Journal of Operational Research in its Applications
Year:2021 | Volume:18 | Issue:2 (69)|Papers Count:8

Just-in-time scheduling problem on a single batch processing machine is investigated in this research. Batch processing machines can process more than one job simultaneously and are widely used in semi-conductor industries. Due to the requirements of just-in-time strategy, the minimization of total earliness and tardiness penalties is considered as the criterion. It is an acceptable criterion for both manufacturer and customer. Since the research problem is proven to be NP-hard, the main objective of this research is to develop metaheuristic algorithms for finding efficient upper bounds for industry sized instances. Two algorithms are proposed for the research problem: a Hybrid Genetic Algorithm (HGA), and a Greedy Randomized Adaptive Search Procedure (GRASP). A dynamic programming approach is developed to sequence the batches in these algorithms. The computational results, based on available test problems in the literature, demonstrate that the proposed algorithms are effective, especially for large sized instances. The average percentage error of HGA is 6. 82% and the corresponding value for GRASP is 11. 64%. The results also show that the performance of the proposed algorithms is more considerable when the job sizes are small.

Optimization problems have dedicated a branch of research to themselves for a long time ago. In this field, multiobjective programming has special importance. Since in most real-world multiobjective programming problems the possibility of determining the coefficients certainly is not existed, multiobjective linear programming problems with interval coefficients are investigated in this paper. Corresponding to such problems, four solution concepts, (, )  A b necessarily weak efficient, (, )  A b necessarily efficient, (, , )  A b C necessarily weak efficient and (, , )  A b C necessarily efficient, are introduced. Moreover, necessary and sufficient conditions for recognizing such solutions are presented. Finally, the efficiency of the results is investigated in some numerical examples.

The rapid development of cities and the increasing urban population in recent decades have made urban planning, management and control more difficult. This problem becomes much more complicated at the time of natural disasters, especially when accompanied by social anomalies. Hence, it is important to ride emergency aid vehicles in crisis situations. In this research, the issue of riding vehicles is considered, taking into account the failure of the route and the failure of rescue vehicles, along with a number of operational constraints. In this study, a two-objective mathematical model for road failure and vehicle failure is considered. In the first objective function, the total amount of customers lost due to congestion in facilities and obstruction of communication paths is minimized. Also, in the second objective function, the average of trip times per unit of time is minimized. It also shows the performance (likelihood of failure) of the used vehicle. In the following, the proposed model was solved for several problems in different dimensions using the two-objective colonial competition meta-heuristic algorithm with MATLAB software and the results were compared with the two-objective genetic algorithm.

In the real world, determining the exact amount of supply and demand in transportation problems due to changing economic conditions may be difficult; but we will be dealing with inaccurate data when transporting essential goods in the event of natural disasters or the movement of military equipment during a war. In this case, in order to deal with data uncertainty and proper description of the parameters in the transport problem, we will need new approaches. One approach is to use grey systems theory and grey parameters in modeling such problems. Accordingly, in this paper, a new approach to solve the problem of grey transport is introduced, which determines the answer as grey numbers without the need to whiten the parameters with an approach based on comparing gray numbers. As a result, the uncertainty of the input data will be well reflected in the obtained answers. To show the efficiency of the proposed method, examples are given and solved with the proposed method.

Planning can be defined as a set of coordinated, coherent, and feasible activities to achieve specific and predetermined goals over a given time frame and based on available facilities. Implementation of economic development planning models is very important, according to prevailing conditions in society and the constraints in society. One of the models used in economic development planning is the mathematical planning model. In this article, we use a mathematical programming optimization model and we run it for real Iranian data over the period 2008-2012. Value added is defined by the Cobb-Douglas model, and the results of the model report labor force share, capital share and total factor productivity of the factors of production. According to the model results, if the Iranian economy could achieve higher value added, then Iran's economic growth in 2012 would have changed from-6. 6% to 3. 1%. Based on the results, the efficiency score of each section has calculated and we rank the sections based on their efficiency score.

Using data envelopment analysis models, in addition to determining the relative efficiency, identifies the weaknesses of the organization in different criteria and, by presenting the desired values, determines the organization's policy towards efficiency and productivity improvement. One of the non-radial models in evaluating the performance and computing the efficiency of firms is called the BAM model. Despite the advantages and disadvantages of the BAM model in calculating DMU’ s efficiency, it is not able to compare and rank efficient units. Based on this fact, this paper develops the BAM model and presents a new model called SupBAM for ranking the decision units. In addition, the important and practical properties of the BAM model and the SupBAM model are presented and proven. Performance evaluation and ranking of some industrial parks illustrates the capabilities of the proposed model and the accuracy of the properties and theorems expressed.

The efficiency of metaheuristic algorithms has a direct relationship with their parameters setting, so that the incorrect selection of completely effective algorithmic parameters could make them inefficient. In this research, the combination of Taguchi approach and the Data Envelopment Analysis (DEA) method are applied to enhance the efficiency of the genetic algorithm to solve the Reentrant Permutation Flow Shop (RPFS) problem. Various scenarios are formed to select genetic algorithm’ s operators for units under evaluation. First, using the Taguchi method for each unit, the optimal parameters are specified with the goal of minimizing the objective function (maximum tardiness). Then the effective units are determined and ranked in order to specify the best operators of the algorithm according to the optimal objective function in the shortest possible time. This research can be used as a method for setting the parameters of other evolutionary and metaheuristic algorithms in order to avoid the disadvantages of the trial and error methods.

The business environment where organizations operate is changing constantly. To achieve competitive advantage, companies should use new strategies; one of these strategies is the supply chain management approach. The performance of the entire supply chain will be affected by improvement of information flow. According to this, the purpose of this paper is to provide a model for evaluating the efficiency of information flow in the supply chain of SAPCO. The indicators were first determined through reviewing the literature in order to assess the efficiency of supply chain information flow and their validity was then checked. Mathematical modeling was performed using goal programming after introducing DMU as well as model inputs and outputs; and goal programming model was implemented by means of GAMS software. The results of model solving were ultimately determined. According to the comparison of the results of the four periods, at the first stage, DMU No. 3 has the highest efficiency in year 1394 and DMU No. 2 has the lowest efficiency in the year 1395. At the second stage, DMU No. 1 has the highest efficiency in the years 1395 and 1396, and DMU No. 4 has the lowest efficiency in the year 1394. At the third stage, DMU No. 1 has the highest efficiency in the years 1394 to 1397 and DMU No. 2 has the highest efficiency in the year 1397, and DMU No. 3 has the lowest efficiency in the year 1394. At the fourth stage, DMU No. 4 has the highest efficiency in the years 1395 to 1397 and DMU No. 8 has the lowest efficiency in the year 1397. At the fifth step, DMU No. 6 has the highest efficiency in the year 1395 and DMU No. 1 has the lowest efficiency in the year 1396.