Alireza Ariyazand; Hamed Soleimani; Farhad Etebari; Esmaeil Mehdizadeh
Abstract
Scheduling is a vital part of daily life that has been the focus of attention since the 1950s. Knowledge of scheduling is a very important and applicable category in industrial engineering and planning of human life. In the field of education, scheduling, and timetabling for best results in classroom ...
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Scheduling is a vital part of daily life that has been the focus of attention since the 1950s. Knowledge of scheduling is a very important and applicable category in industrial engineering and planning of human life. In the field of education, scheduling, and timetabling for best results in classroom teaching is one of the most challenging issues in university programming. As each university has its own rules, policies, resources, and restrictions a unique model of scheduling and timetabling cannot implement. This can cause more complexity and challenging point which needs to be considered scientifically. This study presents a sound scientific model of timetabling and classroom scheduling to improve faculties’ desirability based on days, times, and contents preferences. A sample in Parand branch of Islamic Azad university chooses using the Bat metaheuristic algorithm. By considering the limitations, some unchangeable constraints regarding the specific rules and minimal linear delimitation of the soft constraints of the model, using the appropriate meta-heuristic algorithm to reduce the model run time to a minimum. The results show that the algorithm achieves better results in many test data compared to other algorithms due to meeting many limitations in the problem coding structure. The Bat algorithm is compared with four other algorithms while comparing the results of solving the proposed mathematical model with five metaheuristic algorithms to evaluate the performance. In this research, a multi-objective model is presented to maximize the desirability of professors and to solve the model using Bat, Cuckoo Search, Artificial bee colony, firefly, and Genetic algorithms. In this research 40 different runs of each algorithm were compared, and conclusions were drawn. Modeling has been solved with GAMS and MATLAB software and using the bat meta-heuristic algorithm. It is concluded that in this model, the bat algorithm is the most appropriate algorithm with the shortest time, which has caused the satisfaction of the professors of the educational departments of this academy.
Ruhollah Ebrahimi Gorji; Hamed Soleimani; Behrouz Afshar-Nadjafi
Abstract
In today's competitive market, reducing costs and time is one of the most important issues that has occupied the minds of managers and researchers. This issue is especially important in the field of supply chain management and transportation because by reducing time and cost, manufacturers and service ...
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In today's competitive market, reducing costs and time is one of the most important issues that has occupied the minds of managers and researchers. This issue is especially important in the field of supply chain management and transportation because by reducing time and cost, manufacturers and service providers can gain a competitive advantage over competitors. Accordingly, vehicle routing issues are one of the most important issues in this field because it is directly related to the time of service or product delivery and also by optimizing the network, reduces the cost of the entire network. Therefore, in this study, the intention was to evaluate the problem of vehicle routing (trucks) by considering the time constraints and using a multi-objective approach. Therefore, we discussed each of the factors separately based on the issue. The results of this study show. In this research, the model with two objective functions will be solved by two metaheuristic algorithms NSGA-II and MOPSO Managers are concerned with time and cost management in today's competitive markets, which is seen as a source of competitive advantage. The present study aims to find a solution to a bi-objective function model by employing two metaheuristic algorithms, NSGA-II and MOPSO. Additionally, a criterion for comparing algorithms is presented. The findings show that the MOPSO algorithm yields the optimal solution. The contribution of the present study in comparison with other previous studies can be summarized as follows: Environmental protection based on reducing pollution and its effects as well as reducing costs. Finding the desired route taking into account the complexity and difficulty of the route. Managers are concerned with time and cost management in today's competitive markets, which is seen as a source of competitive advantage. The present study aims to find a solution to a bi-objective function model by employing two metaheuristic algorithms, NSGA-II and MOPSO. Additionally, a criterion for comparing algorithms is presented. The findings show that the MOPSO algorithm yields the optimal solution. The contribution of the present study compared to other previous studies can be environmental protection and cost reduction that the two factors are compared and the results of the two methods are analyzed.
Behrooz Khorshidvand; Hamed Soleimani; Mir Mehdi Seyyed Esfahani; Soheil Sibdari
Abstract
This paper addresses a novel two-stage model for a Sustainable Closed-Loop Supply Chain (SCLSC). This model, as a contribution, provides a balance among economic aims, environmental concerns, and social responsibilities based on price, green quality, and advertising level. Therefore, in the first stage, ...
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This paper addresses a novel two-stage model for a Sustainable Closed-Loop Supply Chain (SCLSC). This model, as a contribution, provides a balance among economic aims, environmental concerns, and social responsibilities based on price, green quality, and advertising level. Therefore, in the first stage, the optimal values of price are derived by considering the optimal level of advertising and greening. After that, in the second stage, multi-objective Mixed-Integer Linear Programming (MOMILP) is extended to calculate Pareto solutions. The objectives are include maximizing the profit of the whole chain, minimizing the environmental impacts due to CO2 emissions, and maximizing employee safety. Besides, a Lagrangian relaxation algorithm is developed based on the weighted-sum method to solve the MOMILP model. The findings demonstrate that the proposed two-stage model can simultaneously cope with coordination decisions and sustainable objectives. The results show that the optimal price of the recovered product equals 75% of the new product price which considerably encourages customers to buy it. Moreover, to solve the MOMILP model, the proposed algorithm can reach to exact bound with an efficiency gap of 0.17% compared to the optimal solution. Due to the use of this algorithm, the solution time of large-scale instances is reduced and simplified by an average of 49% in comparison with the GUROBI solver.
N. Shirazi; M. Seyyed Esfahani; H. Soleimani
Volume 2, Issue 1 , June 2015, , Pages 27-40
Abstract
This paper considers a three-stage fixed charge transportation problem regarding stochastic demand and price. The objective of the problem is to maximize the profit for supplying demands. Three kinds of costs are presented here: variable costs that are related to amount of transportation cost ...
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This paper considers a three-stage fixed charge transportation problem regarding stochastic demand and price. The objective of the problem is to maximize the profit for supplying demands. Three kinds of costs are presented here: variable costs that are related to amount of transportation cost between a source and a destination. Fixed charge exists whenever there is a transfer from a source to a destination, and finally, shortage cost that incurs when the manufacturer does not have enough products for supplying customer’s demand. The model is formulated as a mixed integer programming problem and is solved using a multicriteria scenario based solution approach to find the optimal solution. Mean, standard deviation, and coefficient of variation are compared as the acceptable criteria to decide about the best solution.