Manufacturing and Logistics
Taha Hossein Hejazi; Mirmehdi Seyyed-Esfahani; Hurieh Dezhahang; Donya Ramezani
Abstract
The challenge of designing a closed-loop supply chain (CLSC) under conditions of uncertainty and partial disruptions is complex and demanding. The concept of a closed-loop supply chain involves integrating reverse logistics into the traditional forward supply chain to establish a sustainable and environmentally ...
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The challenge of designing a closed-loop supply chain (CLSC) under conditions of uncertainty and partial disruptions is complex and demanding. The concept of a closed-loop supply chain involves integrating reverse logistics into the traditional forward supply chain to establish a sustainable and environmentally friendly system. However, uncertainties and partial disruptions create significant obstacles to achieving an efficient and dependable CLSC. In order to address these challenges, the concept of chance constraint is introduced, allowing for the consideration of probabilistic uncertainties in decision-making. The goal is to develop a robust CLSC model capable of effectively managing uncertain parameters such as demand, rate of return, and product quality. The Markowitz method is utilized to address uncertainty in the objective function by combining the mean with a coefficient of standard deviation. The study's results demonstrate that incorporating uncertainty into the model leads to increased profitability compared to the deterministic model. The uncertain model is more responsive to demands and considers the dynamics of confidence inventory, leading to improved decision-making. Strategic decisions, such as the number of production, distribution, and destruction facilities, remain consistent in both models. However, the capacity of destruction centers in the uncertain model is slightly smaller due to the consideration of uncertain product quality. Furthermore, incorporating uncertainty into the model has contributed to enhancing the model's clarity and facilitating improved decision-making. This increase in profitability can be attributed to the model's heightened responsiveness to demands, as well as its dynamic approach to managing confidence inventory.
Operations Research
Hajar Shirneshan; Ahmad Sadegheih; Hasan Hosseini-Nasab; Mohammd Mehdi Lotfi
Abstract
Due to the importance of the health field, the problem of determining the shift scheduling of care providers has been addressed in many studies, and various methods have been proposed to solve it. Considering different skills and contracts for care providers is one of the essential issues in this field. ...
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Due to the importance of the health field, the problem of determining the shift scheduling of care providers has been addressed in many studies, and various methods have been proposed to solve it. Considering different skills and contracts for care providers is one of the essential issues in this field. Given the uncertainty in patients' demands, it is a crucial issue as to how to assign care providers to different shifts. One area facing this uncertainty is the provision of services to cancer patients. This study develops a stochastic programming model to account for patient demand uncertainty by considering different skills and contracts for care providers. In the first step, care providers are assigned to work shifts, then, in the second step, the required overtime hours are determined. The sample average approximation method is presented to determine an optimal schedule by minimizing care providers' regular and overtime costs with different contracts and skills. Then, the appropriate sample size is 100, determined based on the Monte Carlo and Latin Hypercube methods. In the following, the lower and upper bounds of the optimal solution are calculated. As the numerical results of the study show, the convergence of the lower and upper bounds of the optimal solution is obtained from the Latin Hypercube method. The best solution is equal to 189247.3 dollars and is achieved with a difference of 0.143% between the upper bound and lower bounds of the optimal solution. The Monte Carlo simulation method is used to validate the care provider program in the next stage. As shown, in the worst case, the value of the objective function is equal to 197480 dollars.
Transportation
Mohamad Ebrahim Tayebi Araghi; Fariborz Jolai; Reza Tavakkoli-Moghaddam; Mohammad Molana
Abstract
The Location Routing Problem (LRP), Automatic Guided Vehicle (AGV), and Uncertainty Planner Facility (UPF) in Facility Location Problems (FLP) have been critical. This research proposed the role of LRP in Intelligence AGV Location–Routing Problem (IALRP) and energy-consuming impact in CMS. The ...
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The Location Routing Problem (LRP), Automatic Guided Vehicle (AGV), and Uncertainty Planner Facility (UPF) in Facility Location Problems (FLP) have been critical. This research proposed the role of LRP in Intelligence AGV Location–Routing Problem (IALRP) and energy-consuming impact in CMS. The goal of problem minimization dispatching opening cost and the cost of AGV trucking. We set up multi-objective programming. To solve the model, we utilized and investigate the Imperialist Competitor Algorithm (ICA) with Variable Neighborhood Search (VNS). It is shown that the ICAVNS algorithm is high quality effects for the integrated LRP in AGVs and comparison, with the last researches, the sensitivity analysis, and numerical examples imply the validity and good convexity of the purposed model according to the cost minimization.
