A model to optimize rack layout in a retail store

ABSTRACTA retail store's layout affects a shopper's visual experience and correspondingly the time spent in the store, navigation through the aisles, and allocation of attention and money across departments and categories. We show that alternate rack layouts allow for more of a rack's facing to appear in the shopper's visual field. To this end, we introduce a set of visual–spatial statistics comprised of visual measures (exposure and intensity) and spatial measures (space and aspect ratio) as a way to quantify the effect a retail layout has on a shopper's visual experience. We present both analytical and algorithmic approaches to capture the dynamics of a travelling shopper's field of regard with respect to a static rack layout. Results for the case of unidirectional shopper travel suggest that racks oriented at 30° from the direction of travel exhibit nearly 250% increase in exposure when compared to 90° racks; for bidirectional traffic, acute orientations still provide up to 150% higher exposure. Interestingly, rack orientations amenable to higher visual measures are not necessarily the best with respect to spatial measures. We illustrate the use of our approach to analyse a real-store layout from a nearby mass merchandiser.

The entry of digitalization into the business world (e-commerce) in the past decade has become a serious challenge for retail store owners (offline stores) in Indonesia. Because, e-commerce provides efficient time and convenience in choosing the goods needed, which in turn makes people start leaving retail stores in fulfilling their life needs. However, not a few consumers are dissatisfied with e-commerce services between the expected products and those received. This study aims to determine the effect of visual merchandising and store layout on the performance of Matahari Department Store. The analytical method used is multiple linear regression with the t test. The results showed that store layout had a significant positive effect on company performance and visual merchandising had a significant positive effect on company performance.

The purpose of this paper is to optimize the tourism path to make the distance shorter. The article first constructed a model for tourism route planning and then used particle swarm optimization (PSO), genetic algorithm (GA), and ant colony algorithms to solve the model separately. Finally, a simulation experiment was conducted on tourist attractions in the suburbs of Taiyuan City to compare the path optimization performance of the three algorithms. The three path optimization algorithms all converged during the process of finding the optimal path. Among them, the ant colony algorithm exhibited the fastest and most stable convergence, resulting in the smallest model fitness value. The travel route obtained through the ant colony algorithm had the shortest distance, and this algorithm required minimal time for optimization. The novelty of this article lies in the enumeration and description of various algorithms used for optimizing travel paths, as well as the comparison of three different travel route optimization algorithms through simulation experiments. Doi: 10.28991/HIJ-2023-04-02-012 Full Text: PDF

The problem of parametric estimation in single-phase transformers is addressed in this research from the point of view of metaheuristic optimization. The parameters of interest are the series resistance and reactance as well as the magnetization resistance and reactance. To obtain these parameters considering only the voltage and the currents measured in the terminals of the transformer, a nonlinear optimization model that deals with the minimization of the mean square error among the measured and calculated voltage and current variables is formulated. The nonlinear programming model is solved through the implementation of a simple but efficient metaheuristic optimization technique known as the black-hole optimizer. Numerical simulations demonstrate that the proposed optimization method allows for the reduction in the estimation error among the measured and calculated variables when compared with methods that are well established in the literature such as particle swarm optimization and genetic algorithms, among others. All the simulations were carried out in the MATLAB programming environment.

Maritime search and rescue (SAR) plays a very important role in emergency waterway traffic situations, which is supposed to trigger severe personal casualties and property loss in maritime traffic accidents. The study aims to exploit an optimal allocation strategy with limited SAR resources deployed at navigation-constrained coastal islands. The study formulates the problem of SAR resource allocation in coastal areas into a non-linear optimization model. We explore the optimal solution for the SAR resource allocation problem under constraints of different ship and aircraft base station settings with the help of an enhanced particle swarm optimization (EPSO) model. Experimental results suggest that the proposed EPSO model can reasonably allocate the maritime rescue resources with a large coverage area and low time cost. The particle swarm optimization and genetic algorithm are further implemented for the purpose of model performance comparison. The research findings can help maritime traffic regulation departments to make more reasonable decisions for establishing SAR base stations.

We evaluate the direct detection of extrasolar giant planets with a two-aperture nulling infrared interferometer, working at angles θ < λ/2B and using a new ratio-of-two-wavelengths technique. Simple arguments suggest that interferometric detection and characterization should be quite possible for planets much closer than the conventional inner working angle, or angular resolution limit. We show that the peak signal from a nulling infrared interferometer of baseline 40 m will often occur inside the null and that the signal variations from path difference fluctuations will cancel to first order in the ratio of two wavelengths. Using a new interferometer simulation code, we evaluate the detectability of all the known extrasolar planets as observed using this two-color method with the proposed Fourier-Kelvin Stellar Interferometer (FKSI). In its minimum configuration FKSI uses two 0.5 m apertures on a 12.5 m baseline and a ±20° field of regard. We predict that ~7 known extrasolar planets are directly detectable using FKSI, with low-resolution spectroscopy (R ~ 20) being possible in the most favorable cases. Spaceborne direct detection of extrasolar giant planets is possible with ~12 m baselines and does not require the much longer baselines provided by formation flying.

The retail landscape in India is witnessing a paradigm shift, particularly in Tier-II cities like Nagpur, where organized retail formats are rapidly challenging traditional market structures. This study presents a comparative analysis of competitive benchmarking among five prominent retail stores: D-Mart, Purushottam Super Bazar, Bapat Supermarket, Punam Super Market, and Pagariya Mart. The primary objective is to evaluate their performance across critical parameters—pricing strategies, product variety, store layout, and customer satisfaction—by integrating marketing analytical perspectives with traditional benchmarking frameworks. ​Adopting a descriptive research design, primary data was collected from 100 respondents through a structured questionnaire using a convenience sampling method. The data was analyzed using percentage analysis, comparative ranking, and SWOT frameworks. The findings reveal a distinct market bifurcation: while national chains like D-Mart hold a significant lead in price competitiveness and inventory depth, local players like Purushottam Super Bazar and Bapat Supermarket retain a competitive edge through superior personalized service, billing speed, and community trust. However, the study identifies a critical gap in the adoption of marketing analytics among local retailers compared to their national counterparts. The research concludes that to sustain growth in a saturated market, local retailers must transition from intuitive management to data-driven decision-making. These insights provide strategic value for retail managers aiming to optimize operational efficiency and enhance customer loyalty in a competitive urban market. ​Keywords: Competitive Benchmarking, Marketing Analytics, Retail Performance, Customer Satisfaction, Nagpur City, Organized Retail.

The present article investigates the effectiveness of evolutionary computation algorithms in a specific optimisation task, namely morphological segmentation of words into subword segments, focusing on the definition of stems and endings. More precisely, particle swarm optimisation (PSO) is compared to an earlier study on the same task using ant colony optimisation (ACO), using a number of different optimisation criteria, for each of which independent experiments are run. In the present article, the system architecture has been revised over earlier implementations, to allow substantially faster simulation times (by several orders of magnitude), which in turn allows the realisation of more iterations. The effect of local search to the PSO final segmentation quality is investigated in detail, with different local search processes being compared in terms of their effectiveness. In addition, issues involving the convergence of PSO are examined, encompassing variants which adopt global versus local training schemes. Experimental results show that, for different datasets, as a rule both PSO and ACO achieve higher segmentation accuracies than manual tuning. A comparison between ACO and PSO is made, over the different criteria used. When focusing on the highest performing criteria, ACO and PSO are comparable, while the system revisions allow the process to be completed much faster. In terms of the highest segmentation accuracy obtained for a specific system configuration, PSO is more effective, by achieving the highest segmentation accuracy amongst all optimisation methods tested.

Massive MIMO technology is recognized as a key enabler for beyond 5G (B5G) and next-generation wireless networks. By utilizing large-scale antenna arrays at the base station (BS), it significantly improves both system capacity and energy efficiency. Despite these advantages, the deployment of a high number of antennas at the BS presents considerable challenges, particularly in the design of signal detectors that can operate with low computational complexity. While the minimum mean square error (MMSE) detector offers optimal performance in these large-scale systems, it suffers from the computational burden that makes its practical implementation challenging. To mitigate this, various iterative methods and their improved versions have been introduced. However, these iterative methods often converge slowly and are less accurate. To address these challenges, this study introduces an improved variant of traditional accelerated over-relaxation (AOR), called optimized AOR (OAOR). AOR is an over-relaxation method, and its performance is highly dependent on its relaxation parameters. To find the optimal parameters, we have developed an innovative approach that integrates a nature-inspired meta-heuristic algorithm known as Particle Swarm Optimization (PSO). Specifically, we introduce a novel variant of PSO that improves upon basic PSO by enhancing the cognitive coefficients to optimize the relaxation parameters for OAOR. These key modifications to the standard PSO improve its ability to explore various solutions efficiently and help to find the optimal parameters more quickly for signal detection. It facilitates the OAOR with faster convergence towards the optimal solution by reducing the error rate, resulting in high detection accuracy and simultaneously decreasing computational complexity from O(K3) to O(K2) making it suitable for modern wireless communication systems. We conduct extensive simulations across various configurations of massive MIMO systems. The results indicate that our proposed method achieves better performance compared to existing techniques. This improvement is particularly evident in terms of both computational complexity and error rate.

This study examines the factors influencing impulse buying behavior among shoppers in organized retail stores in Varanasi, focusing on store layout, promotional activities, consumer mood, availability of cash, and gender differences. Utilizing a sample of eligible responses from 160 shoppers, the study employs multiple regression analysis and independent samples t-tests to investigate these relationships. Key findings indicate that store layout, promotional activities, consumer mood, and availability of cash significantly impact impulse buying behavior, exhibiting strong positive correlations. The regression models reveal that these factors explain a substantial portion of the variance in impulse buying, with R Square values of 0.429, 0.442, 0.545, and 0.531, respectively, all statistically significant at p < 0.001. Store layout and promotional activities enhance product visibility and accessibility, while consumer mood and cash availability act as psychological and financial enablers for impulsive purchases. Conversely, the analysis shows no significant difference in impulse buying behavior between male and female shoppers, as indicated by a non-significant mean difference and p-value of 0.761, suggesting similar impulsive tendencies across genders. The study concludes that retailers can leverage these insights to optimize their marketing strategies, focusing on effective store layouts, targeted promotions, and an understanding of consumer psychology and financial considerations. By doing so, retailers can create an engaging shopping environment that fosters spontaneous purchases, leading to increased sales and improved customer satisfaction. These findings provide valuable implications for retail marketing strategies that maximize impulse buying behavior.

This paper aims at determining the reasons why customers prefer to buy wine online or offline, the first comprising both mobile-commerce (m-commerce) and e-commerce platforms. In particular, this research strives to understand effects of social influence and enjoyment on the intention to purchase wine from online and offline touchpoints. Moreover, the goal is to find out about the differences in the behaviour of French wine buyers and explain the choice of channel buyers prefer to use when they purchase wine.To answer our research questions, data was collected through an online questionnaire. A theoretical model is suggested and tested by the use of SmartPLS3. Online and offline buyers are compared using multi-group analysis (MGA) technique. Wine buyers are compared by the use of one-way ANOVA through SPSS 19.To date no paper has examined the differences between shopping behaviour in a retail store or on e-commerce or m-commerce platforms considering perceived enjoyment and social influence constructs. Hence, this contribution brings to the fore preliminary results on wine shopping behaviour and reveals what drives consumers to make a decision in favour of buying wine online or offline.The results show that fun and social influence affect intention to buy wine from offline touchpoints. Enjoyment also strongly and significantly affects the intention to buy wine from online touchpoints.

PurposeThe purpose of this paper is to investigate the role of in-store mobile usage on purchase decision making in order to understand whether and to what extent the use of the device changes the shopper behavior in terms of planned and unplanned purchases even across different retail store formats.Design/methodology/approachData were obtained using a structured questionnaire from 406 respondents interviewed in hypermarket and discount stores, after the checkout. Data were measured through t-tests and the analysis of variance.FindingsThe mobile intensifies a process of preparation making it popular and the same across the different store formats, confirming how the growing convergence, making store formats less distinctive in the eyes of the consumer, has somewhat flattened and standardized the pre-shopping out-of-store preparation.Practical implicationsThe pervasiveness and the versatility of the mobile, and its ability to influence the decision-making processes, leads to important managerial questions and implications regarding the effectiveness of in-store marketing initiatives and the need to review the mix of out-of-store and in-store investments, with the knowledge that the consumer will continue to become even more prepared and well informed in the future.Originality/valueMobile devices could be used out-of-store, as a tool for shopping preparation, and in-store as a tool for self-regulation. Therefore, it becomes crucial to understand how the mobile influences the decision-making process as well as the buying behavior of shoppers.

It has come to the attention of the publisher that the article: Coskun, M., Gupta, S. and Burnaz, S. (2020), “Store disorderliness effect: shoppers' competitive behaviours in a fast-fashion retail store”, International Journal of Retail & Distribution Management, Vol. 48 No. 7, pp. 763-779. https://doi.org/10.1108/IJRDM-06-2019-0193, did not reference a source drawn upon: Coskun, M. (2019), “The Impact of Retail Store Environmental Cues on Shopper Behaviour”, PhD Thesis, Istanbul Technical University. https://polen.itu.edu.tr/bitstream/11527/18530/1/557686.pdf. Our guidelines make it clear that articles must be fully referenced.

The objective of feeder reconfiguration on distribution system during normal operation is to find the best switch operation plan for reducing power losses and enhancing service reliability. By changing on/off status of sectionalizing-switches and tie-switches on a distribution system, feeder reconfiguration can be achieved. Also, the configured system must satisfy operation conditions as well as restrictions. Since the reconfiguration is done by changing the status of switches, it can be categorized as a discrete combinational optimization problem. Particle swarm optimization (PSO) is the one of the methods that can be used to solve optimal problems. Typical PSO is designed for continuous functions optimization, it is not designed for discrete functions optimization. Therefore, the operators of PSO algorithm must be reviewed and redefined to fit the application field of distribution feeder reconfiguration. This paper proposed a method which modifies the operators of PSO's formula based on the characters of both status of switches and shift operator to construct the binary coding particle swarm optimization for feeder reconfiguration. The test results show that the proposed method can apply to feeder reconfiguration effectively.

Hose-drawn traveler (HDT) is a significant type of sprinkler irrigation equipment in China. In arid and remote areas, where grid power is unreliable or unavailable, the water pumping system of the HDT typically depends on a complementary power supply (CPS) system integrating batteries, photovoltaic (PV) panels, and gasoline generators. However, the configuration of the CPS system is often determined empirically, which can lead to increased costs and compromised operational reliability. This paper aims to optimize the configuration of the CPS system based on the power demand of the water pumping system in HDT. We propose an optimization model for component sizing that considers both the annual costs and reliability of the power supply system. An improved particle swarm optimization (PSO) algorithm, incorporating a penalty function, is employed to determine the optimal configuration of the CPS system. The proposed optimization approach is then applied to an HDT operating in three typical regions in China: Golmud, Beijing, and Harbin. Finally, a comparative analysis of the configurations and annual cost of two CPS systems, PV-battery (PVB) and PV-battery-gasoline (PVBG), is conducted. Results show that for a given region, season, and power of water pump, the PVBG system needs a smaller PV panel area and lower battery capacity than the PVB system. For a fixed region and season, increasing power of water pump leads to a more significant rise in the annual cost of the PVB system than in that of the PVBG system. At high pump power levels, the PVBG system is more cost-effective, resulting in a lower annual cost than the PVB system. In Golmud, under identical seasonal and pump power conditions, both systems have lower annual cost than in Beijing and Harbin, suggesting that Golmud is a more suitable region for deploying CPS systems. The proposed method provides a reference for designing power supply systems of HDT irrigation.