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Inventory management is an essential component of any business, but it can be difficult for businesses today to determine the ideal quantity level required to avoid shortages and to reduce waste. With the maximization of profit, if the backorder quantity is minimized, then this policy will be most preferred and economical. Likewise with the minimization of holding cost, the policy is such that the total order quantity is minimized. The model is formulated as a multi-objective linear programming problem with four objectives: maximizing profit, maximizing total ordering quantity, minimizing the holding cost in the system, minimizing total backorder quantity. The constraints are included with budget limitation, space restrictions and constraint on cost of ordering each item. When converting a fuzzy model to a crisp model, we employ the ranking function approach and graded mean integration method. In order to reduce stock out situations, the lowest optimal quantity level to place in the inventory is also determined using weighted sum and the ε -constraint method. To prevent shortages, the ordering quantity is increased. Minimizing holding costs and back order quantity together enhance the model's profit. Budgetary restrictions, space limitations, and a pricing restriction on ordering each item are all included in the constraints. In a fuzzy context, the proposed inventory model turns into a difficulty of many criteria decision-making. To transform the data from the fuzzy model to the crisp model, the ranking function approach using the triangular fuzzy number, trapezoidal fuzzy number and triangular intuitionistic fuzzy number and graded mean integration methods are used. The optimal solution is obtained using numerical demonstration. Pareto optimal solutions using genetic algorithm for different objective functions are included. Sensitivity analysis of the model is carried out to discuss the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Stochastic optimization approaches benefit from random variance to produce a solution in a reasonable time frame that is good enough for solving the problem. Compared with them, deterministic optimization methods feature faster convergence rates and better reproducibility but may get stuck at a local optimum that is insufficient to solve the problem. In this paper, we propose a group-based deterministic optimization method, which can efficiently achieve comparable performance to heuristic optimization algorithms, such as particle swarm optimization. Moreover, the weighted sum method (WSM) is employed to further improve our deterministic optimization method to be multi-objective optimization, making it able to seek a balance among multiple conflicting circuit performance metrics. With a case study of three common analog circuits tested for our optimization methodology, the experimental results demonstrate that our proposed method can more efficiently reach a better estimation of the Pareto front compared to NSGA-II, a well-known multi-objective optimization approach. [ABSTRACT FROM AUTHOR]

Quality, Health, Safety, and Environment (QHSE) management is essential for all companies, especially service providers. Besides operating within their own facilities, they also work in external units of contracting companies. This work aims to propose global indicators by segment (QHSE) and a global indicator for the integrated system and monitor its efficiency and evolution over time. For this purpose, a case study was analyzed in a repair, maintenance, and inspection service company in the oil segment, operating in Brazil. The prioritization of criteria and subdivision of indicators was carried out by a team of company specialists using the Weighted Sum Method (WSM) decision-making technique. The intention was to obtain a systemic view of the results of global indicators by segment (QHSE) through data collection, identifying points of greater relevance for the company, and proposing recommendations to increase the company's efficiency. The proposed global indicators allow assessing the organization's performance, enabling the identification and dissemination of best management practices for the Integrated Management System (IMS). In the analysis of the results using data from 2021 to 2022, it is observed that the use of the proposed global indicators met the needs of the management system. [ABSTRACT FROM AUTHOR]

Abstract For successful animal husbandry, farmers need to monitor the health status of animals on a daily basis, as well as ensure the safety and protection of animals. In this regard, the problem of choosing the optimal system for monitoring the condition of farm animals in the modern market is urgent. The practical value of the scientific article is to analyze the problems of stakeholders and, based on the needs, conduct a comparative analysis of various modern systems for monitoring the condition of small cattle using the weighted sum method. The object of the study was the process of monitoring the condition of small cattle using RFID tags, as well as the ability to monitor their health depending on the chosen approach. The subject of the study was a livestock enterprise that raises small cattle. It was found that the health of small cattle directly affects the financial stability of the farm. In the presented study, the authors analyze the problems of stakeholders and, based on the needs of stakeholders, conduct a comparative analysis of various modern systems for monitoring the condition of small cattle using the weighted sum method. The results obtained will help farmers make informed decisions in the future when choosing the most appropriate monitoring option for more effective and high-quality monitoring of the condition of small cattle, thereby reducing the risk of diseases and increasing profits for companies.

This paper provides a review of vessel route planning with a focus on safety considerations and the complexity of multi-objective decision-making processes. This complexity arises from the difficulty of finding an appropriate balance between several objectives and safety concerns, often conflicting, that adequately reflects the preferences of the decision makers. The maritime industry faces the challenge of enhancing vessel route optimization for safety, operational efficiency, and cost-effectiveness. We thus describe quantitative methods to find routes that effectively balance multiple objectives, including safety, fuel consumption, and route duration. A significant focus is on the complexity of multi-criteria decision making in this area, highlighting various methodologies for balancing the different objectives. Safety is critical in this context, involving a thorough consideration of navigational risks, environmental factors, and compliance with International Maritime Organization regulations. Specifically, we introduce quantitative approaches for integrating key safety aspects into the decision-making process, including dynamic stability, the probability of bow slamming, and the occurrence of green water.

The market of courier, express and parcel services (CEP) is characterized by the presence of requests for the transfer of goods and documents. During the COVID-19 pandemic, the global postal and CEP infrastructure played a key role in enabling governments to respond to the emerging challenges. In this paper, the competitiveness and the rank of four observed CEP service providers are analyzed using ratio indicators, the Ordinal Priority Approach method (OPA) and the Weighted Sum Method (WSM). We use the OPA method because there is no need for a pairwise comparison matrix and no need for normalization. The paper aims to examine the liquidity, economy and profitability of CEP service providers to get a true picture of the situation in the market of postal and CEP services in the Serbia. In the analysis of business performance and the implementation of the OPA method, the opinions of experts were taken into account. This paper yields a key insight: the identification of the top-performing provider within the group of service providers is accomplished through the utilization of both the OPA and WSM methods. Sensitivity analysis is shedding light on the varying impact of the provider’s rank on the final ranking of the CEP providers.

A groundbreaking study employs interval arithmetic to address the challenging multi-objective interval traveling salesperson problem. Customizing methods like a nearest neighbor, branch and bound, two-way heuristics, and dynamic programming effectively resolve this complex problem. Preserving interval values without the need for classical form conversion is a significant advantage. Researchers validated this approach through extensive experiments, consistently demonstrating superior outcomes compared to existing methods. These algorithmic approaches were optimized for Python 3.11 64-bit to enhance processing speed and efficiency. [ABSTRACT FROM AUTHOR]

Optimal warehouse design is the most substantial issue for many companies, because of increasing their efficiency and productivity. Nowadays, a warehouse is just undefined as a site for goods storage and delivery but is taken into account as a unit interacting with other sectors. Recently, cross-docking warehouses have been widely used since they differ from traditional warehouses in terms of good deposition volume, storage and delivery times, mitigating procurement, exploitation of resources, and accelerating service pace. This study proposes a mathematical model for transit warehouse layout and design to minimize travel distances, downsize the free space of the warehouse and make retailers satisfied. Small-sized problems are solved by the augmented epsilon-constraint and the weighted sum methods, while NSGA-II and MOGWO algorithms are proposed for large-sized problems. Comparison between algorithms indicates higher efficiency of NSGA-II rather than MOGWO algorithm to solve cross-docking warehouse design for allocating stores to floor locations. [ABSTRACT FROM AUTHOR]

The study aims to determine heritage tourism potential in the Kashmir valley, a developing region, to lower the impact of mass tourism on the natural environment. The potential was assessed using criterion‐based indicators. After a Delphi survey, Content Validation Ratio determined indicator retention. Field trips and secondary sources identified the sites. The sites with cultural significance were prioritized with other supporting factors. The Weighted Sum Method assessed the heritage site potential. It was calculated for each criterion: Cultural, Physical, and Natural. Then, total potential was classified into three groups on the basis of equal intervals depicting high, medium, and low, where high ranges from 0.628 to 0.756, medium from 0.502 to 0.628, and low from 0.375 to 0.502. Being the capital city and hub of different activities, Srinagar district of the study area has maximum of the sites with high heritage potential. Owing to its long history, the study region has many heritage sites; however, its tourism potential has yet to be tapped. The study emphasizes the significance of shifting surges in tourism forms in managing sustainable tourism development. [ABSTRACT FROM AUTHOR]

The market of courier, express and parcel services (CEP) is characterized by the presence of requests for the transfer of goods and documents. During the COVID-19 pandemic, the global postal and CEP infrastructure played a key role in enabling governments to respond to the emerging challenges. In this paper, the competitiveness and the rank of four observed CEP service providers are analyzed using ratio indicators, the Ordinal Priority Approach method (OPA) and the Weighted Sum Method (WSM). We use the OPA method because there is no need for a pairwise comparison matrix and no need for normalization. The paper aims to examine the liquidity, economy and profitability of CEP service providers to get a true picture of the situation in the market of postal and CEP services in the Serbia. In the analysis of business performance and the implementation of the OPA method, the opinions of experts were taken into account. This paper yields a key insight: the identification of the top-performing provider within the group of service providers is accomplished through the utilization of both the OPA and WSM methods. Sensitivity analysis is shedding light on the varying impact of the provider's rank on the final ranking of the CEP providers. [ABSTRACT FROM AUTHOR]

A multi-objective optimization problem (MOP) is a simultaneous optimization of more than one real-valued conflicting objective function subject to some constraints. Most MOP algorithms try to provide a set of Pareto optimal solutions which are equally good in terms of the objective functions. The set can be infinite, and hence, analysis and choice task of one or several solutions among the equally good solutions is hard for a decision maker (DM). In this paper, a new scalarization approach is proposed to select a Pareto optimal solution for convex MOPs such that the relative importance assigned to its objective functions is very close together. In addition, two decision-making methods are developed to analyze convex and non-convex MOPs based on evaluating a set of Pareto optimal solutions and the relative importance of the objective functions. These methods support the DM to rank the solutions and obtain one or several of them for real implementation without having any familiarity about MOPs. [ABSTRACT FROM AUTHOR]

In this article, we consider a particular case of bi‐objective optimization (BOO), called bi‐objective minimization (BOM), where the two objective functions to be minimized take only positive values. As well as for BOO, most of the methods proposed in the literature for solving BOM focus on computing the Pareto‐optimal solutions representing different trade‐offs between two objectives. However, it may be difficult for a central decision‐maker to determine the preferred solutions due to the huge number of solutions in the Pareto set. We propose a novel criterion for selecting the preferred Pareto‐optimal solutions by introducing the concept of ρ$$ \rho $$‐Nash Fairness (ρ$$ \rho $$‐NF$$ NF $$) solutions inspired by the definition of proportional fairness. The ρ$$ \rho $$‐NF$$ NF $$ solutions are the feasible solutions achieving some proportional nash equilibrium between the two objectives. The positive parameter ρ$$ \rho $$ is introduced to reflect the relative importance of the first objective to the second one. For this work, we will discuss existential and algorithmic questions about the ρ$$ \rho $$‐NF$$ NF $$ solutions by first showing their existence for BOM. Furthermore, the ρ$$ \rho $$‐NF$$ NF $$ solution set can be a strict subset of the Pareto set. As there are possibly many ρ$$ \rho $$‐NF$$ NF $$ solutions, we focus on extreme ρ$$ \rho $$‐NF$$ NF $$ solutions achieving the smallest values for one of the objectives. Then, we propose two Newton‐based iterative algorithms for finding extreme ρ$$ \rho $$‐NF$$ NF $$ solutions. Finally, we present computational results on some instances of the bi‐objective travelling salesman problem (BOTSP) and the bi‐objective shortest path problem. [ABSTRACT FROM AUTHOR]

Key Performance Indicators (KPIs) help measure objectives critical to business sustainability. This research takes place in a toy factory’s injection moulding department that has yet to develop indicators for subcontractor performance. Poor subcontractor performance significantly impacts the injection moulding production. In the last three months, the delivered quantity exceeded the target by 27%, delivery time consistency is only 78%, there were 1.31% rejects, and inventory accuracy and material efficiency were 81% and 93% respectively. The objective of the study is to determine the KPI and develop an evaluation procedure for the injection moulding supplier and select the best supplier. Multi-criteria decision-making, i.e., weighted sum method, was used. Five employees were involved in determining the criteria weights. The evaluation was based on each supplier’s performance. A written KPI for the company's subcontractor and performance evaluation procedures were developed. There were six subcontractors of which subcontractor H received the highest score (78.42) and subcontractor G received the lowest score (69.42). The subcontractor performance needs to be improved in the future. In the interest of the company’s sustainability, it is recommended that this subcontractor performance evaluation be institutionalised to support subcontractor performance. Successful subcontractors support the business performance.

Structural engineers must encounter multiobjective optimization (MOO) problems in designs including a design of reinforced concrete (RC) columns, where several design objectives must be satisfied to meet contractors’ interests and produce a sustainable design. MOO applications in structural engineering practice are uncommon despite their high demand. Multiobjective population methods have been mainly investigated. However, previous studies have mostly neglected the stopping criteria and convergence of these algorithms. This study proposes a novel gradient-based algorithm – ANN-based Lagrange optimization and its implementation in designs of circular reinforced concrete columns where three objectives, such as cost, CO2 emissions, and column weight, are minimized simultaneously. This study integrates each single-objective function extracted from an artificial neural network (ANN) into a global Lagrange function, a unified function of objectives (UFO), using the trade-off fractions. The Lagrange multiplier method is adopted to deal with constrained conditions by using Newton–Raphson iteration to solve Karush-Kuhn-Tucker (KKT) conditions. The proposed algorithm brought in a set of optimal results capturing multiple objectives, known as a Pareto frontier, which are compared with those obtained using the Nondominated Sorting Genetic Algorithm – II (NSGA-II), showing that two algorithms calibrated to each other. Overall, the ANN-based Lagrange optimization algorithm exhibited better convergence than NSGA-II.

This work develops a computational platform to evaluate the ecotourism potential of tourist sites (alternatives) under multiple indicators (criteria). Such a platform is a generalized multi-attribute decision-making approach based on the integration of Criteria Importance Through Intercriteria Correlation (CRITIC) and the weighted sum method (WSM) under a stochastic environment. In this proposed approach, a randomly generated decision matrix is formed, derived from the initial judgments of decision-makers. Then, the priority weights of the criteria are obtained through CRITIC, while the WSM generates the global priority vector of alternatives. A stochastic process based on Monte Carlo simulation generates a large set of global priority vectors. The aggregation of these vectors yields the final priorities of the alternatives. Contextually, it generates a single-valued composite ecotourism potential index which provides the overall performance of the potential ecotourism sites. Evaluating 47 tourist sites under eight indicators demonstrates the proposed approach. Findings and insights are discussed. [ABSTRACT FROM AUTHOR]

This study proposes an under-frequency load-shedding (UFLS) scheme based on a binary Archimedes Optimization Algorithm (BAOA) and the Weighted Sum Method (WSM) to maintain the stability of an islanded distribution system. These methods consider stability indices and load priorities to ensure effective load shedding during frequency deviations. The BAOA determines the optimal load shedding based on the stability index and power mismatch that minimizes the impact on critical loads while maintaining system stability in an islanded distribution system. The WSM determines the rank of the load to be shed based on four criteria: the load priority, the load category, the stability index, and the load size. Each load is assigned a weight based on its priority. These weight variables determine the order in which loads are shed during frequency deviations. The effectiveness of the proposed UFLS was tested on an 11 kV Malaysian distribution network with two mini hydro distributed generation systems. A comparative study was conducted based on five result outputs, including the number of loads shed, the size of the loads shed, the frequency undershoot, the frequency overshoot, and the time taken to achieve a stable frequency in three cases: base load, peak load, and peak load with photovoltaics (PV). The proposed UFLS showed the best results for 11 of 15 outputs (73.3%) for islanding events and 9 of 15 outputs (60%) for overloading events. The voltage profile and stability index, also, were improved after the proposed UFLS was applied. [ABSTRACT FROM AUTHOR]

Sectorization is concerned with dividing a large territory into smaller areas, also known as sectors. This process usually simplifies a complex problem, leading to easier solution approaches to solving the resulting subproblems. Sectors are built with several criteria in mind, such as equilibrium, compactness, contiguity, and desirability, which vary with the applications. Sectorization appears in different contexts: sales territory design, political districting, healthcare logistics, and vehicle routing problems (agrifood distribution, winter road maintenance, parcel delivery). Environmental problems can also be tackled with a sectorization approach; for example, in municipal waste collection, water distribution networks, and even in finding more sustainable transportation routes. This work focuses on sectorization concerning the location of the area's centers and allocating basic units to each sector. Integer programming models address the location-allocation problems, and various formulations implementing different criteria are compared. Methods to deal with multiobjective optimization problems, such as the ϵ -constraint, the lexicographic, and the weighted sum methods, are applied and compared. Computational results obtained for a set of benchmarking instances of sectorization problems are also presented. [ABSTRACT FROM AUTHOR]

The Kashmir Valley is immensely susceptible to soil erosion due to its diverse topography and unstable geological formations in the Himalayan region. The present study helps in assessing the spatial distribution and prioritizing soil erosion zones in the Central Kashmir region covering the Sindh and Dachigam catchments. The study implemented the GIS-based analytic hierarchy process (AHP) and weighted sum method (WSM) using datasets of precipitation, geological map, soil map, and satellite imagery and derived eleven factors (topographical derivatives, LULC, soil, drainage, rainfall, lithology, wetness index and greenness of an area). The ratings and weightage were proven to be unbiased and reliable based on the observed value of the consistency ratio (CR) (i.e., 0.07). The study depicts 41% of the total area to be extremely vulnerable to soil erosion. The slope varies from 0–62° with mean of 22.12°, indicating 467.99 km2 (26%) and 281.12 km2 (15%) of the area under high and very high susceptible zones, respectively. The NDVI and NDWI maps indicate soil erosion severity covering an area of 40% and 38%, respectively, in highly susceptible zones. High drainage density and curvature zones were observed in 18.33% and 22.64% of the study area, respectively. The study will assist in the planning and implementation of conservation measures.

In general, customers are looking to receive their orders in the fastest time possible and to make purchases at a reasonable price. Consequently, the importance of having an optimal delivery time is increasingly evident these days. One of the structures that can meet the demand for large supply chains with numerous orders is the hierarchical integrated hub structure. Such a structure improves efficiency and reduces chain costs. To make logistics more cost-effective, hub-and-spoke networks are necessary as a means to achieve economies of scale. Many hub network design models only consider hub type but do not take into account the hub scale measured using freight volume. This paper proposes a multi-objective scheduling model for hierarchical hub structures (HHS), which is layered from top to bottom. In the third layer, the central hub takes factory products from decentralized hubs and sends them to other decentralized hubs to which customers are connected. In the second layer, non-central hubs are responsible for receiving products from the factory and transferring them to central hubs. These hubs are also responsible for receiving products from central hubs and sending them to customers. Lastly, the first layer contains factories responsible for producing products and providing for their customers. The factory uses the flexible flow-shop platform and structure to produce its products. The model’s objective is to minimize transportation and production costs as well as product arrival times. To validate and evaluate the model, small instances have been solved and analyzed in detail with the weighted sum and ε-constraint method. Consequently, based on the mean ideal distance (MID) metric, two methods were compared for the designed instances.

This paper proposes a novel daily energy management system for optimization dispatch and operation control of a typical microgrid power system. The multi-objective optimization dispatch problem is formulated to simultaneously minimize the operating cost, pollutant emission level as well as the power loss of conversion devices. While satisfying the system load and technical constraints, ensure high penetration of renewable energy and optimal scheduling of charging/discharging of battery storage system based on a fuzzy logic approach. The weighted sum method is adopted to obtain Pareto optimal solutions, then a fuzzy set theory is employed to find the best compromise solution. Ant lion optimizer method is considered to solve the formulated problem. To prove the efficacy and robustness of the proposed algorithm, a comparison of the performance of ant lion optimizer algorithm with other known heuristic optimization techniques has been investigated. The results obtained show that the proposed algorithm outperforms the other heuristic techniques in solving the multi-objective optimization dispatch problem. They also reveal that a better compromise between the considered contradictory objective functions is achieved when priority is given to the generation of the internal microgrid’s sources with an equivalent contribution rate of 68.45% of generated power from both fuel cell and micro-turbine, whereas the contribution rate of external grid is limited to 11.72%.

The Kashmir Valley is immensely susceptible to soil erosion due to its diverse topography and unstable geological formations in the Himalayan region. The present study helps in assessing the spatial distribution and prioritizing soil erosion zones in the Central Kashmir region covering the Sindh and Dachigam catchments. The study implemented the GIS-based analytic hierarchy process (AHP) and weighted sum method (WSM) using datasets of precipitation, geological map, soil map, and satellite imagery and derived eleven factors (topographical derivatives, LULC, soil, drainage, rainfall, lithology, wetness index and greenness of an area). The ratings and weightage were proven to be unbiased and reliable based on the observed value of the consistency ratio (CR) (i.e., 0.07). The study depicts 41% of the total area to be extremely vulnerable to soil erosion. The slope varies from 0–62° with mean of 22.12°, indicating 467.99 km2 (26%) and 281.12 km2 (15%) of the area under high and very high susceptible zones, respectively. The NDVI and NDWI maps indicate soil erosion severity covering an area of 40% and 38%, respectively, in highly susceptible zones. High drainage density and curvature zones were observed in 18.33% and 22.64% of the study area, respectively. The study will assist in the planning and implementation of conservation measures. [ABSTRACT FROM AUTHOR]

This paper presents a new mathematical model of the green closed-loop supply chain network (GCLSCN) during the COVID-19 pandemic. The suggested model can explain the trade-offs between environmental (minimizing CO2 emissions) and economic (minimizing total costs) aspects during the COVID-19 outbreak. Considering the guidelines for hygiene during the outbreak helps us design a new sustainable hygiene supply chain (SC). This model is sensitive to the cost structure. The cost includes two parts: the normal cost without considering the coronavirus pandemic and the cost with considering coronavirus. The economic novelty aspect of this paper is the hygiene costs. It includes disinfection and sanitizer costs, personal protective equipment (PPE) costs, COVID-19 tests, education, medicines, vaccines, and vaccination costs. This paper presents a multi-objective mixed-integer programming (MOMIP) problem for designing a GCLSCN during the pandemic. The optimization procedure uses the scalarization approach, namely the weighted sum method (WSM). The computational optimization process is conducted through Lingo software. Due to the recency of the COVID-19 pandemic, there are still many research gaps. Our contributions to this research are as follows: (i) designed a model of the green supply chain (GSC) and showed the better trade-offs between economic and environmental aspects during the COVID-19 pandemic and lockdowns, (ii) designed the hygiene supply chain, (iii) proposed the new indicators of economic aspects during the COVID-19 outbreak, and (iv) have found the positive (reducing CO2 emissions) and negative (increase in costs) impacts of COVID-19 and lockdowns. Therefore, this study designed a new hygiene model to fill this gap for the COVID-19 condition disaster. The findings of the proposed network illustrate the SC has become greener during the COVID-19 pandemic. The total cost of the network was increased during the COVID-19 pandemic, but the lockdowns had direct positive effects on emissions and air quality. [ABSTRACT FROM AUTHOR]

Academic Curriculum Balancing is an alternative for building curricular paths, with a focus on planning the student's workload, by assigning subjects to periods in a balanced way in relation to the number of credits and respecting the prerequisites. This work presents the development of a multi-objective model entitled Balancing Academic Curriculum with Subjects Failure Data, which has three objectives: distribute credits more evenly between periods, bring interrelated disciplines closer and more adequately balance disciplines with high retention. The model was transcribed using an exact Mixed Quadratic Integer Programming and Constraint Programming, and given its multi-objective feature, the Weighted Sum method was applied in the achievement of the solutions, constituted in the Pareto frontier. The experimental results obtained by tests in a higher education curriculum provided significant improvements in the balance of student workload. A computational tool that encompasses the new model was developed, in order to enable the application of the proposed approach in real scenarios, favoring learning and completion of the course in the ideal time. In an survey carried out with students and managers, the fulfillment of the objectives of tool and its applicability remained demonstrated. [ABSTRACT FROM AUTHOR]

This paper introduces a novel application of the Wild Horse Optimizer (WHO) to energy management in a typical grid-connected micro-grid (MG) with different types of distributed generation sources, a battery storage device, and loads. The problem is formulated as a nonlinear multi-objective optimization problem with various equality and inequality constraints. The optimal solutions are defined by the following two objective functions: (1) minimization of the total operating costs for the MG, and (2) minimization of the total emission of pollutants in the MG. The multi-objective management problem is transformed into a single-objective problem using the weighted sum method to obtain a Pareto optimum. Simulation results, obtained using the WHO algorithm, are compared with those obtained using other popular optimization algorithms. It is shown that the WHO algorithm provides effective, robust and high-quality solutions. [ABSTRACT FROM AUTHOR]

In general, customers are looking to receive their orders in the fastest time possible and to make purchases at a reasonable price. Consequently, the importance of having an optimal delivery time is increasingly evident these days. One of the structures that can meet the demand for large supply chains with numerous orders is the hierarchical integrated hub structure. Such a structure improves efficiency and reduces chain costs. To make logistics more cost-effective, hub-and-spoke networks are necessary as a means to achieve economies of scale. Many hub network design models only consider hub type but do not take into account the hub scale measured using freight volume. This paper proposes a multi-objective scheduling model for hierarchical hub structures (HHS), which is layered from top to bottom. In the third layer, the central hub takes factory products from decentralized hubs and sends them to other decentralized hubs to which customers are connected. In the second layer, non-central hubs are responsible for receiving products from the factory and transferring them to central hubs. These hubs are also responsible for receiving products from central hubs and sending them to customers. Lastly, the first layer contains factories responsible for producing products and providing for their customers. The factory uses the flexible flow-shop platform and structure to produce its products. The model's objective is to minimize transportation and production costs as well as product arrival times. To validate and evaluate the model, small instances have been solved and analyzed in detail with the weighted sum and ε-constraint method. Consequently, based on the mean ideal distance (MID) metric, two methods were compared for the designed instances. [ABSTRACT FROM AUTHOR]

The fulfilment of forest functions is significantly affected by forest management, especially the applied harvest regeneration systems. The interest rate can significantly influence forest regeneration planning but was rarely tested. We examined how different interest rates (0%, 0.5%, 1%, 1.5%, 2%, 2.5% and 3%) affect the fulfilment of multiple forest functions and hence, the decision on the optimal forest regeneration system for multiple criteria. The analysis was performed based on the simulation of 450 different harvest-regeneration systems applied to a secondary spruce dominated forest stand and the multi-criteria a posteriori decision analysis. The outputs from the growth simulator were used to quantify the fulfilment of timber production and non-production functions. Three selected indicators (Soil expectation value, Shannon index of species diversity, and height to diameter ratio) were analysed on the base of single-criteria optimisation based on maximising a single indicator and the methods of multi-criteria optimisation of the weighted sum method and Pareto front with the goal to harmonise the fulfilment of three different functions. The results indicated that lower interest rates favoured single tree cutting systems with relatively long rotation cycles (140–160 years), while higher interest rates favoured strip cutting with shorter rotation cycles (90–100 years).

Taguchi first developed the quality loss function to better estimate the economic losses incurred by manufacturers and customers caused by quality characteristics being off-target. The quality loss function measures the quality loss caused by a deviation of a quality characteristic from its defined target value. Several researchers have proposed different revised loss functions for overcoming some flaws of the Taguchi loss function. This paper recommends a new family of quality loss functions, which is very flexible, simple, and easy to implement. Three real case studies demonstrated the usability and capabilities of the proposed new loss function for quantifying and predicting quality losses.

Farm tourism is an emerging agenda within the rural tourism literature due to its potential in diversifying income on farms. Despite such potential, issues associated with sustainability became a direct consequence of its growth. However, developing an overarching sustainability evaluation framework is scarce in the domain literature, with an exemption from a recently reported work proposing fuzzy multiple criteria decision-making (MCDM) methods to generate a composite performance score. This work augments the previous computational framework by reducing the mental workload of stakeholders and decision-makers in eliciting judgments required in the evaluation process. It integrates the fuzzy full consistency method (FUCOM) and fuzzy weighted sum method (WSM) into a holistic platform that assigns an aggregate sustainability performance of farm tourism sites. The fuzzy FUCOM assigns the priority weights of sustainable tourism indicators, while the fuzzy WSM generates the composite score. Integrating fuzzy set theory in the evaluation platform addresses the vagueness and uncertainty in the decision-making process. Five farm tourism sites in the Philippines are evaluated to demonstrate the proposed methodology. Findings show that the indicators associated with capability training of local tourism personnel, employment opportunities for the locals, development of an integrated tourism and development plan, and curbing waste generation are deemed the most crucial sustainability indicators. The application of the proposed framework generates the composite sustainability indices for all sites, which can be used for comparing various sites' performance, identifying hotspots, and benchmarking for best practices. Policy insights for these findings are discussed. Finally, a comparative analysis shows that the proposed method is comparable to other time-consuming MCDM methods. [ABSTRACT FROM AUTHOR]

This paper proposes a new concept in transportation problem in which if the negligible amount quantity is transported through a distribution center, then the decision maker (DM) cannot deliver that negligible amount quantity through the particular distribution center and the DM puts some restriction on transportation problem. Here, we develop six transportation models with budget at each destination, of which three models are without restriction and another three models are with restriction. Also, apart from source, demand and capacity constraints, an extra constraint on the total budget at each destination is also imposed. Here, all the parameters in Models 1 and 2 are crisp in nature and are solved in crisp environment, whereas all the parameters in Models 3–6 are interval type-2 fuzzy and random in nature, respectively, and are solved in uncertain environment. To reduce Models 3–6 into its crisp equivalent, we use the expected value of fuzzy number and chance constraint programming technique, respectively. Weighted sum method is also used to give the preference of the objective function and a gradient-based optimization technique-generalized reduced gradient (GRG) method are applied and using LINGO-13 software to get the optimal solutions. A numerical example is provided to illustrate the models and programming. Finally, a sensitivity analysis is presented for Models 1 and 2 with respect to the weight function. [ABSTRACT FROM AUTHOR]

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This paper proposes a novel daily energy management system for optimization dispatch and operation control of a typical microgrid power system. The multi-objective optimization dispatch problem is formulated to simultaneously minimize the operating cost, pollutant emission level as well as the power loss of conversion devices. While satisfying the system load and technical constraints, ensure high penetration of renewable energy and optimal scheduling of charging/discharging of battery storage system based on a fuzzy logic approach. The weighted sum method is adopted to obtain Pareto optimal solutions, then a fuzzy set theory is employed to find the best compromise solution. Ant lion optimizer method is considered to solve the formulated problem. To prove the efficacy and robustness of the proposed algorithm, acomparison of the performance ofant lion optimizer algorithm with other known heuristic optimization techniques has been investigated. The results obtained show that the proposed algorithm outperforms the other heuristic techniques in solving the multi-objective optimization dispatch problem. They also reveal that a better compromise between the considered contradictory objective functions is achieved when priority is given to the generation of the internal microgrid's sources with an equivalent contribution rate of 68.45% of generated power from both fuel cell and micro-turbine, whereas the contribution rate of external grid is limited to 11.72%. [ABSTRACT FROM AUTHOR]

This paper deals with the multi-objective optimization dispatch (MOOD) problem in a DC microgrid. The aim is to formulate the MOOD to simultaneously minimize the operating cost, pollutant emission level of (NOx, SO2 and CO2) and the power loss of conversion devices. Taking into account the equality and inequality constraints of the system. Two approaches have been adopted to solve the MOOD issue. The scalarization approach is first introduced, which combines the weighted sum method with price penalty factor to aggregate objective functions and obtain Pareto optimal solutions. Whilst, the Pareto approach is based on the implementation of evolutionary multi-objective optimization solution. Single and multi-objective versions of multi-verse optimizer algorithm are, respectively, employed in both approaches to handle the MOOD. For each time step, a fuzzy set theory is selected to find the best compromise solution in the Pareto optimal set. The simulation results reveal that the Pareto approach achieves the best performances with a considerable decrease of 28.96 $/day in the daily operating cost, a slight reduction in the power loss of conversion devices from 419.79 kWh to 419.29 kWh, and in less computational time. While, it is noticing a small increment in the pollutant emission level from 11.54 kg/day to 12.21 kg/day, for the daily microgrid operation. This deviation can be fully covered when comparing the cost related to the treatment of these pollutants, which is only 5.55 $/day, to the significant reduction in the operating cost obtained using the Pareto approach.

This study proposes an under-frequency load-shedding (UFLS) scheme based on a binary Archimedes Optimization Algorithm (BAOA) and the Weighted Sum Method (WSM) to maintain the stability of an islanded distribution system. These methods consider stability indices and load priorities to ensure effective load shedding during frequency deviations. The BAOA determines the optimal load shedding based on the stability index and power mismatch that minimizes the impact on critical loads while maintaining system stability in an islanded distribution system. The WSM determines the rank of the load to be shed based on four criteria: the load priority, the load category, the stability index, and the load size. Each load is assigned a weight based on its priority. These weight variables determine the order in which loads are shed during frequency deviations. The effectiveness of the proposed UFLS was tested on an 11 kV Malaysian distribution network with two mini hydro distributed generation systems. A comparative study was conducted based on five result outputs, including the number of loads shed, the size of the loads shed, the frequency undershoot, the frequency overshoot, and the time taken to achieve a stable frequency in three cases: base load, peak load, and peak load with photovoltaics (PV). The proposed UFLS showed the best results for 11 of 15 outputs (73.3%) for islanding events and 9 of 15 outputs (60%) for overloading events. The voltage profile and stability index, also, were improved after the proposed UFLS was applied.

The presented article points out the possibilities of elimination of agricultural brownfield in the East Slovak region through the resumption of production by growing fast-growing woody plants, specifically Paulownia and apple trees. Based on a detailed analysis of the necessary investment costs associated with the resumption of production and the prediction of sales of produced Paulownia and apple trees, the article presents a quantification of economic payback time from the proposed three options for resumption of crop production in the agricultural brownfield. Based on clearly identified determinants of decision-making on the optimal possibility of the revitalization of agricultural brownfield in interaction with the evaluation of the so-called weighted sum (multicriteria decisionmaking method), the article concludes by pointing out the effective possibility of resuming crop production in the revitalized area of the agricultural brownfield. The results revealed that the growth of Paulownia for the production of biomass is the best method of agricultural brownfield remediation. [ABSTRACT FROM AUTHOR]

The extraction of PV cell and module parameters is vital for the accurate simulation of PV systems. PV cell exhibits non-linear nature of I – V and P – V curves. Hence, parameters estimated at the optimum value of the current error criterion, such as root mean square error (RMSE) of current, may not give accurate power estimation at maximum power point (MPP). In this work, the shortcomings of the single objective optimization (SOO) approach based parameter estimation methods are assessed using two different problem formulations: compound objective function and weighted sum. The results from the SOO approach indicate that weightage assigned to objectives such as RMSE and the percentage relative power Error at MPP (% RPE) has impacted the accuracy of parameter estimation. The selection of weightage for each objective is indeed a difficult choice to make. Moreover, the obtained cumulative value of RMSE and % RPE is high. In order to overcome these shortcomings of the SOO approach, a multi-objective optimization (MOO) approach based parameter estimation is formulated. Then formulated MOO problem is solved by using the proposed nondominated sorting cuckoo search optimization (NSCSO). Combining nondominated sorting, a crowded comparison technique, and Lévy flight characteristics, the NSCSO algorithm is proposed, which is inspired by both the NSGA-II and NSCS algorithms. Results indicate that the proposed parameter estimation method based on the MOO approach using NSCSO has given the optimum value for cumulative RMSE and % RPE in comparison with the SOO approach using the CSO algorithm. The proposed method is also validated on various PV models: PVM 752 (GaAs thin film) cell, STM6-40 (monocrystalline), and STP6-120/36 (polycrystalline) silicon modules. • The multi objective optimization approach based parameter estimation of PV cell and module is proposed. • A new NSCSO algorithm is proposed. • Weighted Sum method is applied to formulated objective functions for parameter estimation of PV cell and module. • The deficiency of single objective optimization based parameter estimation of PV cell is established. • The performance of the NSCSO algorithm is compared with NSCS and NSGA-II algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a novel optimization approach for a day-ahead power management and control of a DC microgrid (MG). The multi-objective optimization dispatch (MOOD) problem involves minimizing the overall operating cost, pollutant emission levels of (NOx, SO2 and CO2) and the power loss cost of the conversion devices. The weighted sum method is selected to convert the multi-objective optimization problem into a single optimization problem. Then, analytic hierarchy process (AHP) method is applied to determine the weight coefficients, according to the preference of each objective function. The system’s performance is evaluated under both grid connected and standalone operation mode, considering power balancing, high level penetration of renewable energy, optimal scheduling of charging/discharging of battery storage system, control of load curtailment and the system technical constraints. Ant lion optimizer (ALO) method is considered for handling MOOD, and the performance of the proposed algorithm is compared with other known heuristic optimization techniques. The simulation results prove the effectiveness and the capability of the developed approach to deal better with the coordinated control and optimization dispatch problem.They also revealed that economically running the MG system under grid connected mode can reduce the overall cost by around 4.70% compared to when it is in standalone operation mode.

The proliferation of electric vehicles (EVs) has led to an increased demand for strategically located EV charging stations (EVCSs) to ensure a balanced and accessible charging network. The siting of EVCS involves a multifaceted process that includes technological, economic, social, geographical, and environmental factors. An EVCS network should deliver high-quality service, alleviate drivers' range anxiety, and be compatible with renewable energy system integration, while also accounting for technical infrastructure and future expansion expectations. Resting areas along highways, equipped with amenities and grid connections, emerge as potential sites for EVCS installation. Accordingly, this study presents an EVCS siting framework along highways, integrating expert opinions from diverse disciplines. The proposed method employs expert opinions to weigh site selection criteria and then uses a clustering-based approach to identify suitable locations for EVCS siting, utilizing the weighted sum method. According to the experts, the most important criterion is determined as the service level of the candidate point with a weight of 0.375, followed by traffic density (0.218), and proximity to connection roads (0.215). The method is tested both on a test highway and the Edirne-Ankara highway, which is the most heavily used in Türkiye. The results demonstrate that the test highway identifies 13 out of 18 optimal locations using the clustering strategy. Similarly, the siting method identifies 18 out of 31 optimal EVCS locations along the Edirne-Ankara highway ensuring the distance constraints. This approach is scalable and adaptable for application on highways in various countries where EVCS infrastructure is still developing. [Display omitted] • An EVCS siting framework along highways is proposed incorporating expert opinions. • The method covers economic, technological, geographical, environmental, and social factors. • The weighted sum method is implemented to solve the EVCS siting problem. • The method is implemented as a case study for the most heavily used highway of Turkey. • 18 out of 31 points are identified as the optimal EVCS locations along the highway. [ABSTRACT FROM AUTHOR]

Most of the time, people experience difficulty in identifying the best shortest paths to reach their destination while travelling. This becomes the main motive to research the shortest path identification problem. In this work, an undirected network is considered for the research evaluation of the proposed method. Previously, various types of techniques have been developed by many researchers for this shortest path issue in a networking environment. However, most of them exhibit computational complexity issues when updating their weights for every iteration. Based on this observation, the proposed work plans to solve SPP (Shortest Path Problem) in an undirected network using Fuzzy Dijkstra's approach based on improved WSM (Weighted Sum Method) with heuristic optimization. Here, the weight updating process of WSM is optimized by Sea Lion Optimization approach in order to improve the working performance of WSM while combining with Fuzzy Dijkstra method. The proposed concept is evaluated in MATLAB, and the performance evaluation is done using time consumption, energy utilization, network lifetime and prediction accuracy by comparing it with Dijkstra, Fuzzy-MPSO and WSDA methods. [ABSTRACT FROM AUTHOR]

Copyright of Acta Sting is the property of Akademie STING, o.p.s. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The important factor for the supply chain management of fresh products is partner selection. Environment protection is also an important factor, however the factor is not taken into account by the traditional supplier selection. Therefore, the supplier selection standard includes the green standard in this paper. Our work is to investigate an optimal mathematical modeling for green partner selection. The four targets of the proposed model are cost, product quality, green appraisal score and time. The proposed genetic algorithm with multi-targets are to search the set of optimum solutions using by weighted sum method. The proposed model introduces a supply chain network structure to analysis average number Pareto-optimal solutions with genetic algorithm for the four problems. It is pointed out that the variation of f2 and f3 with f1 and f4 is kept within obvious ranges. This practical result highlights the fact that the effects of the fact that effects of f2 and f3 are important factors affecting the performance supply chain network of fresh product. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shenzhen University Science & Engineering is the property of Editorial Department of Journal of Shenzhen University Science & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)