Supply chain management
Fatemeh Kangi; Seyed Hamid Reza Pasandideh; Esmaeil Mehdizadeh; Hamed Soleimani
Abstract
In recent years, the expansion of social responsibility concept, increased environmental considerations, economic incentives and governmental pressure on manufacturers for waste management have caused organizations to focus attention on the development of closed-loop supply chains (CLSC) and reverse ...
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In recent years, the expansion of social responsibility concept, increased environmental considerations, economic incentives and governmental pressure on manufacturers for waste management have caused organizations to focus attention on the development of closed-loop supply chains (CLSC) and reverse logistics (RL) processes. The adoption of these approaches, will enable organizations to simultaneously meet economic, social and environmental goals and consider the manufacturing cycle from supply and production to reuse of products. Hence, this study deals with an optimization model within the framework of a multi-echelon, multi-product and multi-period CLSC with hybrid facilities where cross-docking strategy and vehicle routing with soft time windows have been included in the model. In the problem defined as a MILP model, decisions are made simultaneously at three levels of strategic, tactical and operational. Furthermore, to tackle the NP-hard problem and achieve near-to-optimal results in reasonable time, two meta-heuristic algorithms, NRGA and MOPSO are developed and the algorithms’ parameters are tuned using the Taguchi method. Finally, the computational results are examined by the performance measures and statistical analysis and the sensitivity analysis is performed regarding the impacts of demand and rate of returned product on the objective functions’ values.
Operations Research
Vahid Razmjooei; Iraj Mahdavi; Selma Gutmen
Abstract
A Cellular Manufacturing System (CMS) is a suitable system for the economic manufacture of part families. Scheduling the manufacturing cells plays an effective role in successful implementation of the manufacturing system. Due to the fact that in the CMS, bottleneck machine and human resources are two ...
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A Cellular Manufacturing System (CMS) is a suitable system for the economic manufacture of part families. Scheduling the manufacturing cells plays an effective role in successful implementation of the manufacturing system. Due to the fact that in the CMS, bottleneck machine and human resources are two important factors, which so far have not been studied simultaneously in a mathematical model, there should be a model to consider them. Therefore, this research develops a bi-objective model for CMS in a three-dimensional space of machine-part and human resources. The main objective is to minimize the maximum completion time of all tasks in the system and reduce the number of intercellular translocation based on bottleneck machines’ motion and human resources. Due to the NP-hardness of the studied problem, applying the conventional solution methods is very time-consuming, and is impossible in large dimensions. Therefore, the use of metaheuristic methods will be useful. The accuracy of the proposed model is investigated using LINGO by solving a small example. Then, to solve the problem in larger dimensions, a hybrid Multi-Objective Tabu Search-Genetic Algorithm (MO-TS-GA) is designed and numerical results are reported for several examples.
Scheduling
Manizheh Teimoori; Houshang Taghizadeh; Jafar Pourmahmoud; Morteza Honarmand Azimi
Abstract
Air traffic management is an important job and often faces various problems. One of the most common problems in this area is the issue of aircraft sequencing, which is a multi-dimensional problem due to the large number of flights and their different positional conditions. Previously proposed models ...
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Air traffic management is an important job and often faces various problems. One of the most common problems in this area is the issue of aircraft sequencing, which is a multi-dimensional problem due to the large number of flights and their different positional conditions. Previously proposed models were based on First Come, First Service (FCFS) have not considered the time factor, resulting in increased delay penalties. In this regard, this article proposes a model in which the time factor is one of the factors that is managed and additional costs due to delay will be eliminated. This paper proposed the Multi-Objective Grey Wolf Optimization (MOGWO) algorithm to evaluate three objective functions such as the airport runway efficiency, the apron and parking costs, and the fuel consumption costs. The proposed algorithm compared with well- known NSGA-II (non–dominated Sorting Genetic Algorithm). The obtain results represented that in the case of using all the data for the first, second and third-objective function, MOGWO performs better than NSGA-II. The brilliant results demonstrated the superiority of the proposed model. In this study, using the proposed model, the data set of Shahid Hasheminejad International Airport in Mashhad was analyzed.
Engineering Optimization
Burak Omer Saracoglu; Miguel De Simón Martín
Abstract
The design of Renewable Energy Power Plants (REPPs) is crucial not only for the investments' performance and attractiveness measures, but also for the maximization of resource (source) usage (e.g. sun, water, and wind) and the minimization of raw materials (e.g. aluminum: Al, cadmium: Cd, iron: Fe, silicon: ...
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The design of Renewable Energy Power Plants (REPPs) is crucial not only for the investments' performance and attractiveness measures, but also for the maximization of resource (source) usage (e.g. sun, water, and wind) and the minimization of raw materials (e.g. aluminum: Al, cadmium: Cd, iron: Fe, silicon: Si, and tellurium: Te) consumption. Hence, several appropriate and satisfactory Multi-Objective Problems (MOPs) are mandatory during the REPPs' design phases. MOPs related tasks can only be managed by very well organized knowledge acquisition on all REPPs' design equations and models. The proposed MOPs need to be solved with one or more multi-objective algorithm, such as Multi-Objective Evolutionary Algorithms (MOEAs). In this respect, the first aim of this research study is to start gathering knowledge on the REPPs' MOPs. The second aim of this study is to gather detailed information about all MOEAs and available free software tools for their development. The main contribution of this research is the initialization of a proposed multi-objective evolutionary algorithm knowledge acquisition system for renewable energy power plants (MOEAs-KAS-F-REPPs) (research and development loopwise process: develop, train, validate, improve, test, improve, operate, and improve). As a simple representative example of this knowledge acquisition system research with two selective and elective proposed standard objectives (as test objectives) and eight selective and elective proposed standard constraints (as test constraints) are generated and applied as a standardized MOP for a virtual small hydropower plant design and investment. The maximization of energy generation (MWh) and the minimization of initial investment cost (million €) are achieved by the Multi-Objective Genetic Algorithm (MOGA), the Niched Sharing Genetic Algorithm/Non-dominated Sorting Genetic Algorithm (NSGA-I), and the NSGA-II algorithms in the Scilab 6.0.0 as only three standardized MOEAs amongst all proposed standardized MOEAs on two desktop computer configurations (Windows 10 Home 1709 64 bits, Intel i5-7200 CPU @ 2.7 GHz, 8.00 GB RAM with internet connection and Windows 10 Pro, Intel(R) Core(TM) i5 CPU 650 @ 3.20 GHz, 6,00 GB RAM with internet connection). The algorithm run-times (computation time) of the current applications vary between 20.64 and 59.98 seconds.
