Worst Location Research Articles

Optimal Location of Solar Photovoltaic Plants Using Geographic Information Systems and Multi-Criteria Analysis

Nowadays, solar energy is considered to be one of the most developed renewable energy sources, and its production capacity has increased in recent years. To optimize yields and production, the correct selection of the location of these plants is essential. This research develops a methodological proposal that allows for detecting and evaluating the most appropriate places to implement solar photovoltaic plants almost automatically through GIS tools. A multi-criteria analysis is proposed to analyze large extensions of land with ten duly weighted criteria that cover the energy and territorial requirements that any installation must meet. The method assigns each site a location coefficient that reflects the weighting of the chosen criteria so that the value ordered from highest to lowest reflects the best to the worst location. Unlike other research works that can be considered similar, the methodological proposal is much more consistent than traditional alternatives as it uses a multi-criteria analysis and a weighting mechanism that is also statistically consistent, objective, and based on logical criteria. This innovative methodology is applied to Cantabria (north of Spain), although it could be used for other contexts.

Improved Differential Evolution Algorithm Guided by Best and Worst Positions Exploration Dynamics.

The exploration of premium and new locations is regarded as a fundamental function of every evolutionary algorithm. This is achieved using the crossover and mutation stages of the differential evolution (DE) method. A best-and-worst position-guided novel exploration approach for the DE algorithm is provided in this study. The proposed version, known as "Improved DE with Best and Worst positions (IDEBW)", offers a more advantageous alternative for exploring new locations, either proceeding directly towards the best location or evacuating the worst location. The performance of the proposed IDEBW is investigated and compared with other DE variants and meta-heuristics algorithms based on 42 benchmark functions, including 13 classical and 29 non-traditional IEEE CEC-2017 test functions and 3 real-life applications of the IEEE CEC-2011 test suite. The results prove that the proposed approach successfully completes its task and makes the DE algorithm more efficient.

Enhancing the effectiveness of bioaerosol disinfection in indoor environments by optimizing far-UVC lamp locations based on Markov chain model

Far-ultraviolet C (far-UVC) light is an effective and safe disinfection method for bioaerosol control in occupied indoor environments. The installation location of a far-UVC lamp strongly influences the spatial distribution of far-UVC irradiance, and thus the effectiveness of bioaerosol disinfection. To assist the design process, this study developed a fast prediction approach based on the Markov chain model for optimizing the installation locations of far-UVC lamps in order to enhance the disinfection effectiveness for indoor bioaerosol control. Experiments were conducted in an environmental chamber to validate the proposed simulation-based optimization approach. The results show that the proposed method can correctly predict the disinfection efficiency when compared with experimental data, and optimizing the installation location of the far-UVC lamp increased the disinfection efficiency by 54 % compared with the worst location. As an application, the validated method was then used to design the installation location of a far-UVC lamp in a real conference room. The results show that installing the far-UVC lamp in the optimal location can increase the disinfection efficiency by 48 % compared with the worst installation location. Therefore, optimizing the far-UVC lamp location using the proposed Markov chain model can enhance the effectiveness of bioaerosol disinfection in indoor environments.

Differential Evolution with Group-Based Competitive Control Parameter Setting for Numerical Optimization

Differential evolution (DE) is one of the most popular and widely used optimizers among the community of evolutionary computation. Despite numerous works having been conducted on the improvement of DE performance, there are still some defects, such as premature convergence and stagnation. In order to alleviate them, this paper presents a novel DE variant by designing a new mutation operator (named “DE/current-to-pbest_id/1”) and a new control parameter setting. In the new operator, the fitness value of the individual is adopted to determine the chosen scope of its guider among the population. Meanwhile, a group-based competitive control parameter setting is presented to ensure the various search potentials of the population and the adaptivity of the algorithm. In this setting, the whole population is randomly divided into multiple equivalent groups, the control parameters for each group are independently generated based on its location information, and the worst location information among all groups is competitively updated with the current successful parameters. Moreover, a piecewise population size reduction mechanism is further devised to enhance the exploration and exploitation of the algorithm at the early and later evolution stages, respectively. Differing from the previous DE versions, the proposed method adaptively adjusts the search capability of each individual, simultaneously utilizes multiple pieces of successful parameter information to generate the control parameters, and has different speeds to reduce the population size at different search stages. Then it could achieve the well trade-off of exploration and exploitation. Finally, the performance of the proposed algorithm is measured by comparing with five well-known DE variants and five typical non-DE algorithms on the IEEE CEC 2017 test suite. Numerical results show that the proposed method is a more promising optimizer.

Effect of openings on simply supported reinforced concrete skew slabs using finite element method

Abstract Reinforced concrete skew slabs are commonly used in bridges due to space constraints in motorways and in congested urban areas. Such slabs also often need to contain openings for architectural requirements or services, and this study seeks to determine the effect of these openings on the strength of the skew slabs. The finite element method was used to analyze 13 cases of slabs with skew angles of 45°. The first and second cases were used to validate the results with experimental work. Other cases studied the effect of the position and shape of openings on the strength of the skew slabs under both one-point and four-point loading conditions. The study also showed that the worst location for the opening was near the obtuse corners since this is where most of the load is transferred. With respect to the shape of openings, three different shapes with the same area were used: skew, circle, and square shapes. It was found that the effect of the opening shape depends on how many steel reinforcements are removed, as well as the appearance of negative cracking brought on as a result of the opening. Overall, the square shape introduces the smallest reduction in the skew slab strength compared with a slab with no opening. Accordingly, the study recommends that the strength of skew slabs can best be maintained if square openings are employed near the acute corners, alongside negative steel reinforcement to avoid cracking.

An algorithm to determine the most suitable location to construct new commercial harbours

In the planning of a new harbour, much attention should be given to minimising the transportation time and cost during the lifetime of the harbour. An algorithm is proposed for determining the most suitable location for constructing new commercial harbours and ports. To determine the most suitable location for new harbours locally, a simplified simulation method is prepared for computing the characteristics of sea waves. In the proposed global model, the distribution of population, the transportation cost, the capacities of existing harbours and the demands for export/import cargos are employed as the main factors. In this model, an index called the total transportation price index (TTPI) is introduced. TTPI is calculated and examined for any potential location where the new harbour or port might be constructed. The proposed model has been validated by applying it to a case study for the conditions of several big commercial harbours of Iran’s Persian Gulf coastline. The case study indicates that the position of the port of Imam Khomeini is the most technically suitable; and that the Chahbahar port has the worst location for serving the entire country and should be limited to serving locally the nearby provinces.

Robust IRS-Aided Secrecy Transmission With Location Optimization

In this paper, we propose a robust secrecy transmission scheme for intelligent reflecting surface (IRS) aided communication systems. Different from all the existing works where IRS has already been deployed at a fixed location, we take the location of IRS as a variable to maximize the secrecy rate (SR) under the outage probability constraint by jointly optimizing the location of IRS, transmit beamformer and IRS phase shifts with imperfect channel state information (CSI) of Eve, where we consider two cases: a) the location of Eve is known; b) only a suspicious area of Eve is available. We show a critical observation that CSI models are different before and after IRS deployment, thus the optimization problem could be decomposed and solved via a two-stage framework. For case a), in the first stage, universal upper bounds of outage probabilities only related to the location of IRS are derived which can be optimized via successive convex approximation (SCA) method. In the second stage, we develop an alternative optimization (AO) algorithm to optimize beamformer and phase shifts iteratively. For case b), we propose a Max-Min SR scheme based on two-stage framework, where the location of IRS is optimized based on the worst location of Eve. Simulation results indicate the importance of the location of IRS optimization.

Sustainable Design of Advanced Driver Assistance Systems Based on Optimization and Empirical Studies on Full-Size Light-Duty Pickup Trucks

Advanced Driver Assistance Systems (ADAS) have become increasingly commonplace in all types of vehicle models in the last decade. The majority of studies have focused on smaller vehicles with gross vehicle weight rating (GVWR) under 6,000lbs, predominantly sedans, and heavy trucks, GVWR over 26,000lbs for their ADAS evaluations. While it is sensible to use this style of vehicle because it is ubiquitous worldwide for a typical vehicle body style, these studies neglect full-size light-duty pickup trucks (FSLDPTs), GVWR 6,001 – 10,000lbs, which are abundant on the roads in the United States, 18% of the vehicle population. The differences in mass, center of gravity, and utilitarianism of these vehicles allows for unique conditions for studying the effects of ADAS. This work combines the evaluations the effectiveness of ADAS in FSLDPTs across seven vehicle models, representing 18% of registered vehicles in the US, for their reduction of injury severity for occupants during accidents involving fatalities and the economics of the cost benefits of ADAS at reducing the severity of injuries for occupants of FSLDPTs for multiple different brands. This work utilizes the findings for the best and worst location to be impacted on a full-size light-duty pickup truck based on an optimization of simulated accidents based on data from the seven main FSLDPTs in this class of vehicles. The objective is to use these results for the development of future designs of ADAS technologies and their placement on a FSLDPT. While these methods could be applied to any vehicle, the FSLDPT was investigated as they represent 18% of registered vehicles in the United States. This work will specifically discuss the sustainability in terms of financial, social (human health) and environmental impact of design and manufacturing ADAS for FSLDPTs to achieve an increase in safety over non-ADAS equivalent FSLDPTs.

A comprehensive evaluation of the vertical triplex-tube heat exchanger with PCM, concentrating on flow direction, nanoparticles and multiple PCM implementation

To mitigate the intermittency of renewable resources and tackle with discrepancy in supply and demand, heat storage systems offer a promising solution. Among various storage systems, latent heat storage attracted a lot of attention. In these systems, the phase change material plays the major role in the storage mechanism. In the present study, a three-dimensional numerical study was performed on a triplex-tube vertical unit containing paraffin RT50 and RT82. The aim of this study was to investigate the effect of fluid direction, temperature, and employment of two materials and nanoparticles. According to the results, during the melting process, regardless of the inlet fluid temperature of 368 or 373 K, the use of bottom injection for both tubes can accelerate the phase change rate by up to 13%. The temperature of the inlet fluid, by increasing by 5°compared to 368 K, is capable of providing a growth of almost 24% in different inlet directions. In the axial arrangement of two materials, the best result is obtained if the material is used with higher melting temperature at higher altitudes and top injection mode. However, the best type of placement is the radial positioning in which the material with the higher melting temperature is adjacent to the outer tube. In this case, on average, a 30% improvement is achieved compared to the worst location. The results of applying nanoparticles in the state of single-material and double-materials at 3 different concentrations showed the performance improvement of 10% to 18.5% compared to the corresponding cases without nanoparticles.

On the Optimal Deployment of Power Beacons for Massive Wireless Energy Transfer

Wireless energy transfer (WET) is emerging as an enabling green technology for Internet of Things (IoT) networks. WET allows the IoT devices to wirelessly recharge their batteries with energy from external sources such as dedicated radio frequency transmitters called power beacons (PBs). In this paper, we investigate the optimal deployment of PBs that guarantees a network-wide energy outage constraint. Optimal positions for the PBs are determined by maximizing the average incident power for the worst location in the service area since no information about the sensor deployment is provided. Such network planning guarantees the fairest harvesting performance for all the IoT devices. Numerical simulations evidence that our proposed optimization framework improves the energy supply reliability compared to benchmark schemes. Additionally, we show that although both, the number of deployed PBs and the number of antennas per PB, introduce performance improvements, the former has a dominant role. Finally, our proposal allows to extend the coverage area while keeping the total power budget fixed, which additionally reduces the level of electromagnetic radiation in the vicinity of PBs.

Determination of Worst Location of Telecommunication Tower over Residential Apartment with Introduction of Outrigger and Wall Belt Supported System at Bikaner City under Seismic Loading

Determination of Worst Location of Telecommunication Tower over Residential Apartment with Introduction of Outrigger and Wall Belt Supported System at Bikaner City under Seismic Loading

Effects of Roof Bolter Canopy Air Curtain on Airflow and Dust Dispersion in an Entry Using Exhaust Curtain Ventilation

A recent study conducted by the National Institute for Occupational Safety and Health (NIOSH) evaluated the roof bolter canopy air curtain (CAC) system in a blowing face ventilation system, demonstrating its effectiveness and illustrating the CAC protection zones. This study evaluates the roof bolter machine CAC while operating in an exhausting face ventilation system. This study considers two similar locations to allow comparison with the previous blowing face ventilation (Zheng et al., Min Metal Explor 36(6):1115–1126, (2019)) study: (1) a roof bolter machine bolting the roof at 20 ft (6.1 m) from the face and (2) a roof bolting machine bolting at 4 ft (1.2 m) from the face. The environment introduces 6.0 mg/m3 of respirable dust to represent the roof bolting machine operating downstream of the continuous miner. However, the exhausting face ventilation uses an exhaust curtain with 9000 cfm (4.25 m3/s) of air. Two roof bolter machine working positions are simulated with the use of dual drill heads in the inward position for two inside bolts and in the outward position for two outside bolts. The influence of the CAC on airflows and dust dispersion is evaluated with the CAC operating at 250 cfm (0.12 m3/s) with dust reductions ranging from 39.5 to 82.8%. When the roof bolter machine operated close to the face, increasing CAC airflow was required for adequate protection since the dust reductions can be as low as 39.5%. Additional CAC airflows of 350 cfm (0.17 m3/s) and 450 cfm (0.21 m3/s) were evaluated and demonstrated that dust reductions increased to 59.7% (350 cfm) and 72.0% (450 cfm) for the worst location where the roof bolter operators located.

Emergency scour repairs and performance at the Route 37 viaducts, NJ, USA

Route 37 links the New Jersey (USA) mainland to beach communities in Seaside Heights and is an emergency evacuation route. Carrying three lanes of traffic in each direction, the adjacent bridges are approximately 4900 ft (1490 m) long. Following the devastation of Superstorm Sandy in 2012, a special underwater inspection revealed severe scour damage at 23 piers, necessitating emergency scour countermeasures. The worst location suffered 15 ft (4·6 m) of local scour with 10 ft (3 m) of timber pile exposure. A fast-track inspection, analysis and design, expedited client responses, continuous coordination and excellent team cooperation achieved timely repairs to the worst-affected locations, while keeping the bridges open for much-needed access to the barrier island for storm recovery efforts. Emergency repairs included installation of grout-filled fabric formed bags installed in situ around the pile caps as formwork and filling voids beneath the caps with grout. Precast solid armouring blocks were then installed around the toe of the grout bags. These repairs preserved not only the most severely undermined piers, but also provided a resilient, long-term repair. Recent underwater inspections verified the effectiveness of the repairs, which have encouraged material deposition and replenished the natural bay bottom.

Sustainability benchmarking for logistics center location decision

PurposeAs in line with eco benchmarking, the purpose of this paper is to solve a location selection problem in an emerging country by applying sustainability benchmarking principles.Design/methodology/approachA hybrid multi-criteria decision-making method, fuzzy AHP and Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE), is used as methodology to make sustainability benchmarking for logistics center location selection.FindingsIt is revealed that according to AHP and PROMETHEE calculations, Kemalpasa is determined as the most appropriate location from the sustainable perspectives. Torbali is specified as the worst location to construct a logistics center in terms of benchmarking criteria based on sustainability concerns. Based on these numerical results, managerial implications are presented with a sustainability benchmarking view.Originality/valueThe main originality of this study is integrating one of the relatively new topics, sustainability benchmarking, with a popular area, logistics center location selection.

Repackaged “urban renewal”: Issues of spatial equity and environmental justice in new construction, suburban neighborhoods, and urban islands of infill

ABSTRACTThis study examines environmental justice issues; the tie between infill, sprawl, and inequity; and race and class discrimination as a hindrance to urban sustainability in newly constructed “starter home” neighborhoods in Charlotte, North Carolina, commonly called “cookie-cutter” development. Major findings include a discriminatory practice of locating neighborhoods meant for lower-income and workforce families adjacent to preexisting locally unwanted land uses (LULUs), including environmental hazards and other negative elements in the built environment such as industrial uses or heavy manufacturing, thus placing the most vulnerable populations at greater risk. We find that new construction surrounding the Interstate 485 loop spurred economic development in exurban areas but syphoned it from older inner-ring areas and that the worst location for new starter home neighborhoods is within predominantly low-income urban neighborhoods already challenged on a number of fronts. Rather than acting as a catalyst for positive change, the opposite occurred, and the surrounding problems, instability, and disinvestment spread into the new areas. In such situations, starter homes embody repackaged urban renewal as a new model: islands of suburban-style infill surrounded by decline.

Determination of Best Location for Elevated Tank in Branched Network

The research focuses on determination of best location of high elevated tank using the required head of pump as a measure for this purpose. Five types of network were used to find the effect of the variation in the discharge and the node elevation on the best location. The most weakness point was determined for each network. Preliminary tank locations were chosen for test along the primary pipe with same interval distance. For each location, the water elevation in tank and pump head was calculated at each hour depending on the pump head that required to achieve the minimum pressure at the most weakness point. Then, the sum of pump heads through the day was determined. The results proved that there is a most economical location where the energy consumption is minimum. This location joined with the branched line that containing the most weakness point. The best location didn’t join with the highest demand location unless this location containing the most weakness point. The results indicated that the moving of tank away from best location in pump direction result in pump head increasing that exceed the increasing in pump head when the tank moves in the opposite direction. The location of tank beside the pump station was the worst location. Also, the results showed that as the distance between the pump and the highest demand become shorter, the required pump head become less. The uniform demand distribution required the least amount of pump head, it required minimum head of (554)m while the networks, that have highest demand at distance 200m,400m, and 1000m from the pump station, required minimum head of 651m, 682m, and 726m respectively.

LOCATION AS A FACTOR OF THE GROWTH OF DEVELOPMENT ACTIVITY

The studies conducted by the author indicate the significance of location for the growth of development activity. Foreign literature also emphasises the impact of this factor on the possibility of the growth of development activity. Location is often understood as the position of the real property in the given city. This paper deals with the issue of the “external” location, i.e. the developers' choice concerning the location of their undertakings in particular cities. Each city has its own characteristics and defined specificity which can encourage or discourage developers to undertake an activity in this particular city. In this context, the goal of the paper is an attempt to define the best and the worst location among Polish province capitals for development activity using taxonomic methods.

A new preference voting method for sustainable location planning using geographic information system and data envelopment analysis

Supply chain operations with sustainability considerations have become an increasingly important issue in recent years and location planning for sustainable development plays an important role in guiding future of local, regional and national systems. Geographic information system is a technology for making better decisions about location. To solve location planning problem, in this paper, a new preference aggregation algorithm using complementary slackness condition and discriminant analysis is developed. Applying a voting system for solving sustainable location planning problem is new and cannot be found in literature. Using geographic information system and factor analysis, a multiple attribute decision making problem is developed. In this paper, multiple attribute decision making problems are solved by our proposed method for ranking a voting system and also the sustainable location is obtained. A case study demonstrates efficiency of proposed method. For this purpose, the proposed method is used to determine the most appropriate locations for constructing agro-industries in Markazi province. In our real application, ten main criteria using obtained variances and eigen values are recognized. Seven locations are selected and ranked. Results show that Komain is the best location for constructing agro-industry and Komijan is the worst location.

Exploring avoidance strategies and neighbourhood topologies in particle swarm optimisation

Particle swarm optimisation (PSO) is a stochastic optimisation algorithm in which particles evaluate solutions in a problem space and converge on the best known solution. This paper presents a PSO variant with avoidance of worst locations (AWL). The particles in PSO AWL remember the worst previous positions as well as the best. This new information changes the motion of the particles and results in spending less time exploring areas which are known to have the worst fitness. A small influence from the worst locations leads to the best performance. The performance of PSO AWL is promising compared to the standard PSO. The PSO AWL also performs significantly better compared to previous implementations of worst location memory. This paper also explores the effect of static vs. dynamic topology on the PSO AWL. It is found that the dynamic topology, gradually increasing directed neighbourhoods (GIDN), greatly improves the performance of PSO AWL.

Exploring avoidance strategies and neighbourhood topologies in particle swarm optimisation

Particle swarm optimisation (PSO) is a stochastic optimisation algorithm in which particles evaluate solutions in a problem space and converge on the best known solution. This paper presents a PSO variant with avoidance of worst locations (AWL). The particles in PSO AWL remember the worst previous positions as well as the best. This new information changes the motion of the particles and results in spending less time exploring areas which are known to have the worst fitness. A small influence from the worst locations leads to the best performance. The performance of PSO AWL is promising compared to the standard PSO. The PSO AWL also performs significantly better compared to previous implementations of worst location memory. This paper also explores the effect of static vs. dynamic topology on the PSO AWL. It is found that the dynamic topology, gradually increasing directed neighbourhoods (GIDN), greatly improves the performance of PSO AWL.