scholarly journals Supply Chain Optimization Considering Sustainability Aspects

2021 ◽  
Vol 13 (21) ◽  
pp. 11873
Author(s):  
Mohammad Ali Beheshtinia ◽  
Parisa Feizollahy ◽  
Masood Fathi
Keyword(s):  
Supply Chain ◽  
Real Life ◽  
Transportation Cost ◽  
Mixed Integer ◽  
Test Problems ◽  
Improved Genetic Algorithm ◽  
Supply Chain Optimization ◽  
Environmental Aspect ◽  
Use Of Resources ◽  
Milp Model

Supply chain optimization concerns the improvement of the performance and efficiency of the manufacturing and distribution supply chain by making the best use of resources. In the context of supply chain optimization, scheduling has always been a challenging task for experts, especially when considering a distributed manufacturing system (DMS). The present study aims to tackle the supply chain scheduling problem in a DMS while considering two essential sustainability aspects, namely environmental and economic. The economic aspect is addressed by optimizing the total delivery time of order, transportation cost, and production cost while optimizing environmental pollution and the quality of products contribute to the environmental aspect. To cope with the problem, it is mathematically formulated as a mixed-integer linear programming (MILP) model. Due to the complexity of the problem, an improved genetic algorithm (GA) named GA-TOPKOR is proposed. The algorithm is a combination of GA and TOPKOR, which is one of the multi-criteria decision-making techniques. To assess the efficiency of GA-TOPKOR, it is applied to a real-life case study and a set of test problems. The solutions obtained by the algorithm are compared against the traditional GA and the optimum solutions obtained from the MILP model. The results of comparisons collectively show the efficiency of the GA-TOPKOR. Analysis of results also revealed that using the TOPKOR technique in the selection operator of GA significantly improves its performance.

scholarly journals Local Blood Supply Chain Optimization: A Case Study

2020 ◽  
Vol 18 (4) ◽  
Author(s):  
Reza Babazadeh ◽  
Ali Sabbaghnia ◽  
Fatemeh Shafipour
Keyword(s):  
Supply Chain ◽  
Blood Supply ◽  
Blood Donation ◽  
Programming Model ◽  
Human Life ◽  
Mixed Integer ◽  
Model Parameters ◽  
Supply Chain Optimization ◽  
Blood Supply Chain ◽  
Proposed Model

: Blood and its products play an undeniable role in human life. In recent years, although both academics and practitioners have investigated blood-related problems, further enhancement is still warranted. In this study, a mixed-integer linear programming model was proposed for local blood supply chain management. A supply network, including temporary and fixed blood donation facilities, blood banks, and blood processing centers, was designed regarding the deteriorating nature of blood. The proposed model was applied in a real case in Urmia, Iran. The numerical results and sensitivity analysis of the key model parameters ensured the applicability of the proposed model.

scholarly journals Supply chain optimization with variable demand by considering financial criteria and scenarios

2017 ◽  
Vol 26 (44) ◽  
pp. 21 ◽  
Author(s):  
John Willmer Escobar
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Net Present Value ◽  
Programming Model ◽  
Supply Chain Design ◽  
Mixed Integer ◽  
Supply Network ◽  
Distribution Centers ◽  
Supply Chain Optimization ◽  
Starting Point

This paper contemplates the supply chain design problem of a large-scale company by considering the maximization of the Net Present Value. In particular, the variability of the demand for each type of product at each customer zone has been estimated. As starting point, this paper considers an established supply chain for which the main problem is to determine the decisions regarding expansion of distribution centers. The problem is solved by using a mixed-integer linear programming model, which optimizes the different demand scenarios. The proposed methodology uses a scheme of optimization based on the generation of multiple demand scenarios of the supply network. The model is based on a real case taken from a multinational food company, which supplies to the Colombian and some international markets. The obtained results were compared with the equivalent present costs minimization scheme of the supply network, and showed the importance and efficiency of the proposed approach as an alternative for the supply chain design with stochastic parameters.

scholarly journals Supply Chain Optimization for Farmer-Bepari System of Agricultural Products in Bangladesh

2019 ◽  
pp. 1-13
Author(s):  
Mohammad Khairul Islam ◽  
Md. Mahmud Alam ◽  
Mohammed Forhad Uddin
Keyword(s):  
Product Distribution ◽  
Labour Cost ◽  
Agricultural Products ◽  
Raw Material ◽  
Mixed Integer ◽  
Optimization Strategy ◽  
Supply Chain Optimization ◽  
Material Cost ◽  
Production And Distribution ◽  
Milp Model

This study, for the Farmer-Bepari system of agricultural products in Bangladesh, can be formulated as a mixed integer linear programming (MILP) model. Further, it will be investigated that the significant impact of profit the attributes such as labour cost, fertilizer cost, the raw material cost of different firms and also to estimate the product distribution in different locations. To solve this MILP model, with the help of a branch and bound algorithm by using A Mathematical Programming Language (AMPL). To investigate the model we have to collect data from seven locations of three districts in Bangladesh. Also, a numerical example presented this study, which objectives illustrate the models. From the sensitivity of the production, if the raw material cost, labour cost and fertilizer cost increase is about 5%, then decrease the profit by MILP model have 0.004%, 1.6% and 1.2% respectively. Labour cost is a significant factor in profit, which changes the profit more than the raw material cost and fertilizer cost of the product. The results are helping decision-makers to identify the desired agricultural production and distribution structure optimization strategy.

scholarly journals Five-Echelon Multiobjective Health Services Supply Chain Modeling under Disruption

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Farnaz Javadi Gargari ◽  
Mahjoube Sayad ◽  
Seyed Ali Posht Mashhadi ◽  
Abdolhossein Sadrnia ◽  
Arman Nedjati ◽  
...  
Keyword(s):  
Supply Chain ◽  
Optimization Model ◽  
Optimization Problem ◽  
Programming Model ◽  
Transportation Cost ◽  
Real Data ◽  
Mixed Integer ◽  
Fixed Cost ◽  
Hub Location ◽  
Goal Programming Model

Medicine unreliability problem is taken into consideration as one of the most important issues in health supply chain management. This research is associated with the development of a multiobjective optimization problem for the selection of suppliers and distributors. To achieve the purposes, the optimal quota allocation is determined with respect to disruption of suppliers in a five-echelon supply chain network and consideration of the distributor centers as a hub location-allocation mode. The objective of the optimization model is involved in simultaneous minimization of transactions costs dealing with suppliers, expected purchasing costs from suppliers, expected percentages of delayed and returned products in each distributor, as well as transportation cost in each echelon and fixed cost for distributor centers, and finally maximization of the expected scores for suppliers and high priority of product customers. The optimization problem is formulated as a mixed-integer nonlinear programming model. The proposed optimization model is utilized to investigate a numerical case study for asthma-specific medicines. The analyzing procedure is conducted based on the collected real data from Cobel Darou pharmaceutical company in 2019. Furthermore, a fuzzy multichoice goal programming model is considered to solve the proposed optimization model by R optimization solver. The numerical results confirmed the authenticity of the model.

scholarly journals Machine-Learning Methods to Select Potential Depot Locations for the Supply Chain of Biomass Co-Firing

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6554
Author(s):  
Diana Goettsch ◽  
Krystel K. Castillo-Villar ◽  
Maria Aranguren
Keyword(s):  
United States ◽  
Machine Learning ◽  
Supply Chain ◽  
Ghg Emissions ◽  
The United States ◽  
Mixed Integer ◽  
Total Cost ◽  
Computational Burden ◽  
Milp Model

Coal is the second-largest source for electricity generation in the United States. However, the burning of coal produces dangerous gas emissions, such as carbon dioxide and Green House Gas (GHG) emissions. One alternative to decrease these emissions is biomass co-firing. To establish biomass as a viable option, the optimization of the biomass supply chain (BSC) is essential. Although most of the research conducted has focused on optimization models, the purpose of this paper is to incorporate machine-learning (ML) algorithms into a stochastic Mixed-Integer Linear Programming (MILP) model to select potential storage depot locations and improve the solution in two ways: by decreasing the total cost of the BSC and the computational burden. We consider the level of moisture and level of ash in the biomass from each parcel location, the average expected biomass yield, and the distance from each parcel to the closest power plant. The training labels (whether a potential depot location is beneficial or not) are obtained through the stochastic MILP model. Multiple ML algorithms are applied to a case study in the northeast area of the United States: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Multi-Layer Perceptron (MLP) Neural Network. After applying the hybrid methodology combining ML and optimization, it is found that the MLP outperforms the other algorithms in terms of selecting potential depots that decrease the total cost of the BSC and the computational burden of the stochastic MILP model. The LR and the DT also perform well in terms of decreasing total cost.

Sustainable Design of a Multi-Echelon Closed Loop Supply Chain under Uncertainty for Durable Products

2021 ◽  
Vol 13 (19) ◽  
pp. 11126
Author(s):  
Abbas Al-Refaie ◽  
Yasmeen Jarrar ◽  
Natalija Lepkova
Keyword(s):  
Supply Chain ◽  
Stochastic Model ◽  
Environmental Sustainability ◽  
Closed Loop ◽  
Transportation Cost ◽  
Return Rate ◽  
Raw Material ◽  
Mixed Integer ◽  
Closed Loop Supply Chain ◽  
Durable Products

The increased awareness of environmental sustainability has led to increasing attention to closed loop supply chains (CLSC). The main objective of the CLSC is to capture values from end-of-life (EOL) products in a way that ensures a business to be economically and environmentally sustainable. The challenge is the complexity that occurrs due to closing the loop. At the same time, considering stochastic variables will increase the realism of the obtained results as well as the complexity of the model. This study aims to design a CLSC for durable products using a multistage stochastic model in mixed-integer linear programming (MILP) while considering uncertainty in demand, return rate, and return quality. Demand was described by a normal distribution whereas return rate and return quality were represented by a set of discrete possible outcomes with a specific probability. The objective function was to maximize the profit in a multi-period and multi-echelon CLSC. The multistage stochastic model was tested on a real case study at an air-conditioning company. The computational results identified which facilities should be opened in the reversed loop to optimize profit. The results showed that the CLSC resulted in a reduction in purchasing costs by 52%, an annual savings of 831,150 USD, and extra annual revenue of 5459 USD from selling raw material at a material market. However, the transportation cost increased by an additional annual cost of 6457 USD, and the various recovery processes costs were annually about 152,897 USD. By running the model for nine years, the breakeven point will be after three years of establishing the CLSC and after the annual profit increases by 1.92%. In conclusion, the results of this research provide valuable analysis that may support decision-makers in supply chain planning regarding the feasibility of converting the forward chain to closed loop supply chain for durable products.

A Humanitarian Green Supply Chain Management Considering Minimum Cost and Time

2021 ◽  
Vol 8 (2) ◽  
pp. 63-82
Author(s):  
Dipanjana Sengupta ◽  
Amrit Das ◽  
Uttam Kumar Bera ◽  
Anirban Dutta
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Solution Method ◽  
Real Life ◽  
Optimal Solution ◽  
Minimum Cost ◽  
Transportation Cost ◽  
Chain Management ◽  
Green Transportation ◽  
Humanitarian Supply Chain

Disaster is the sudden problem of the world. There is no time bound. By disaster, all the creatures of the earth are affected. Here, the authors have tried to show some issues which are related to the natural calamities and green transportation. The main investigation of the paper is to describe about humanitarian supply chain management with optimized transportation cost, time, and carbon emission. Here a real-life problem of flood affected area has been chosen. When such disasters happen, quick response can reduce the devastation and save lives, and thus, it requires fulfilling the basic humanitarian needs of the affected population. In such case, organizations should also maintain the emission of the vehicles in safe range to mitigate the further disaster by pollution. A multi-objective solid transportation problem considering cost, time, and emission has been presented here. To solve the problem, this paper has used goal programming method and pareto optimal solution method. A comparison of results is also shown later. Some managerial insights are drawn to describe the situation.

Closed supply network modelling for end-of-life ship remanufacturing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Prem Chhetri ◽  
Mahsa Javan Nikkhah ◽  
Hamed Soleimani ◽  
Shahrooz Shahparvari ◽  
Ashkan Shamlou
Keyword(s):  
Supply Chain ◽  
End Of Life ◽  
Closed Loop ◽  
Payback Period ◽  
Mixed Integer ◽  
Supply Chain Network ◽  
Supply Network ◽  
Closed Loop Supply Chain ◽  
Content Type ◽  
Milp Model

PurposeThis paper designs an optimal closed-loop supply chain network with an integrated forward and reverse logistics to examine the possibility of remanufacturing end-of-life (EoL) ships.Design/methodology/approachExplanatory variables are used to estimate the number of EoL ships available in a closed-loop supply chain network. The estimated number of EoL ships is used as an input in the model and then it is solved by a mixed-integer linear programming (MILP) model of the closed-loop supply chain network to minimise the total logistic costs. A discounted payback period formula is developed to calculate the length of time to recoup an investment based on the investment's discounted cash flows. Existing ship wrecking industry clusters in the Western region of India are used as the case study to apply the proposed model.FindingsThe MILP model has optimised the total logistics costs of the closed-loop supply network and ascertained the optimal number and location of remanufacturing for building EoL ships. The capital and variable costs required for establishing and operating remanufacturing centres are computed. To remanufacture 30 ships a year, the discounted payback period of this project is estimated to be less than two years.Practical implicationsShip manufacturing businesses are yet to re-manufacture EoL ships, given high upfront capital expenditure and operational challenges. This study provides management insights into the costs and benefits of EoL ship remanufacturing; thus, informing the decision-makers to make strategic operational decisions.Originality/valueThe design of an optimal close loop supply chain network coupled with a Bayesian network approach and discounted payback period formula for the collection and remanufacturing of EoL ships provides a new integrated perspective to ship manufacturing.

scholarly journals A Hybrid Method for Modeling and Solving Supply Chain Optimization Problems with Soft and Logical Constraints

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Paweł Sitek ◽  
Krzysztof Bzdyra ◽  
Jarosław Wikarek
Keyword(s):  
Supply Chain ◽  
Hybrid Method ◽  
Optimization Problems ◽  
Programming Model ◽  
Combinatorial Problem ◽  
Search Space ◽  
Mixed Integer ◽  
Optimization Models ◽  
Supply Chain Optimization ◽  
Logical Constraints

This paper presents a hybrid method for modeling and solving supply chain optimization problems with soft, hard, and logical constraints. Ability to implement soft and logical constraints is a very important functionality for supply chain optimization models. Such constraints are particularly useful for modeling problems resulting from commercial agreements, contracts, competition, technology, safety, and environmental conditions. Two programming and solving environments, mathematical programming (MP) and constraint logic programming (CLP), were combined in the hybrid method. This integration, hybridization, and the adequate multidimensional transformation of the problem (as a presolving method) helped to substantially reduce the search space of combinatorial models for supply chain optimization problems. The operation research MP and declarative CLP, where constraints are modeled in different ways and different solving procedures are implemented, were linked together to use the strengths of both. This approach is particularly important for the decision and combinatorial optimization models with the objective function and constraints, there are many decision variables, and these are summed (common in manufacturing, supply chain management, project management, and logistic problems). TheECLiPSesystem with Eplex library was proposed to implement a hybrid method. Additionally, the proposed hybrid transformed model is compared with the MILP-Mixed Integer Linear Programming model on the same data instances. For illustrative models, its use allowed finding optimal solutions eight to one hundred times faster and reducing the size of the combinatorial problem to a significant extent.

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