supply chain management
Maedeh Fasihi; Seyed Esmaeil Najafi; Reza Tavakkoli-Moghaddam; Mostafa Hahiaghaei-Keshteli
Abstract
The supply chain management is an important factor in current competitive market. In recent years, the shortage of resources for answering an increasing food demand has increased researchers’ attention to the food supply chain. Given the importance of fish in the Household Food Basket, the development ...
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The supply chain management is an important factor in current competitive market. In recent years, the shortage of resources for answering an increasing food demand has increased researchers’ attention to the food supply chain. Given the importance of fish in the Household Food Basket, the development of aquaculture and recycling of returned goods in reverse logistics would significantly help with preserving water resources, as well as sustainable development. Therefore, government agencies and aquaculture industry beneficiaries are interested in reverse logistics. This study is focused on the optimization of a closed-loop supply chain of fish. To this end, a new bi-objective mathematical model is proposed that both minimizes total costs and maximizes fulfilling customers demand in uncertainty situation. Several well-known multi-objective meta-heuristic algorithms and a proposed hybrid meta-heuristic algorithm are applied to identify Pareto solutions. The solutions are then compared in terms of performance metrics. Also, the epsilon-constraint method and sensitivity analysis are used to validate the algorithms and evaluate the performance of the model. Lastly, the VIKOR method is used to select the superior method. To demonstrate the capability of the proposed model, a closed-loop supply chain of trout in northern Iran is investigated as a case study. The results show that the developed model could be effective in reducing the costs and increasing customer satisfaction.
SEYYED ALI Mirnezhad; Parham Azimi; Ahmad Yousefi Hanoomarvar
Abstract
In the present study, the redundancy allocation problem (RAP) of series-parallel system has been investigated to maximize the system's availability. To achieve the research objective, budget, weight and volume constraints, and the maximum and minimum number of elements assigned to each subsystem have ...
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In the present study, the redundancy allocation problem (RAP) of series-parallel system has been investigated to maximize the system's availability. To achieve the research objective, budget, weight and volume constraints, and the maximum and minimum number of elements assigned to each subsystem have been considered. The main innovation of this research is to consider the failure and repair rates of components with non-exponential distribution function in the process of optimization. Taking into account failure and repair rate via non-exponential distribution function makes it impossible to calculate accessibility using mathematical relations. Therefore, the present study has used simulation method to calculate system availability. Since the simulation has no optimization capability On the other hand, in the redundancy allocation problem, it is necessary to evaluate the system availability recurrently in order to find the optimal solution. Further, due to the high degree of difficulty of developed mathematical function, the genetic metaheuristic algorithm was used to solve it. Finally, the efficiency of the genetic algorithm was measured against particle swarm algorithm and simulated annealing algorithm. To compare fairly, the parameters affecting the algorithms are adjusted using the Taguchi method and the algorithms are in their best practice. The computational results prove the high ability of the genetic algorithm in optimizing the concerned problem.
Mehdi Yazdani
Abstract
This paper deals with the problem of two-stage assembly flow shop scheduling with considering sequence-independent setup times .The objective is to minimize totalcompletion times of all orders. In this problem, there are several orders for one type of product. Each ordered product is formed of several ...
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This paper deals with the problem of two-stage assembly flow shop scheduling with considering sequence-independent setup times .The objective is to minimize totalcompletion times of all orders. In this problem, there are several orders for one type of product. Each ordered product is formed of several different parts. At first, the parts are manufactured in a flow shop stage with some different machines and then they are assembled into a final product on a single machine. This paper presents three meta-heuristic algorithms, namely Parallel Variable Neighborhood Search (PVN) Artificial Immune Algorithm (AIA) and Simulated Annealing (SA), for solving under studied problem. The Taguchi experimental design method as an optimization technique is employed to tune different parameters and operators of presented algorithms. Also, Numerical experiments are used to evaluate the performance of the proposed algorithms. The results show that the PVNS algorithm performs better than the other algorithms
Ali Mohtashami; Amir Hossein Niknamfar
Abstract
Hazardous materials which are materials due to their chemical and physical properties impose significant risk to the safety of people and the environment. It's more complex routing transport of such material than normal materials. The Combination of the two subjects as the problem of locating and routing, ...
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Hazardous materials which are materials due to their chemical and physical properties impose significant risk to the safety of people and the environment. It's more complex routing transport of such material than normal materials. The Combination of the two subjects as the problem of locating and routing, it has created unified system to locating- routing problems. These problems determined optimal number and location of facilities at the same time and also set the optimal number of vehicles and their routes. The purpose of this study was design a network for the transportation of hazardous materials and includes supply levels, distribution (hub) and customers. Hence, it presented a mathematical model in order to minimizing costs and risk simultaneously. Hazardous materials sent from supplier to the hubs and deliveries to customers from there via routing by road transportation. It should be mentioned that in proposal model the hubs been locating In order to validate the model, prepared code GAMS In software and for the exact solution, sample problems with various dimensions were produced in the form of smart and random. For this purpose, was written an algorithm design in Matlab software. According to the problem was NP-Hard, presented a hybrid algorithm based on simulated annealing and genetic algorithms to solve large-scale. At the end of research the proposed algorithm were compared with the results of exact solution.
Behnam Vahdani
Abstract
Today, intense competition in global markets has forced companies to design and manage of supply chains in a better way. Since the role of three factors: location, routing and inventory is important to continue the life of a supply chain so, integration of these three elements will create an efficient ...
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Today, intense competition in global markets has forced companies to design and manage of supply chains in a better way. Since the role of three factors: location, routing and inventory is important to continue the life of a supply chain so, integration of these three elements will create an efficient and effective supply chain. In this study, we investigate the problem of supply chain network design that including routing and inventory problem consist of flow allocation, vehicle routing between facilities, locating distribution centers and also consider the maximum coverage for respond to customer demand. Proposed mathematical model is a nonlinear mixed integer programming model for location-routing-inventory problem in the four-echelon supply chain by considering the multiple conflicting goals of total cost, travel time and maximum coverage. In order to solve the proposed model, three meta-heuristic algorithms (MOPSO, MSGA_II and NRGA) has been used. The accuracy of mathematical model and proposed algorithms are evaluated through numerical examples
Mahnaz Afrasiyabi; Ahmad Sadeghi
Abstract
Models presented in inventory management, encompass varied parameters. Primary factor in classic models related to determination of the economical ordering quantity (EOQ) and the economical production quantity (EPQ), is to consider parameters like the setup cost, the holding cost and the demand rate, ...
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Models presented in inventory management, encompass varied parameters. Primary factor in classic models related to determination of the economical ordering quantity (EOQ) and the economical production quantity (EPQ), is to consider parameters like the setup cost, the holding cost and the demand rate, to be fixed. This characteristic leads to a great difference among the quantity of the economical ordering obtained in classic models and real-word conditions. For instance, It should be stated that not only the holding costs of spoiled and useless products are not always fixed, but also, they would be increased by passing time. This article is an attempt to develop classical EOQ and EPQ models by considering holding and purchasing cost as an increasing continuous function of the ordering cycle time. Due to the complexity of the considered problem, two meta-heuristic algorithms, including Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO) are developed. Optimizing service level is considered as one of main apprehension in management science, that’s why increasing service level optimization would be evaluated as the second objective. As the performance of meta-heuristic algorithms is significantly influenced by calibrating their parameters, Taguchi methodology has been used to tune the parameters of the developed algorithms
amir abas najafii; faramarz shamsnatari; mohammad najafi
Volume 11, Issue 29 , July 2013, , Pages 1-20
Abstract
A Resource Investment Problem is a project scheduling problem recently considered. In this issue, in contrast with other project scheduling, the project availability of needed resources level is considered decision variable and the goal is to find a schedule and resource requirement level. Researches ...
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A Resource Investment Problem is a project scheduling problem recently considered. In this issue, in contrast with other project scheduling, the project availability of needed resources level is considered decision variable and the goal is to find a schedule and resource requirement level. Researches regarding this field are related to optimizing an objective. In this paper, resource investment problem is studied for simultaneous optimization minimizing projectspan and project resource costs. Two multi-objective meta-heuristic algorithms, two process sub-population genetic algorithms and multi-population genetic algorithm are proposed to find solutions. According to evaluation criteria, the function of two algorithms is computationally compared and.
S.M. Ali Khatami Firouzabadi; Amin Vafadar Nikjoo
Volume 10, Issue 27 , January 2012, , Pages 44-67
Abstract
In this research, we use Artificial Bee Colony (ABC) algorithm tosolve cell to switch assignment problem (CTSAP) that is NP-hard. InCTSAP, there are cells and switches in which cells locations arepredetermined. The objective of problem is optimal assigning of cellsto switches with minimum cost. Here, ...
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In this research, we use Artificial Bee Colony (ABC) algorithm tosolve cell to switch assignment problem (CTSAP) that is NP-hard. InCTSAP, there are cells and switches in which cells locations arepredetermined. The objective of problem is optimal assigning of cellsto switches with minimum cost. Here, we have two kinds of costs,handoff and cabling costs. Call handling capacity for every switchesare given and equal. The model of our work is single homed that iseach cell must connect to only one switch. The mathematical model isbinary and nonlinear.The program is coded by MATLAB 7.8.0 (R2009a). After estimatingparameters values of model, approving performance accuracy of codeand adjusting control parameters, the efficiency of algorithm bydetermining experimental problems compared to Ant ColonyOptimization (ACO) that is one of the best for solving this problem.Results show satisfactory performance of ABC algorithm
