Search

The COVID-19 pandemic has increased the demand for life-saving devices known as 'ventilators,' which help critically ill patients breathe. Owing to the high global demand for ventilators and other medical equipment, many Indian nonmedical equipment companies have risen to meet this demand. This unexpected demand for ventilators during the COVID-19 pandemic, similar to that for other EOL electronic medical devices, has become a severe problem for the nation. Consequently, the healthcare industry must efficiently handle EOL ventilators, which can be outsourced to 3PRLPs. 3PRLPs play a vital role in a company's reverse logistics activities. This study emphasises the 3PRLP selection process as a complex decision-making problem and the optimisation of order allocation to qualified 3PRLPs. As a result, this study proposes a two-phase hybrid decision-making problem. First phase combines the two multi-attribute decision-making methods to select 3PRLPs based on their assessed SPS and Second phase, the evaluated SPS was utilised as one of the objectives of a multi-objective linear programming model to allocate orders to the selected 3PRLPs. To solve the proposed model, both classical and modern approaches were used. The results show that the proposed framework can be successfully implemented in the current scenario of the healthcare industry. [ABSTRACT FROM AUTHOR]

[Objective Land use change affects ecosystem services value (ESV). The aims of this study are to examine land use change, to assess ESV under multiple scenarios, and to optimize land management and maintain ecological security. [Methods] Firstly, four scenarios were set up to find the optimal solution of land use structure under different objectives through multi-objective programming (MOP). And then, the spatial distribution of land use in 2030 was predicted with the help of PLUS model. Moreover, ESV was evaluated by the equivalent factor method. Lastly, spatial autocorrelation was used to analyze the clustering characteristics, while Pearson correlation coefficient was used to explore the synergistic relationship of trade- offs. [Results] (1) From 2000 to 2020, land use types in Yinchuan City were dominated by cropland and grassland, with construction land expanding by 473.1 km², cropland and grassland decreasing. (2) The natural evolution scenario and economic priority scenario increase in construction land and decrease in grassland. The ecological priority scenario increases in forest and grassland, and decreases in cropland by 363.9 km². The sustainable development scenario has almost the same cropland and increases in forest and water area. (3) ESV shows a decreasing trend, and the overall ESV of the four scenarios is 4.718 4 billion yuan, 4.540 4 billion yuan, 5.392 4 billion yuan, and 4.971 1 billion yuan, respectively, with hydrological regulation and climate regulation dominating. (4) There are extensive and significant synergistic relationships among the 11 ESV, with low synergies between food production and others. [Conclusion] Sustainable development scenario reversing the downward trend of ESV and achieving high economic benefits can provide a basis for planning. Strengthening the protection of forest and water area is conducive to the improvement of ESV. [ABSTRACT FROM AUTHOR]

In the hybrid rotating seru production system (HRSPS), how to match multi-skilled workers and operation units is an important problem. The operation units include rotating serus and assembly line processes. This paper proposes a flexible matching method of multi-skilled workers and operation units considering possible absences of multi-skilled workers in the HRSPS. First, the principles of matching scheme generation and the principles of matching scheme adjustment under absences are proposed. Second, according to the preference information of multi-skilled workers and operation units, the preference degree calculation methods are proposed. Furthermore, a multi-objective programming model for the flexible matching of multi-skilled workers and operation units is constructed, and an improved multi-objective particle swarm optimization algorithm (IMOPSO) is proposed to solve the model better. The numerical experiment results demonstrate that the method proposed in this paper can generate and adjust matching schemes in a relatively short time, and what is more, the obtained matching schemes and matching adjustment schemes have advantages in the satisfaction of multi-skilled workers and operation units. Therefore, the method proposed in this paper is feasible and effective. [ABSTRACT FROM AUTHOR]

In this paper, we present two methods to find the strictly efficient and weakly efficient points of multi-objective programming (MOP) problems in which their objective functions are pseudo-convex and their feasible sets are polyhedrons. The obtained efficient solutions in these methods are the extreme points. Since the pseudo-convex functions are quasi-convex as well, therefore the presented methods can be used to find efficient solutions of the (MOP) problem with the quasi-convex objective functions and the polyhedron feasible set. Two experimental examples are presented. [ABSTRACT FROM AUTHOR]

Introduction: Due to the expansion of population, economic progress, urbanization, increasing food demands, and diversification of food systems, resources are being excessively exploited and degraded. This is compounded by the challenges posed by climate change and limited resources, as well as inadequate management practices. The concept of water-energy-food (WEF) nexus management recognizes the interdependencies among various resources, such as water, food, and energy, in order to promote sustainable resource management. By establishing a harmonious balance among different objectives, this approach aims to safeguard the well-being of both human societies and the environment, ensuring the fulfillment of needs and the preservation of benefits for both parties. Methods: In this study, the water-energy-food (WEF) nexus approach is applied to the Sistan plain, located in the southeastern region of Iran, to effectively redistribute production resources within the agricultural sector. The methodology employed is multi-objective programming, which incorporates various goals. These objectives encompass maximizing farmer revenue and energy derived from food production (measured in calories), while simultaneously minimizing greenhouse gas (GHG) emissions, irrigation water consumption, and overall energy consumption throughout the 2018–2019 crop year. Results: The findings of this study demonstrate that implementing the water-energy-food (WEF) nexus approach in the Sistan plain yields positive outcomes. Despite a reduction in the cultivation area, there is a notable shift towards growing more nutritious crops. This shift not only contributes to food security but also increases crop calorie production from 457.16 million to 565.19 million. Consequently, there is a decrease in irrigation water consumption from 261.62 million to 260.48 million cubic meters, energy consumption from 1400.13 million to 1396.81 million MJ per hectare, and greenhouse gas (GHG) emissions from 0.014 million to 0.0139 million tons per hectare. Discussion: Analyzing the physical and economic productivity reveals that GHG emissions had the highest productivity in terms of both physical and economic measures in Zahak County. As the WEF nexus approach aims to preserve and prevent environmental degradation, it is recommended to implement development and bio-balance policies utilizing this approach to ensure environmental conservation. [ABSTRACT FROM AUTHOR]

The selection of human assets for teams significantly impacts the success and profitability of projects. Industrial Revolution 4.0 (4IR) and the post-COVID-19 conditions impose requirements on virtual collaboration, bots-human collaboration, and teleworking projects. The matrix-structured organization faces challenges in this process because it requires weighing various criteria from distinct perspectives. Accordingly, an inappropriate team selection process can result in high costs or failure. Team member competency criteria are identified based on 4IR, in this study. The study also evaluates the theory of generations based on the fact that project teams consist of members from different generations, each with unique characteristics. To this end, a multi-objective allocation model is presented that maximizes competency level while minimizing costs, considering the organizational structure, the 4IR, the post-COVID-19 era condition, and the generation theory. The study attempts to provide decision-makers in multiple-project organizations with a realistic picture to make a trade-off between the cost and competency level of teams. The linear best-worst method (BWM) is used to weigh the competency criteria. Regarding the developed bi-objective model, the Augmented ε- Constraint (AUGMECON) method is utilized to solve the problem. The model is also validated using the Iran Mall project. The findings indicate that younger generations have almost 1.3 more competence scores in virtual communication than older generations. Also, the organization should increase expenditures by 7.1% to reach the highest level of competency. [ABSTRACT FROM AUTHOR]

Purpose: Generally, selecting an investment portfolio with appropriate returns that is also secure and auditable has been one of the issues raised in recent decades. For this purpose, the present research proposes an appropriate approach using ideal and anti-ideal values, ideal values, as well as maximum deviations of each objective, considering the sample in the examined market, fuzzy goals, interval fuzzy values for each asset, and their combination with satisfaction functions, fuzzy ideal planning, and weighting objectives using expert decision-makers' opinions, as well as the development of fuzzy basic weighting method. It seeks to select an investment portfolio in the digital currency market.Methodology: In this research, a new approach to selecting an investment portfolio based on uncertain data and multi-objective uncertain planning is proposed, and ultimately, the proposed approach is implemented in the digital currency market for portfolio selection.Findings: The results of the present study show that the proposed model of investment portfolio compared to the base model not only led to higher returns but also had higher audibility and better risk control. In other words, the proposed model outperformed the base model in all the objectives under study.Originality/Value: As distinguishing features of the proposed model of this research, one can mention: 1) constructing and using fuzzy distribution functions and calculating ideal values and expected ranges for all desired objectives considering the conditions of the examined market research using simple mathematical modeling, 2) utilizing the experience of financial market experts in planning model for selecting suitable investment portfolios in emerging financial markets, 3) presenting an approach to calculating portfolio risk in conditions of information scarcity in the problem environment using fuzzy theory, 4) development of the fuzzy benchmark-criterion method for weighting the objectives under study in the problem considering the expertise of financial market experts, and 5) simple modeling, considering interval fuzzy values in the model, and being usable for all individuals with different levels of investment knowledge.

Multi-objective mathematical programming subject to box constraints on system parameters is an important problem in the fields of computer-aided design and decision-making theory, when there is an apparent conflict between multiple preference criteria. In this work, mathematical programming methodology has been proposed using a modern algorithm to solve multi-objective mathematical problems. The proposed algorithm to perform the ranking and classification procedures for the set of alternative solutions with boundary constraints is based on the concepts of parametric multi-aspect modeling of flexible complex systems. Interactive software has been implemented in the .Net environment to aid the decision-maker to identify the preference criteria and constraints on the system parameters, and then to choose the best solution from the morphological set depending on the developed algorithm. In addition, an illustrative numerical example solved by the proposed algorithm is presented at the end of this work.

Intelligent algorithms have shown promise in supporting marketing strategy decisions through data mining. However, existing methods have primarily relied on expertise, lacking autonomous decision-making abilities. Consequently, a marketing strategy decision model based on particle swarm optimization and multi-objective programming is proposed. This study first explores the potential for integrating particle optimization and multi-objective programming models partially, and then assesses the overall effectiveness of each marketing strategy by defining a fitness function. Subsequently, the particle swarm optimization algorithm is employed to search for and optimize decision variables to identify the optimal combination of marketing strategies. Finally, several simulation experiments are conducted using external real data. The research findings indicate that the algorithm's error rate in this study was initially 0.23. However, after 500 training sessions, it decreased to 0.08 and maintained a relatively low level. The proportion of marketing strategy revenue increased by 15.2 percentage points between 0 and 100 training sessions, then remained relatively stable at over 30%. Its revenue proportion continued to rise during the training process, significantly surpassing that of other algorithms. [ABSTRACT FROM AUTHOR]

Land use conflict is an inevitable and objective phenomenon during regional development, with significant impacts on both regional economic growth and ecological security. Scientifically assessing the spatiotemporal evolution of these conflicts is essential to optimize land use structures and promote sustainable resource utilization. This study employs multi-period land use/land cover remote sensing data from China to develop a model for the measurement of land use conflict from the perspective of the landscape ecological risk. By applying the optimal landscape scale method to determine the most appropriate analysis scale, this research investigates the spatiotemporal evolution characteristics of land use conflicts in the Central Yunnan Urban Agglomeration from 2000 to 2020. Furthermore, by integrating the Patch-Generating Land Use Simulation (PLUS) model with the Multi-Objective Programming (MOP) algorithm, this study simulates the spatial patterns of land use conflict in 2030 under four scenarios: Natural Development (ID), Economic Development (ED), Ecological Conservation (PD), and Sustainable Development (SD). The findings reveal that, from 2000 to 2020, the proportion of areas with strong and moderately strong conflict levels in the Central Yunnan Urban Agglomeration increased by 2.19%, while the proportion of areas with weak and moderately weak conflict levels decreased by 1.45%, underscoring the growing severity of land use conflict. The predictions for 2030 suggest that the spatial pattern of conflict under various scenarios will largely reflect the trends observed in 2020. Under the ID scenario, areas with weak and moderately weak conflict levels constitute 57.5% of the region; this increases by 0.85% under the SD scenario. Conversely, areas experiencing strong and moderately strong conflict levels, which stand at 33.02% under the ID scenario, decrease by 1.04% under the SD scenario. These projections indicate that the SD scenario, which aims to balance ecological conservation with economic development, effectively mitigates land use conflict, making it the most viable strategy for future regional development. [ABSTRACT FROM AUTHOR]

In this study, the supply chain system of the food supply chain (FSC) is assessed. Initially, the operations of farmers are examined using the Data Envelopment Analysis (DEA) technique. Then, locations of relatively efficient farmers are used as potential collection sites for the distribution at the downstream operations. Next, the Multi-Objective Optimization Model is proposed to examine location and distribution channels under the sustainability paradigm. That is, the economic criterion is primarily considered by focusing on minimizing the total supply cost. The social criterion is next deliberated by evaluating the equality aspect of farmers for the fair proportion of vegetable supply. Then, the environmental criterion is incorporated by assessing the CO2 emissions of food transportation activity. Additionally, a trade-off analysis is analyzed to investigate the conflicting behavior of model using the Non-Preemptive Programming method. The sensitivity analysis is further examined by varying the time-requirement parameter to verify the model functionalities. Finally, the regional case study in Thailand based on the food supply data is applied to validate the model. Given that farmers are distant from diverse locations, the results of our study can provide a strategic choice for key decision-makers in the FSC network under sustainability consideration. [ABSTRACT FROM AUTHOR]

One of the most important decisions that farmers make is the allocation of resources in an optimal manner, which is often done by determining the optimal cropping pattern. The purpose of this study was to present a cultivation model compatible with the agricultural ecosystem of Shiraz Plain, Fars Province, Iran, by quantifying the environmental effects of agricultural production using the Life Cycle Assessment (LCA) approach. The results of LCA showed that cultivation of crops such as lentils, onions, and tomatoes had the most negative environmental effects. The ecosystem quality index for crops in this plain varied between 0.03 and 3.64 PT. The highest negative impact of crop cultivation on the quality of the ecosystem was attributed to onion, tomato, and rain-fed lentils. The results of multi-objective planning showed that farmers can achieve their economic objectives and policymakers’ environmental goals through reducing the area under cultivation. By changing the cropping pattern towards the suggested pattern for Shiraz Plain, an average decrease of 5.60% in profit was expected. However, this change is an effective step in controlling consumption of water, chemical fertilizers, and pesticides. Achieving sustainable agriculture in terms of economic and environmental indicators is possible by reducing the cropland area and economic profit by 18.05% and 11.43%, respectively. [ABSTRACT FROM AUTHOR]

Multi-objective mathematical programming subject to box constraints on system parameters is an important problem in the fields of computer-aided design and decision-making theory, when there is an apparent conflict between multiple preference criteria. In this work, mathematical programming methodology has been proposed using a modern algorithm to solve multi-objective mathematical problems. The proposed algorithm to perform the ranking and classification procedures for the set of alternative solutions with boundary constraints is based on the concepts of parametric multi-aspect modeling of flexible complex systems. Interactive software has been implemented in the .Net environment to aid the decision-maker to identify the preference criteria and constraints on the system parameters, and then to choose the best solution from the morphological set depending on the developed algorithm. In addition, an illustrative numerical example solved by the proposed algorithm is presented at the end of this work. [ABSTRACT FROM AUTHOR]

Purchasing and procurement managers should make informed decisions in selecting materials at the right time, in sufficient quantities, and at affordable prices. Supplier selection and order allocation (SSOA) is a vital aspect of purchasing and procurement processes. In this research, the techniques and decision-making methods used in SSOA from peer-reviewed journals published from 2021 to 2023 are examined. This research explores the publications through three major categories, including literature reviews (LR), deterministic optimization (DO) models, and uncertain optimization (UO) models. The related operations research techniques are also discussed. Furthermore, observations, conclusions, and suggestions for future studies are provided with details. [ABSTRACT FROM AUTHOR]

A significant challenge in asset management is the selection of investment projects for infrastructures, which often relies on subjective judgement and lacks structured decision support methods. This challenge is particularly complex in water systems due to the diverse and heterogeneous nature of the components requiring investment. While the infrastructure value index (IVI) is widely used to characterise assets and support investment decisions in the water sector, its application in optimisation models for generating efficient project portfolios remains unexplored. To address this research gap, this study introduces optimisation models for generating investment portfolio plans in water systems' asset management. The proposed approach includes two mixed‐integer linear programming (MILP) models that determine optimal solutions and an evolutionary algorithm that offers sub‐optimal alternative investment selection plans to provide decision‐makers with additional choices for balancing optimal outcomes. The primary contribution of this research is the combined utilisation of MILP and evolutionary algorithms, integrating the IVI into the decision‐making process. These tools provide decision‐makers with structured methods for defining investment plans and minimising the subjective elements typically associated with such processes. To illustrate the effectiveness of the models, a case study is presented involving a pumping station of a Portuguese water company. The results demonstrate the practical application and benefits of the proposed approach in optimising investment decisions. This research contributes to advancing asset management practices by integrating quantitative optimisation techniques and leveraging the IVI, thereby enhancing the objectivity and efficiency of investment planning in water systems' asset management. [ABSTRACT FROM AUTHOR]

Population growth has led to more food demand, especially meat. Designing a supply chain, especially a meat one, is complicated due to the uncertainty of food demand and the perishability of meat. To this aim, we develop a multi-objective mixed-integer linear programming model. The developed model contains four echelons, i.e., farms, slaughterhouses, retailers, and customers. The first objective function minimizes the total costs, the second objective minimizes the distribution time, and the third objective minimizes the network's non-resiliency simultaneously. An enhanced version of the augmented e-constraint method is employed to solve the suggested model, and a set of Pareto-optimal solutions is found. This study also explores the impact of using the robust possibilistic approach in modeling a supply chain network under uncertainty. Numerical experiments demonstrate that the robust optimization approach brings significantly superior outcomes in comparison to the conventional deterministic approach, and the model provides a practical and valuable tool for real-world supply chain challenges. [ABSTRACT FROM AUTHOR]

Tourists going out for a trip encounter various uncertainties, such as weather conditions, road conditions, the tourists' consumption budget, travel time, and other uncertainties. Tourists focus on the goals of minimizing travel time and consumption costs while maximizing personal satisfaction with the route; therefore, the multi-objective programming model for an uncertain tourism route problem is established based on uncertainty theory. The objectives of the model are to minimize the travel time and consumption cost and to maximize the tourists' satisfaction with the route. According to inverse uncertainty distribution, the model can be transformed into a traditional programming model and solved by the ant colony algorithm (ACO). Finally, in order to solve the uncertain tourism route programming problem, a numerical example is given to show the application of the model. [ABSTRACT FROM AUTHOR]

In the offensive operations of the red and blue sides, in order to fully seize the battlefield air superiority, the blue side air defense fire units are usually taken as the first key targets. Therefore, it is of great significance to accurately predict the activity law of the blue side air defense fire units and scientifically design the joint reconnaissance and attack actions. Firstly, the maneuver support mode adopted by the air defense fire unit is systematically analyzed, the defense capability calculation formula is given based on the definition of defense capability, and the grid network model is introduced to complete the quantitative calculation. Based on the support scope constraint, position resource constraint and maneuver process constraint, the shortest maneuver route and the highest defense capability are taken as the optimization objectives for the actual support requirements. A prediction model for maneuvering route of ground air defense fire units is established. Secondly, aiming at the problem of solving the nonlinear model, the model linearization transformation and solving method are proposed. Then, on the basis of the optimal transfer route, an algorithm for constructing maneuver route library is designed to generate the probabilistic prediction scheme of blue side maneuver route and analyze the maneuver law of blue side air defense fire units. Finally, based on the case simulation, the feasibility and global efficiency of the designed model and method are verified, which can effectively predict the maneuvering route of blue side anti-aircraft fire units. [ABSTRACT FROM AUTHOR]

This paper is mainly concerned with some of the theoretical aspects of equitable multi-objective optimization. By using the equitability preference structure, we discuss some properties of the equitably nondominated set, such as nonemptiness, external stability and connectedness. Also, we introduce the concept of proper equitable nondominance, and show that these solutions can be obtained by minimizing a weighted sum of the sort of objective functions where all weights are positive and decreasing. Moreover, we present a hybrid scalarization problem to generate equitably nondominated solutions. This method also provides a necessary condition for the existence of properly equitable nondominated solutions. [ABSTRACT FROM AUTHOR]

In multi-stage processes, classical Data Envelopment Analysis (DEA) acts like a black box and measures the efficiency of decision-making units (DMUs) without considering the internal structure of the system. Unlike classical DEA, recent studies have shown that the overall system efficiency scores are more meaningful if researched using the Network DEA (NDEA) methodology. NDEA performs simultaneous efficiency evaluations of sub-processes and the entire system. Recently, the composition method integrated with multi-objective programming (MOP) has been preferred by many authors to alleviate the drawbacks of earlier methods such as decomposition, slack-based measure (SBM) and the system-centric approach. This study proposes a novel approach incorporating Multi-Choice Conic Goal Programming into the NDEA (MCCGP-NDEA). It provides a more accurate representation of the Pareto front by revealing potential Pareto optimal solutions which are overlooked by the composition methods. Due to its ability to modify stage weights based on the decision makers' (DMs) preferences, it is likely to gather more samples from the Pareto surface. Computational results on available benchmark problems confirm that the proposed MCCGP-NDEA is a viable alternative to existing methods. [ABSTRACT FROM AUTHOR]

This academic paper delves into the intricacies of dining out and dietary management, aiming to provide an effective solution to the multifaceted challenges faced by regular out-diners in Taiwan. The research centers on the development and application of an innovative hybrid model that combines the analytic hierarchy process (AHP) and multi-objective programming (MOP) to facilitate dietary planning, thereby accommodating individual preferences, expert insights, and the recommended daily intake levels for six categories of essential nutrients. The model is designed to enable a dynamic adjustment of preference coefficients when decision-makers provide their preferences, resulting in personalized dietary recommendations. However, it is noted that solutions adhering to predefined budget constraints may sometimes fall short of identifying entirely satisfactory dietary combinations. Furthermore, a significant challenge identified in this study pertains to the availability of food products at chain restaurants and stores. These products often exhibit deficiencies in essential nutrients while offering an excess of dietary energy. The research reveals that when individuals adhere to recommended dietary combinations, they can attain nutrient intake levels that closely approximate suggested values. In this study, the AHP–MOP model demonstrates enhanced stability and superior adherence to the principles of healthy dietary planning, ultimately yielding dietary combinations associated with a higher perceived value within the same budgetary constraints than the MOP-only model. With the regional limitation of model, the study underscores the potential for enhancing the model's practicality by expanding the product database, thereby contributing to the improved dietary well-being of regular out-diners. [ABSTRACT FROM AUTHOR]

Multi-objective transportation problems (MOTPs) are mathematical optimization problems that involve simultaneously considering multiple, often conflicting objectives in transportation planning. Unlike traditional transportation problems, which typically focus on minimizing a single objective such as cost or distance, MOTPs aim to balance multiple objectives to find the optimal solution. These problems appear in various real-world applications such as logistics, supply chain management, and transportation, where decision-makers need to consider multiple criteria when designing transportation networks, routing vehicles, or scheduling deliveries. The primary challenge lies in the uncertainty in real-world transportation scenarios, where logistics involve factors beyond cost and distance. We investigated a multi-choice solid fractional multi-objective transportation problem (MCSF-MOTP) based on supply, demand, and conveyance capacity, where the coefficients of the objective functions were of the multi-choice type due to uncertainty. To address this uncertainty, the proposed model employed the Newton divided difference interpolation polynomial method, and the suitability of this model was validated through a numerical illustration employing a ranking approach. [ABSTRACT FROM AUTHOR]

Sustainable management of water, energy, and food (WEF) under climate change will be a significant challenge for arid agricultural systems. This study developed a fractional non-linear multi-objective programming (FNLMOP) model to optimize resource allocation and improve agricultural sustainability in these systems under climate change. The model was designed in the framework of the WEF nexus to simultaneously improved energy productivity (profit/energy), and water productivity (profit/water), while mitigating environmental damage (damage to groundwater resources/output) and ensuring food security in an arid watershed in Iran. The long Ashton research station weather generator (LARS-WG) and the coupled model intercomparison project 6 (CMIP6) were employed to project climate parameters for both future dry and wet conditions. The sustainability of the optimal solutions was then assessed using a hybrid criteria importance through intercriteria correlation (CRITIC)-VIKOR approach. The optimal solutions revealed a reduction in the land under cultivation and produced less water-intensive crops. The optimization model can ensure WEF security, enhancing agricultural system sustainability by optimizing crop cultivation patterns and resource allocation. Current crop choices were highly inefficient with the bigger changes being from the current crops to optimal crops. Climate change showed a substantial but lesser influence on optimal crop choice.

The temporal variability and spatial heterogeneity characteristics of the water-agriculture-ecology (WAE) nexus system have aggravated the difficulties in its synergetic management. Besides, in the inland river basin, the surface water and groundwater are tightly linked by the combination of canal and well irrigation. To address these issues, a spatiotemporal equilibrium-water transformation based water-agriculture-ecology nexus synergetic management (SEWT-WAE) model was proposed by incorporating a spatio-temporal robust optimization method and linear water transformation model. The SEWT-WAE model was then applied to the Aksu River Basin, an inland river basin of Xinjiang, China. The results indicated that the SEWT-WAE model was highly effective in achieving spatiotemporal equilibrium in groundwater balance and ecological water utilization, as well as in the integrated management of surface water and groundwater across upstream and downstream regions. The optimal synergetic management scheme was obtained based on the coordinated development degree. Compared to the current situation: (i) the irrigation amount provided by the surface water (groundwater) in the Tabei (Tanan) irrigation district was increased (decreased) by 21.4 % (70.2 %); (ii) the irrigated areas of grain crops and gardens were increased by 30.4 % and 20.1 %, respectively, while the irrigated area of cotton was decreased by 19.4 %; (iii) the ecological water utilization of the Populus euphratica forest was increased by 17.81 %. Overall, this study presents a new optimization model for achieving spatiotemporal equilibrium and conjunctive use of surface water and groundwater and provides decision support for WAE nexus synergetic management in the inland river basin.

Land use change in emerging nations raises landscape ecological risks (LERS), hastens the deterioration of urban and rural ecosystem services, endangers human well-being, and undermines sustainable development in the face of rapidly increasing urbanization. Here, using the Chengdu Plain as the study area and long time series data from 2000 to 2020, the optimal time span is selected for multi-context spatio-temporal simulation. The land use map of Chengdu Plain in 2025 under the four scenarios of “Natural Development” (ND), “Economic Priority Development” (END), “Ecological Priority Development” (ELD), and “Sustainable Development” (SD) was simulated, and the multi-indicator landscape ecological risk index (ERI) was generated to compare and analyze the differences between land use and landscape ecological risk under different policy preferences. Subsequently, the land use data from 2025 to 2040 were simulated, the landscape ecological risk pattern was mapped, and the spatial and temporal evolution analysis from 2010 to 2040 was conducted to explore the spatial evolution law of land use change and landscape ecological risk. Based on the results, the high ecological risk aggregation areas are prone to appear in END scenarios, whereas medium-ecological risk aggregation areas are more likely to appear in ELD scenarios, and the government should focus its policy on arable land protection. Moreover, the land use pattern of cultivated land surrounding construction land and forested land surrounding cultivated land, caused by the irrational single-core development pattern and the policy of returning farmland to forests, has exacerbated the landscape ecological risk of the Chengdu Plain, constituting a unique landscape ecological risk pattern. It’s also important to remember that the Chengdu Plain’s less economically developed regions need to focus on the high-quality development of ecological land use. We adopted high-precision simulation methods to simulate the complex land use changes in rapidly urbanizing areas and explored the spatial evolution law and causes of landscape ecological risk evolution in the context of land use changes, with the intention of offering a solid theoretical foundation for such areas’ future planning in developing nations.

Multi-objective transportation problems (MOTPs) are mathematical optimization problems that involve simultaneously considering multiple, often conflicting objectives in transportation planning. Unlike traditional transportation problems, which typically focus on minimizing a single objective such as cost or distance, MOTPs aim to balance multiple objectives to find the optimal solution. These problems appear in various real-world applications such as logistics, supply chain management, and transportation, where decision-makers need to consider multiple criteria when designing transportation networks, routing vehicles, or scheduling deliveries. The primary challenge lies in the uncertainty in real-world transportation scenarios, where logistics involve factors beyond cost and distance. We investigated a multi-choice solid fractional multi-objective transportation problem (MCSF-MOTP) based on supply, demand, and conveyance capacity, where the coefficients of the objective functions were of the multi-choice type due to uncertainty. To address this uncertainty, the proposed model employed the Newton divided difference interpolation polynomial method, and the suitability of this model was validated through a numerical illustration employing a ranking approach.

To cope with the potential threat caused by climate change, reducing carbon dioxide (CO2) emissions, which are mainly derived from fossil fuels, is the top priority in curbing global warming. Taiwan claims that its target for intended nationally determined contribution is to achieve a 50% reduction in the level of business-as-usual greenhouse gas emissions by 2030, which is equivalent to a decrease in emissions by 20% compared to the 2005 level. To reach the intended nationally determined contribution target for 2030, planning a long-term project is necessary. Therefore, the study proposes a multi-objective optimization model to program the industrial structure of Taiwan from 2022 to 2030 for a near-optimal trade-off between gross domestic product and CO2 emissions. The results indicate that industries with high emission rates, such as the chemical material and primary metal industries, must impose actions to significantly reduce emissions. In addition, high energy-intensive industries should not expand their scales to maintain sustainable development. On the contrary, the electrical machinery industry should be further developed. The findings can provide helpful information for policymakers and serve as a reference for future industrial development in Taiwan. [ABSTRACT FROM AUTHOR]

Bilevel programming is characterized by the existence of two optimization problems in which the constraint region of the upper level problem is implicitly determined by the lower level optimization problem. This hierarchical design of optimization is suitable to model a large number of real-life applications. However, when dealing with a non linear multi-objective optimization context, new complexities arise due to conflicting objectives. In this paper, an exact method is described to solve an integer indefinite quadratic bilevel maximization problem with multiple objectives at the upper level, where the objective functions at both levels are the product of two linear functions. The algorithm suggested aims to produce a set of efficient solutions by employing a branch and cut approach. It optimizes the indefinite quadratic problem of the upper level within the feasible region of the original problem in an iterative manner. Then, it introduces the Dantzig cut technique to identify the optimal solution for the integer indefinite quadratic bilevel programming problem. Additionally, the algorithm utilizes an efficient cut that reduces the search process for obtaining the set of efficient solutions of the main problem, along with a branching constraint for the integer decision variables. The algorithm was implemented and tested on instances generated randomly, yielding positive outcomes. [ABSTRACT FROM AUTHOR]

Objective The availability of energy is a vital aspect of a nation's economic and social development, with energy consumption serving as a telling metric of the level of prosperity that can be achieved. However, the conventional systems of electricity production that rely on large, centralized power plants have become inadequate in recent years due to the high expenses of production, air pollution, and poor energy quality. In response to these challenges, smart grids have emerged and offer several advantages. Effective management of electricity demand is critical in the context of smart grids, and the implementation of demand-response techniques plays an instrumental role in achieving this objective. These programs enhance energy consumption patterns during peak load times, resulting in appropriate pricing and grid reliability. There are two distinct categories of load response programs: price-oriented and incentive-oriented. In the scope of this research, we focuse on the former, which relies on real-time pricing. Our objective is to develop a multi-objective mathematical model that considers load response programs for smart energy grids. Methods The study employs a scenario-based approach and classifies the parameters into two distinct categories: deterministic and non-deterministic. Wind speed, solar radiation, energy demand, and local electricity prices are marked as non-deterministic due to their nature. As each non-deterministic parameter adheres to a specific probability distribution, a scenario is created for each parameter based on its corresponding distribution. Subsequently, a mathematical multi-objective model is developed that aimes to minimize operating costs, reduce pollution emissions, and minimize peak load, along with the related constraints. After collecting the required data, the model is run using the GAMS programming language. In addition, the study evaluates the impact of load response programs on enhancing objective functions. Results The study findings demonstrate that the implementation of smart grids, accompanied by active consumer participation in load response programs, can result in a significant reduction in operating costs, pollution emissions, and peak load. Additionally, the study indicates that a higher level of consumer participation in load response programs can enhance the overall effectiveness of the programs. Specifically, the study shows that a 20% increase in consumer participation resulted in a 15%, 17%, and 13% improvement in operating costs, pollution emissions, and peak load reduction, respectively. Conclusion Smart grids represent a modern digital solution that streamlines the transfer of electricity between suppliers and consumers in the realm of energy transmission. This advanced system enables the regulation of home appliances, promoting energy conservation and cost- effectiveness, while simultaneously enhancing the reliability of the energy transmission network. Governments may opt to implement smart grids as a strategic solution to address complex issues such as energy independence, global warming, and pollution emissions. [ABSTRACT FROM AUTHOR]

Given the contradiction between the rapid growth of products and the modest recovery rate of end-of-life products, there is a pressing need to understand the societal significance of establishing a reverse logistics network for end-of-life products. This research constructs an open-loop five-tier reverse logistic network model encompassing customers, centres for collection, disassembly and inspection, remanufacturing, and disposal. A multi-objective mixed-integer nonlinear programming model under uncertainty has been developed. Unlike previous research, this model accounts for surrounding residents' disutility of facilities while simultaneously minimizing economic costs and environmental impact. Besides, uncertainty theory is introduced in solving the proposed model. More specifically, the formulated model converts all uncertain variables into uncertain distributions by implementing the uncertain multi-objective programming method. Furthermore, a customised non-dominated sorting genetic algorithm III (NSGA-III) is proposed and is employed for the first time to address facility selection and recycling volume distribution within the network. The model is then validated using a real-life case study focusing on end-of-life vehicles in Changchun, China. This research could assist decision-makers in both governmental and private sectors in achieving a balanced approach to the interests of the economy, environment, and local communities comprehensively when designing reverse supply chains.

One of the main challenges of food bank warehouses in developing countries is to determine how to allocate perishable products to beneficiary agencies with different expiry dates while ensuring food safety, meeting nutritional requirements, and minimizing the shortage. The contribution of this research is to introduce a new multi-objective, multi-product, and multi-period perishable food allocation problem based on a single warehouse management system for a First Expired-First Out (FEFO) policy. Moreover, it incorporates the temporal aspect, guaranteeing the dispatch of only those perishable products that meet the prescribed minimum quality standards. A weighted sum approach converts the multi-objective problem of minimizing a vector of objective functions into a scalar problem by constructing a weighted sum of all the objectives. The problem can then be solved using a standard constrained optimization procedure. The proposed mixed integer linear model is solved by using the CPLEX solver. The solution obtained from the multi-objective problem allows us to identify days and products experiencing shortages. In such cases, when there is insufficient available inventory, the total quantity of product to be dispatched is redistributed among beneficiaries according to a pre-established prioritization. These redistributions are formulated as integer programming problems using a score-based criterion and solved by an exact method based on dynamic programming. Computational results demonstrate the applicability of the novel model for perishable items to a real-world study case.

Purchasing and procurement managers should make informed decisions in selecting materials at the right time, in sufficient quantities, and at affordable prices. Supplier selection and order allocation (SSOA) is a vital aspect of purchasing and procurement processes. In this research, the techniques and decision-making methods used in SSOA from peer-reviewed journals published from 2021 to 2023 are examined. This research explores the publications through three major categories, including literature reviews (LR), deterministic optimization (DO) models, and uncertain optimization (UO) models. The related operations research techniques are also discussed. Furthermore, observations, conclusions, and suggestions for future studies are provided with details.

The aim this study is presenting the solution methodology of multiobjective fuzzy transportation problem with fuzzy decision variables, where all the input parameters and decisions variables of the programming problems are assumed to be triangular fuzzy number and triangular fuzzy decision variables respectively. Moreover the objectives under considerations are minimization of cost of transportation and minimization of shipping time under fuzzy environment. The fuzziness of the objective functions and the fuzzy constraints of the programming problem are defuzzified using the ranking function and the equality property between two fuzzy numbers, respectively. The consequent crisp multi-objective fuzzy transportation problem is tackled by employing fuzzy mathematical programming approach. Finally fuzzy decision is made after solving the resultant mathematical programming problem using LINGO(Schrage and LINDO Systems (1997)) software. Illustrative numerical example is presented in support of the proposed methodology.

The focus of this paper is to suggest a solution methodology for a fully fuzzy multi-objective quadratic programming problem. Solving the provided mathematical model using known classical methods is extremely difficult. To solve the present mathematical programming problem, three major approaches are suggested. In first step, we used arithmetic operations between two fuzzy parameters and variables. The importance is given in the next step to handle fuzzy part of objective functions by ranking functions and after the completion of the second step, the fuzzy part of the fuzzy constraints tackled by the inequality property of between two triangular fuzzy numbers. Finally, the transformed multi-objective quadratic mathematical programming problem is solved using a weighted fuzzy goal programming approach. The final solution of the suggested model is derived using existing methodology and softwares. The working procedures of the proposed method is further discussed using numerical example. [ABSTRACT FROM AUTHOR]

Xinjiang, located in the arid region of the northwest, is an important agricultural province and water resources are an important guarantee and a necessary condition for the development of local agriculture. Taking Shache irrigation area of Xinjiang as an example, this paper discussed the possibility of promoting the sustainable development of agricultural economy in the irrigation area by optimizing the crop planting structure and rationally using water resources according to the “14th Five-Year Plan” for Kashgar region and the outline planning document for the long-term goals in 2035. Specifically, from the three perspectives of agricultural ecological benefits, economic benefits and irrigation water consumption, the multi-objective comprehensive optimization model was built in this research to adjust and analyze the crop planting structure in Shache irrigation area. The results show that a) the optimization of crop planting structure in the current year can increase carbon sequestration by 0.8 t, economic benefits by 584 538 800 Yuan and save 1 110 200 m³ of total agricultural irrigation water. b) After the multi-objective planning of the crop planting area in 2025 and 2035, the ecological and economic benefits will be greatly improved, the water saving effect will be remarkable and the purpose of adjusting the crop planting structure will be achieved. [ABSTRACT FROM AUTHOR]

Shipbrokers play a key role in maritime industry by acting as intermediates between shipping companies and the market. They undertake various chartering, buying or selling operations. In this paper, we propose a mathematical programming approach for the evaluation and selection of shipbrokers. Specifically, the score of each ship broker is a composite measure that is derived by aggregating a set of performance criteria, e.g., reputation, etc. The developed mathematical programming models enable the aggregation and weighting of the criteria. We employ three optimization models to explore the effect of different weighting schemes on the scores and ranking of the shipbrokers. The models that provide a common set of weights for all the shipbrokers establish the appropriate ground for comparisons among them. Also, our models facilitate the incorporation of user priorities over the criteria in the form of weight restrictions. The proposed approach is illustrated by assessing seven shipbroker offers for selling a dry-bulk ship using four criteria, namely revenue, brokerage fee, brokerage time and terms & conditions. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an exact algorithm for optimizing a linear fractional utility function over the efficient set of a multi objective integer quadratic problem. The algorithm is based on the "Branch and Cut" principle, which combines the branching process to ensure decision variables' integrity and efficient cuts built off the non-increasing gradients' directions of objective functions to eliminate inefficient integer solutions. The proposed approach accelerates the convergence to the efficient solution that optimizes the utility function. After presenting and describing the algorithm, a detailed didactic example is illustrated, followed by an experimental study to validate our approach and show computational costs. [ABSTRACT FROM AUTHOR]