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Providing appropriate home health care is one of the increasing concerns in the health care organizations. Home health care provides various services for disabled or elderly individuals at their homes. Also, dealing with the current critical situation of the coronavirus disease (COVID-19) due to the limited capacity of hospitals and the feeling of insecurity in crowded places, home health care is more recommended. This paper addresses a Home Health Care Routing and Scheduling Problem (HHCRSP) with two modes of transportations including public and private modes. Also, multi-depot version of the problem is studied to enhance the service delivery in scattered points. In this study, a mathematical model is presented based on a Mixed Integer Linear Programming (MILP) whose objective function is minimization of the sum of the travel distance and overtime costs. Furthermore, three meta-heuristic algorithms including Invasive Weed Optimization (IWO), Grasshopper Optimization Algorithm (GOA), and Simulated Annealing (SA) are presented for solving large-sized problems. Since the performance of meta-heuristic algorithms depends on setting the parameters, the Taguchi method is used to statistically set parameters of the developed algorithms. The computational results have shown that the proposed IWO has worked better than the other two proposed algorithms statistically. [ABSTRACT FROM AUTHOR]

The Vehicle Routing Problem (VRP) is one of well-established and important topics in supply chain and logistics networks. The location-routing decision-making has a significant role to optimize the traveling cost per distance, total traveling time, and the number of vehicles in the supply chain networks. One of the recent advances in these systems refers to container terminals in the global trade centers. The new contributions of this model are to consider the routes of two types of ships (Tramp and Liner) with different properties and the time windows in order to minimize costs, simultaneously. The proposed model is solved by an exact solver using LINGO software and because of inherent complexity of the problem proposed in the real-world cases, the Genetic Algorithm (GA) is used to find an optimal/global solution in a reasonable span of time. Finally, an in-depth analysis and discussion are provided to conclude the main findings and practical implications of the results. [ABSTRACT FROM AUTHOR]

In this paper, we present a multi-stage hybrid model for analysing a supply chain network (SCN) collapse recovery possibility. In the first stage of the model, we analyse the ability of an SCN to fulfil its customers required due date (RDD). As soon as the SCN defaults to timely fulfil their customers RDD, the second stage of the model is triggered to measure the collapse recovery possibility (CRP) of the SCN. Then, we calculate the final collapse recovery possibility (FCRP) of the SCN. Since the operation times, customer demand and external supply of raw material are uncertain, we use fuzzy triangular numbers to estimate the value of foregoing parameters. Consequently, we employ fuzzy program evaluation and review technique (FPERT) to calculate the completion time of SCN operations. In the third stage, for the critical elements of the SCN obtained from FPERT, the SC simulator is developed to provide a dynamic view of the SC and assesses the impact of decisions recommended by the SC fuzzy models on SC performance. Moreover, an empirical example is presented to validate the effectiveness of the proposed model. Finally, we conduct sensitivity analysis on the parameters employed in the model to analyse the behaviour of each parameter. [ABSTRACT FROM AUTHOR]

In this paper, we present a fuzzy multiple criteria decision making (FMCDM) model known as fuzzy balancing and ranking. In contrast to other MCDM models, our proposed model does not require the weights of decision making criteria. First, we appraise the performance of alternatives against criteria via linguistic variables which are expressed as triangular fuzzy numbers. The foregoing model obtains the alternative rankings through a four-stage process. Second, an outranking matrix is derived indicating that the frequency with which one alternative is superior to all other alternatives based on each criterion. Third, the outranking matrix is triangularised to obtain an implicit pre-ordering or provisional order of alternatives. Fourth, the provisional order of alternatives is subjected to various screening and balancing operations that require sequential application of a balancing principle to the so-called advantages-disadvantages table that combines the criteria with the pair-wise comparisons of alternatives. Additionally, to demonstrate the procedural implementation of the proposed model and its effectiveness, we apply it on a case study regarding the problem of supplier selection. [ABSTRACT FROM AUTHOR]

In this paper, a novel model is presented for machines and automated guided vehicles' simultaneous scheduling, which addresses an extension of the blocking job shop scheduling problem. An artificial neural network approach is used to estimate machine's breakdown indexes. Since the model is strictly NP-hard and because objectives contradict each other, two developed meta-heuristic algorithms called \fuzzy multi-objective invasive weeds optimization algorithm" and \fuzzy multi-objective cuckoo search algorithm" with a new chromosome structure which guarantees the feasibility of solutions are developed to solve the proposed problem. Since there is no benchmark available on literature, three other metaheuristic algorithms are developed with a similar solution structure to validate performance of the proposed algorithms. Computational results showed that developed fuzzy multi-objective invasive weeds optimization algorithm had the best performance in terms of solving problems compared to four other algorithms. [ABSTRACT FROM AUTHOR]

In today's ever-changing and highly uncertain environment, organizations depend on research and development (R&D) activities to adapt intensive growth of technology. One of the most important and trickiest tasks of any developing firms is to define new projects. This process gets difficult when it comes to choosing an appropriate portfolio from a set of candidate projects. Since organizations are faced with limited resources of R&D and budget constraints, they have to choose a project portfolio that mitigates the corresponding risk and enhances the overall value of portfolio. Therefore, the purpose of this study is to introduce a practical model to select the best and the most proper project portfolio while considering project investment capital, return rate, and risk. The ever-changing and highly uncertain environment of projects is addressed by utilizing interval type-2 fuzzy sets (IT2FSs). In this paper, a new model of R&D project evaluation is first introduced. This model includes a new risk-return index. This model is then extended in project portfolio selection, and as a result, a new model of R&D project portfolio selection is proposed under uncertainty. Constraints and limitations of R&D project portfolio selection are comprehensively addressed. In this model, lower semi-variance is applied to consider risk of proposed projects. Therefore, this paper offers a new model that applies IT2FSs to handle uncertainty, uses semi-variance to assess risk, synchronously considers risk and return in its selection process, and addresses the considerations and limits of real-world problems. Eventually, to verify the proposed model, a numerical example of the existing literature is solved with the model, and the results are compared. The first proposed model is used to prioritize proposed R&D projects of a gas and oil development holding firm as a real case study. To illustrate further, a practical example is also provided to demonstrate the applicability of the proposed project portfolio selection model. [ABSTRACT FROM AUTHOR]

Organizations need to evaluate project proposals and select the ones that are the most effective in reaching the strategic goals by considering sustainability issue. In order to enhance the effectiveness and the efficiency of project oriented organizations, in this paper a new multi-objective decision making (MODM) approach of sustainable project portfolio selection is proposed which applies interval-valued fuzzy sets (IVFSs) to consider uncertainty. In the proposed approach, in addition to sustainability criteria, other practical criteria including non-financial benefits, strategic alignment, organizational readiness and project risk are incorporated. The presented approach consists of three main parts: In the first part, a novel composite risk re turn index based on the IVFSs is introduced and used to form the first model to evaluate the financial re turn and risk of the proposed projects. In the second p a rt, a new risk reduction compromise ratio model is introduced to evaluate projects versus non-financial criteria. Finally, an MODM model is presented to form the overall objective function of the approach. In order to make the approach more suitable for real-world situations, a group of applicable constraints is included in the proposed approach. The constraints are based on limitations and issues existing in practical project portfolio management. Due to importance of uncertainty and risk in project portfolio selection, they are addressed separately in three parts of the approach. In the first part, a novel downside risk measure is introduced and applied to assess financial risk of projects. In the second part of the approach, not only project risk is accounted for as a criterion, bu t also a new method is introduced to control and limit the risk of uncertainty and to use the advantages of IVFSs. Finally, the proposed IVF-MODM approach is applied to select the optimal sustainable project portfolio in real case study of a holding company in a developing country. The results show that the approach can successfully address highly uncertain environments. Moreover, risk has been fully explored from different perspectives Eventually, the approach provided the decision makers with more flexibility in focusing on financial and non-financial criteria in the selection process. [ABSTRACT FROM AUTHOR]

This paper investigates the optimal multi-product dynamic pricing and inventory policies over a multi-period planning horizon with deteriorating products and a fuzzy demand function. The objective is maximization of the discount profit. A dynamic programming model is presented to determine retail price and replenishment quantities. Also, due to the existence of uncertainties in the values of parameters, such as cost, deterioration rate, and the optimal strategies in general, they cannot be obtained with high feasibility. Thus, the concept of fuzzy set theory can be applied to cope whit this issue. Since the presented model is a fuzzy partial deferential equation, three novel fuzzy expansion methods, including Jacobi polynomials, airfoil polynomials, and fuzzy collocation methods, are proposed for solving this problem. Finally, this paper carries out various computational experiments to assess the proposed model and solution approaches. [ABSTRACT FROM AUTHOR]

Multiple attributes group decision making (MAGDM) is regarded as the process of determining the best feasible solution by a group of experts or decision makers according to the attributes that represent different effects. In assessing the performance of each alternative with respect to each attribute and the relative importance of the selected attributes, quantitative/qualitative evaluations are often required to handle uncertainty, imprecise and inadequate information, which are well suited to represent with fuzzy values. This paper develops a VIKOR method based on intuitionistic fuzzy sets with multi-judges and multi-attributes in real-life situations. Intuitionistic fuzzy weighted averaging (IFWA) operator is used to aggregate individual judgments of experts to rate the importance of attributes and alternatives. Then, an intuitionistic ranking index is introduced to obtain a compromise solution to solve MAGDM problems. For application and validation, this paper presents two application examples and solves the practical portfolio selection and material handling selection problems to verify the proposed method. Finally, the intuitionistic fuzzy VIKOR method is compared with the existing intuitionistic fuzzy MAGDM method for two application examples, and their computational results are discussed. [ABSTRACT FROM AUTHOR]

Selecting the best project in construction industry is a complex decision problem in which numerous conflicting factors should be considered for the assessment. Among well-known models in the soft computing field, artificial intelligence (AI) can be suggested to yield better predictions than traditional methods. For this purpose, this paper introduces an effective AI model based on new neural networks and fuzzy logic to improve the decisions for projects owners. A computationally AI model, namely locally linear neuro-fuzzy (LLNF), is proposed to precisely predict the overall performance of projects in construction industry. Proposed model can be properly employed for the long term prediction of performance data in construction industry. Finally, the model is applied to facilitate the assessment process in a real case study. To demonstrate the applicability of the proposed AI model, the computational results in terms of the performance and accuracy are compared to two widely-used regression methods. [ABSTRACT FROM AUTHOR]

This paper proposes a model on the basis of VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methodology as a compromised method to solve the Multi-Objective Large-Scale Nonlinear Programming (MOLSNLP) problems with block angular structure involving fuzzy coefficients. The proposed method is introduced for solving large scale nonlinear programming in fuzzy environment for first time. The problem involves fuzzy coefficients in both objective functions and constraints. In this method, an aggregating function developed from LP-metric is based on the particular measure of "closeness" to the "ideal" solution. The solution process is composed of two steps: First, the decomposition algorithm is utilized to reduce the q-dimensional objective space into a one-dimensional space. Then a multi-objective identical crisp non-linear programming is derived from each fuzzy non-linear model for solving the problem. Second, for finding the final solution, a single-objective large-scale nonlinear programming problem is solved. In order to justify the proposed method, an illustrative example is presented and followed by description of the sensitivity analysis. [ABSTRACT FROM AUTHOR]

The use of third-party logistics (3PL) providers is regarded as new strategy in logistics management. The relationships by considering 3PL are sometimes more complicated than any classical logistics supplier relationships. These relationships have taken into account as a well-known way to highlight organizations' exibilities to regard rapidly uncertain market conditions, follow core competencies, and provide long-term growth strategies. Choosing service providers has been considered as a notable research area in the last two decades. The review of the literature represents that neural networks have proposed better performance than traditional methods in this area. Therefore, in this paper, a new enhanced artificial intelligence (AI) approach is taken into consideration to assist the decision making for the logistics management which can be successfully presented in cosmetics industry for long-term prediction of the real performance data. The presented AI approach is based on modern hybrid neural networks to improve the decision making for the 3PL selection. The model can predict the overall performance of the 3PL according to least squares support vector machine and cross validation technique. In addition, the proposed AI approach is given for the 3PL selection in a real case study for the cosmetics industry. The computational results indicate that the proposed AI approach provides high performance and accuracy through the real-life situations prediction along with comparing two other two well-known AI methods. [ABSTRACT FROM AUTHOR]

In this research, we address the application of uncertainty programming to design a multi-site, multi-product, multi-period, closed-loop supply chain (CLSC) network. In order to make the results of this article more realistic, a CLSC for a case study in the iron and steel industry has been explored. The presented supply chain covers three objective functions: maximization of profit, minimization of new products' delivery time, collection time and 'disposal time of used products, and maximizing flexibility. To solve the proposed model, an interactive hybrid solution methodology is adopted through combining a hybrid fuzzy-stochastic programming method and a fuzzy multi-objoctive approach. Finally, the numerical experiments are given to demonstrate the significance of the proposed model and the solution approach. [ABSTRACT FROM AUTHOR]

Mathematical modeling of supply chain operations has proven to be one of the most complex tasks in the field of operations management and operations research. Despite the abundance of several modeling proposals in the literature; for vast majority of them, no effective universal application is conceived. This issue renders the proposed mathematical models inapplicable due largely to the fact that real-life supply chain problems are set forth in restrained terms or represented less strikingly than they would bear out. This paper is triggered to bridge this gap by proposing a universal mixed integer linear programming (MILP) framework which to large extent simulates many realistic considerations in vehicle routing scheduling problems in cross-docking systems which might have separately been attempted by other researchers. The developed model is pioneer in excogitating the vehicle routing scheduling problem with the following assumptions: a) multiple products are transported between pick-up and delivery nodes, b) delivery time-intervals are imposed on each delivery node, c) multiple types of vehicles operate in the system, d) capacity constraints exists for each vehicle type, and finally e) vehicles arrives simultaneously at cross-docking location. Moreover, to solve the model a hybrid solution methodology is presented by combining fuzzy possibilistic programming and stochastic programming. Finally, in order to demonstrate the accuracy and efficiency of the proposed model, an extensive sensitivity analysis is performed to scrutinize its parameters' demeanors. [ABSTRACT FROM AUTHOR]

Decision-making analysis methods are employed to find the best option among feasible alternatives where an amount of alternatives versus criteria is introduced as only one value level with stationary numerical value. In real-world decision situations, the condition of multi-segment problems may exist in practice. In this paper, a new method is proposed to rank the alternatives in Multiple Criteria Decision-Making (MCDM) problems, where the amount of alternatives to the criteria can be represented by several segments. Hence, a multi-segment decision matrix can be obtained. Moreover, the proposed method, based on Simple Additive Weight (SAW), can be employed to solve the decision problems, where the amount of alternatives versus the assessment criteria at each level is introduced as a function of some parameters. These functions can be regarded as linear, exponential, and trigonometric. Finally, three real case studies are given to demonstrate the solution procedure of the proposed method and then a sensitivity analysis for each case is reported. [ABSTRACT FROM AUTHOR]