Mathematical Optimization Models
Fatemeh nikkhoo; Ali Husseinzadeh Kashan; ehsan nikbakhsh; bakhtiar ostadi
Abstract
Purpose: The order picking problem is very important as one of the logistics activities of the warehouse. This problem is defined as collecting orders from different warehouse locations to respond to customers' orders in the shortest possible time. The purpose of this paper is to provide a multi-objective ...
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Purpose: The order picking problem is very important as one of the logistics activities of the warehouse. This problem is defined as collecting orders from different warehouse locations to respond to customers' orders in the shortest possible time. The purpose of this paper is to provide a multi-objective mathematical programming model for integrating the decisions of batching, routing, scheduling of pickers with the problem of packaging in multi-warehouse environment. The objective functions include minimization of the delivery times and total order picking costs.
Methodology: In this research, first by reviewing the literature in the field of order picking, the research gaps of the problem have been identified. Then, taking into account the main constraints of the problem, a multi-objective mathematical model has been formulated for the multi-warehouse order picking problem. To solve the problem, the classic Benders decomposition algorithm and the accelerated Benders decomposition algorithm have been used. To validate and applicability of the proposed model, the data related to the warehouses of a company producing sanitary products in Iran was used as a case study and its results were reported in the article.
Findings: The results of the proposed model indicate that CPLEX is able to solve these problems up to small sizes in an acceptable time. Also, the numerical results show the performance of the Benders decomposition algorithm and the accelerated Benders algorithm as suitable alternatives for solving the model in the large-sized problems. The calculation results obtained from the implementation of the solution methods for the proposed model showed that in terms of the number of iterations and the calculation time, the accelerated Benders algorithm had better results than the classic Benders algorithm.
Originality/Value: In this research, for the first time, the order picking problem with considerations of the integrity of operational decisions has been formulated in the form of a multi-objective mathematical model for a multi-warehouse environment. In this article regarding the solution method, exact solution approaches have been used for the first time considering the structure of the problem. The computation results show that the proposed algorithms are efficient and suitable methods for problem solving.
Scheduling Modeling
Ali Husseinzadeh Kashan; Saeed Afkhami; Parisa Maroofkhani
Abstract
Purpose: In this research due to the importance of the U-shaped assembly line balancing and, on the other hand, the importance of human factors and setup times, we want to develop a bi-objective mathematical model minimize the cycle time and the total cost.Methodology: Since the research problem is shown ...
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Purpose: In this research due to the importance of the U-shaped assembly line balancing and, on the other hand, the importance of human factors and setup times, we want to develop a bi-objective mathematical model minimize the cycle time and the total cost.Methodology: Since the research problem is shown to be NP-hard, NSGA-II, which is a population-based algorithm, and also SPEA-II are used to solve the problem.Findings: A mathematical model for the problem on hand is developed. We solve the problem using NSGA-II and SPEA-II. We use four criteria for analyzing the results of the mathematical model and evaluating the performance of the multi-objective evolutionary algorithms. The experimental results demonstrate that NSGA-II is superior to SPEA-II.Originality/Value: A bi-objective mathematical model for the U-shaped assembly line balancing problem considering setup-times and workers' skill is developed, and the problem is solved using two algorithms.
Scheduling Modeling
Morteza Farhadi Sartangi; Ali Husseinzadeh Kashan; Hassan Haleh; Abolfazl Kazemi
Abstract
Purpose: Order picking operation is one of the most well-known labor and cost intensive internal logistics processes. Withdrawal of the order in response to customer need is defined in order to collect a set of orders from storage zone in the shortest possible time. The purpose of this research is to ...
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Purpose: Order picking operation is one of the most well-known labor and cost intensive internal logistics processes. Withdrawal of the order in response to customer need is defined in order to collect a set of orders from storage zone in the shortest possible time. The purpose of this research is to provide a scientific and practical basis considering the constraints that enforce to achieve an acceptable level of performance in order picking systems. This is done by building a Mixed Integer Linear Programming (MILP) formulation and developing an adapted solution method suited to the structure of the problemMethodology: First, by reviewing the literature in the field of order picking systems, sufficient knowledge has been obtained at the operational level, and with emphasis on warehouse management constraints, a MILP formulation is proposed by integrating order batching and picker routing. After validating the model and solving it through GAMS software, due to the nature of the problem, which is an NP-hard type, the problem is solved with an efficient algorithm, which is a grouping version of the league championship algorithm, and the results are compared. To develop the algorithm, operators are fit to the specific structure of the problem, i.e., the assignment of orders (items) to order pickers (groups)Findings: Developing a multi-period MILP formulation for multi-trip picker routing, assuming for the first time the possibility of product replenishment and limited access to pickers. For large-scale problem instances, the league championship algorithm is used. The results indicate the effective capability and efficiency of this algorithm for solving large test problem instances.Originality/Value: The issue of multi-period order picking and multi-trip routing of pickers is considered for the first time in this paper. Because of the limited number of pickers, this must be taken into account in modeling. The assumption of product replenishment is also considered for the first time in this article and its modeling has been done. In this way, orders enter the warehouse over time, during different periods, and are placed in a predetermined positions. The limited access to pickers in each period is also discussed for the first time in this paper. Finally, the objective function of minimizing the total tardiness, which is in line with the needs of the industry, is also introduced in this paper. Regarding the solution method, a league championship metaheuristic algorithm is presented which takes into account the problem structure (which corresponds to the structure of grouping problems) and solution generation operators have been developed to maintain the new solution.
Optimization in science and engineering
Leila Hasan-Beigi Dashtbayaz; Isa Nakhai Kamalabadi; Ali Husseinzadeh Kashan; Sakine Beigi
Abstract
Purpose: In CNC machines, changes in machining conditions such as speed and feed rate will also change the operating time. Changes in these conditions also result in changes in the production cycle time and production costs. Tool life is also sensitive to these changes. Appropriate machining time is ...
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Purpose: In CNC machines, changes in machining conditions such as speed and feed rate will also change the operating time. Changes in these conditions also result in changes in the production cycle time and production costs. Tool life is also sensitive to these changes. Appropriate machining time is generally determined by assuming a certain lifetime for CNC machine tools to minimize production costs. However, minimizing costs usually results in increased machining time and lower output rates.Methodology: In this research, the optimal machining time is determined using a bi-objective model including minimizing the cost and total production time of a robotic cell with a CNC machine and a material handling robot. It has assumed that identical productions are produced in this robotic cell. Using the Epsilon constraint method, the proposed model is coded in GAMS software and its results are reported.Findings: In this research, the lifespan of the CNC machine tools can be considered as a determined or probable value. The results showed that decreasing the operation time at different speeds does not necessarily impose the same cost on the system. Therefore, it is necessary to be more careful in choosing the appropriate machining time for different tools and parts. Paying attention to the rate of suddenly tool breakdowns is also important in choosing the appropriate time for machining. Using a set of non-dominated solutions, it is possible to determine the appropriate machining time in different parts to achieve a suitable level of problem objectives.Originality/Value: In this research, for the first time, the failure rate of the tool as one of the cost factors in the robotic cell has been added to the cost function of a production cycle and its effect on determining the appropriate machining time has been investigated.
multi objective decision making
Zahra Shoaei Naeini; Parastoo Mohammadi; Ali Husseinzadeh Kashan
Abstract
The oil consortium in Iran is one of the most important approaches to implementing the huge projects of the petrochemical industry. But the selection of suitable and expert partners is one of the most common bottlenecks in such cooperation networks. The purpose of this paper is to provide a practical, ...
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The oil consortium in Iran is one of the most important approaches to implementing the huge projects of the petrochemical industry. But the selection of suitable and expert partners is one of the most common bottlenecks in such cooperation networks. The purpose of this paper is to provide a practical, yet simple, solution for the decision makers to be able to choose the right candidate from the selected candidates. For this purpose, a three-stage model has been designed. In the first stage, the criteria affecting the choice of partner from the perspective of experts and reviewing the literature were first collected and weighed with the help of SWARA method. In the next stage, the ranking of partners (6 domestic and 4 foreign companies) was based on a set of decision-making methods such as COPRAS, VIKORA, SWA, TOPSIS, ARAS, MOORA, and multi-MOORA. The final stage integrated the ranking results based on the Copeland. In the end, financial capability and debt ratio and repayment potential were introduced as the most important criteria and sub-criteria. Also, partner 3 was selected as the best candidate by Copeland. Finally, in order to measure the performance of the integration of results, the Spearman correlation coefficient was used and the results of high test affinity and integration of results were obtained. Therefore, it can be said that the approach used has performed well.
meta-heuristic algorithms
Ehsan Aghdaee; Ali Husseinzadeh Kashan
Abstract
In managing a project, reliable prediction is an essential element for success. Project managers are always looking for controlling their projects to make sure the the project is within acceptable limits. For a long time, the earned value management (EVM) for pursuing time performance and the cost of ...
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In managing a project, reliable prediction is an essential element for success. Project managers are always looking for controlling their projects to make sure the the project is within acceptable limits. For a long time, the earned value management (EVM) for pursuing time performance and the cost of the project has been used. However, using this method to valuate project time performance by utilizing the time performance index (SPI) by researchers and practitioners has been faced with serious criticism. Therefore, the present study proposes a framework for assessment and prediction of the temporal performance of each of the thread activities in project management. In this framework, using the multi objective league championship algorithm (MOLCA), the initial plan of the projects is optimized and then via using the Kalman Filter prediction method, project execution planning is done such that the projects in conditions of uncertainty could be forecasted and ahead horizon being demonstrated accurately with the least error for project managers. In this paper, in order to ensure the quality of the solutions, the output of the algorithm is compared with genetic algorithms (NSGII) and particle swarm optimization (MOPSO), where results demonstrate the superiority of the proposed algorithm.