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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]

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]

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]

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]