adel aazami

Linguistic variables (LVs) provide a reliable expression of cognitive information. By inheriting the advantages of LVs, we can express uncertain and incomplete cognitive information in multiple attribute decision-making (MADM), and they do so better than existing methods.  In the decision-making process, we can consider decision experts’ (DEs’) bounded rationality, such as cognition toward loss caused by the DEs’ cognitive limitations during the decision process. Therefore, it is necessary to propose a novel cognitive decision approach to handle MADM problems in which the cognitive information is expressed by LVs. In this paper, we employ LVs to represent uncertain and hesitant cognitive information. Then, we propose a mathematical programming approach to solve the MADM problems where attributes or cognitive preferences are not independent.  Moreover, the validity and superiority of the presented approach are verified by dealing with a practical problem.

The objective of this manuscript is to introduce an innovative methodology for addressing multiple attribute group decision-making (MAGDM) problems utilizing interval-valued intuitionistic fuzzy sets (IVIFS). The proposed approach solves the problem using a mathematical programming methodology. In the present investigation, a group decision-making problem characterized by IVIF multiple attributes is conceptualized as a linear programming model and resolved expeditiously. The models that are being proposed have been reformulated into two analogous linear programming (LP) models through the application of a variable transformation and the concept of aggregation operators. The obtained LP models are solvable by common approaches. The principal benefit of the suggested methodology is its facilitation of decision-makers (DM) in identifying an alternative that exhibits optimal performance, and the decision-making process does not rely on DM knowledge. Application of the proposed method is represented in a decision-making problem, and the results are compared with similar methods, proving the compatibility of the proposed method with previous ones. The solid and understandable logic with computational easiness are the main advantages of the proposed method.

This research focuses on the integrated production-inventory-routing planning (PIRP) problem, which persuades necessary decisions to study the supply chains (SCs). Previous research studies confirm that corporations coping with production, inventory, and routing problems, can remarkably decrease the total costs and meet the customers' demands efficaciously. Currently, because of severe obligations, corporations must consider environmental factors and cost optimization in their activities. Accordingly, in this article, a green PIRP (GPIRP) is addressed using mixed-integer linear programming (MILP), which simultaneously takes into account the economic and social decisions of the SCs. Furthermore, because the SCs routing-oriented problems belong to the NP-hard categories, we propose a two-phase heuristic solution method; in the first phase, the inventory and production decisions are determined using MILP formulation. The second phase seeks to find optimal vehicle routing and transportation decisions using a genetic algorithm (GA). Two main deals leading to this paper's unique position are to develop a bi-objective MILP model for the GPIRP and present a novel hybrid two-phase heuristic solution method that sequentially utilizes the CPLEX solver and the proposed GA. To validate the computational performance of the proposed solution method, we conduct a case study from the Ahvaz Sugar Refinery Company in Iran to demonstrate the advantages of the formulated model. Moreover, we handle sensitivity analyses to study the effectiveness of the suggested method for the large-sized examples

Vehicle routing problem (VRP) is about finding optimal routes for a fleet of vehicles in order that they can meet the demands for a set of given customers by traveling through those paths. This problem is one of the most important and most applicable problems of transportation and logistics scope. In this paper, green vehicle routing and scheduling problem with heterogeneous fleet including reverse logistics in the form of collecting returned goods along with weighted earliness and tardiness costs is studied to establish a trade-off between operational and environmental costs. In this regard, a mixed integer non-linear programming (MINLP) model is proposed at the first stage; then its accuracy and correct functioning are evaluated by solving some examples. Since this problem is categorized as a NP-hard problem, a genetic algorithm (GA) is suggested in order to find near-optimal solutions for large instances in a rational computational time. Eventually, the performance of the GA is evaluated in comparison with solving the mathematical model for small-sized problems. Analysis of the results considering two criteria, solutions quality and computational times, indicates the satisfactory operation of the proposed algorithm in a proper computational time.

In operations research, bi-level programming is a mathematical modeling which has another optimization problem as a constraint. In the present research, regarding the current intense competition among large manufacturing companies for achieving a greater market share, a bi-level robust optimization model is developed as a leader-follower problem using Stackelberg game in the field of aggregate production planning (APP). The leader company with higher influence intends to produce new products, which can replace the existing products. The follower companies, as rivals, are also seeking more sales, but they do not have the intention and ability to produce such new products. The price of the new products is determined by the presented elasticity relations between the uncertain demand and price. After linearization, using the KKT conditions, the bi-level robust model is transformed into an ordinary uni-level model. Due to the NP-hard nature of the problem, Benders decomposition algorithm (BDA) is proposed for overcoming the computational complexities in large scale. Finally, using the real data of Sarvestan Sepahan Co as a leader company, the validity of the developed model as well as efficiency and convergence of the BDA are investigated. The computational results clearly show the efficiency and effectiveness of the proposed BDA.

Manufacturing company’s decisions regarding the quantity of production and inventory is a production planning and inventory control problem. Decisions about transferring products are expressed as a transportation and routing problem. Considering these three problems together results in an integrated production-inventory-routing planning (PIRP) which is one of the important supply chain problems. Companies which solve their PIRP problems better can decrease the cost of their products and gain more competitive advantage compared to other competing companies. Based on today’s strict regulations, companies must take into account environmental considerations in addition to economical ones in all their processes, from production until supply. Thus, an appropriate transportation planning can prevent the excessive increase in costs and decrease environmental pollution. Therefore, this study integrates the problem of decreasing environmental pollution with PIRP and develops a green PIRP (GPIRP) problem which efficiently considers the economic and social dimensions of production and supply. This problem is modeled in an integrated manner using the MILP approach. In order to show the applicability of the developed model and its practicability of environmental aspect, a case study is conducted on Ahvaz Sugar Refinery, Iran. Finally, some managerial insights are derived from the computational results.

Mixed-integer polynomial programming (MIPP) problems are one class of mixed-integer nonlinear programming (MINLP) problems where objective function and constraints are restricted to the polynomial functions. Although the MINLP problem is NP-hard, in special cases such as MIPP problems, an efficient algorithm can be extended to solve it. In this research, we propose an algorithm for global optimization of the MIPP problems, in which, first, the MIPP is reformulated as a multi-parametric programming by considering integer variables as parameters. Then, the optimality conditions of resulting parametric programming give a parametric polynomial equations system (PES) that is solved analytically by Grobner Bases (GB) theory. After solving PES, the parametric optimal solution as a function of the relaxed integer variables is obtained. A simple discrete optimization problem is resulted for any non-imaginary parametric solution of PES, which the global optimum solution of MIPP is determined by comparing their optimal value. Some numerical examples are provided to clarify proposed algorithm and extend it for solving the MINLP problems. Finally, a performance analysis is conducted to demonstrate the practical efficiency of the proposed method.

The job shop scheduling problem (JSP) is one of the most difficult problems in traditional scheduling because any job consists of a set operations and also any operation processes by a machine. Whereas the operation is placed in the machine, it is essential to be considering setup times that the times strongly depend on the various sequencing of jobs on the machines. This research is developed a two-objective model to solve JSP with sequence-dependent setup times (SDST). Considering SDST and optimizing of the both objectives simultaneously (makespan and maximum tardiness) bring us closer to natural-world problems. The ε-constraint method is applied to solve the mentioned two-objective model. A set of numerical data is generated and tested to validate the model’s efficiency and flexibility. The developed model can efficiently use for solving JSPs in the real world, especially for manufacturing companies with having setup and delivery time’s constraints.

Bi-level programming is a mathematical programming, which there is another optimization problem at its constraints. According to the current fierce competition between large production companies to obtain a greater share of the market, this study develops a bi-level robust optimization model as the leader and the follower using Stackelberg game in the field of production planning. The leader company with higher leverage has decided to produce some new products that can be replaced with the existing products. The follower companies as a competitor, similar to the leader company, are looking to sell more. The follower companies do not have any intent and ability to produce such new products. Prices of the new products are determined using the tensile relations, which presented between the uncertain demand and price, creating the game between two levels of the model. After the linearization, the bi-level robust model is transformed to standard single-level model using conditions of Karush–Kuhn–Tucker (KKT). Finally, the accuracy and efficiency of the developed model have been verified by using the real data of Sarvestan Sepahan Company in Isfahan as the leader in the competitive market.

In the current competitive conditions, all the manufacturers’ efforts are focused on increasing the customer satisfaction as well as reducing the production and delivery costs; thus, there is an increasing concentration on the structure and principles of supply chain (SC). Accordingly, the present research investigated simultaneous optimization of the total costs of a chain and customer satisfaction. The basic innovation of the present research is in the development of the hierarchical location problem of factories and warehouses in a four-level SC with multi-objective approach as well as the use of the multi-objective evolutionary metaheuristic algorithms. The main features of the resulting developed model would include determination of the number and location of the required factories, flow of the raw material from suppliers to factories, determination of the number and location of the distribution centers, flow of the material from factories to distribution centers, and finally allocation of the customers to distribution centers. In order to obtain optimal solutions of the model, a multi-objective hybrid particle swarm algorithm (MOHPSO) was presented; then, to assess performance of the algorithm, its results were compared with those of the NSGA-II algorithm. The numerical results showed that this algorithm had acceptable performance in terms of time and solution quality. On this basis, a real case study was implemented and analyzed for supplying the mountain bikes with the proposed algorithm.

In this paper, multi-site aggregate production planning for the production of perishable products such as gifts of New Year, calendars and maturities is determined by postponement policy in uncertainty conditions. The production process for these products is proposed to be divided into two phases including the production of final and semi-finished products with applying the concept of postponement. So, there are three production activities including direct production, the production of semi-finished products and final assembly. Also, a robust optimization model to solve aggregate production planning problem for these products is developed. Finally, a set of real data from a calendar producing company in Tehran called “NIK Calendar” is used to validate and show the efficiency of the proposed model. Results show that the proposed model of this paper can be used for similar factories which are active in the field of aggregate production planning with considering uncertainty in the parameters.