A hybrid Markov process-mathematical programming approach for joint location-inventory problem under supply disruptions

2018 ◽  
Vol 52 (4-5) ◽  
pp. 1147-1173 ◽  
Author(s):  
Ehsan Dehghani ◽  
Mir Saman Pishvaee ◽  
Mohammad Saeed Jabalameli
Keyword(s):  
Supply Chain ◽  
Markov Process ◽  
Mathematical Programming ◽  
Simulated Annealing Algorithm ◽  
Mixed Integer ◽  
Programming Approach ◽  
Second Phase ◽  
Optimization Approach ◽  
Inventory Problem ◽  
Joint Location

This paper introduces a joint location-inventory problem, in which facilities become temporarily unavailable. A hybrid approach based on the Markov process and mathematical programming techniques is presented to design the distribution network of a supply chain in an integrated manner. In the first phase, the Markov process derives some performance features of inventory policy. In the second phase, using outputs of the Markov process, the location-inventory problem is formulated as a mixed-integer nonlinear programming model. Moreover, a robust possibilistic programming approach is utilized, which is able to provide a more stable supply chain structure under almost all possible values of imprecise parameters. Since the proposed problem is complicated to solve by means of exact methods, we develop a simulated annealing algorithm in order to find near-optimal solutions in reasonable computational times. The obtained computational results reveal the efficiency and effectiveness of the proposed solution approach. Finally, some insights are provided and the performance of the proposed robust optimization approach is compared to traditional possibilistic chance constrained method.

scholarly journals A multi-objective optimization of closed-loop supply chain problem with vehicle routing

2021 ◽  
Vol 6 (2) ◽  
pp. 121-130
Author(s):  
Shahul Hamid Khan ◽  
Vivek Kumar Chouhan ◽  
Santhosh Srinivasan
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Consumer Products ◽  
Product Recovery ◽  
Business Strategies ◽  
Mixed Integer ◽  
Programming Approach ◽  
Closed Loop Supply Chain ◽  
Competitive Edge ◽  
Chain Problem

Product recovery has become significant business strategies to increase a competitive edge in business and also in the society. Parts from discarded products due to rapid advancement and post-consumer products before & after end-of-life (EOL) are recovered to reduce landfill waste and to have become a part of circular economy. Product recovery is made possible with the help of Closed-loop supply chain (CLSC). This paper concentrates on multi-period, multi-product, and multi-echelon Closed Loop Green Supply Chain (CLGSC) network. A bi-objective (cost and emission) Mixed Integer Linear Programming (MILP) model has been formulated for the network and has been optimized using Goal Programming approach and Genetic Algorithm. Results are discussed for providing some managerial insights of the model.

scholarly journals A scenario-based optimization model for planning and redesigning the sale and after-sales services closed-loop supply chain

2020 ◽  
Author(s):  
Nazanin Esmaeili ◽  
Ebrahim Teimoury ◽  
Fahimeh Pourmohammadi
Keyword(s):  
Supply Chain ◽  
Customer Satisfaction ◽  
Optimization Model ◽  
Linear Optimization ◽  
Spare Parts ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Warranty Period ◽  
Linear Optimization Model ◽  
Mixed Integer Linear Optimization

In today's competitive world, the quality of after-sales services plays a significant role in customer satisfaction and customer retention. Some after-sales activities require spare parts and owing to the importance of customer satisfaction, the needed spare parts must be supplied until the end of the warranty period. In this study, a mixed-integer linear optimization model is presented to redesign and plan the sale and after-sales services supply chain that addresses the challenges of supplying spare parts after the production is stopped due to demand reduction. Three different options are considered for supplying spare parts, including production/procurement of extra parts while the product is being produced, remanufacturing, and procurement of parts just in time they are needed. Considering the challenges of supplying spare parts for after-sales services based on the product's life cycle is one contribution of this paper. Also, this paper addresses the uncertainties associated with different parameters through Mulvey's scenario-based optimization approach. Applicability of the model is investigated using a numerical example from the literature. The results indicate that the production/procurement of extra parts and remanufacturing are preferred to the third option. Moreover, remanufacturing is recommended when the remanufacturing cost is less than 23% of the production cost.

Sustainable tire supply chain network design and operations planning using a multi objective , scenario-based optimization approach

2021 ◽  
Author(s):  
Reza Yousefi Zenouz ◽  
Aboozar Jamalnia ◽  
Mojtaba Farrokh Farrokh ◽  
Mastaneh Asadi
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Technology Selection ◽  
Mixed Integer ◽  
Programming Approach ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Economic Dimension ◽  
Operations Planning ◽  
Multi Objective

Abstract Each year, millions of tires reach their end of life. Worn-out tires are either buried or burned, both of which harm the environment through polluting the air and groundwater. Companies need to consider their social responsibility, such as employment and regional development, and the environmental impact of their activities when making strategic and operational decisions. This study addresses the closed-loop supply chain network design (SCND) and operations planning problem with regard to the three dimensions of sustainability using a mathematical programming approach. The options of retreading, recycling, and energy recovery together with the use of green technologies are considered to minimize the environmental impacts. The proposed decision model can help supply chain managers in tire manufacturing industry make better-informed decisions in order to achieve the three-fold objectives of sustainability. The developed mathematical model turns out to be a multi-objective, multi-echelon, and multi-product mixed integer linear programming. The model is solved using the Lp-metric method and CPLEX solver. The scenario approach is used to address the uncertainty in demand of new products and the rate of return of worn-out tires. The model solutions are the optimal location of the facilities considering population density and unemployment rate in addition to economic dimension, the optimal amount of allocation, the flow of materials, and the best green technology selection. Sensitivity analysis is also conducted to validate the model and test the robustness of the obtained solutions. Finally, managerial implications are provided.

A Self-Learning Particle Swarm Optimization for Robust Multi-Echelon Capacitated Location–Allocation–Inventory Problem

2019 ◽  
Vol 18 (04) ◽  
pp. 677-694 ◽  
Author(s):  
Erfan Babaee Tirkolaee ◽  
Javad Mahmoodkhani ◽  
Mehdi Ranjbar Bourani ◽  
Reza Tavakkoli-Moghaddam
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Upper And Lower Bounds ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Inventory Problem ◽  
Swarm Optimization ◽  
Location Allocation ◽  
Self Learning ◽  
Capacitated Location

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.

scholarly journals Fuzzy Reliability Optimization for 2-Hub Center Problem with Cluster-Based Policy and Application in Cross-Border Supply Chain Network Design Using TS Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Chenyi Yan ◽  
Xifu Wang ◽  
Kai Yang
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Reliability Optimization ◽  
Supply Chain Network ◽  
Center Problem ◽  
Fuzzy Reliability ◽  
Hub Location Problem ◽  
Cross Border

As information and communication technology evolves and expands, business and markets are linked to form a complex international network, thus generating plenty of cross-border trading activities in the supply chain network. Through the observations from a typical cross-border supply chain network, this paper introduces the fuzzy reliability-oriented 2-hub center problem with cluster-based policy, which is a special case of the well-studied hub location problem (HLP). This problem differs from the classical HLP in the sense that (i) the hub-and-spoke (H&S) network is grouped into two clusters in advance based on their cross-border geographic features, and (ii) a fuzzy reliability optimization approach based on the possibility measure is developed. The proposed problem is first modeled through a mixed-integer nonlinear programming (MINLP) formulation that maximizes the reliability of the entire cross-border supply chain network. Then, some linearization techniques are implemented to derive a linear model, which can be efficiently solved by exact algorithms run by CPLEX for only small instances. To counteract the difficulty for solving the proposed problem in realistic-sized instances, a tabu search (TS) algorithm with two types of move operators (called “Swap I” and “Swap II”) is further developed. Finally, a series of numerical experiments based on the Turkish network and randomly generated large-scale datasets are set up to verify the applicability of the proposed model as well as the superiority of the TS algorithm compared to the CPLEX.

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