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Several real-life problems are comprised of finite single-server acyclic queueing networks. The performance optimization of these queueing networks has been the subject of several studies. The present study extends the minimization of the total buffer area and overall service rates in the network simultaneously with the maximization of throughput. It is well known that these three objectives are conflicting. This fact leads to a multi-objective approach that the literature in the area has already addressed. Nevertheless, this study aims to demonstrate that there are algorithms with low computational costs that can produce solutions more efficiently than those obtained previously. Furthermore, the provided solutions can enhance throughput by solving a stochastic knapsack problem. The greedy procedure utilizes a technique of redistributing buffers between the queues, ensuring that the overall capacity is less than or equal to the previous overall capacity; thus, one objective is improved (the throughput) without compromising the other objective (total buffer allocation). Several computational experiments attest to the quality of this proposition. In addition, we provide a comparison with previously proposed solutions. [ABSTRACT FROM AUTHOR]

Comprehensive data on machines is essential for digitization in the industry. Since standardized machine elements are used in most machines, integrating them with sensors provides the opportunity to acquire data comprehensively from in-situ. The sensor integration must not change the element’s core function and the standardized mechanical interfaces. Hence, sensors must be fully integrated and self-sufficient. Regarding bolts as widely used machine element, a solution that combines all the aforementioned requirements does not yet exist. A main problem in developing sensor-integrating machine elements and especially bolts is to overcome the conflicting objectives in defining design space for mechanical and sensory functions. There is a lack of an approach to model the effects of the design space parameters on the mechanical and sensory functions. This paper proposes an optimization function to aid mechanical and electrical engineers in resolving conflicting objectives by balancing function fulfillment when determining design space parameters. Therefore, the effects of the parameters on the mechanical and sensory functions are modelled using FE-analysis and composing an optimization function with weights. This function provides optimal design space parameters. With respect to mountability boundary conditions the optimum for equal weights is at diameters 13.2mm both, increasing v. Mises stress by 29% and strain at sensor position by 80%. The location of the optimum is very dependent on proper weighting, which resembles a prioritization of the mechanical versus the sensory function fulfillment. This enables engineers to find optimal parameters by balancing the mechanical and the sensory function fulfillment.

UAV mission planning is one of the core problems in the field of UAV applications. Currently, mission planning needs to simultaneously optimize multiple conflicting objectives and take into account multiple mutually coupled constraints, and traditional optimization algorithms struggle to effectively address these difficulties. Constrained multi-objective evolutionary algorithms have been proven to be effective methods for solving complex constrained multi-objective optimization problems and have been gradually applied to UAV mission planning. However, recent advances in this area have not been summarized. Therefore, this paper provides a comprehensive overview of this topic, first introducing the basic classification of UAV mission planning and its applications in different fields, proposing a new classification method based on the priorities of objectives and constraints, and describing the constraints of UAV mission planning from the perspectives of mathematical models and planning algorithms. Then, the importance of constraint handling techniques in UAV mission planning and their advantages and disadvantages are analyzed in detail, and the methods for determining individual settings in multiple populations and improvement strategies in constraint evolution algorithms are discussed. Finally, the method from the related literature is presented to compare in detail the application weights of constrained multi-objective evolutionary algorithms in UAV mission planning and provide directions and references for future research.

Two different applications of Jaynes' maximum entropy framework, maxEnt, are given. The purpose is to show how maxEnt stimulates questions about the relations between the formal structure of the maxEnt model and the practical, possibly political, considerations which arise when deciding the objective function and the constraints. While these points do not lead to computationally complicated models they do raise important questions about the relationship between model form and the environment in which it is used. The first example is from urban and transportation planning. The use of maxEnt to derive the allocation of trips, the gravity model, brought benefits of clarity but also raised an important question with policy implications: should journey cost rightly be a behavioural constraint in the model or should it be considered an output? The policy implications are discussed. The second case was initially stimulated by personnel selection and raises the possibility that it is not always clear what maxEnt should be optimising, what should we be minimally discriminating about? The issue is quite general and is illustrated by the choice of a scheme for water resource allocation. [ABSTRACT FROM AUTHOR]

Goal: The present study aimed to demonstrate the applicability of the fuzzy goal programming to frame the decision support system for the decision-makers to deal with the real-life problem of the agriculture sector namely the apple cultivation planning problem and to obtain an optimum solution. Design / Methodology / Approach: The proposed method occurred within the apple-producing sector in the Kashmir valley of India and included the collection of data through interviews and surveys with various farmers. Also, the results were drawn with the help of LINGO 18.0. Results: The current finding implies that all of the desired objectives have been met, as well as an optimal solution. The proposed model offers a significant approach for designing plans to determine various agricultural activities in a fuzzy decision environment. Finally, the current study conducts a case study in the apple cultivation sector to obtain various competing objectives. Sensitivity analysis was also performed on its preferential weight parameters. Limitations of the investigation: It should be noted that the parameters related to production cost, transportation cost, and cost of material may change over the years. The selling price may also vary according to the quality of apples and over the years. Because of natural factors, the annual production of apples may also vary. Practical implication: The current study shows that using fuzzy goal programming techniques in an apple production process has a huge potential to increase farmers' income. It can be concluded that the current study can help decision-makers to deal with real-life planning issues in the agricultural sector. Future research can also take advantage of other initiation strategies. OriginalityNalue: The study looks at the applicability of the fuzzy goal programming paradigm in a new field of apple production in the agriculture sector. According to our knowledge, this is the first time the optimization model has been used in the apple cultivation sector. [ABSTRACT FROM AUTHOR]

Goal: The present study aimed to demonstrate the applicability of the fuzzy goal programming to frame the decision support system for the decision-makers to deal with the real-life problem of the agriculture sector namely the apple cultivation planning problem and to obtain an optimum solution. Design / Methodology / Approach: The proposed method occurred within the apple-producing sector in the Kashmir valley of India and included the collection of data through interviews and surveys with various farmers. Also, the results were drawn with the help of LINGO 18.0. Results: The current finding implies that all of the desired objectives have been met, as well as an optimal solution. The proposed model offers a significant approach for designing plans to determine various agricultural activities in a fuzzy decision environment. Finally, the current study conducts a case study in the apple cultivation sector to obtain various competing objectives. Sensitivity analysis was also performed on its preferential weight parameters. Limitations of the investigation: It should be noted that the parameters related to production cost, transportation cost, and cost of material may change over the years. The selling price may also vary according to the quality of apples and over the years. Because of natural factors, the annual production of apples may also vary. Practical implication: The current study shows that using fuzzy goal programming techniques in an apple production process has a huge potential to increase farmers' income. It can be concluded that the current study can help decision-makers to deal with real-life planning issues in the agricultural sector. Future research can also take advantage of other initiation strategies. Originality/Value: The study looks at the applicability of the fuzzy goal programming paradigm in a new field of apple production in the agriculture sector. According to our knowledge, this is the first time the optimization model has been used in the apple cultivation sector.

Paper is aimed to examine the impacts of conflicting objectives on the StateOwned Enterprises (SOEs) performance. The study motivated by the potential differences between government as the shareholders and management as the operator of SOEs. Government as shareholders may include politician, bureaucrats, and other government’s agents or ministry who have different interests and objectives toward SOEs. The government objectives are mainly dominated by socio-economic expectations. Meanwhile, management may bring his/her own interest and objectives which dominated by market and economic expectation. The conflict may appear and affect the performance. Using the case of the Indonesia public listed SOEs, this study examines in the extent to which the conflicting objectives between government and management stated objectives affect the performance; and which objectives dominate the business. The Statistic tools are used to examine the impacts of government and management conflicting objectives on SOEs performance during the period 0f 2004 to 2018. The period is selected particularly to see whether the changes of political regime contribute to the conflicting objectives. The study indicates that government objectives dominate the business activities which significantly affect the performance of SOEs.

We investigate the Ojibwe experience of the logging era in the Great Lakes region (ca. 1860-1925) through a historical case study of forest management on the Bad River reservation, located in northern Wisconsin. Although primary forest clearance reshaped ecological, cultural, and political landscapes throughout the region, the Ojibwe experienced the logging era in ways that differed from their white neighbors. As treaty-defined land cessions catalyzed changes to the Bad River livelihood and economy, reservation forests took on a new meaning: the stands became the primary source of untapped capital for the band and the Indian Agency. In accordance with the fiducial responsibility of tribal trust doctrine, the Indian Agency was charged to manage reservation forest resources for the long-term benefit of tribal members. Harvest of the Bad river stands (ca. 1894-1922) produced 1.25-1.5 billion board feet of pine, other softwood, and hardwood timber, valued at approximately $7 million. And yet, Bad River reservation forests were largely depleted by 1925, while generating minimal lasting return for tribal members. We investigate why this failure occurred at Bad River. Our study revealed multiple reasons: 1) upheaval of the traditional Ojibwe economy, which degenerated into a timber dependent economy; 2) ineffective supervision of Indian agents by senior Indian Agency officials; 3) corruption by the designated timber contractor, which was tolerated by the Indian Office; 4) Indian Agency assumptions that Ojibwe culture should be reshaped to fit the model of Anglo-American settled agriculture. During the early 20th century, the agency vacillated between forestry practices designed to maximize economic return and those characteristic of scientific forestry, thus demonstrating its competing resource management objectives. These twin goals—conserving forests for the future and cutting forests to hasten assimilation—conflicted with one another, and the result was disastrous for both the Bad River reservation forest and economy.

• We study people's perceptions of agricultural policy goals. • We conduct a survey in Switzerland on the importance of eight policy goals. • Animal welfare is perceived as the most important goal. • Differences between the Swiss language regions indicate cultural differences. • Policy needs to take into account cultural differences within the population. Agricultural policy goals should be legitimised by the population, as agriculture is an important recipient of governmental support in Europe. Questions arise as to how people assess these policy goals, which factors affect the perception of agricultural policy goals and to what extent cultural differences influence this assessment. We address these questions by conducting an online survey among 1,542 respondents in the German-, French- and Italian-speaking parts of Switzerland. We find that the multifunctional role of agriculture is strongly anchored in the population's perception of agricultural policy. The stated preferences of the participants show that increasing animal welfare is clearly the most important agricultural policy goal for Swiss citizens. Controlling for a range of sociodemographic characteristics and personal attitudes, we find evidence of differences between language regions and thus of cultural differences in the assessment of individual agricultural policy goals. For example, compared to respondents in the other two language regions, German-speaking respondents found increasing domestic food production significantly more important, whereas reducing food prices for consumers, increasing farmers' income, and reducing greenhouse gas emissions were perceived as significantly less important goals. Environmental attitudes were closely related to the perceived importance of agricultural policy goals; thus, we can expect heated discussions in the future, especially if environmental objectives continue to be missed. [ABSTRACT FROM AUTHOR]

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Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

This study addresses the problem of distributed formation control for a multi‐agent system with collision avoidance between agents and with obstacles, in the presence of various constraints. The authors proposed solution incorporates a control Lyapunov function (CLF) into a distributed model predictive control scheme, which inherits the strong stability property of the CLF and optimises the formation performance. For each agent, the formation tracking objective is formulated through the CLF, while the collision avoidance objective being explicitly considered as constraints. A relaxation parameter is introduced into the CLF condition to make the trade‐off between the two conflicting objectives. The terminal constraint is constructed based on the concept of velocity obstacle, which characterises the set of states that lead to collisions. They show that the terminal constraint together with the relaxed CLF‐based constraint guarantees the recursive feasibility and stability of the multi‐agent system for almost any prediction horizon. Furthermore, the theoretical effectiveness and advantageous implementation properties are demonstrated through simulation for multi‐agent formation control with several obstacles. [ABSTRACT FROM AUTHOR]

Energy efficiency (EE) is considered as one of the pivotal uplink (UL) performance metrics for 5G dense networks. It consists of two conflicting objectives that are recognised as benefit–cost ratio: spectral efficiency and energy consumption. Accordingly, network design tradeoff is the key challenge for future networks. In this paper, we aim to jointly maximise network spectral efficiency and minimise power consumption with respect to the following design parameters: base station (BS) density, users' number, equipped antennas' number, and signal‐to‐noise power ratio without losing service quality. The performance of the ultra‐dense network is characterised on the basis of the Pareto optimality concept through the following benchmarks: (i) studying impact of exhausted power on the deployed hardware elements. (ii) Validating the total EE performance through Monte‐Carlo simulation within a low cost of processing time. (iii) The proposed approach is compared against single objective scheme to show the significance of the design tradeoff. Furthermore, we will introduce a detailed mathematical analysis for UL power policy and channel estimation for reliable pilot reusing. Simulation results will show that our proposed solution guarantee remarkable EE performance via reducing the number of deployed BSs without scarificing service quality. [ABSTRACT FROM AUTHOR]

Disaster management studies have demonstrated that housing reconstruction programs often contain conflicting objectives. Yet, insufficient knowledge still exists about how conflicts between objectives appear and escalate during the reconstruction process. The purpose of this article is to explain the causes and consequences of these conflicts and to reveal how recovery programs can prevent them. After developing the analytical framework of underlying and intensifying factors of conflicting objectives , this qualitative inquiry examines the housing reconstruction program conducted after the 2003 earthquake in Bam. Empirical results show three sets of conflicts between economic, social, cultural, and built environment objectives, and reveal how they led to repeating simple modular housing units, which partially destroyed a rich and historic architectural landscape, ignoring households’ needs, identities, culture, and traditions and damaging irreversibly Bam's unique urban fabric. Results reveal unsolved controversies in the post-disaster reconstruction field and the lack of participatory decision-making before disasters as the underlying factors of such conflicting objectives. On the other hand, conflicts between stakeholders as well as challenges in participation processes during the reconstruction process intensified conflicts between recovery objectives. Findings recommend preventing the rapid establishment of new organizations in the post-disaster stage and finding a balance between professionals’ and lay community's knowledge to fulfill short and long-term recovery objectives. [ABSTRACT FROM AUTHOR]

Due to advancements in the wireless networking technologies, a lot of new wireless services are demanding the radio spectrum for their usage. The radio spectrum is considered as a limited natural resource for radio communications, and it became crowded. In fact, this resource is not limited, rather the scarcity of the radio spectrum arises because of inefficient and fixed utilization of this valuable resource. This inefficient and under-utilization of radio spectrum has diverted the attentions of the researchers towards the different techniques in order to access the spectrum dynamically and adaptively. The concept of Cognitive Radio (CR) can be implemented by making the communication devices intelligent, in order to use the available radio spectrum efficiently. The efficient implementation of cognitive radios into the wireless networks gives rise to a tremendous number of optimization problems with multiple objectives. In most of the cases, these objectives are conflicting with each other and need to be addressed carefully. In this article, we will present the importance of multi-objective optimization (MOO) in cognitive radio networks (CRNs). We will discuss about the fundamental differences in single objective optimization (SOO) and MOO techniques, in order to solve MOO problems associated with CRNs. We will differentiate between the possible relations such as, conflicting, supporting and design dependent, among these objectives. A comparative discussion will also be provided about optimization types, solution approaches/algorithms and different combining techniques to combine multiple objectives in order to address MOO problems those comes with CRNs. [ABSTRACT FROM AUTHOR]

This paper proposes a novel two-level particle swarm optimization algorithm for multi-objective optimization (MO-TLPSO) employed to a challenging problem of bioinformatics i.e. RNA sequence-structure alignment. Level one of the proposed approach optimizes the dimension of each swarm which is sequence length for the addressed problem, whereas level two optimizes the particle positions and then evaluates both the conflicting objectives. The conflicting objectives of the addressed problem are obtaining optimal multiple sequence alignment as well as optimal secondary structure. Optimal secondary structure is obtained by TL-PSOfold, the structure is further used for computing the contribution of base pairing of individual sequence and the co-variation between aligned positions of sequences so as to make the structure closer to the natural one. The results are tested against the popular softwares for pairwise and multiple alignment at BRAlibase benchmark datasets. Proposed work is so far the first multi-objective optimization based approach for structural alignment of multiple RNA sequences without converting the problem into single objective. Also, it is the first swarm intelligence based approach that addresses sequence-structure alignment issue of RNA sequences. Simulation results are compared with the state-of-the-art and competitive approaches. MO-TLPSO is found well competent in producing pairwise as well as multiple sequence-structure alignment of RNA. The claim is supported by performing statistical significance testing using one way ANOVA followed by Bonferroni post-hoc analysis for both kind of alignments. [ABSTRACT FROM AUTHOR]

Numerous problems encountered in real life cannot be actually formulated as a single objective problem; hence the requirement of Multi-Objective Optimization (MOO) had arisen several years ago. Due to the complexities in such type of problems powerful heuristic techniques were needed, which has been strongly satisfied by Swarm Intelligence (SI) techniques. Particle Swarm Optimization (PSO) has been established in 1995 and became a very mature and most popular domain in SI. Multi-Objective PSO (MOPSO) established in 1999, has become an emerging field for solving MOOs with a large number of extensive literature, software, variants, codes and applications. This paper reviews all the applications of MOPSO in miscellaneous areas followed by the study on MOPSO variants in our next publication. An introduction to the key concepts in MOO is followed by the main body of review containing survey of existing work, organized by application area along with their multiple objectives, variants and further categorized variants.

Decision-making in Indonesian fishery management with an ecosystem approach requires trade-offs between conflicting objectives. For small pelagic fisheries of Indonesia, this study addressed common decision-making problems in determining optimal levels of fishing effort adjustment and integrated management of fishery and fish processing objectives using multiple-goal bioeconomic programming models. In the effort adjustment, the analysis optimised among the conflicting ecological and economic objectives using the allocation of the effort across two of Indonesia's Fisheries Management Areas (FMAs). An optimal fishing effort adjustment could increase fish stock biomass, fishery production, and fishery profit in both FMAs together. For managing the integrated fishery combining fishing and fish processing, the analysis determined the best-compromise solution to the conflicting objectives, while optimising the vessel size, the allocation of vessels within and beyond provincial jurisdictional waters, and the fish processing capacity. The result suggested not targeting increased employment as a fishery management objective to avoid increases in fishing pressure and the number of vessel workers that would lead to potential decreases in fisher family income. The programming models could be expanded to address the nation-wide issues of unbalanced fishing pressure and integrated fishery management by using an ecosystem approach to fisheries to sustain small pelagic fish resources while balancing ecological and human well-being. [ABSTRACT FROM AUTHOR]

Rebuilding depleted fish stocks and preventing the collapse of fisheries are major challenges for most coastal countries. In addition to human-induced factors, interactions between fish stocks within a food web add further complexity to the task of stock rebuilding. In this study we use a stylized bioeconomic model of a multi-stock fishery to study how different management objectives are affected by the nature of stock interactions and to identify potential trade-offs between multiple objectives in stock rebuilding. The results show that the type and strength of stock interactions determine directly the trade-offs between the biological and economic objectives of the fishery as well as the short-term and long-term objectives in stock rebuilding. Compared to a single species perspective, the opportunity cost of hastening the speed of stock rebuilding by reducing the fishing capacity is lower when the depleted stocks have a competitive relationship and higher when the interdependence between the stocks is predatory-prey or mutually beneficial. Our model results further show that stock interactions directly influence whether full or partial rebuilding of depleted stocks is achieved and whether the biomass of rebuilt stocks remains above the management target over time. Even a simple form of competitive or predator-prey interaction can prolong the duration of the rebuilding process and reverse initial rebuilding success or prevent it entirely, underlying the importance of stock interactions for the rebuilding of fisheries. [ABSTRACT FROM AUTHOR]

In recent years, many-objective optimization problems (i.e. more than three objectives) have attracted the interests of many researchers. The main difficulties of many-objective optimization problems lie in high computational cost, stagnation in search process, etc. It is almost impossible to design an algorithm effective for all problems. However, for some problems, especially for problems with redundant objectives, it is possible to design effective algorithms by removing the redundant objectives and keeping the non-redundant objectives so that the original problem becomes the one with much fewer objectives. To do so, first, a multi-objective evolutionary algorithm-based decomposition is adopted to generate a smaller number of representative non-dominated solutions widely distributed on the Pareto front. Then the conflicting objective pairs are identified through these non-dominated solutions, and the redundant objectives are determined by these pairs and then removed. Based on these, a fast non-redundant objectives generation algorithm is proposed in this paper. Finally, the experiments are conducted on a set of benchmark test problems and the results indicate the effectiveness and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]