Developing a bi-objective location-allocation-inventory problem for humanitarian relief logistics considering maximum allowed distances limitations

Author(s):  
Armin Cheraghalipour ◽  
Saba Farsad ◽  
Mohammad Mahdi Paydar
Author(s):  
Aida Rezaei ◽  
Tina Shahedi ◽  
Amir Aghsami ◽  
Fariborz Jolai ◽  
Hamidreza Feili

Integrating strategic and tactical decisions to location-allocation and green inventory planning by considering e-commerce features will pave the way for supply chain managers. Therefore, this study provides an effective framework for making decisions related to different levels of the dual-channel supply chain. We provide a bi-objective location-allocation-inventory optimization model to design a dual-channel, multi-level supply chain network. The main objectives of this study are to minimize total cost and environmental impacts while tactical and strategic decisions are integrated. Demand uncertainty is also addressed using stochastic modeling, and inventory procedure is the periodic review . We consider many features in inventory modeling that play a very important role, such as lead time, shortage, inflation, and quality of raw materials, to adapt the model to the real conditions. Since a dual-channel supply chain is becoming more important for sustainable economic development and resource recovery, we combine online and traditional sales channels to design a network. We generate five test problems and solve them by using the augmented ε-constraint method. Also, the Grasshopper optimization algorithm was applied to solve the model in a reasonable time for a large size problem. In order to provide managerial insights and investigate the sensitivity of variables and problem objectives with respect to parameters, sensitivity analysis was performed.


2019 ◽  
Vol 18 (04) ◽  
pp. 677-694 ◽  
Author(s):  
Erfan Babaee Tirkolaee ◽  
Javad Mahmoodkhani ◽  
Mehdi Ranjbar Bourani ◽  
Reza Tavakkoli-Moghaddam

This paper addresses a multi-echelon capacitated location–allocation–inventory problem under uncertainty by providing a robust mixed integer linear programming (MILP) model considering production plants at level one, central warehouses at level two, and the retailers at level three in order to design an optimal supply chain network. In this model, the retailer’s demand parameter is uncertain and just its upper and lower bounds within an interval are known. In order to deal with this uncertainty, a robust optimization approach is used. Then, a self-learning particle swarm optimization (SLPSO) algorithm is developed to solve the problem. The results show that the proposed algorithm outperforms the exact method by providing high quality solutions in the reasonable amount of computational runtime.


2020 ◽  
Vol 102 ◽  
pp. 340-350 ◽  
Author(s):  
Erfan Babaee Tirkolaee ◽  
Iraj Mahdavi ◽  
Mir Mehdi Seyyed Esfahani ◽  
Gerhard-Wilhelm Weber

2010 ◽  
Vol 207 (2) ◽  
pp. 750-762 ◽  
Author(s):  
Zhishuang Yao ◽  
Loo Hay Lee ◽  
Wikrom Jaruphongsa ◽  
Vicky Tan ◽  
Chen Fei Hui

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 471
Author(s):  
Rafael B. Carmona-Benítez

The capacitated p-median transportation inventory problem with heterogeneous fleet (CLITraP-HTF) aims to determine an optimal solution to a transportation problem subject to location-allocation, inventory management and transportation decisions. The novelty of CLITraP-HTF is to design a supply chain that solves all these decisions at the same time. Optimizing the CLITraP-HTF is a challenge because of the high dimension of the decision variables that lead to a large and complex search space. The contribution of this paper is to develop a dimensionality-reduction procedure (DRP) to reduce the CLITraP-HTF complexity and help to solve it. The proposed DRP is a mathematical proof to demonstrate that the inventory management and transportation decisions can be solved before the optimization procedure, thus reducing the complexity of the CLITraP-HTF by greatly narrowing its number of decision variables such that the remaining problem to solve is the well-known capacitated p-median problem (CPMP). The conclusion is that the proposed DRP helps to solve the CLITraP-HTF because the CPMP can be and has been solved by applying different algorithms and heuristic methods.


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