Home health care (HHC) companies serve as the alternative to hospitals aiming to provide customers with medical care at home. A crucial challenge for HHC providers is to optimize routes and schedules ...for their caregivers to serve customers. Inspired by the practices in the HHC industry, this paper addresses a multi-objective home healthcare routing and scheduling problem (HHRSP) with several conflicting objectives: minimizing routing cost and improving service consistency and workload balance. We refer to the problem as a multi-objective consistent home healthcare routing and scheduling problem (MoConHHRSP). To be more practical, uncertain travel and service times are also considered and defined based on uncertainty theory. Next, the uncertain programming model for the proposed MoConHHRSP is formulated and then reduced to its deterministic equivalent. Due to the NP-hard essence of the problem, an improved multi-objective artificial bee colony (IMOABC) metaheuristic, integrating the large neighborhood search heuristic and an adapted non-dominated solution set update strategy into the multi-objective artificial bee colony (MOABC) framework, is developed. Finally, a series of numerical experiments are conducted to illustrate the competitive performance of the designed algorithm by comparing it with other multi-objective algorithms from multiple evaluation metrics. Furthermore, the trade-off analysis reveals that a better caregiver consistency can be achieved at a high price of total costs and workload balance, while a great improvement on the workload balance can be provided with little deterioration in caregiver consistency. In many cases, low total costs and a high level of workload balance can be achieved simultaneously.
•A new home healthcare optimization problem with service consistency is proposed.•Uncertain travel and service times are considered and dealt by uncertainty theory.•Our algorithm performs well compared with other multi-objective algorithms.•The trade-off analysis reveals some managerial insights.
In view of different types of online rumors, when the governance institutions (government departments) intervene to achieve the least cost and the best effect is a core issue of rumor governance. ...Based on the perspective of the government, under the premise of limited human, material and financial resources, and considering the different impacts of government intervention opportunities on the spread of different types of rumors, this paper establishes an uncertain multi-objective network rumor transmission intervention timing model, so as to achieve the optimal allocation of government intervention opportunities for different types of online rumors, minimize the total cost and maximize the inhibition effect of rumor spreading. Then, the equivalent form of the model is given, and the model is solved. In addition, numerical experiments are conducted on three types of rumors during the COVID-19 pandemic. The experimental results show that when the types of online rumors and the focus of government departments on cost and governance effect are different, the optimal time for intervention will change. Finally, combined with the experimental results, corresponding countermeasures and suggestions are put forward for the supervision of online rumors by government departments.
Uncertainty theory provides a new tool to deal with the shortest path problem with nondeterministic arc lengths. With help from the operational law of uncertainty theory, this paper gives the ...uncertainty distribution of the shortest path length. Also, it investigates solutions to the
α
-shortest path and the most shortest path in an uncertain network. It points out that there exists an equivalence relation between the
α
-shortest path in an uncertain network and the shortest path in a corresponding deterministic network, which leads to an effective algorithm to find the
α
-shortest path and the most shortest path. Roughly speaking, this algorithm can be broken down into two parts: constructing a deterministic network and then invoking the Dijkstra algorithm.
Interval programming is a commonly used technique in real-world situations. Its related theories and methods have been widely researched. There are a variety of approaches for assessing solutions of ...an interval programming problem due to the particularity of intervals. It is well-known that different assessing approaches may produce different optimal solution(s) for the same interval programming problem, and it is rather difficult to choose from these assessing approaches for users, especially for those who have little knowledge about interval arithmetic, which greatly restricts its extensive applications.
In this paper, we develop an ensemble framework for assessing solutions of interval programming problems. At the start, interval dominance rules are defined, and their correlations are described via exclusion, inclusion and equivalence; then, a rule reduction strategy is developed through inspecting the impact of different rules on the sorting of solutions, and a novel ensemble dominance relation for interval programming is proposed to evaluate solutions; furthermore, their complexities are analyzed; finally, the experimental results empirically validate the correctness and effectiveness of the proposed framework.
•Developing framework for reducing purchasing risks associated with suppliers.•Combination of grey system theory and uncertainty theory is used.•It neither requires any probability distribution nor ...fuzzy membership function.•It selects the most appropriate suppliers and allocates optimal purchase quantity.
Supplier selection in supply chain is critical strategic decision for organization’s success and has attracted much attention of both academic researchers and practitioners. Supplier selection problem consists of stochastic and recognitive uncertainties. However, the requirement of large sample size and strong subject knowledge to build suitable fuzzy membership function restrict the applicability of probability and fuzzy theories in supplier selection problem. In response, this study proposed a new tool for supplier selection. In this paper, we applied the combination of grey system theory and uncertainty theory which neither requires any probability distribution nor fuzzy membership function. The objective of this paper is to develop framework for reducing the purchasing risks associated with suppliers. The proposed supplier selection method not only selects the most appropriate supplier(s) but also allocate optimal purchase quantity under stochastic and recognitive uncertainties. An example is shown to highlight the procedure of the proposed model at the end of this paper.
This paper discusses a joint problem of optimal project selection and scheduling in the situation where initial outlays and net cash inflows of projects are given by experts’ estimates due to lack of ...historical data. Uncertain variables are used to describe these parameters and the use of them is justified. A new mean-variance and a mean-semivariance models are proposed considering relationship and time sequence order between projects. In order to solve the complex problems, the methods for calculating uncertain lower partial semivariance and higher partial semivariance values are introduced and a hybrid intelligent algorithm which integrates genetic algorithm with cellular automation is provided. In addition, two examples are presented to illustrate the application and significance of the new models, and numerical experiments are done to show the effectiveness of the proposed algorithm.
•Network DEA is considered in uncertain environment.•All external and internal inputs and outputs are provided by an expert.•Uncertainty theory initiated by B. Liu is considered as the modeling ...approach.•Model is devised and sensitivity analysis is carried out.•The model is implemented on a simple illustrative example.
In the Network data envelopment analysis, in addition to external inputs and outputs of a decision-making unit, interactions between internal subprocesses are also respected. These interactions could be an essential part of an efficiency assessment of decision-making units, while in many circumstances, they are represented as undetermined quantitative values by the experts of the domain. Uncertainty theory, initiated in 2007 and then completed in 2009, is an axiomatic mathematical framework for formalizing human reasoning. Here, we consider the network DEA at which both external and internal data are provided by an expert and design a novel procedure in the efficiency evaluation process of decision-making units using the uncertainty theory. We consider the basic model for a typical small instance, and a crisp counterpart is provided. The presented numerical example could be evidence of the applicability of the methodology in practice.
•Three models are proposed for uncertain covering location problem.•The crisp equivalences of the models are discussed.•Uncertainty distribution of the covered demand is studied.
In practical ...location problems on networks, the response time between any pair of vertices and the demands of vertices are usually indeterminate. This paper employs uncertainty theory to address the location problem of emergency service facilities under uncertainty. We first model the location set covering problem in an uncertain environment, which is called the uncertain location set covering model. Using the inverse uncertainty distribution, the uncertain location set covering model can be transformed into an equivalent deterministic location model. Based on this equivalence relation, the uncertain location set covering model can be solved. Second, the maximal covering location problem is investigated in an uncertain environment. This paper first studies the uncertainty distribution of the covered demand that is associated with the covering constraint confidence level α. In addition, we model the maximal covering location problem in an uncertain environment using different modelling ideas, namely, the (α, β)-maximal covering location model and the α-chance maximal covering location model. It is also proved that the (α, β)-maximal covering location model can be transformed into an equivalent deterministic location model, and then, it can be solved. We also point out that there exists an equivalence relation between the (α, β)-maximal covering location model and the α-chance maximal covering location model, which leads to a method for solving the α-chance maximal covering location model. Finally, the ideas of uncertain models are illustrated by a case study.
Product configuration is widely practiced and is very effective when offering mass customisation production. This strategy assists manufacturers in understanding customers' preferences and demands, ...presenting product relevant rules, choosing alternative components and modules, and constructing theoretical models. However, the uncertainties (such as lead-time uncertainty) are not fully considered in the product configuration. To fill this gap, we develop an uncertain product configuration model based on uncertain lead-time and time-sensitive demand using uncertain programming. Another contribution is that we explored the interdependence between uncertain lead-time and outsourcing strategy. This uncertain mixed-integer programming is solved by using CPLEX 12.8. During a series of numerical experiments, we found that the profit and the sourcing strategies are sensitive to the uncertain lead-time and the outsourcing strategy helps companies reduce losses due to uncertainty.
One of the primary concerns in most decision making problems is the uncertainty associated with the input parameters. The existence of uncertainty may lead to some unrealistic results, which may make ...the final decision even more difficult. This paper presents an application of robust optimization technique to a recently developed model named Best-Worst method. The resulted robust approach is formulated as a linear programming where it can be solved using any commercial software package. The proposed model has been implemented on several instances which exist in the literature and the preliminary results have indicated that a small perturbation may influence the finally ranking, significantly.
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