Industrial Mathematics
Mohammad Shafiekhani; Alireza Rashidi Komijan; Hassan Javanshir
Abstract
In this paper, a new type of vehicle routing problem in the valuable commodity transportation industry is modeled considering the route risk constraint. The proposed model has two objective functions for risk minimization. In the first objective function, three concepts are presented, which are: 1) the ...
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In this paper, a new type of vehicle routing problem in the valuable commodity transportation industry is modeled considering the route risk constraint. The proposed model has two objective functions for risk minimization. In the first objective function, three concepts are presented, which are: 1) the vehicle does not travel long distances in the first three moves because it carries more money, 2) to serve the same branch on two consecutive days, at the same time 3) The bow should not be repeated in two consecutive days. This reduces the possibility of determining a fixed pattern for the service and increases the security of the service. In the second objective function, risk is a function of the amount of money, the probability of theft and the probability of its success. To solve the proposed model, two different meta-heuristic algorithms including genetic algorithm and ant colony optimization algorithm have been used. In computational testing, the best parameter settings are determined for each component and the resulting configurations are compared in the best possible settings. The validity of the answers of the algorithms has been investigated by generating different problems in various dimensions and using the real information of Shahr Bank. The results show that the genetic algorithm provides better results compared to the ant colony algorithm with an average of 0.93% and a maximum of 1.87% difference with the optimal solution.
Engineering Optimization
Elnaz Farhang zad; Reza Ehtesham Rasi; Davood Gharakhani
Abstract
This paper examines the use of hybrid metaheuristic algorithms to optimize order quantity in a single manufacturer-multi-supplier two-level JIT supply chain in production system. Over the years, production systems have largely been controlled by either MRP (Material Requirements Planning), JIT (Just ...
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This paper examines the use of hybrid metaheuristic algorithms to optimize order quantity in a single manufacturer-multi-supplier two-level JIT supply chain in production system. Over the years, production systems have largely been controlled by either MRP (Material Requirements Planning), JIT (Just in Time) or OPT (Optimized Production Technology) paradigm. In the supply chain environment, traditional material demand planning does not consider the supplier's supply capacity and economic benefits, which is not conducive to the long-term cooperation of upstream and downstream enterprises in the supply chain. The main goal of this paper is to optimize ordering batches based on MRP and JIT in supply chain. There is limited research designing and optimizing the supply chain / procurement network. This study is among the first to integrate supplier selection to optimize performance indicators in supply chain network design considering minimization of total cost of JIT supply chain order batch coordination adjustment model. The BOM constraints and MRP formulation principles of product production are followed to minimize the supply chain the total cost of downstream companies’ inventory, transportation, out of stock, and crashing is the target. The MRP-led supply chain ordering batch collaborative optimization model is constructed; the manufacturer’s main production plan is adjusted to change the procurement plan to obtain supplier supplies according to the scheme, an improved discrete particle swarm optimization algorithm and genetic algorithm is designed to solve the model; the feasibility of the model is verified by an example. The effectiveness of the algorithm is proved through the analysis and comparison of the algorithm results.
Game theory
Mohammad Shafiekhani; Alireza Rashidi Komijan; Hassan Javanshir
Abstract
The process of transferring money from the treasury to the branches and returning it at specific and limited periods is one of the applications of the Vehicle Routing Problem (VRP). Many parameters affect it, but choosing the right route is the key parameter so that the money delivery process is carried ...
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The process of transferring money from the treasury to the branches and returning it at specific and limited periods is one of the applications of the Vehicle Routing Problem (VRP). Many parameters affect it, but choosing the right route is the key parameter so that the money delivery process is carried out in a specific period with the least risk. In the present paper, new relationships are defined in the form of three concepts in order to minimize route risk. These concepts are: 1) the vehicle does not travel long routes in the first three movements, 2) a branch is not served at the same hours on two consecutive days, and 3) an arc should not be repeated on two consecutive days. The proposed model with real information received from Bank Shahr has been performed for all branches in Tehran. Because the VRP is an NP-Hard problem, a genetic algorithm was used to solve the problem. Different issues in various production dimensions were solved with GAMS and MATLAB software to show the algorithm solution quality. The results show that the difference between the genetic algorithm and the optimal solution is an average of 1.09% and a maximum of 1.75%.
Scheduling
Vahid Bahmani; Mohammad Amin Adibi; Esmaeil Mehdizadeh
Abstract
This paper provides an integrated model for a two-stage assembly flow shop scheduling problem and distribution through vehicle routing in a soft time window. So, a mixed-integer linear programming (MILP) model has been proposed with the objective of minimizing the total cost of distribution, holding ...
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This paper provides an integrated model for a two-stage assembly flow shop scheduling problem and distribution through vehicle routing in a soft time window. So, a mixed-integer linear programming (MILP) model has been proposed with the objective of minimizing the total cost of distribution, holding of products, and penalties of violating delivery time windows. To solve this problem, an improved meta-heuristic algorithm based on whale optimization algorithm (WOA) has been developed. A comparison of the integrated and non-integrated model in a case study of industrial gearboxes production shows that the integrated model compared to the non-integrated model has saved 15.6% and 13.6% in terms of delay time and total costs, respectively. Computational experiments also indicate the efficiency of improved WOA in converging to optimal solution and achieve better solution in comparison to the genetic algorithm (GA).
Operations Research
Saeed Khalili; Masood Mosadegh Khah
Abstract
This study presents a new mathematical optimization model using queuing theory to determine the hotel capacity in an optimal manner. For this purpose, a Knapsack model based on the queuing theory is proposed. In this regard, after simulating a hotel's reception system with the help of queuing models ...
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This study presents a new mathematical optimization model using queuing theory to determine the hotel capacity in an optimal manner. For this purpose, a Knapsack model based on the queuing theory is proposed. In this regard, after simulating a hotel's reception system with the help of queuing models and using a limited two-dimensional Knapsack model, the capacity and an optimum number of rooms are obtained. Since the proposed model is too complex on large scales, a modified Genetic Algorithm (GA) approach enhanced by Taguchi method is employed to solve the problem. The obtained results indicate that unlike previous studies, the proposed models can be applied to different scenarios.
Engineering Computations
Giampietro Fabbri; Matteo Greppi
Abstract
In the present work, an innovative hybrid solar panel is proposed, which can be used to pave floors or to cover roofs. A particular heat sink is employed, which gives robustness to the panel and provides a better heat transfer effectiveness with respect to tube heat exchangers. The geometry of the heat ...
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In the present work, an innovative hybrid solar panel is proposed, which can be used to pave floors or to cover roofs. A particular heat sink is employed, which gives robustness to the panel and provides a better heat transfer effectiveness with respect to tube heat exchangers. The geometry of the heat sink which is employed in the panel is optimized with the help of a numerical model and a genetic algorithm. Some optimization examples are shown. The velocity and temperature distributions on the heat sink cross section are also investigated. The presented hybrid panel allows till 20% increase in the electrical efficiency with respect to a simple photovoltaic panel. Moreover, it can be easily installed under every environmental condition due to its robustness and resistance to water infiltration.
Fuzzy optimization
Seyedeh Maedeh Mirmohseni; Seyed Hadi Nasseri; Mohammad Hossein Khaviari
Abstract
In this paper, dynamic programming for sequencing weighted jobs on a single machine to minimizing total tardiness is focused, to significance of fuzzy numbers field, and importance of that for decision makers who are facing on uncertain data, combination of dynamic programming and fuzzy numbers is applied. ...
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In this paper, dynamic programming for sequencing weighted jobs on a single machine to minimizing total tardiness is focused, to significance of fuzzy numbers field, and importance of that for decision makers who are facing on uncertain data, combination of dynamic programming and fuzzy numbers is applied. A random scheduling problem with fuzzy processing times is given and solved. In addition, algorithm consuming time during solving same category problem and different sizes are analyzed that for large problem CPU time usage is extremely unaffordable. Therefore demonstration of near-exact heuristic method such as Genetic Algorithm (GA) appears. In this paper sufficient discussion around solving this kind of problems and their algorithms analysis and a combination between Dynamic Programming (DP) and genetic algorithm as a newly born method is proposed that stand on DP performance and genetic algorithm search power, and finally comparison on the recent developed method has been held. Then this method can deal with real-world problem easily. Thus, decision makers actually can use this modification of dynamic programming for coping with un-crisp problem.
