Operations research is a commonly used method in many subjects nowadays. One applicable domain of operation research is the problem of facility layout and location. In this paper, a new mathematical programing model is developed for an optimal facility location and assignment. The model includes two objective functions. The first one minimizes the total material handling and fixed costs of facility location. Because of the importance of energy and the main role of fossil fuel in transportation, the second objective function, minimizes the total cost of fuel consumption. To consider the real condition in the proposed model, the cost of fuel, is considered to increase stepwise gradually. In the proposed model the coefficients of objective function are considered to be probabilistic and some of constraints to be fuzzy variables. Using a new approach, this model can be changed to a robust model. To prove the applicability of the model, it is examined for a real condition of facility location.

In this paper, a two-echelon supplier-manufacturer system has been studied through net present value (NPV) approach. The production rate is finite and constant in both echelons. Also it is assumed that there is a lead-time between the first echelon and it is getting to the second echelon. It is also assumed that the lot-size of manufacturer (second echelon) is m times larger than the supplier’s factors (first echelon), and the supplier can receive wares (the raw material) from the manufacturer in a cycle through several shipments, due to the point that shortage is not allowed. So, it is supposed that the supplier’s production rate is greater than manufacturer’s. The aim is to determine the optimal lot-size of each echelon such that the NPV of the total cost of system is minimized. After approximating the NPV objective function via Maclaurin expansion in both zero and non-zero lead-time cases, an exact algorithm is presented to find optimal solution of the presented model. Based on the results, the two approaches of average cost and NPV do not lead to a same result, and non-equivalency is occurred in this case.

In this paper, we consider scheduling of project networks under minimization of total weighted resource tardiness penalty costs. In this problem, we assume constrained resources are renewable and limited to very costly machines and tools which are also used in other projects and are not accessible in all periods of time of a project. In other words, there is a dictated ready date as well as a due date for each resource such that no resource can be available before its ready date but the resources are allowed to be used after their due dates by paying penalty costs depending on the resource type. We also assume, there is only one unit available of each resource type and no activity needs more than it for execution. The goal is to find a schedule with minimal total weighted resource tardiness penalty costs. For this purpose, we present a hybrid metaheuristic procedure based on the greedy randomized adaptive search algorithm and path-relinking algorithm. We develop reactive and non-reactive versions of the algorithm. Also, we use different bias probability functions to make our solution procedure more efficient. The computational experiments show the reactive version of the algorithm outperforms the non-reactive version. Moreover, the bias probability functions defined based on the duration and precedence relation characteristics give better results than other bias probability functions.

Background: Over the past decade, many hospitals have adopted hybrid operating rooms (ORs). As resources are limited, these ORs have to prove themselves in adding value. Current estimations on standard OR costs show great variety, while cost analyses of hybrid ORs are lacking. Therefore, this study aims to identify the cost drivers of a conventional and hybrid OR and take a first step in evaluating the added value of the hybrid OR. Methods: A comprehensive bottom-up cost analysis was conducted in five Dutch hospitals taking into account: construction, inventory, personnel and overhead costs by means of interviews and hospital specific data. The costs per minute for both ORs were calculated using the utilization rates of the ORs. Cost drivers were identified by sensitivity analyses. Results: The costs per minute for the conventional OR and the hybrid OR were € 9. 45 (€ 8. 60-€ 10. 23) and € 19. 88 (€ 16. 10-€ 23. 07), respectively. Total personnel and total inventory costs had most impact on the conventional OR costs. For the hybrid OR the costs were mostly driven by utilization rate, total inventory and construction costs. The results were incorporated in an open access calculation model to enable adjustment of the input parameters to a specific hospital or country setting. Conclusion: This study estimated a cost of € 9. 45 (€ 8. 60-€ 10. 23) and € 19. 88 (€ 16. 10-€ 23. 07) for the conventional and hybrid OR, respectively. The main factors influencing the OR costs are: total inventory costs, total construction costs, utilization rate, and total personnel costs. Our analysis can be used as a basis for future research focusing on evaluating value for money of this promising innovative OR. Furthermore, our results can inform surgeons, and decision and policy-makers in hospitals on the adoption and optimal utilization of new (hybrid) ORs.

Objective: Selecting suppliers of industrial systems equipment to minimize the total construction and operation costs and implement it for Feed Water Multi-state System (FWS) of Heat Recovery Steam Generator (HRSG) boilers in Mapna Company. Methods: Using the Markov chain model results in a mathematical programming model and then analyzing the sensitivity of this model. First, using the Markov chain, different system states are drawn. Then, using the state transfer rate (failure rate and repair rate) of the equipment, the probability of the system being in any of these states is determined parametrically. Then, the Markov chain model results are added to the other constraints of a mathematical model the problem and solved by GAMS software. The effect of cost parameter changes on the optimal solution is calculated individually and then compared in a general graph. Results: The highest construction and operation costs are in ascending order: 1-the equipment purchasing price, 2-the production capacity reduction cost, 3-the system construction delay penalty, and 4-the system shutdown cost. Also, the relationship between the optimal solution to these parameters is linear in a wide range of changes. Conclusion: All costs have a direct effect on the design of the Feed Water System (purchase, delay, capacity reduction, and shutdown), but the cost of purchasing equipment has the most significant effect on the total cost. It is therefore recommended to focus entirely on reducing this cost.

The use of pipelines is the most effective and safest method of transporting large-scale petroleum products from the refinery to the storage and distribution centers. Efficient and optimal planning in multi-product pipelines is important from economic, social, and strategic points of view. In this paper, a Mixed Integer Linear Programming (MILP) model with a continuous time framework is presented to determine not only the batches' optimal injection and volume, but also their sequence and timing. The goal is to minimize the costs of pumping, storage, mixing, and delayed demands regarding all operational constraints in the problem. This paper investigates the planning of transportation of petroleum products via a onedirection multi-product pipeline with an injection source at the origin and several distribution centers along the way. The supposition of this problem regards a multi-period planning horizon with a time window for the total demands of the distribution centers, which must be provided until the end of the program horizon. The case study is a pipeline with a length of 457 km that delivers 6 petroleum products to 4 distribution centers. Two examples of the mentioned case study are presented to demonstrate the advantages of using the Mixed Integer Linear Programming model. The evaluation and validation of the plan is confirmed by comparing it with the operational plan realized in Iran Oil Pipelines and Telecommunications Company. The results imply a significant improvement in pipeline scheduling and cost reduction by applying the mathematical model.

In recent years, the increase in electricity consumption in the household sector and the increase in the purchase price of electricity have led to the creation of an energy management system in smart homes. The smart home energy management system schedules the equipment for the next 24 hours. This paper examines a smart home that incorporates new technologies like interruptible loads, electric vehicles, energy storage systems, renewable energy sources, and smart devices. In this paper, the smart home energy management system, knowing its consumption needs and the current price of electricity in the electricity market, provides the power it requires by purchasing from the power grid or using the production power of renewable energy resources. Additionally, the smart home energy management system can sell its excess production power to the grid in a few hours. This paper aims to reduce the operating costs of a smart home during the day, and it has done so in several different scenarios. The effect of charging and discharging electric vehicle, energy storage system, and renewable energy resources in reducing costs has been investigated in these scenarios.

Given the increasing human need for health systems and the costs of using such systems, the problem of optimizing health-related systems has attracted the attention of many researchers. One of the most critical cases in this area is the operating room scheduling. Much of the cost of health systems is related to operating room costs. Therefore, planning and scheduling of operating rooms can play an essential role in increasing the efficiency of health systems as well as reducing costs. Given the uncertain factors involved in such matters, attention to uncertainty in this problem is one of the most critical factors in the results. In this study, the problem of the daily scheduling of the operating room with uncertain surgical time was investigated. For minimizing overhead costs and maximizing the number of surgeries to reduce patients' waiting time, after introducing a mathematical model, a chance-constrained programming approach is used to deal with its uncertainty. In this study, also, a harmony search algorithm is proposed to solve the model because of its NP-Hardness. By performing the numerical analysis and comparing the presented algorithm result with a genetic algorithm, the results show that the proposed algorithm has a better performance.

The main goal of this research is to compare sticky operating costs, economic costs and the effect of depreciation expenses of emergence of costs stickiness phenomenon among firms accepted in Tehran Stock Exchange. We used the pattern devised by Anderson et al., linear pattern devised by them and Weiss’ s firm-level criterion for testing hypotheses. Research hypotheses have been based on a statistic sample including 91 firms during 11 years from 2004 to 2014 and tested using multivariate regression patterns and general combined data stratification. Results of this research using pattern devised by Anderson and et al., indicated that operating costs are significantly stickier than economic costs. Moreover, operating costs after deducting depreciation are less sticky than operating costs before deducting depreciation. Results using the linear pattern devised by Anderson et al., aren’ t significant. According to firm-level criterion devised by Weiss, there isn’ t also significant difference between operating costs and economic costs as well as operating costs after deducting depreciation and before deducting depreciation costs regarding their stickiness.