Search For Best Solution Research Articles

Cumulative Capacitated Colored Traveling Salesman Problem.

A colored traveling salesman problem (CTSP) is a generalization of the well-known multiple traveling salesman problem, which introduces colors to distinguish the accessibility of its cities to salesmen. This work proposes a city/customer-centric model called cumulative capacitated CTSP (C2-CTSP) to tackle some practical problems with fast response requirements. Its hypergraph and mathematical programming formulations are developed for the first time. A general variable neighborhood search (GVNS) metaheuristic is designed to solve it. Specifically, greedy backtracking is proposed to initialize a solution taking into account the cumulative cost and two constraints including colors and capacities. Next, 2-swap, reinsertion, and double-bridge operations are randomly selected and carried out to execute the perturbation. Moreover, neighborhood-list-2-opt, relocation move, and generalized partition crossover are organized as variable neighborhood descent to constitute the local search for better solutions. Extensive experiments are conducted to compare the proposed GVNS with four genetic algorithms, two hybrid ant colony systems, two variable neighborhood search methods, and a perturb-based local search in 20 regular and random cases. The statistical results demonstrate that GVNS is superior to all competitors tuned by irace package in terms of both search ability and convergence rate. In addition, the study of six GVNS variants lacking different operators validates the significant role of each corresponding operator in GVNS's outstanding performance.

Cybernetic Model Design for the Qualification of Pharmaceutical Facilities

In this paper, an integrated cybernetic model for managing qualification activities when commissioning pharmaceutical facilities is created, focusing on defining critical factors that provide all the prerequisites for the start of the production process. An eight-year research and work on complex projects in the pharmaceutical industry is integrated into a scientific research endeavor focused on the qualification of pharmaceutical facilities. The newly designed cybernetic model for the qualification of pharmaceutical facilities is flexible and adaptive and has the most adequate elements so far recognized in practice and enables the qualification of smart facilities, in accordance with the concept of Pharma 4.0. Additionally, it meets the requirements of the regulatory authorities; therefore, it constantly initiates the search for better solutions and process improvements. Moreover, it is universal and, thus, applicable to all reconstructions in the pharmaceutical industry. The application of the designed model has been implemented in practice and has shown outstanding results, as it combines diversity and sustainability in project management. Also, the model focuses on indicating aspects that include risk management, scientific approach, experimental testing, numerical simulations, as well as the possibility of optimization and energy saving.

A maximum entropy deep reinforcement learning method for sequential well placement optimization using multi-discrete action spaces

Well placement optimization is a crucial method to solve the planar conflicts in reservoir development, mainly to determine the optimal well locations and drilling sequence to maximize the economic benefits of reservoir development. However, the current well placement optimization methods face the problems of high-dimensional discretization of optimization variables and lack of effective incentives for policy exploration, which make it challenging to improve the global optimization ability (the ability to jump out of locally optimal solutions in time and continuously search for better solutions in the whole optimization process) and real-time adjustability of the well placement optimization methods under limited numerical simulation times. In this paper, we propose a new sequential well placement optimization method, based on the Discrete Soft Actor-Critic Algorithm (DSAC), which incorporates the maximum entropy mechanism to formulate well placement and drilling sequencing schemes more efficiently and maximize the net present value (NPV) over the entire life cycle of reservoir development. Specifically, the method models the well placement optimization problem as a Markov Decision Process (MDP) and achieves sequential well placement optimization by training a Deep Reinforcement Learning (DRL) agent that maps reservoir states to a stochastic policy of well placement variables as well as evaluates the value function of the current policy. The DRL agent can determine the optimal infill well location in real-time based on the reservoir state at different times during the development process, thus obtaining the optimal drilling sequence. The proposed method in this paper has two innovations. First, by reconstructing the large-scale discrete action space of well placement optimization variables into multi-discrete action spaces, and with the maximum entropy mechanism, policy exploration is encouraged to improve the global optimization capability. Second, the trained policy can swiftly adapt the subsequent well placement scheme for a specific state of the target reservoir without the requirement to initiate training from scratch, which can realize the offline application of the trained policy and has better real-time adjustability. To verify the effectiveness of the proposed method, it is tested in 2D and 3D reservoir models. The results show that DSAC not only outperforms the gradient-based optimization method, classical evolutionary algorithms, and existing reinforcement learning proximal policy optimization (PPO) method in terms of global optimization ability but also shows better real-time adjustability of the trained policy when applied offline.

A proposal towards a VNS-based decision support tool for large scale location-covering-type problems

We describe a number of typical features, constraints and objectives, frequently appearing in spatial system-design of maximum covering-type, such as emergency response systems. We then indicate some heuristic local search methods topped by a Variable Neighbourhood Search to construct and search for good solutions. These ideas were intended to form the basis for a possibly multi-objective spatial decision support system.

Research On the No-Wait Flow Shop Scheduling Problem Based on Simulated Annealing Algorithm.

This paper focuses on the No-Wait Flow Shop Scheduling Problem and conducts research based on an improved Simulated Annealing Algorithm. The No-Wait Flow Shop Scheduling Problem is of great significance in practical production, but its solution complexity is high, requiring the search for appropriate optimization methods. In this study, a solution based on an improved Simulated Annealing Algorithm is proposed. Simulated Annealing Algorithm, as a global optimization method, introduces random perturbations to the current solution and accepts or rejects the new solution based on Metropolis criterion to escape local minima and search for better solutions. In this paper, the termination condition of the improved Simulated Annealing Algorithm is enhanced. Specifically, if no better solution is obtained after a certain number of consecutive temperature calculations or when the maximum number of outer loop iterations is reached, the algorithm terminates and outputs the result. This improvement aims to increase efficiency and reduce unnecessary computations.By comparing the results with other heuristic algorithms, it is found that the solution based on the improved Simulated Annealing Algorithm can effectively provide better scheduling solutions for the No-Wait Flow Shop Scheduling Problem, thereby improving production efficiency, reducing costs, and optimizing resource utilization.

Dynamic Weight and Mapping Mutation Operation-Based Salp Swarm Algorithm for Global Optimization

The salp swarm algorithm imitates the swarm behavior of salps during navigation and hunting that has been proven the superiority of search for best solution. However, although it has sufficient global search ability, it is still worth paying attention to problems of falling into local optima and lower convergence accuracy. This paper proposes some improvements to the salp swarm algorithm that are based on a nonlinear dynamic weight and the mapping mutation operation. Firstly, the nonlinear dynamic weight is helpful for further optimizing the transition from exploration to exploitation and alleviating the local optima stagnation phenomena. Secondly, utilizing a mapping mutation operation can increase the diversity of followers in algorithm, to avoid getting trapped into the local optima during the search and provide a better optimal solution. The proposed algorithm is characterized by a stronger global optimization capability and high convergence accuracy. Finally, to confirm the effectiveness of the proposed algorithm, comparative experiments based on other well-known swarm-based algorithms and each improvement for the original algorithm are conducted. The quantitative results and convergence curves among several algorithms demonstrate that the enhanced algorithm with the nonlinear dynamic weight and mapping mutation operation can outperform the original algorithm.

Innovative Fabrication of Hollow Microneedle Arrays Enabling Blood Sampling with a Self-Powered Microfluidic Patch

Microneedles are gaining a lot of attention in the context of sampling cutaneous biofluids such as capillary blood. Their minimal invasiveness and user-friendliness make them a prominent substitute for venous puncture or finger-pricking. Although the latter is suitable for self-sampling, the impracticality of manual handling and the difficulty of obtaining enough qualitative sample is driving the search for better solutions. In this context, hollow microneedle arrays (HMNAs) are particularly interesting for completely integrating sample-to-answer solutions as they create a duct between the skin and the sampling device. However, the fabrication of sharp-tipped HMNAs with a high aspect ratio (AR) is challenging, especially since a length of ≥1500 μm is desired to reach the blood capillaries. In this paper, we first described a novel two-step fabrication protocol for HMNAs in stainless steel by percussion laser drilling and subsequent micro-milling. The HMNAs were then integrated into a self-powered microfluidic sampling patch, containing a capillary pump which was optimized to generate negative pressure differences up to 40.9 ± 1.8 kPa. The sampling patch was validated in vitro, showing the feasibility of sampling 40 μL of liquid. It is anticipated that our proof-of-concept is a starting point for more sophisticated all-in-one biofluid sampling and point-of-care testing systems.

A Review – Some Approaches to Combat Salt Stress in Wheat Crop

Wheat constitutes a central position for ensuring food and nutritional security; however, rapidly rising soil and water salinity pose a serious threat to its production globally. Salinity stress is a universal dilemma that is happening due to climate change. It affects hectares of arable land. Main focus regarding improving salinity tolerance in plants has been given to Na+ exclusion/ Na+ compartmentalization and enhanced ROS system. Besides this, ameliorative activity of phytohormones, nutrients, amino acids and organic osmolytes has also been widely studied. Exploring traits in wild genotype aids search for better solutions. Based upon phenotype screening, novel genes involving salinity tolerance will be easily identified. Moreover, selected mutants can be used to validate the functions of salt genes. Wheat plants utilize a range of physiological biochemical and molecular mechanisms to adapt under salinity stress at the cell, tissue as well as whole plant levels to optimize the growth, and yield by off-setting the adverse effects of saline environment. Recently, various adaptation and management strategies have been developed to reduce the deleterious effects of salinity stress to maximize the production and nutritional quality of wheat. Thereby, this review highlights effects of salt tolerance, physiological mechanisms behind salt tolerance and transgenic wheat that are potential indicators of salinity stress tolerance.

A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm

Hyperspectral image (HSI) analysis has become one of the most active topics in the field of remote sensing, which could provide powerful assistance for sensing a larger-scale environment. Nevertheless, a large number of high-correlation and redundancy bands in HSI data provide a massive challenge for image recognition and classification. Hybrid Rice Optimization (HRO) is a novel meta-heuristic, and its population is approximately divided into three groups with an equal number of individuals according to self-equilibrium and symmetry, which has been successfully applied in band selection. However, there are some limitations of primary HRO with respect to the local search for better solutions and this may result in overlooking a promising solution. Therefore, a modified HRO (MHRO) based on an opposition-based-learning (OBL) strategy and differential evolution (DE) operators is proposed for band selection in this paper. Firstly, OBL is adopted in the initialization phase of MHRO to increase the diversity of the population. Then, the exploitation ability is enhanced by embedding DE operators into the search process at each iteration. Experimental results verify that the proposed method shows superiority in both the classification accuracy and selected number of bands compared to other algorithms involved in the paper.

Multi-objective optimization algorithm assisted by metamodels with applications in aerodynamics problems

The optimization algorithms when used in real engineering problems involving high fidelity numeric simulations often require a large number of numerical assessments to achieve a good approximation of the optimal solution. The computational time needed to find this solution may be unfeasible in these problems. The metamodel assisted algorithms have been used to accelerate optimization problems using different strategies to find the optimum. For single objective problems, CORS (Constrained Optimization using Response Surfaces) was developed with basis on the iterative generation of distance constraints to explore and exploit the design space, such that convergence to a global optimum is mathematically guaranteed. In this paper, a multi-objective optimization strategy based on metamodel construction using radial basis functions, MO-CORS, is presented. It takes the advantage of the CORS strategy in multi-objective problems to perform the effective detection of the non-dominated set extreme points, for the subsequent filling of empty spaces between these extremes. Metamodels are used strategically in an iterative sampling process to guide the search for better solutions and to determine where in the domain the next objective function evaluations should be performed. The evaluations carried out on the expensive functions also allow improving metamodel construction in the promising regions at each iteration. Results obtained in test problems and in aerodynamic problems applications show that the developed algorithm is an effective tool to accelerate single and multi-objective optimization problems and that the use of the CORS strategy inside MO-CORS was relevant in helping it to attain solutions not found by other optimization algorithms.

Generic Tabu Search

The Multiple Sequence Alignment problem is considered to be an NP-Hard problem, requiring initially a specific encoding schema and design, as for any other of its siblings, to implement and run any of the main categories of heuristic. This paper intends to discuss our proposed generic implementation of the Tabu Search algorithm, a heuristic procedure proposed by Fred Glover to solve discrete combinatorial optimization problems. In this research, we try to coordinate and synchronize different designs/implementations discussed in many literatures, with some of the references mentioned in this paper. The basic idea is to avoid that the search for best solutions stops when a local optimum is found, by maintaining a list of non-acceptable or forbidden (taboo) solutions/costs, called Tabu list or Short-Term Memory (STM). In our algorithm, we attempt to add some executions tracing functionalities in order to help later analysis for initial parameters tuning. On the other hand, we propose to include the concept of a list called Long-Term Memory (LTM), so that some of the best solutions found so far can be saved, for search diversification.

Organisation’s communication activity and the level of process maturity – an empirical approach

Purpose: The authors aimed to identify the characteristics of a process organisation, and to determine the organisation's activity in various areas, including communication. Design/methodology/approach: The research methods used in the article was an online survey. Findings: The conducted research is the first step in assessing the relations that occur between the level of process maturity of an organisation and the level of communication activity. In the surveyed organisations there is a high awareness of the need and importance of processes. Research limitations/implications: The study of corporate image and the study of reputation and prestige in the area of internal communication activity. A group of about 64% of organisations declared such activity, although to varying degrees. About 33% of respondents declared these activities as strategic. It should be assumed that they are the result of image activities, activities focused on reputation and prestige. It can be assumed that these are activities of public relations or corporate communication. Admittedly, further research reveals the lack of certain processes, which are important and determine the strategic character of these activities (e.g., lack of CSR, lack of employer branding). Practical implications: Achieving more and more process maturity through process improvement should be treated as a cyclical activity, which is oriented towards the continuous search for better solutions. Organisations that intend to improve their processes, and thus gain greater process maturity, should continually take care to shape open communication, both within the company and in the external environment. Originality/value: Strategic achievement of an integrated state of internal and external communication is based on constructive dialogue and is determined by the number of different processes, the level of communication and the level of their process maturity.

Optimum Synthesis of Four-Bar Mechanism by Using Relative Angle Method: A Comparative Performance Study

In this paper, the dimensional synthesis of the four-bar mechanism for path generation is formulated using the relative angle motion analysis and the link geometry parameterization with Cartesian coordinates. The Optimum Dimensional Synthesis using Relative Angles and the Cartesian space link Parameterization (ODSRA+CP) is stated as an optimization problem, and the solution is given by the differential evolution variant DE/best/1/bin. This study investigates the behavior and performance of such formulation and performs a comparative empirical study with the well-known synthesis method based on vector-loop equation motion analysis where different modifications in the metaheuristic algorithms are established in the literature to improve the obtained solution. Five study cases of dimensional synthesis for path generation with and without prescribed timing are solved and analyzed. The empirical results show that the way of stating the optimization problem in the ODSRA+CP significantly improves the search process for finding promising solutions in the optimizer without requiring algorithm modifications. Therefore, it is confirmed that the optimizer search process in the optimal synthesis of mechanisms is not the only way of improving the obtained solutions, but also the optimization problem formulation has a significant influence on the search for better solutions.

A branch‐and‐dive heuristic for single vehicle snow removal

AbstractThis paper deals with planning of a tour for a vehicle to clear a certain set of streets in a city of snow. Our previous results on the problem contain a heuristic based on reformulation to an asymmetric traveling salesman problem (ATSP) which yields feasible solutions and upper bounds, and a relaxation of a MIP model for obtaining lower bounds. The goal now is to try to improve the solutions and bounds. In this paper we describe a branch‐and‐dive heuristic which is based on branch‐and‐bound principles. We discuss how branching can be done so that the fixations can be utilized in both the relaxation and the ATSP model, and how the search for better solutions can be done. The heuristic has been implemented and applied to real life city networks. The method is shown to outperform two other heuristics for the ATSP with precedence constraints.

A parallel multi-objective genetic algorithm with learning based mutation for railway scheduling

Railway system is a reliable and efficiency major public transportation. It is supported by many countries since it has a less environmental effect compared to another type of transportation. As the railway networks have become larger and more complex with increasing passenger demand, both aspects from the passenger satisfaction and operational cost need to be satisfied. This paper proposes a Parallel Multi-objective Evolutionary Algorithm with Hybrid Sampling Strategy and learning-based mutation to solve the railway train scheduling problem. Learning techniques have been coupled with a multi-objective genetic algorithm to guide the search for better solutions. In this paper, we incorporate a learning-based algorithm into a mutation process. The evaluation process is divided into sub-process and calculated by a parallel computational unit using GPU CUDA framework. Two sets of numerical experiments based on a small-scale case of Thailand ARL transit line and a larger case of BTS transit network are implemented to verify the effectiveness of the proposed approaches. The experimental results show the effectiveness of the proposed algorithm comparing to sequential CPU computational and two classical multi-objective evolutionary algorithms. With the same number of operating trains, the proposed algorithm can obtain schedule with less average waiting time and the time used for computational is significantly reduced.

Hybrid Micro Genetic Multi-Population Algorithm With Collective Communication for the Job Shop Scheduling Problem

This paper presents a hybrid genetic algorithm with collective communication (HGACC) using distributed processing for the job shop scheduling problem. The genetic algorithm starts with a set of elite micro-populations created randomly, where the fitness of these individuals does not exceed a tuned upper bound in the makespan value. The computational processes distribute the micro-populations collectively. In the micro-populations, each individual’s search for good solutions is directed toward the solution space of the fittest individual, identified by an approximation of genetic traits. In each generation of the genetic algorithm, the best individual from each micro-population migrates to another micro-population to maintain diversity in populations. Changes in the genetic sequence are applied to each individual by the simulated annealing algorithm (iterative mutation). In this paper, the results obtained show that the genetic algorithm achieves excellent results, as compared to other genetic algorithms. It is also better than other non-genetic meta heuristics or competes with them.

Prediction of Status Particulate Matter 2.5 Using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSO

Air pollution is the entry or inclusion of living things, energy substances, and other components into the air. Moreover, Air pollution is the presence of one or several contaminants in the outside atmospheric air such as dust, foam, gas, fog, smoke or steam in large quantities with various properties and time intervals of the contaminants in the air resulting in disturbances to the lives of humans, plants or animals. One of the parameters measured in determining air quality is PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> . However, PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> has a higher probability of being able to enter the lower respiratory tract because small particle diameters can potentially pass through the lower respiratory tract. In this paper, we will get two different insight. First, the probability of status change using Markov chain and second, forecasting by using VAR-NN-PSO. More details we classify by three classifications no risk (1-30), medium risk (30-48), and moderate (>49) in Pingtung and Chaozhou. This data is starting from January 2014 to May 2019 and it can be modeled with the Markov chain. At the same time, we perform Hybrid VAR-NN-PSO to forecast PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> in Pingtung and Chaozhou. In this optimization, the search for best solutions is carried out by a population consisting of several particles. Based on the results of the discussion, opportunities for the transition from monthly status change are obtained continuous stochastic time with a stationary probability distribution. Regarding the VAR-NN-PSO, we obtained the mean absolute percentage error (MAPE) 3.57% for PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> data in Pingtung and 4.87% for PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> data in Chaozhou, respectively. This model can be predicted to forecasting 180 days ahead. Besides, the population in PSO has generated randomly with the smallest value and the high value the accuracy.

Task allocation based modified bacterial foraging algorithm for maximising reliability of a heterogeneous distributed system

modified bacterial foraging algorithm for allocation and scheduling tasks on processors and links with high reliability is proposed (MBFA). MBFA ensure high reliability without redundancy of processors or links by using an efficient and simple structure. MBFA is improved by adding an efficient model to deal with the weak solution (i.e., allocation with low reliability). Also a simulated annealing technique is integrated into MBFA in order to strengthen the search of best solution. The efficiency and effectiveness of MBFA are tested using random generated problems. MBFA is tested over the published approaches HPSO and GAA using density parameter (DS), number of nodes (N) and the communication to computation ratio parameter (CCR). The results show the superiority of MBFA over the published approaches in all test cases.

Epilogue

This dissertation investigated regional-design practices that evolved in the context of Dutch national planning frameworks in the period between the 1980s and the 2010s. Questions concerned whether and how practices influenced these planning frameworks, and the extent to which practices were influenced by shifts that frameworks underwent over time. The main aim of the dissertation was to develop an enhanced understanding of interrelations between regional design and spatial planning, so the performances of regional design in the spatial-planning realm can be better anticipated. The personal motivation of this dissertation lies in my professional experience as a regional designer. I am trained as an architect and gained experience as an urban designer during my early professional carrier. In 2005, I became Chief Designer at South Wing Studio (Atelier Zuidvleugel), a publicly funded think tank which was asked to explore regionalisation and planning in the Southern part of the Dutch Randstad region by means of design. I was used to feeling doubt while designing buildings, public spaces and neighbourhoods. In the meantime, I learned from design theory that it is natural for a designer to doubt. The built environment is an intricate, complex system; there are always a multitude of alternative paths to use during a search for good solutions. However, doubt was accelerated in regional-design practice, due to its concern about large-scale areas and costly public works, and its position in a multi-actor and highly political setting. Every design step accomplished became intensely debated. Every step generated surprises, unforeseen reactions; encouraging at times, discouraging at others.

Multilevel Shared Management for Policy Design and Problem Solving. Case of an Idle Group – Released from Estonian Prisons

The focus of the paper is on the application of the subordinates involving governance (SIG) model and its design in a certain context through problem identification method. The proposed social involvement model is combining shared governance with independent actors and traditionally subjected (subordinate) parties as counterparts. As an example of the policy design mechanics, the case of socialization of returners from prison is chosen to build a model: an opportunity and preparedness in finding legal engagement for the (re)entry into society are the key components that may tackle high recidivism rate in the region. Seemingly, the current system already has a shared governance model in use, government working with NGOs and with the Estonian Unemployment Insurance Fund. This cooperation however has resulted with no substantial results from the perspective of rehabilitation and (re)socialization – Estonia remains one of the high incarceration and recidivism areas in Europe. The paper argues that current target in data collection (knowledge formation) and in preparation for (re)integration is set following institutional interests instead of systemic logic with a focus on results. The search for better solutions must therefore continue.