مقالات رزومه دکتر امیرسامان خیرخواه

In this paper, a bi-objective mixed-integer non-linear programming (MINLP) model is proposed to design a P2P electricity market for price-based energy management incorporating incentives and time-based demand response programs (DRPs), considering the active involvement of retailers. The proposed mathematical model was investigated for a hypothetical smart microgrid connected to the upstream grid, based on real data from Tehran, Iran. The results indicated that due to the Iranian government's policies on renewable energy, in all scenarios, a significant portion of the retail income comes from selling electricity to the wholesale market, and a considerable part of prosumers' costs is attributed to the deployment of energy generation systems to supply energy to the MG However, it can be argued that participating in the P2P market plays a significant role in reducing prosumers' costs. Furthermore, investigating the impacts of DRPs revealed that their implementation and price elasticity of demand increased retail and overall MG profit by up to 10.3% and 39.98%, respectively. Additionally, implementing DRPs benefits consumers (without DERs) in the MG.

Increasing concerns about environmental issues, resources constraint and social issues of employees have moved organizations to review production strategies and facility layout to provide an arrangement that takes into account all dimensions of sustainability (economic, social, and environmental). Therefore, in this research, a multi-objective mathematical model has been developed to achieve a balance between reducing layout costs, reducing electrical energy consumption, and improving the safety of the production environment for the operators. One prominent feature of the proposed model is the consideration of the strategy of scaling the processing speed of operations in machines alongside process routing. This is combined with concepts such as machine reliability, workload balancing, and operator allocation. In order to validate and assess the usability of the proposed model, the LP-metric method and examples derived from the relevant literature have been utilized. Considering the complexity of the model and the limitations of the GAMS software in providing timely solutions for large-scale problems, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) has been employed. Finally, the proposed approach was used as a real case study in a gas stove and industrial oven production workshop. The results show a 62% savings in production costs from applying the proposed method.

The production routing problem is created by combining the two problems of lot sizing and vehicle routing. Previous research has examined the effectiveness of this combination in reducing costs. In this paper, the production routing problem is considered with the aim of minimizing the risk of production and distribution of hazardous materials. Attention to social and environmental criteria related to sustainability is being increased by researchers today. Hazardous materials are harmful to human health and the environment. Accidents related to these materials often have far-reaching adverse consequences. Risk is a danger measurement criterion for operations related to these materials.

The problem is modeled as a mixed integer program. The risk function in the proposed model is nonlinear and depends on the load of the machine, the population risk, and the type of hazardous material. Given that solving the mathematical model with the nonlinear objective function is more difficult, this function is approximated by a piecewise linear function.

In this research, 8 standard instances have been used to evaluate and solve the model and compare the two nonlinear and linear models. The results show that by using the approximate model, a better answer can be achieved at the same time. Through sensitivity analysis, the effect of changes to production capacity and warehouses on risk has also been looked into.

Originality/Value:

 This research proposed the production routing problem for hazardous materials according to sustainability criteria using a nonlinear mathematical model and uses a piecewise linear approximation to solve the model.

The design of facility layout and production planning accordingly is significantly important in the manufacturing industries. This type of design in traditional units is mainly based on the lowest cost of moving materials and effective environmental indicators, and social factors are ignored in the planning and design of facility layout; Therefore, usually, the arrangement does not match the human capacity, the intensity of the employees' work is not scientific and it causes a lot of damage to the physical and mental health of the employees for a long time. Considering the importance of the mentioned items, paying attention to biological issues such as energy, which is one of the main concerns of the country, and social issues, such as the safety of the operator's work environment, which is followed by governmental and non-governmental associations, can be considered a positive and new step in the layout design.

Design/methodology/approach: 

In this paper, a multi-objective mathematical programming model has been developed in a dynamic cellular manufacturing system, taking into account the minimization of the costs of moving and rearranging the facilities, the minimization of the risks of the work environment (the potential risk of placing machinery in a certain place), and the consumption of electrical energy in product production operations. To make the model closer to reality, other issues related to the production environment such as hiring and firing operators in each period, training, wages and their allocation are also included in the model. The proposed model is solved by designing samples in different dimensions, applying the epsilon constraint method and meta-heuristic algorithm NSGA-II and MOPSO, using performance indicators, and associated comparison and analysis by T-test.

Performance indices (spacing metric, diversification metric, mean ideal distance, CPU time) in solution methods were calculated and compared based on information inspired by the subject literature and the design of 15 problems with different dimensions. The results indicated that the quality of the solutions obtained from the NSGA-II algorithm was better than other methods and in terms of the solution time, the solution was reached at least two times earlier than the epsilon constraint method.

Research limitations/implications: 

For future studies, it is suggested to investigate the performance of other meta-heuristic algorithms to solve the proposed model; Also, the combination of other real-world industrial factors such as financial and energy resource limitations, facility idle time, and unequal facility dimensions can be considerably valuable for future research. In addition, the uncertainty of the parameters, which is one of the main and practical issues in the real world, can be added to the model in terms of demand or processing time.

Practical implications:

 The dynamic cellular manufacturing system is one of the well-known manufacturing systems that provides the possibility of increasing the level of reactivity, flexibility and agility in production to compete in the market, but with the growth of industrial development and the increasing attention of societies to the amount of energy consumption and the conditions of employees in production with a new challenge. It has been faced in such a way that it should include sustainability issues from the very beginning and in the design of the facility layout. The proposed model has provided a stable, dynamic and optimal layout by creating a balance between the dimensions of sustainability (cost, energy and layout safety) in manufacturing industries.

Social implications:

 Ignoring social issues and mismanagement of human resources has resulted in unforeseen costs for manufacturing companies. This study attempts to propose a model that is closer to reality and provides a more accurate and safe arrangement by considering various dimensions of human resource management in production, such as hiring, firing, training, wages, and allocation, which are effective tools for decision-making in organizations, alongside workplace safety.

Originality/value:

 In this paper, a nonlinear mixed integer programming model is proposed for the facility layout problem in a dynamic and sustainable cellular space. In this model, the amount of energy consumption, which is one of the main issues that manufacturing industries deal with, has been addressed and has helped industries in improving their performance. Two approaches have been proposed for this issue. The amount of energy used to move the machines and the amount of energy used in processing each part/product based on the selection of the best route. In this model, the best production route is selected in such a way that, in addition to reducing production costs, energy consumption in moving parts and processing each operation should be reduced.

Hospitals are one of the most important organizations in the country, and nurses are one of their main pillars with high workloads and stress, nurses are exposed to many injuries. Ergonomic load on nurses can be reduced by an optimal job rotation and shift scheduling plan. Although these two approaches have been proposed separately, in past research articles, they have not been considered at the same time.

In this article, a two-objective mathematical model is presented for simultaneous optimization of job rotation and shift scheduling of nurses. The first objective is to minimize the maximum ergonomic load and the second objective is to minimize the total mental-psychological load on nurses. The comprehensive criterion approach has been used to integrate the objectives and reach a single objective model. The proposed model has been used in a case study and the parameters of the model have been quantified using the Movement and Assistance of Hospital Patients (MAPO) index and the Nordic questionnaire as well as the Depression- Anxiety- Stress Scale )DASS( method.

The program obtained from the mathematical model was compared with the program available in the studied hospital, which was done manually. The results showed a decrease in the ergonomic load of nurses in the new program compared to the existing situation.

Compared to the conventional method, the proposed integrated method can lead to a greater reduction of occupational injuries.

The main purpose of this paper was to provide a mathematical model of the production routing problem by applying environmental protection policies. The target company consists of two independent departments of production and distribution. These subsets are managed locally, and the two-way communication between them forms a Stackelberg game. The first subset (distribution company) is the first level decision maker and determines the route for vehicles and the amount of product transfer to each customer. The distribution company pursues two goals, minimizing costs of distribution, maintenance, and vehicle emissions. As the limited production capacity prevents the manufacturer from meeting all the demands of the distribution sector, the distribution company can procure products from the subsidiary manufacturing company or provide alternative products by paying more from other manufacturers, so in this case, it will receive compensation from the manufacturer. At a lower level, the second subset (manufacturer) schedules production with the aim of minimizing production and maintenance costs. Finally, the multi-objective bi-level problem-solving algorithm is described and developed based on the bi-level fuzzy goal programming approach. Based on numerical analysis results, the utility of final decisions regarding both the distributor and manufacturer perspective is sensitive to the cost of alternative products and also compensation. In order to improve the agreement between the first and second level decision-makers, the proposed algorithm considers tolerances that can be adjusted to achieve more executive plans. Numerical results show that the answers of the bi-level fuzzy goal programming method are equal or very close to the worst answer.

This paper presents a new mixed integer nonlinear mathematical model to design dynamic cellular manufacturing systems (DCMSs). In the dynamic environment, the entire planning horizon is divided into multiple smaller periods, and each period has dierent product mix and part demand. In reality, production quantity may not be equal to the demand as it may be satis ed from inventory or backorders. Thus production quantity should be determined through PP decisions in order to determine the number and type of machines to be installed in manufacturing cells. Consequently, the manufactured cells should be recon gured in each period. Design and implementation of an eective CMS involves many issues such as cell formation (CF), production planning (PP), layout design, and scheduling. The proposed model is concurrently making the CF and PP decisions and incorporates several design features including multi-period production planning, alternate routings, system recon guration, duplicate machines, lot splitting, workload balance among machines and cells, cell size limits, and material ow between machines. Machine capacity is also considered. Capacity requirement and the number of required machines in each cell are de ned as decision variables and for the rst time, calculated based on ow shop perspective. The model forms independent cells and determines all related decision variables during each period of the time horizon according to the availability of the machines at the beginning of the rst period. The objective is to minimize the total costs of intra-cell material handling, machine operating, maintenance and setup, cell recon guration and forming, inventory holding and backorder, and production. Linearization techniques are used to transform the suggested non-linear programming model into a linearized formulation. Using ow shop perspective to calculate capacity requirement is considered in this paper for the rst time and so to verify the performance of the proposed model, a numerical example with randomly generated data is solved by a branch-and-bound (B&B) method under the Lingo 12.0 software.

In this paper, we consider the problem of hazardous material distribution where the distributer chooses the routes on the network, and a regulatory agency controls the behavior of the distributer to traverse the specified routes. In these circumstances, the distributer wants to select some routes to minimize the total distributing costs. Most of the time, this occurs due to selecting risky arcs in which more individuals are exposed to risk. To prevent this and increase the capability to deal with the risk of hazardous material transportation through roads, the regulatory agency obliges carriers to traverse through the most secure arcs, though imposing more distribution costs.
In reality, two decision makers may have different perceptions or asymmetric information about the network. To apply network interdiction models for real situations, we introduce the information asymmetry for these types of vehicle routing network interdiction problems and investigate its benefits and risks. For this purpose, a bi-level programming model is proposed and two bi-level meta-heuristics are suggested to solve this Stachelberg interdictor-evader game. The computational results showed that the developed co-evolutionary meta-heuristic algorithm could be more effective and more rational than the developed Bi-GA for these problems.

In this study, the stochastic cell formation problem with developing model within queuing theory with stochastic demand, processing time and reliability has been presented. Machine as server and part as customer are assumed where servers should service to customers. Since, the cell formation problem is NP-Hard, therefore, deterministic methods need a long time to solve this model. In this study, genetic algorithm and modified particle swarm optimization algorithm are presented to solve problems. Because the metaheurstic algorithms quality depends strongly on selected operators and parameters, design of experiment is done for set parameters. The deterministic method of branch and bound algorithm is used to evaluate the results of modified particle swarm optimization algorithm and the genetic algorithm.Evaluates indicate better performance of the proposed algorithms in quality the metaheurstic algorithms final solution and solving time in comparing with the method of Lingo software’s branch and bound. Ultimately, the results of numerical examples indicate that considering reliability has significant effect on block structures of machine-part matrixes.

In this paper, a bi-objective model is developed to deal with an aggregate production planning problem in a multi product, multi period supply chain including multiple suppliers, factories and demand points. This bi-objective model aims to minimize the total cost of supply chain including inventory costs, manufacturing costs, work force costs, hiring, and firing costs, and maximize the minimum of producer's reliability by considering probabilistic lead times, to improve performance of the system and achieve a more reliable production plan. Since the proposed bi-objective model is NP-hard, a Pareto-based multi-objective imperialist competitive algorithm (MOICA) is used. To evaluate the performance of presented algorithm, non-dominated sorting genetic algorithm (NSGA-II) is applied, too. The results show the capability and efficiency of proposed algorithm in finding Pareto solutions.

In today’s competitive word, it is so important to offer a new way in the cycle of production and supply by which the maximum efficiency will be achieved. The problem of applying cross-docking operations in logistics and supply chain network design, as a new and applicable approach, has attracted much attention in both academic and industrial areas. The matter making this strategy be highly welcomed, is the cross-docking role in shortening transportation cycle, controlling the shipment costs and on-time delivery of orders causing value in different aspects and improved service level. In this review paper, we first present a clear definition about cross-docking operations and then all the problems involving in the related field are classified and reviewed.

This article proposes a method which could improve the performance of an Emergency Department (ED) in a large hospital in Tehran، where the long waiting times cause patients’ and ED staff ’s dissatisfaction. ED is considered as a bottleneck in hospitals. To reduce long waiting time and improve patient satisfaction، application of system improvement methods is required. Using discrete- event simulation، it first models patient flow and identifies bottlenecks. In simulation model، patient arrival is assumed to be non-homogenous and operations of medical tests are detailed and consequently patient queues and virtual specimen queues can be considered separately. The analytic hierarchy process (AHP) was used to specify weights for each criterion and PROMETHEE to rank scenarios. Based on simulation results and important criteria، what-if scenarios were designed to evaluate system reactions. Using this method، 10 scenarios were designed. Implementing the best scenario would reduce waiting time by approximately 6%. Conclustion: Considering ED overcrowding، simulation is used to reflect current situation. Based on the simulation reports، what-if scenarios were used to design scenarios which could improve ED performance. Multi criteria decision making method was used to find the best scenario based on criteria like waiting time، utilization، leave with own responsibility (LWOR) and cost.

Nowadays, according to the importance of safe transportation in permanent economic development, using new approaches which lead to improving the safety and quality of roads is vital. Many experts believe that intelligent transportation systems are the suitable solution to achieve this goal. One of the transportation system’s intelligence component is determination of the roads safe speed limits which directly affect the roads traffic volume and safety. In this paper the determination of safe speed limits and the effective indexes are discussed by introducing a heuristic approach for defining safe speed limits dynamically based on the roads conditions. This approach aims to reduce accidents caused by the drivers’ unawareness of the roads conditions or the drivers’ inexperience in determining safe speed. In this approach the safe speed is determined with considering the difference between the roads current and the ideal conditions. Finally, a numerical example is applied to describe the proposed approach.

In this article، the continuous - review inventory control system has been studied. A new constraint of demand dependent on the average percent of product shortage has been added to the problem. It means that the average demand has a direct relationship with shortage in a period. This constraint، which is related to the costs of credit loss of the organization due to product shortage، has been considered in the inventory model. In this paper، the mathematical model of this problem has been presented and then، two heuristic approaches based on the genetic and simulated annealing algorithms are developed. Computational results indicate that the simulated annealing algorithm can provide better results compare to the genetic algorithm.

Although investment in research and development (R&D) is a promising tool for technology centered organizations through achieving their objectives, resource constraints make organizations select between their pool of R&D projects. Evaluating and selecting research projects are decision maker's continual challenge referring to the importance of research projects alignment with organization strategic objectives. Although financial return is a common criterion applied in many cases of project evaluation, technologic and strategic alignment and project’s probability of success are critical criteria. Project portfolio management is a tool to identify and select projects and to determine to what extent they should be invested and which projects should be stopped in a certain period of time. In this study, the authors developed ae Data Envelopment Analysis-Balanced Scoreboard (DEA-BSC) model to support the R&D project portfolio evaluation and selection. The study describes the application of the developedproject portfolio selection method to research and development (R&D) projects of the National Iranian Gas Company (NIGC). The R&D project portfolio selection results were evaluated and validate by an expert panel of gas industry.

Nowadays, due to the increasing rate of environmental complexities and the growing number of effective factors in making appropriate decisions, making decisions based on scientific principles has obtained a specific status among the top managers. Opting for the best consultancy firm, as one of the most important decisions the top managers would make for receiving consultancy services, has changed into a vital decision regarding the complications of the current business atmosphere and the effects of the quality of such services on the future sustenance of an economic enterprise. In this paper, in order to make a proper decision to opt for an appropriate consultant in devising strategic plans, a nine-step procedure is presented the most important advantages of which are the pre-qualification of the alternatives and the classification of decision-making criteria into general and specific groups. The proposed procedure has been investigated and analyzed utilizing the real data collected from Tehran Beautification Organization (TBO). The results show that the proposed procedure for selecting consultants for strategic plans in urban management is of high applicability and replicability.

One of the most important aspects of multi echelon inventory management systems is related to spare part; actually, in the real world, it is more economical to repair equipment than replace them. Also, it is possible to benefit from discounts when more spare parts purchased from an external supplier. So, this paper considers a multi-echelon spare-part inventory system with backorders and quantity discount of external supplier and applies a network approach for modeling and solving these problems. Numerical example shows the efficiency of this algorithm.