Scientia Iranica
Volume:30 Issue: 2, Mar-Apr 2023

Health care centers are one of the most important municipal facilities that are directly involved in providing personal and social health. In this paper, a new Robust Mathematical Modeling is simultaneously provided to locate and efficiently allocate healthcare facilities, including different service levels of medical care, concerning the characteristics in normal and disaster situations. Furthermore, to help victims and prevent overcrowding in hospitals and medical centers during disasters, the establishment of temporary and outpatient centers is allowed for emergency provision of basic services. Also, the possibility to send equipment and medical teams to these centers is considered in the proposed model. Since the considered problem is included as NP-Complete problems, to solve the problem, two metaheuristic algorithms, harmony search algorithm and hybrid Tabu search combined with variable neighborhood search algorithm, and a lower bound based on Lagrangian relaxation method are presented. Finally, to examine the proposed algorithms, a number of sample problems are randomly generated in small and large sizes and the results of exact solutions and the lower bound resulting from the Lagrangian relaxation method are evaluated and compared with the results of the meta-heuristic algorithms. The results show the good performance of the proposed algorithms.

Application of an absolute supply chain model does not invalidate the possibility of few defective items in a supplied lot, therefore it becomes essential to conduct an inspection process for segregating the defective items, subsequently such segregated items are sold at discounted price. Shortages mainly occur with sudden demand or erratic production capacity, and player’s decisions are influenced by it. In this paper, the shortage is considered as a seller’s decision variable and demand is receptive to selling price and marketing expenditure of the buyer. Player’s interaction will be reviewed and determined as non-cooperative Stackelberg game. Further, a supply chain model is endured to substantiate the interaction and democracy among buyer and seller in the supply chain and is pitched by non-cooperative game theoretical approaches. The Stackelberg game approach is used in the non-cooperative method where one player acts as leader and another as follower. Hereafter, unanimous numerical examples along with sensitivity analysis are exhibited to compare amidst two different models with and without shortages to demonstrate the significance of the paper.

In this paper, single product single machine systems under Markovian deterioration of machine condition throughout a specified finite planning horizon are studied. It is assumed that the machine is subject to random failures and that any maintenance activities carried out in a period, reduces the system’s potential production capacity during that period. Furthermore, it is assumed that the machine is minimally repaired at failure, and PM is carried out, after inspection, to restore the machine to an ‘as-good-as-new’ status. The objective of the study is to find the optimal intervals for inspection and preventive maintenance (PM) activities in condition-based maintenance (CBM) planning with a discrete monitoring framework subject to minimize the sum of inspection, PM, minimal repair, and backlog costs. To achieve the goal, a stochastic dynamic programming model that enumerates demand is presented calling the demand-driven CBM model. The numerical results show that this model decreases the total cost significantly that depends on the demand and the unit backlog cost, which is an increasing and a concave function in the unit backlog cost regardless of the initial machine state.

A common way to address customer concerns in the post-warranty period is to provide an extended warranty. Although sometimes the manufacturer is reluctant to offer an extended warranty, an agent takes on this task to maintain market share. In this regard, a three-level-servicing-contract among manufacture, agent and customer is presented in which both warranty period and useful life of the product are considered as function of age and usage. The proposed model considers two approaches to control the number of product failures and reduce cost: (1) the technology level used in manufacturing as an effective factor in product reliability; and (2) non-periodic maintenance activities to maintain the product reliability at an acceptable level. In addition, in this study, to calculate the costs imposed on each side of the contract more accurately, the time-value-of-money is considered in the calculation of financial flows. To illustrate the effectiveness of the proposed approach, three comparative studies are provided. The first comparative study shows the impact of the provision of an extended warranty, while the second comparative study proves the importance of preventive maintenance to reduce costs. The results of the last one show the effect of considering the time-value-of-money in calculating cash flows.

This paper studies, investment portfolio of two players in the banking system in a two-level game, and eventually determines the optimal portfolios of investors using the Markowitz model. This two-level game includes bank C as the leader of the game and customers of this bank as the game followers. The investment portfolios of the leader player include investment in competitor banks (A and B), foreign exchange market, real estate market, and stock. The data related to the mentioned assets covered 2010-2020, where the optimal investment portfolios of the players was first determined using GAMS and genetic meta-heuristic algorithm. Next, the problem was solved again using the meta-heuristic algorithms of PSO and IWO. Eventually, the optimal algorithm was chosen using TOPSIS multi-criteria decision-making. The results of 3 algorithms indicated that the optimal portfolio for the leader player consisted of investment in properties, securities, and competitor banks respectively.

Outsourcing is recognized as a tool to gain strategic advantages. Maintenance outsourcing is a common practice in many industries, including chemical, petroleum, petrochemical, and medical equipment manufacturing. Nevertheless, outsourcing is associated with many risks. In the present study, based on the system dynamics, we designed a model to identify variables, influencing the effectiveness of equipment, efficacy, and profitability. We also examined the extent of the effects of these variables and assessed their relationships to decide on maintenance outsourcing in gas refineries. First, the influential variables were identified by reviewing the literature and considering the experts’ opinions. Next, a system dynamics model was designed, and the optimal values of the variables were investigated by creating five different scenarios. The results showed how the investigated variables affected our goals and how we could achieve them by keeping the values of these variables close to those determined in the selected scenarios. If the variable of equipment effectiveness was preferred by the managers, scenario-3 would be selected, as the equipment effectiveness reached its maximum level in this scenario. On the other hand, if the efficacy and profitability variables were preferred, scenario 4 would be selected in which efficacy and profitability were at maximum levels.

In this paper, a new algorithm for solving bi-level optimization problems is presented. This algorithm can obtain the optimal or near-optimal solution for any bi-level optimization problem. The decision variables of the first and second level models can be both integers and continuous. In this method, by solving a certain number of the bi-objective programming model and then solving the corresponding second-level model, a bi-level feasible solution that is either optimal or near-optimal is identified. To evaluate the efficiency of the algorithm, the value of the objective function, as well as its computation time in different instances, are compared with exact methods as well as evolution-based methods. The numerical results confirm the high efficiency of the proposed algorithm.

Particle Swarm Optimization (PSO) has many successful applications on solving continuous optimization problems. It has been adapted to solve discrete optimization problems using different variants, such as integer PSO (IPSO), discrete PSO (DPSO) and integer and categorical PSO (ICPSO). ICPSO, a recent PSO variant, uses probability distributions instead of the solution values. In this study, we applied ICPSO algorithm to solve dynamic integrated process planning, scheduling and due date assignment (DIPPSDDA) problem which is a higher integration level of well-known problems which are integrated process planning and scheduling (IPPS) and scheduling with due date assignment (SWDDA). Briefly, due date assignment function is integrated to IPPS problem as the third manufacturing function in DIPPSDDA. Furthermore, DIPPSDDA performs scheduling function in a dynamic environment in where jobs arrive to shop floor in any time. The objective of DIPPSDDA problem is to minimize the earliness, tardiness and given due dates length. Since the experimental results show that ICPSO does not find better solutions, crossover and mutation operators used in genetic algorithm were implemented to ICPSO, namely modified ICPSO (MICPSO). Finally, experimental results indicate that the proposed MICPSO provides better performance as compared to genetic algorithm, ICPSO and modified discrete PSO.

Perishable products may expire if their holding time exceeds their shelf-life. In this study, along with designing a forward flow to distribute perishable products; remained perished products at retailers can be gathered for recycling during distributing fresh products. To mitigate the waste, recycled products are offered to a secondary market. A mathematical model for this Closed-Loop Location-Routing-Inventory Problem (CL-LRIP) is developed by considering multi-compartment trucks, simultaneous pickup and delivery, technology selection, and risk of urban traffic. Based on three sustainability pillars, three objective functions are considered. This way, the interests of the network's three main stakeholders are embedded. The proposed model is solved by the Torabi-Hassini method. Two evolutionary algorithms, including Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and a new hybrid one, are also developed to solve large-sized cases of the NP-complete problem. Statistical tests show the superiority of the hybrid algorithm in the computational time (CT) metric, which is about 0.4 NSGA-II’s CT. The results indicate the importance of closing the network loop for perishable products. Finally, the sensitivity analysis determined that 83.33 % decrease in recycled product’s sale price causes 9.08% increase in costs, 2.77% decrease in environmental side-effects, and 5.16% decrease in social objectives, which are significant.

According to the significant role of ports, port-hinterland distribution network considering various parameters, has come under the spot of attention in the recent years. This paper, considering intermodal transport, along with the possibility of constructing new inland terminals where transportation mode changes, aims to investigate the subject of port-hinterland freight distribution network. To this aim, considering the volume of exported freight being delivered as well as imported freight received, a multi-objective intermodal model has been developed for Iran's case study. In this model, it has been assumed that in addition to the existing railway and road routes in the country, new railway and road routes could be constructed as well. The first objective function involves minimizing the cost of transportation along with the cost of constructing an inland terminal. The second objective function involves minimizing CO_2 released during freight transport. The certain model of the problem has been described, first and uncertainty conditions in amounts of import demand and export supply has been taken into account. A robust modeling approach has been used. Therefore, data of goods imported or exported to/from Iran were collected and solved using robust model in GAMS software; then the results were analyzed and investigated.

Recently, energy demand forecasting has emerged as a signicant area of researchbecause of its prominent impact on greenhouse gases (GHGs) emissionand global warming.The problems of load forecasting are characterized by complexand nonlinear nature and also long-term historical dependency. Up to now,several approaches from statistical to computational intelligent have been appliedin this research led. The literature agrees with the fact that deep learningapproach is more capable in dealing with these characteristics among existingapproaches. However, the recent state-of-the-art deep network models are notrobust against dierent historical dependency. In this study, we propose a graphframework based on parallel DeepNet branches to tackle this challenge. Thisframework consists of multi parallel branches in which dierent kind of networkscan be incorporated. The parallel recurrent branches represent the historical dependencyof determinants individually and this leads to better performance incase of dierent historical dependency in data. In this case study, the performanceof the proposed model is examined through a comparison study withthe state-of-the-art deep network models. The comparison resulted in that theproposed framework can improve the load forecasting by a signicant marginon average.

The presence of extreme events gives rise to outrageous results regarding population parametersand their estimates using traditional moments. Traditional moments are usually influenced by extremeobservations. In this paper, we propose some new calibration estimators under L-Moments scheme for variance which is one of the most important population parameters. Some suitable calibration constraints under double stratified random sampling are also defined for these estimators. Our proposed estimators based on L-Moments are relatively more robust in presence of extreme values. The empirical efficiency of proposed estimators is also calculated through simulation. Covid-19 pandemic data from January 22, 2020, to August 23, 2020, is considered for simulation study.

So far, numerous studies have been developed to evaluate the performance of decision-making units through DEA technique in different places, but most of these studies have measured the performance of decision-making units by efficiency criteria. The productivity is considered as a key factor in the success and development of decision-making units and its evaluation is more comprehensive than efficiency evaluation. Recently, the productivity has been considered in DEA technique. The productivity in these studies is often evaluated through the productivity indexes. These indexes require at least two time periods and also the two important elements of efficiency and effectiveness in these studies are not significantly evident. There are few studies that measure productivity through efficiency and effectiveness. This few researches also measure the efficiency and effectiveness in two stages separately. So, the purpose of this study is to develop a new approach in the DEA technique in order to measure productivity of decision-making units through efficiency and effectiveness simultaneously, in one stage and interdependently. One case study demonstrates application of the proposed approach in the branches of a Bank. Using proposed approach revealed that efficient branches are not necessarily productive, but productive branches are also efficient.