Worst Solution Research Articles

Analysis of public records of lobbying practices of the ultra-processed sugary food and drink industries in Chile: a qualitative study

Given the role of commercial determinants on sugar consumption and health, this study aimed to describe lobbying practices of the ultra-processed sugary food and drinks industries in Chile between 2014 and2022. Official meetings between ultra-processed sugary food and drinks industries and related commercial actors and Chilean government officials were obtained from the Chilean Lobby Registry. Relevant commercial names were initially identified based on their market share and expanded iteratively based on information from relevant meetings. Qualitative analysis followed a deductive-inductive approach using the Corporate Political Activity Model to identify and classify objectives, framing and action strategies. From 237 records identified, the Ministries of Health, Social Development, and Economy were the most frequently lobbied. Industry representatives sought to achieve their short- and long-term objectives by targeting a diverse range of authorities, including Ministers and Under-secretaries, using different strategies. Framing strategies focused on presenting sugary food and drinks industries as socially responsible and legitimate policy actors and criticised public health initiatives as 'bad solutions'. Action strategies aimed to influence policymaking and nurture corporate reputations. Extensive lobbying took place by the sugary food and drinks industries between 2014 and 2022, a period when major public health policies were being discussed in Chile. Lobbying strategies varied to meet industry objectives and targeted a diverse range of government institutions including high-ranking officials. Tighter regulations to stop inappropriate industry influence in public health policymaking are urgently required. Agencia Nacional de Investigación y Desarrollo (Chile)-PhD Scholarship. University College London-Open Access fees.

A multistart and recombination algorithm for finding many unique solutions to spatial aggregation problems

AbstractSpatial aggregation is essential for applications where data at low level spatial units such as census blocks are grouped into larger regions. This type of problem can be formulated as spatial optimization problems where the goal is to minimize the difference between the grouped regions. These problems are difficult to solve because of their computational intensity. In addition, these problems often have multiple, instead of singular, optimal solutions that have the same or similar objective function values but exhibit different spatial configurations. Existing solution methods often aim to find single solutions to these problems. In this paper, we discuss a new heuristic method that can be used to find a set of unique optimal or near-optimal solutions to spatial aggregation problems. The algorithm consists of two phases. A multistart phase first generates a pool of random solutions to a problem. The size of the pool is specified by the user and contains the number of solutions desired to be found. Each random solution is then improved using an efficient algorithm called give-and-take. The second phase uses a recombination algorithm to create new solutions based on solutions randomly selected from the pool. The worst solution in the pool will be replaced by the new solution if the latter is better and does not exist in the pool. We test this multistart and recombination algorithm (MSRA) using a variety of problems with different sizes and the results suggest the effectiveness of the algorithm in finding multiple unique optimal or near-optimal solutions.

A discrete Jaya algorithm for vehicle routing problems with uncertain demands

Vehicle Routing Problem with Uncertain Demands (VRPUD) is one of the research hotspots in the field of logistics scheduling. In this paper, a Discrete Jaya (DJaya) algorithm is presented for the VRPUD to minimize the total cost. A novel dividing-point-based coding scheme is designed to represent solutions with higher robustness. In addition, an efficient repair strategy is embedded into the decoding process to avoid the failure of producing feasible solutions. The best and worst solutions are employed to generate offspring solutions in DJaya. Several efficient local search methods are also presented to enhance the exploitation ability and increase the diversity of solutions. Based on the benchmark data sets of the VRPUD, numerical simulations are carried out for the proposed DJaya algorithm with uncertain demands. Computational results and comparisons with the state-of-the-art algorithms demonstrate the superiority of the proposed algorithm in solving VRPUD.

Machine learning and evolutionary optimization approach for solving the flexible job-shop scheduling problem

This paper proposes a method of using machine learning and an evolutionary algorithm to solve the flexible job shop problem (FJSP). Specifically, a back propagation (BP) neural network is used as the machine learning method, the most widely used genetic algorithm (GA) is employed as the optimized object to address the machine-selection sub-problem of the FJSP, and particle swarm optimization (PSO) is utilized to solve the operation-order sub-problem of the FJSP. At present, evolutionary algorithms such as the GA, PSO, ant colony algorithm, simulated annealing algorithm, and their optimization algorithms are widely used to solve the FJSP; however, none of them optimizes the initial solutions. Because each of these algorithms only focuses on solving a single FJSP, they can only use randomly generated initial solutions and cannot determine whether the initial solutions are good or bad. Based on these standard evolutionary algorithms and their optimized versions, the JSON object was introduced in this study to cluster and reconstruct FJSPs such that the machine learning strategies can be used to optimize the initial solutions. Specifically, the BP neural networks are trained so that the generalization of BP neural networks can be used to judge whether the initial solutions of the FJSPs are good or bad. This approach enables the bad solutions to be filtered out and the good solutions to be maintained as the initial solutions. Extensive experiments were performed to test the proposed algorithm. They demonstrated that it was feasible and effective. The contribution of this approach consists of reconstructing the mathematical model of the FJSP so that machine learning strategies can be introduced to optimize the algorithms for the FJSP. This approach seems to be a new direction for introducing more interesting machine learning methodologies to solve the FJSP.

Research on Fuzzy C- Clustering Recursive Genetic Algorithm based on Cloud Computing Bayes Function

Aiming at the problems of poor local search ability and precocious convergence of fuzzy C-cluster recursive genetic algorithm (FOLD++), a new fuzzy C-cluster recursive genetic algorithm based on Bayesian function adaptation search (TS) was proposed by incorporating the idea of Bayesian function adaptation search into fuzzy C-cluster recursive genetic algorithm. The new algorithm combines the advantages of FOLD++ and TS. In the early stage of optimization, fuzzy C-cluster recursive genetic algorithm is used to get a good initial value, and the individual extreme value pbest is put into Bayesian function adaptation table. In the late stage of optimization, when the searching ability of fuzzy C-cluster recursive genetic is weakened, the short term memory function of Bayesian function adaptation table in Bayesian function adaptation search algorithm is utilized. Make it jump out of the local optimal solution, and allow bad solutions to be accepted during the search. The improved algorithm is applied to function optimization, and the simulation results show that the calculation accuracy and stability of the algorithm are improved, and the effectiveness of the improved algorithm is verified.

Maxim and Principle Contractualism

I argue that, in order to address the ideal world problem while remaining faithful to our concept of morality, Contractualists should no longer determine which actions I must perform by seeing whether they accord with certain principles for the general regulation of behavior. Instead, I argue, Contractualists should determine whether it is right or wrong for me to perform an action by evaluating any maxim that might be reflected by my action. I call the resulting view “Maxim Contractualism.” It states that an agent’s action is morally required just in case any maxim that he might adopt that involves not performing that action is one that someone could reasonably reject. Finally, I argue that, although Act Contractualism also provides us with the materials to solve the Ideal World Problem, it is a worse solution because it cannot account for the fact that, sometimes, what would happen if I performed an action over time is relevant to whether I am permitted to perform that action right here, right now.

Application of the Two-sided difference quotient in the solution of nonlinear Ill-posed inverse self-adjoint elliptic problem

When we use a discretization by finite differences, to solve differential equations we find problems at the border of the domain of the solution. If the solution is also immersed in a ill-posed inverse problem; we can find very bad solutions. In this paper we apply a discretization of two - sided difference quotients method to solve Ill-posed inverse self-adjoint elliptic problem [Kirsch(2011)]. Some numerical examples showing the effectiveness of this method and we will use mollification techniques to smooth the solutions.

Optimized Sigmoid Functions for Speech Presence Probability and Gain Function in Speech Enhancement

Speech presence probability (SPP) and gain functions such as Wiener filter or MMSE estimators require an estimate of the a-priori signal-to-noise ratio (SNR). However, the estimation of the a-priori SNR is computationally involved and sensitive to noise variations. This paper proposes to approximate the SPP and the overall gain function of a speech enhancement system by using sigmoid functions to reduce the need of estimating the a-prior SNR. By applying an approximation via the sigmoid functions it is shown that only the a-posteriori estimate of SNR is needed, resulting in a low complexity system. The sigmoid function is designed with an optimization algorithm to optimize its parameters with respect to speech quality measures. The optimization algorithm is based on the idea that the solution obtained for a given problem should move towards the best solution and avoid the worst solution. The proposed algorithm requires minimal control parameters and does not require any algorithm specific parameters. Simulation results show that the proposed sigmoid functions achieve good results in terms of speech quality measures when compared with existing methods while providing significantly lower complexity for implementation.

Natural lighting of interiors and sun protection of premises with roof lighting system

The article describes the contemporary issues of natural lighting of interiors of civil and industrial buildings. The major attention is given to the roof lighting system of the buildings in question, as far as this system provides not only increased levels of natural lighting, but sometimes an undesirable heat gains, due to prolonged insolation period and hence, extra solar radiation. It is noted, that this reason leads to design and construct the means of natural lighting in such a way, that to provide interiors with sufficient natural illumination and efficient sun protection at the same time. Some principal conclusions and recommendations were made as a result of analytic studies, presented in the article. For example, it was competently proved that the best solar standing solution for roof lighting installations are sheds, or sawtooth roof structure. The worst solution, to protect interiors from solar overheating are roof skylights. These skylights require extra sun protection in form of jalousies, blinds, etc. The same additional solar shading, though to less extend, is needed to provide additional sun protection for roof monitors.

Comparative Analysis of Multicriteria Decision‐Making Methods for Bus Washing Process Selection: A Case Study

Water is at the core of sustainable development, and its use for human activities, including vehicle washing, should be done in a sustainable way. There are several technical solutions for washing buses offering different performances, making it difficult to choose the one that best meets the requirements of each specific case. The literature on the topic hardly analyzes the choice of the best technical solution for washing buses and does not apply and compare the results of different multicriteria decision‐making (MCDM) methods for the problem. The unique information available is from the different suppliers in the market. Whereby, this work intends to give a technical‐scientific contribution to fulfill this gaps. Therefore, the main objectives of this work are (1) to select the best sustainable technical solutions for washing buses depending on the specific conditions for a case study and (2) to analyze how different multicriteria decision‐making methods behave in the selection process. To achieve these objectives, the problem was approached as a case study in a public transport company in Portugal and the methodology followed the next steps: started with the identification of the different types of commercial technical solutions for washing buses; the company’s experts selected four main criteria: water consumption, operating costs, quality of washing, and time spent; the criteria weights were determined using the fuzzy‐AHP method; then four representative MCDM methods were selected, namely, AHP, ELECTRE, TOPSIS, and SMART; the ranks obtained for the four methods were compared; and a sensitivity analysis was performed. Considering the input data for the criteria and their weights, the results for all the methods showed that the best and the worst solution was the same, mobile portico with a brush and porticoes with three brushes, respectively. Furthermore, the results of the sensitivity analysis performed with disturbances for the weights of each criterion presented that the results are slightly affected and the similarity in rankings for the four MCDM methods was validated by Spearman’s rank correlation coefficient (rs) and Kendall’s coefficient of concordance (W). Considering these results, the SMART method, the less complex one, showed no difference from the others. For that reason, simple methods, such as SMART, in line with other works in the literature perform well in most cases. As a final remark of this work, it can be said that the methodology employed in this project can also be deemed applicable to other similar companies seeking technical solutions for bus or truck washing. Furthermore, the application of the SMART method, the less complex one and the most understandable for people, showed no difference from the others, being able to be applied in similar situations.

On fuzzy fractional quadratic programming problems with an application in the tourism sector

In the corporations working with logistics, many management issues involving optimization require fractional quadratic programming. Further, crisp parameters are unable to model the real life problems, where impreciseness is involved. In the present study, fuzzy fractional quadratic programming problem is studied using a hybrid method that combines analytic and numerical approaches. The original problem is converted to a crisp multiobjective problem, wherein the constraint coefficients are handled by variation of parameter technique through an interactive manner. The proposed method attempts to alleviate existing problems of straightforward conversion to a deterministic model by making constraint parameters vary as per αr∈(0,1]. The flexibility of choices in constraint parameters with αr=0 leading to best solution and αr=1 resulting in worst solution, indicates the robust nature of proposed algorithm. The theorems have been constructed and proved to show the equivalence between the original and transformed problem. A numerical example and a transportation problem in tourism sector switching between both balanced and unbalanced cases is modelled and later solved using the proposed methodology.

Application of the Grey TOPSIS Method to Assess and Select a Contractor in Terms of Occupational Safety Management

Abstract The objective of the article is to analyze the possibility of using the gray TOPSIS method to evaluate and select a contractor due to specific criteria regarding occupational health and safety management (OHSM). The choice of contractor is a problem for the decision-maker to make decisions based on many criteria, and most often, these criteria are very diverse, both quantitative and qualitative. The gray TOPSIS method is based on the use of a measure of the relative distance from the best solution, constituting the pattern, and from the worst solution, constituting the anti-pattern, to sort the solutions, using the Grey System Theory. To evaluate and select the contractor, 12 different criteria were used, describing the key areas of OHSM for the decision-maker. The criteria and their weights were selected taking into account the nature of the work planned to be carried out by the company ordering the work. Z-10 annual reports on working conditions in a given year submitted by enterprises to the Central Statistical Office, expert assessments and occupational health and safety procedures provided by five potential contractors were used as sources of information. The research confirmed that the use of gray numbers as part of the TOPSIS method allows the ranking of potential contractors from the point of view of selected criteria and the selection of the company that best meets the assumed occupational health and safety management criteria.

A Novel Solution to Avoid Mercury Emissions Produced by Highly Contaminated Demolition Debris in Mining Site

Mercury (Hg) emissions into the air can in some cases be a critical problem for public health and environmental protection. Hence, it is crucial to find cost-effective solutions to tackle this issue. An innovative solution is presented in this study using two case studies, diminishing drastically the emissions through covering high Hg-contaminated debris with industrial subproducts (fly-ashes and blast furnace slags). This solution is compared with other two conventional alternatives: (1) removal of the debris to an authorized landfill and (2) in situ encapsulation. The analysis is carried out by comparing costs, carbon footprint and the Environmental–Social–Governance (ESG) impact. It has been noted that the solution proposed has some advantages with respect to conventional alternatives, from both economic and environmental points of view, presenting lower costs and a lower carbon footprint. The worst solution is to remove the debris for ex situ treatment, while the encapsulation represents a middle solution with advantages and disadvantages. The findings of the study can be a step forward in the usage of industrial subproducts, that are currently placed in waste disposal, to solve the problem of sites contaminated by mercury and eliminate its emissions.

An intuitionistic fuzzy weighted influence non-linear gauge system for equipment evaluation under system-of-systems warfare environment

System-of-systems (SoS) warfare has become the main form of modern warfare, where various equipment interact and interconnect to achieve operational goals. The complexity of equipment SoS poses a challenge for evaluating equipment under such warfare environments, as it requires considering multiple elements such as tasks and capabilities to ensure reliable evaluations. This paper proposes a novel equipment evaluation approach for SoS warfare environments based on the intuitionistic fuzzy weighted influence non-linear gauge system (IF-WINGS). The approach employs intuitionistic fuzzy sets (IFS) to represent uncertain information related to tasks, capabilities, and equipment. By extending the WINGS method with IFS, the proposed approach uncovers the interdependencies among the elements within the equipment SoS using the causal relation graph, considering both the strength and influence intensity of these elements. Additionally, the proposed approach evaluates and ranks different alternatives using relative closeness, which takes into account the distance of each alternative from the best and worst solutions, and this enables a more reliable and effective evaluation of alternatives. A novel equipment evaluation approach that considers the interrelationships among various elements within the equipment SoS is introduced based on IF-WINGS. To demonstrate the effectiveness of the proposed method, a case study is presented, and the results are compared to other methods, with Spearman’s rank correlation coefficients of the comparative methods obtained as (0.96,0.96,1.00,1.00), respectively. The results show that the proposed method could provide reliable and robust results for equipment evaluation in SoS warfare environments. By introducing the IF-WINGS method into equipment evaluation under SoS environment, this study provides a novel and effective way that considers the complex relationships among different elements in SoS warfare, enabling reliable and reasonable evaluation. In conclusion, the IF-WINGS method proves to be a valuable tool for assessing equipment performance in complex and interconnected warfare scenarios.

Solutions of economic load dispatch problems for hybrid power plants using Dandelion optimizer

In this paper, an economic load dispatch problem (ELD) is solved for reaching optimal power output of hybrid systems in addition to cost minimization. The systems consider the forbidden working zones (FWZs), dynamic load demand, wind farms, and solar photovoltaic fields (SPs). The cost minimization solutions for the ELD problem are found by applying the Dandelion optimizer (DO), the salp swarm algorithm (SSA), and the particle swarm optimization (PSO). In the study case, the power system consists of six thermal power plants (TPs), two wind farms, and two SPs. In addition, the variation of load demand over 24 hours of one day is applied. DO and SSA can achieve the best cost of $15443.0753 for the first system, but PSO cannot. However, DO is the most stable method reaching the standard deviation of 0.0184 for fifty runs but that of SSA and PSO is about 1.0439 and 8.9664. For the second system, DO can reach smaller cost than PSO by $11.17, $137.74 and $323.09 for the best, mean and worst solutions among fifty found solutions. As a result, DO is strongly recommended for solving the ELD problem.

A Nature Inspired Hybrid Partitional Clustering Method Based on Grey Wolf Optimization and JAYA Algorithm

This paper presents a hybrid meta-heuristic algorithm using Grey Wolf optimization (GWO) and JAYA algorithm for data clustering. The idea is use exploitative capability of JAYA algorithm in the explorative phase of GWO to form compact clusters. Here, instead of using one best and one worst solution for generating offspring, three best wolfs and three worst omega wolfs of the population are used. So, the best wolfs and worst omega wolfs assist in moving the new solutions towards the best solutions and simultaneously helps in staying away from the worst solutions. This enhances the chances of reaching the near optimal solutions. The superiority of the proposed method is compared with five promising algorithms, namely GWO, Sine-Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), JAYA and K-means algorithms. The result obtained from the Duncan’s multiple range test and Nemenyi hypothesis based statistical test confirms the superiority and robustness of our proposed method.

Optimum design of real-size reinforced concrete bridge via charged system search algorithm trained by Nelder-Mead simplex

In this paper, an optimization method based on the combination of Nelder-Mead (NM) simplex concepts and charged system search algorithm (CSS) is proposed and called NM-CSS. NM, as a search technique, involves the iterative formation and transformation of a geometrically arranged set of points known as a simplex to uncover the optimal solution. By incorporating NM into metaheuristic algorithms, the convergence speed and solution quality can be notably boosted. Throughout each iteration, the solutions undergo various operations, including reflection, contraction, and expansion, to enhance the effectiveness of the algorithm. In this algorithm, produced solutions by the CSS are sorted and divided into two groups of good and bad solutions, and then the NM operators are applied to bad solutions to generate the possible best ones. The proposed algorithm is verified through some mathematical benchmark functions. Then, it is implemented on a real-size reinforced concrete bridge to reach an optimum design. To recognize the effective parameters in the design of structural components of reinforced concrete bridges, a sensitivity analysis is carried out. The total cost of materials in piers and the deck is defined as an objective function, and the cross-section area of structural elements and longitudinal reinforcements are chosen as design variables. The results of simulations represent the stability and robustness of the proposed NM-CSS method compared to standard CSS. In other words, utilizing the proposed NM-CSS algorithm in an optimum design of the piers and deck results in saving %3.685 and %2.084 of costs, respectively, in comparison with the results of the standard CSS.

Enhancing the Vulnerability Assessment of Rainwater Pipe Networks: An Advanced Fuzzy Borda Combination Evaluation Approach

A vulnerability assessment system for rainwater pipe networks, comprising 13 indexes, was developed to facilitate the rational allocation and timely updating of urban storm drainage systems. An enhanced Borda combination evaluation method, which considers both the optimal and worst solutions, was proposed, accompanied by the operation procedure and numerical calculation method. Five stormwater systems in Central China were selected as case studies, and their vulnerability was evaluated and compared using five distinct evaluation methods: the entropy weight method, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), the efficacy coefficient method, the fuzzy comprehensive evaluation method, and the improved fuzzy Borda combination evaluation method. The results demonstrated that the correlation coefficients between the fuzzy Borda combination evaluation method and the four individual evaluation methods were equal to or greater than 0.88, indicating strong agreement. Additionally, the compatibility of the combination evaluation method was found to be 0.96. This study holds both theoretical significance and practical value for preventing urban waterlogging and contributes to the development of more resilient urban storm drainage systems.

A decomposition-based multi-objective Jaya algorithm for lot-streaming job shop scheduling with variable sublots and intermingling setting

This study examined the lot-streaming job shop scheduling problem (LSJSP) with variable sublots and intermingling setting, which has rarely been considered in the literature and is beneficial to real industry settings. Upon variable sublots and intermingling setting, a multi-objective mixed-integer linear programming model (MILP) is formulated to achieve a tradeoff between the shortest tardiness and the minimum number of transferred sublots. Considering the NP-hard characteristic of the problem, the MILP model would get restricted in solving medium and large scale instances. Thus building an efficient multi-objective optimization algorithm is desirable. Motivated by the Jaya algorithm and decomposition idea, we propose a new algorithm framework called a decomposition based multi-objective Jaya algorithm (MOJA/D) to achieve a balance between exploration and exploitation. This MOJA/D framework can provide some guidance in algorithm construction for other multi-objective optimization problems. Following the MOJA/D framework, a specific MOJA/D metaheuristic is further designed for coping with the proposed problem. First, a novel forward and backward decoding strategy is designed to implement coevolution among different sub-problems and enhance the population diversity. Second, the Jaya updating mechanism is designed based on problem knowledge to guide the solutions moving towards the best solution and away from the worst solution. Moreover, considering the tradeoff between two conflicting objectives, the problem-specific local search strategies are designed and integrated with the neighborhood based local search strategies to enhance exploitation in the search process. The influence of parameter setting on MOJA/D is investigated by using design of experiments. The statistical results from extensive computational experiments demonstrate the effectiveness of the specific algorithm designs and show that the MOJA/D metaheuristic is superior to the existing algorithms in solving the proposed problem.

Elimination Properties for a Probabilistic Scheduling Problem

In many areas of the economy, we deal with random processes, e.g., transport, agriculture, trade, construction, etc. Effective management of such processes often leads to optimization models with random parameters. Solving these problems is already very difficult in deterministic cases, because they usually belong to the NP-hard class. In addition the inclusion of the uncertainty parameters in the model causes additional complications. Hence these problems are much less frequently studied. We propose a new customized approach to searching the solutions space for problems with random parameters. We prove new, strong properties of solutions, the so-called block elimination properties, accelerating the neighborhood search. They make it possible to eliminate certain subsets of the solution space containing worse solutions without the need to calculate the value of the criterion function. Blocks can be used in the construction of exact and approximate algorithms, e.g., metaheuristics such as tabu search, significantly improving their efficiency.