following relative guidelines from the open literature, to validate and assess the performance of the novel mathematical model. Furthermore, in an effort to produce useful managerial insights, several sensitivity analyses were performed considering different fluctuation rates on key model parameters.

This study presents the Pollution Traveling Salesman Problem with Refueling, a novel optimization problem which integrates two recently proposed variants of the Traveling Salesman Problem: the Pollution Traveling Salesman Problem and the Traveling Salesman Problem with Refueling. The proposed problem captures the operational dynamics of a real-world routing scenario involving a single vehicle originating from a central depot and delivering products to end customers. When considering the vehicle’s fuel tank capacity and fuel consumption during the routing process, the need to visit fuel stations for refueling arises. To address this complex problem, a new mixed integer linear programming model was developed, and the Gurobi solver was employed to solve smaller instances. For the effective resolution of larger practical problem cases, a two-stage double adaptive general variable neighborhood search method was proposed. The proposed methodology exhibits comparable efficiency to a commercial solver, demonstrating notably low execution time requirements. To further assess its performance, a comparative study was conducted on TSPLib instances. In comparison to various solution approaches documented in the open literature, encompassing both VNS-based and alternative methods, our proposed approach consistently yields highly competitive results within low execution times.

Remanufacturing businesses have difficulty in competing and expanding due to the unpredictable quality and performance of remanufactured products. It has turned into a problem area and a bottleneck for the growth of the remanufacturing sector. The control and management of remanufactured assembly quality (RAQ) is considered one of the key factors affecting the quality of remanufactured products. By reviewing existing literature, we have found that in remanufacturing assembly systems, the uncertainty of remanufacturing components, the volatility of remanufacturing assembly processes, the complexity of assembly control, and the diversity of assembly schemes are the main reasons for the difficulty in ensuring assembly quality. However, existing literature lacks research on data management, evolutionary mechanisms of RAQ, and multivariate control. To improve the stability of RAQ and improve the quality of remanufactured products, we propose the construction of an intelligent reasoning mode for precise RAQ control and a tailored, intelligent control method for individual components, providing support for the intelligent control of remanufactured assembly functional modules. This study provides a basis for implementing high-performance assembly technology for remanufactured products. Our findings should encourage the industry's modernization for remanufacturing.

This paper studies the Hierarchical Multi-Switch Multi-Echelon VRP (HMSME-VRP), a newly introduced VRP variant based on a real-world case involving High Capacity Vehicles (HCV). The problem originates from the policies of a distribution company in the Nordic countries where HCVs of up to 34.5 meters and up to 76 tons are allowed. The HMSME-VRP offer a new way to model distribution problems to cover large geographical areas without substantial costs in infrastructure. Furthermore, it adds complexity to the standard VRP and, as such, remains N P-hard and difficult to solve to optimality. Indeed, it has been demonstrated that only very small instances can be solved to optimality by a commercial solver. Thus, in order to handle instances of real-world size, we propose two General Variable Neighborhood Search (GVNS) procedures, the second of which is adaptive, utilizing an intelligent reordering mechanism. In order to evaluate the proposed procedures, 48 benchmark instances of various sizes and characteristics are generated and made publicly available, comprising of clustered, random, and semi-clustered customers. The computational results show that both GVNS procedures outperform the exact solver. Additionally, the adaptive version outperforms the conventional version based on both average and best solutions. Furthermore, we present a statistical analysis to verify the superiority of the adaptive version.

Multi-criteria sequence dependent setup times scheduling problems exist almost everywhere in real modern manufacturing world environments. Among them, Sequence Dependent Setup Times-Multi-Objective Hybrid Flowshop Scheduling Problem (SDST-MOHFSP) has been an intensifying attention of researchers and practitioners in the last three decades. In this paper, we briefly summarized and classified the current standing of SDST-MOHFSP. All publications are categorized regarding the solution methods, as well as the structure of the hybrid flowshop which helps researcher and practitioner to use/modify proper solution algorithm for solving their specific problem. Furthermore, based on the review of the existing papers, the need for future research is recognized. Accordingly, by recognizing the research gaps, a large number of recommendations for further study have been proposed.

Cloud computing and Internet of Things (IoT) are computing technologies that provide services to consumers and businesses, allowing organizations to become more agile and flexible. The potential business values that cloud consumers can achieve depend a lot on the quality of service in the provided cloud services. Therefore, ensuring the quality of service through service-level agreements (SLA) for such cloud-based services is crucial for both the service providers and service consumers. As SLA is critical for cloud deployments and wider adoption of cloud services, the management of SLA in cloud and IoT has thus become an important and essential issue. In this paper we provide an understanding of the current status and maturity level of SLA management in industrial IoT and academic efforts in this field. We also conduct a preliminary survey of current research on SLA management in order to identify open challenges and gaps that need to be addressed in future research directions. In particular, we investigate how to provide useful SLA management support adapted to the maturity level and current industrial practices, and shorten the gap between academia and industry.

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Brain activity can be seen as a time series, in particular, electroencephalogram (EEG) can measure it over a specific time period. In this regard, brain fingerprinting can be subjected to be learned by machine learning techniques. These models have been advocated as EEG-based biometric systems. In this study, we apply a recent Hybrid Focasting Model, which calibrates its if-then fuzzy rules with a hybrid GVNS metaheuristic algorithm, in order to learn those patterns. Due to the stochasticity of the VNS procedure, models with different characteristics can be generated for each individual. Some EEG recordings from 109 volunteers, measured using a 64-channels EEGs, with 160 HZ of sampling rate, are used as cases of study. Different forecasting models are calibrated with the GVNS and used for the classification purpose. New rules for classifying the individuals using forecasting

Estimation of distribution algorithms (EDAs) is a subset of evolutionary algorithms widely used in various optimization problems, known for their favorable results. Each generation of EDAs builds a probabilistic model to represent the most promising individuals, and the next generation is created by sampling from this model. The primary challenge in designing such algorithms lies in effectively constructing the probabilistic model. The mutual exclusivity constraint imposes an additional challenge for EDAs to approach permutation-based problems. In this study, we propose a new EDA called Position-Guided Sampling Estimation of Distribution Algorithm (PGS-EDA) specifically designed for permutation-based problems. Unlike conventional approaches, our algorithm focuses on the positions rather than the elements during the sampling phase. We evaluate the performance of our algorithm on the Permutation Flow-shop Scheduling Problem (PFSP). The experiments conducted on various sizes of Taillar...

Background The layout of an electronics circuit plays an important role in the design and usability of many products (Mihajlovic et al. 2007). In computers, communications, and many other systems, the flip-flops are fundamental building blocks. They are the important timing elements in digital circuits which have great impacts over power consumption and speed. The performance of Flip-Flop influence the performance of whole synchronous circuit,

In this study, effective and fast removal efficiency of carob powder (as an absorbent material from liquid phase) was investigated by focusing on the dangerous paint methylene blue mixture. The surface texture developed adsorbent was revealed to be porous by characterizing done thanks to scanning through electron microscope. Experiment parameters of pH, ultrasonic frequency, particle size, contact time, temperature and initial concentration of dissolved methylene blue dye were investigated accordingly. Thereafter, Box-Behnken design experiment was applied for adsorption experiments. Regression analysis findings demonstrated that the experimental data is good for to the non-linear model with correlation coefficients of correction value at 0.8899 and 0.9830. The maximum adsorption value was determined as around 256.44 mg/g thanks to the Electro-Search Algorithm, a recent Artificial Intelligence based intelligent optimization technique. Additionally, some alternative intelligent optimization algorithms were also used for determining optimum values. According to the results of the study, the carob bean can be used as an alternative adsorbent and the found optimum values can be employed for that purpose.

In this study, effective and fast removal efficiency of carob powder (as an absorbent material from liquid phase) was investigated by focusing on the dangerous paint methylene blue mixture. The surface texture developed adsorbent was revealed to be porous by characterizing done thanks to scanning through electron microscope. Experiment parameters of pH, ultrasonic frequency, particle size, contact time, temperature and initial concentration of dissolved methylene blue dye were investigated accordingly. Thereafter, Box-Behnken design experiment was applied for adsorption experiments. Regression analysis findings demonstrated that the experimental data is good for to the non-linear model with correlation coefficients of correction value at 0.8899 and 0.9830. The maximum adsorption value was determined as around 256.44 mg/g thanks to the Electro-Search Algorithm, a recent Artificial Intelligence based intelligent optimization technique. Additionally, some alternative intelligent optimization algorithms were also used for determining optimum values. According to the results of the study, the carob bean can be used as an alternative adsorbent and the found optimum values can be employed for that purpose.

Estimation of distribution algorithms (EDAs) is a subset of evolutionary algorithms widely used in various optimization problems, known for their favorable results. Each generation of EDAs builds a probabilistic model to represent the most promising individuals, and the next generation is created by sampling from this model. The primary challenge in designing such algorithms lies in effectively constructing the probabilistic model. The mutual exclusivity constraint imposes an additional challenge for EDAs to approach permutation-based problems. In this study, we propose a new EDA called Position-Guided Sampling Estimation of Distribution Algorithm (PGS-EDA) specifically designed for permutation-based problems. Unlike conventional approaches, our algorithm focuses on the positions rather than the elements during the sampling phase. We evaluate the performance of our algorithm on the Permutation Flow-shop Scheduling Problem (PFSP). The experiments conducted on various sizes of Taillar...

This special issue contains 15 papers submitted by the participants of the 8th International Conference on Variable Neighborhood Search (ICVNS 2021), which was held in Abu Dhabi, U.A.E., online due to COVID-19 restrictions, on March 22–24, 2021.

This work introduces the Fleet-size and Mix Pollution Location-Inventory-Routing Problem with Just-in-Time replenishment policy and Capacity Planning. This problem extends the strategic-level decisions of classic LIRP by considering capacity selection decisions and heterogeneous fleet composition. An MIP formulation of this new complex combinatorial optimization problem is proposed and small-sized problem instances are solved using the CPLEX solver. For the solution of more realistic-sized problem instances, a General Variable Neighborhood Search (GVNS)-based framework is adopted. Novel adaptive shaking methods are proposed as intelligent components of the developed GVNS algorithms to further improve their performance. To evaluate the proposed GVNS schemes, several problem instances are randomly generated by following specific instructions from the literature and adopting real vehicles' parameters. Comparisons between these solutions and the corresponding ones achieved by CPLEX are made. The computational results indicate the efficiency of the proposed GVNS-based algorithms, with the best GVNS scheme to produce 7% better solutions than CPLEX for small problems. Finally, the economic and environmental impacts of using either homogeneous or heterogeneous fleet of vehicles are examined.

With the increasing concern due to climate change caused by higher atmospheric concentration of CO2 and other greenhouse gases, reducing environmental impact is becoming more important for every part of society. Manufacturing is responsible for a significant portion of the energy/material consumption and environmental burden and has great potential for contributing to this effort by reducing its impact through green manufacturing. Green manufacturing presents opportunities across the manufacturing enterprise to increase efficient usage of energy and material resources. These opportunities include designing products to consume less materials and energy during manufacturing and use, incorporating more efficient manufacturing processes, streamlining and optimizing manufacturing schedules and plans, and circularizing products. The goal of this paper will be to provide a perspective on the opportunities that exist within green manufacturing through a review of pertinent topics and future directions.

In this study, effective and fast removal efficiency of carob powder (as an absorbent material from liquid phase) was investigated by focusing on the dangerous paint methylene blue mixture. The surface texture developed adsorbent was revealed to be porous by characterizing done thanks to scanning through electron microscope. Experiment parameters of pH, ultrasonic frequency, particle size, contact time, temperature and initial concentration of dissolved methylene blue dye were investigated accordingly. Thereafter, Box-Behnken design experiment was applied for adsorption experiments. Regression analysis findings demonstrated that the experimental data is good for to the non-linear model with correlation coefficients of correction value at 0.8899 and 0.9830. The maximum adsorption value was determined as around 256.44 mg/g thanks to the Electro-Search Algorithm, a recent Artificial Intelligence based intelligent optimization technique. Additionally, some alternative intelligent optimization algorithms were also used for determining optimum values. According to the results of the study, the carob bean can be used as an alternative adsorbent and the found optimum values can be employed for that purpose.

Traditionally vehicles act only as servers in transporting passengers and goods. With increasing sensor equipment in vehicles, including automated vehicles, there is a need to test algorithms that consider the dual role of vehicles as both servers and sensors. The paper formulates a sequential route selection problem as a shortest path problem with on-time arrival reliability under a multi-armed bandit setting, a type of reinforcement learning model. A decision-maker has to make a finite set of decisions sequentially on departure time and path between a fixed origin-destination pair such that on-time reliability is maximized while travel time is minimized. The upper confidence bound algorithm is extended to handle this problem. Several tests are conducted. First, simulated data successfully verifies the method, then a real-data scenario is constructed of a hotel shuttle service from midtown Manhattan in New York City providing hourly access to John F. Kennedy International Airport. ...

Thompson Sampling (TS) is a popular algorithm
used in multi-armed bandit problems. In the standard setting,
it assumes that the rewards associated with each arm are
stationary, which means that the reward distribution remains
fixed throughout the experiment. However, in many real-world
scenarios, the reward distribution can change over time, and
this is known as a non-stationary bandit problem. In this case,
the traditional TS algorithm may not perform well. To address
this issue, several extensions of the standard TS algorithm
have been proposed for non-stationary bandits. One such
extension is the Bayesian Online Changepoint Detection
(BOCD) algorithm. BOCD uses a Bayesian framework to model
the changes in reward distribution and adjust the exploration
and exploitation trade-off accordingly. The BOCD algorithm
maintains a posterior distribution over the possible locations
of the change points in the reward distribution. At each time
step, it uses this posterior to compute the probability that a
change point has occurred. If the probability of a change point
is high, the algorithm explores more to adapt to the new
reward distribution. Otherwise, it exploits more to maximize
its expected reward.

Brain activity can be seen as a time series, in particular, electroencephalogram (EEG) can measure it over a specific time period. In this regard, brain fingerprinting can be subjected to be learned by machine learning techniques. These models have been advocated as EEG-based biometric systems. In this study, we apply a recent Hybrid Focasting Model, which calibrates its if-then fuzzy rules with a hybrid GVNS metaheuristic algorithm, in order to learn those patterns. Due to the stochasticity of the VNS procedure, models with different characteristics can be generated for each individual. Some EEG recordings from 109 volunteers, measured using a 64-channels EEGs, with 160 HZ of sampling rate, are used as cases of study. Different forecasting models are calibrated with the GVNS and used for the classification purpose. New rules for classifying the individuals using forecasting

The physical limitations of CMOS technology triggered several research for finding an alternative technology. QCA is one of the emerging nanotechnologies which is gaining attention as a substitute of CMOS. The main potential of QCA is its ultra low power consumption, less area overhead, and high speed. Majority and inverter gates are the basic gates in QCA, which together works as a universal logic gate to implement any QCA circuit. This paper proposes an efficient methodology for optimal QCA circuit synthesis of arbitrary multi-output boolean functions. A multi-objective genetic algorithm based approach is used to reduce worst case delay and gate count of a QCA circuit. Different importance is given to worst case delay, no. of majority and no. of inverter gates. Several efficient techniques are used in order to achieve the optimal result and reduce the computational complexity furthermore additional methodologies are used to eliminate redundancies from the final solution. Comparison of the obtained results with the existing best techniques indicates, the proposed technique outperforms in terms of worst case delay and gate count.

Restless bandits model the exploration vs. exploitation trade-off in a changing (non-stationary) world. Restless bandits have been studied in both the context of continuously-changing (drifting) and change-point (sudden) restlessness. In this work, we study specific classes of drifting restless bandits selected for their relevance to modelling an online website optimization process. The contribution in this work is a simple, feasible weighted least squares technique capable of utilizing contextual arm parameters while considering the parameter space drifting non-stationary within reasonable bounds. We produce a reference implementation, then evaluate and compare its performance in several different true world states, finding experimentally that performance is robust to time drifting factors similar to those seen in many real world cases.

In this study, effective and fast removal efficiency of carob powder (as an absorbent material from liquid phase) was investigated by focusing on the dangerous paint methylene blue mixture. The surface texture developed adsorbent was revealed to be porous by characterizing done thanks to scanning through electron microscope. Experiment parameters of pH, ultrasonic frequency, particle size, contact time, temperature and initial concentration of dissolved methylene blue dye were investigated accordingly. Thereafter, Box-Behnken design experiment was applied for adsorption experiments. Regression analysis findings demonstrated that the experimental data is good for to the non-linear model with correlation coefficients of correction value at 0.8899 and 0.9830. The maximum adsorption value was determined as around 256.44 mg/g thanks to the Electro-Search Algorithm, a recent Artificial Intelligence based intelligent optimization technique. Additionally, some alternative intelligent optimization algorithms were also used for determining optimum values. According to the results of the study, the carob bean can be used as an alternative adsorbent and the found optimum values can be employed for that purpose.

Extracting an optimal set of parameter values for a MOS device is a complex problem. Since the existence of local minima in the solution space, the traditional methods of parameter extraction rely on gradient techniques may not produce near optimal solutions. Genetic algorithms are well suited for finding near optimal solutions in irregular parameter spaces. In this study*, BSIM3V3 Model C35 process fabricated chips were used, A genetic algorithm have been applied to the problem of device model parameter extraction and are able to be produced models of superior accuracy in much less time and with less reliance on human expertise. The threshold voltage related model parameters and mobility related model parameters were found for BSIM3V3. Extracted parameter values reproduce I-V characteristics.

In order to improve the efficiency of selective disassembly sequence planning for repair, a selective disassembly sequence planning approach based on ant colony algorithm is proposed. Hybrid Precedence Graph(HPG) is constructed to describe the geometric constraints among constituting components and components' stability during disassembly. Then in order to find the target component accurately and quickly, disassembly set for the target is obtained based on HPG. Dynamical candidates set strategy based on precedence relations is adopted to constrain the searching space, and a selective Disassembly Sequence Planning(DSP) approach based on HPG is proposed. Finally, the validity and feasibility of the proposed approach is verified by a study case. Taking geometric constraint and components' stability into count, this approach could be more practical.

Supply chain sustainability refers to the optimal balance between economic, environmental, and societal criteria during decision-making processes. This chapter presents a comprehensive review of recent research contributions on sustainable supply chain optimization. The selected articles have been classified according to several criteria. More specifically, an initial classification has been performed based on the sustainability concerns taken under consideration in each study. Next, a classification has been provided based on decision levels, and finally, the reviewed works have been categorized according to the available solution methods. The chapter is closing with some main insights and suggestions for future research.

Disassembly line is crucial in recovering products in large quantities. It has recently gained a lot of attention due to its role in efficiently recovering valuable materials, components, and subassemblies. Disassembly involves many challenges that further complicate the process. Recent research reported in the literature has focused on balancing disassembly lines. That means the time required for the work content at each station is balanced. However, the inventory generated at various stations may not be balanced. Additional inventory problems arise because of the disparity between the demand of certain components and the actual yield from the products. In this paper, we discuss these and other inventories issues to provide a better understanding of complications resulting from this and provide an approach to overcome them. A numerical example is considered to illustrate the approach.

Reverse logistics is a critical topic that has captured the attention of government, private entities and researchers in recent years. This increase in the concern was driven by current set of government regulations, increase of public awareness, and the attractive economic opportunities. As a result, many corporations have started to comprehend the importance of the recovery process and are taking serious steps in restructuring their supply chain processes to meet the new regulations such as limitations on waste disposal and recycling requirements [1]. Because of the unique problems associated with reverse supply chain and the complex nature of the reverse logistics activities, numerous studies have been carried out in this field. One of those crucial areas is inventory management of end-of-life (EOL) products. Previously, we have assumed deterministic data for demand, supply, and line yields when modeling the inventory problem along the disassembly line. In this paper, we model un...

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End-of-life disassembly has developed into a major research area within the sustainability paradigm, resulting in the emergence of several algorithms and structures proposing heuristics techniques such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Neural Networks (NN). The performance of the proposed methodologies heavily depends on the accuracy and the flexibility of the algorithms to accommodate several factors such as preserving the precedence relationships during disassembly while obtaining nearoptimal and optimal solutions. This paper improves a previously proposed Genetic Algorithm model for disassembly sequencing by utilizing a faster metaheuristic algorithm, Tabu search, to obtain the optimal solution. The objectives of the proposed algorithm are to minimize (1) the traveled distance by the robotic

Sensors embedded into products during the production process have a potential to decrease disassembly yield uncertainty by detecting non-functional or missing components prior to the actual disassembly process. The aim of this study is the quantitative evaluation of the impact of sensor embedded products (SEPs) on the performance of an appliance disassembly line. First, separate design of experiments studies based on orthogonal arrays are performed for the cases with and without SEPs. Discrete event simulation models of both cases were developed to calculate various performance measures under different experimental conditions. Then, the results of pair-wise t-tests comparing the two cases, based on different performance measures, are presented. The results show the superiority of SEPs over conventional products for all performance measures considered in the study.

A way to implement the pull system in a disassembly line is to use a multi-kanban model. The model employs several types of kanbans attached to both components and subassemblies. The heart of the system lies in the kanban routing mechanism which allows routing of kanbans in multi-directions based on real time conditions. This mechanism creates minimum amount of residual inventory while satisfying varying demand levels. It also helps regulate the requests for subassembly from upstream workstations when a breakdown occurs at a workstation. This reduces blockage and starvation of subassemblies at workstations other than the broken workstation. In this paper, we discuss the difficulties involved in utilizing the multi-kanban mechanism. We thoroughly investigate several scenarios of the disassembly line setting including a scenario with common products, a scenario with component discriminating demand, a scenario in the presence of products with multiple precedence relationships, and a scenario with workstation breakdowns. These scenarios represent various disassembly environments that a facility may face when dealing with the disassembly of both single and multiple products on a single line. In each scenario, we examine effectiveness of the multi-kanban model using three performance measures, viz., the inventory level, the level of satisfied demand, and the customer waiting time. We compare these results with the ones generated from the same line that employs a traditional push system. Using simulation, we demonstrate that the overall performance of the disassembly line using multi-kanban mechanism outperforms the disassembly line with the traditional push system.

Product recovery is a new trend that many manufacturers practice to minimize the fast depletion of virgin resources and to realize economical benefits from recovering end-of-life (EOL) products. However, the practice of recovering components and materials is challenging as it often requires disassembly. There are many distinctive complications associated with the disassembly process. One of the complications stems from the disassembly line balancing problem (DLBP). DLBP has recently been actively researched in the literature and several heuristic models have been introduced to provide near optimal work contents at each workstation of the disassembly line. However, due to the disparity between demands for parts and their yields, there are many inventory problems that arise during the disassembly line balancing process. In this paper, we identify the issue of unbalanced inventories generated at various workstations of a disassembly line and discuss how to overcome this. A case example involving a personal computer (PC) is considered for discussion. In order to provide a full analysis of the problem, measures of performances are defined. Measures of performances reflect the state of the system and the ability to meet the demand while maintaining consistent flow of parts. We also discuss and compare various issues associated with the assembly systems and the disassembly systems. While it is clear that the inventory issues surrounding the disassembly line offer a new challenge, the understating that we have gained from solving the traditional inventory problems, nevertheless, provide helpful insights in overcoming this new challenge.

This paper focuses on the disassembly-to-order (DTO) problem. End-of-life (EOL) products are purchased from a number of suppliers in order to be disassembled into individual components to satisfy the demand for specified numbers of components. However, there are a lot of uncertainties that complicates the process. Previous work in the literature solved the DTO problem under multiple uncertainties and a single objective. Other models solved the multicriteria DTO problem with a single uncertainty. This paper develops a multi-criteria DTO model that takes into consideration multiple system uncertainties and solves it using Linear Physical Programming (LPP). The main objective was to find the best combination of take-back EOL products to be purchased from every supplier that would satisfy the demand and achieve the aspiration levels of multiple goals. An example is considered to illustrate the model approach.

Timely and cost-effective acquisition of spare parts at post product life cycle (PPLC) is a challenging task due to limited supply of new spare parts. The most commonly used solution to this problem is the placement of a final order in the beginning of PPLC. Another alternative is the use of disassembled parts from End of Life (EOL) products. In this study, we consider both alternatives at the same time. First, a detailed discrete event simulation (DES) model of a disassembly line is developed. Then, a Genetic Algorithm (GA) is integrated with the DES model for the optimization of Final Order Quantities (FOQs) for critical spare parts.

Finding the best sequence of local search operators that yields the optimal performance of Variable Neighborhood Search is an important open research question in the field of metaheuristics. This paper proposes a Reinforcement Learning method to address this question. We introduce a new hyperheuristic scheme, termed Bandit VNS, inspired by the Multi-armed Bandit, a particular type of a single state reinforcement learning problem. In Bandit VNS, we utilize the General Variable Neighborhood Search metaheuristic and enhance it by a hyperheuristic strategy. We examine several variations of the Upper Confidence Bound algorithm to create a reliable strategy for adaptive neighborhood selection. Furthermore, we utilize Adaptive Windowing, a state of the art algorithm to estimate and detect changes in the data stream. Bandit VNS is designed for effective parallelization and encourages cooperation between agents to produce the best solution quality. We demonstrate this concept's advantages in accuracy and speed by extensive experimentation using the Capacitated Vehicle Routing Problem. We compare the novel scheme's performance against the conventional General Variable Neighborhood Search metaheuristic in terms of the CPU time and solution quality. The Bandit VNS method shows excellent results and reaches significantly higher performance metrics when applied to well-known benchmark instances. Our experiments show that, our approach achieves an improvement of more than 25% in solution quality when compared to the General Variable Neighborhood Search method using standard library instances of medium and large size.

The physical limitations of CMOS technology triggered several research for finding an alternative technology. QCA is one of the emerging nanotechnologies which is gaining attention as a substitute of CMOS. The main potential of QCA is its ultra low power consumption, less area overhead, and high speed. Majority and inverter gates are the basic gates in QCA, which together works as a universal logic gate to implement any QCA circuit. This paper proposes an efficient methodology for optimal QCA circuit synthesis of arbitrary multi-output boolean functions. A multi-objective genetic algorithm based approach is used to reduce worst case delay and gate count of a QCA circuit. Different importance is given to worst case delay, no. of majority and no. of inverter gates. Several efficient techniques are used in order to achieve the optimal result and reduce the computational complexity furthermore additional methodologies are used to eliminate redundancies from the final solution. Comparison of the obtained results with the existing best techniques indicates, the proposed technique outperforms in terms of worst case delay and gate count.

This work introduces the Fleet-size and Mix Pollution Location-Inventory-Routing Problem with Just-in-Time replenishment policy and Capacity Planning. This problem extends the strategic-level decisions of classic LIRP by considering capacity selection decisions and heterogeneous fleet composition. An MIP formulation of this new complex combinatorial optimization problem is proposed and small-sized problem instances are solved using the CPLEX solver. For the solution of more realistic-sized problem instances, a General Variable Neighborhood Search (GVNS)-based framework is adopted. Novel adaptive shaking methods are proposed as intelligent components of the developed GVNS algorithms to further improve their performance. To evaluate the proposed GVNS schemes, several problem instances are randomly generated by following specific instructions from the literature and adopting real vehicles' parameters. Comparisons between these solutions and the corresponding ones achieved by CPLEX are made. The computational results indicate the efficiency of the proposed GVNS-based algorithms, with the best GVNS scheme to produce 7% better solutions than CPLEX for small problems. Finally, the economic and environmental impacts of using either homogeneous or heterogeneous fleet of vehicles are examined.

This study proposes the use of cradle-to-cradle production programming for waste furniture and aims to achieve optimal efficiency by reusing waste furniture and maximizing the corresponding benefits so that the furniture industry is in line with cradle-to-cradle manufacturing. This study is increasingly important to manage products through a comprehensive green reverse logistics system that consists of three procedures, i.e., recycling, reuse, and remanufacturing of waste items. This study proposes a mathematical formula to establish a cradle-to-cradle production programming model for waste furniture. The fuzzy set theory is used to define the attributes that reflect production and market demands such as degree of damage, technical feasibility, market demands, environmental protection laws and regulations, and environmental performance. In addition, particle swarm optimization is adopted to ascertain the optimal profit from waste furniture sales. Through situational simulations and ...

In the reverse supply chain inventory theory, inventory models are concerned with the demand of reusable parts, stock replenishment, ordering cycle, delivery lead time, number of disassembled products, ordering costs. The particularity of these models consists in the occurrence of high uncertainties of the quantity and quality of the returned products and resulting parts. To overcome the problem, an inventory model that incorporates decision variables at proactive and reactive levels is derived and discussed in this paper.

D Flip-Flop as a digital circuit can be used as a timing element in many sophisticated circuits. Therefore the optimum performance with the lowest power consumption and acceptable delay time will be critical issue in electronics circuits. The newly proposed Dual-Edge Triggered Static D Flip-Flop circuit layout is defined as a multi-objective optimization problem. For this, an optimum fuzzy inference system with fuzzy rules is proposed to enhance the performance and convergence of non-dominated sorting Genetic Algorithm-II by adaptive control of the exploration and exploitation parameters. By using proposed Fuzzy NSGA-II algorithm, the more optimum values for MOSFET channel widths and power supply are discovered in search space than ordinary NSGA types. What is more, the design parameters involving NMOS and PMOS channel widths and power supply voltage and the performance parameters including average power consumption and propagation delay time are linked. To do this, the required mat...

This work addresses a novel General Variable Neighborhood Search (GVNS) solution method, which integrates intelligent adaptive mechanisms to re-order the search operators during the intensification and diversification phases, in an effort to enhance its overall efficiency. To evaluate the performance of the new GVNS scheme, asymmetric and symmetric instances of the classic Traveling Salesman Problem (TSP) from the TSPLib were solved. The obtained results of the Double-Adaptive GVNS were compared with those achieved by two single-adaptive GVNS, which use an adaptive mechanism either for the intensification or the diversification phase and with a conventional GVNS. For a fair comparison, all GVNS schemes were structured using the same local search and shaking operators. Moreover, the novel GVNS algorithm was compared with some recent solution methods for the TSP, found in the open literature. The comparative studies revealed the high efficiency of the novel VNS scheme and underlined the significant impact of intelligent mechanisms on the performance of classic metaheuristic frameworks.