An integrated location-inventory model for a spare part’s supply chain considering facility disruption risk and CO2 emission

Abstract

Purpose: Managing the inventory of spare parts is very difficult because of the stochastic nature of part’s demand. Also, only controlling the inventory of the spare part is not enough; instead, the supply chain of the spare part needs to be managed efficiently. Moreover, every organization now aims to have a resilient and sustainable supply chain to overcome the risk of facility disruption and to ensure environmental sustainability. This paper thus aims to establish a model of inventory-location relating to the resilient supply chain network of spare parts.Design/methodology/approach: First, applying queuing theory, a location-inventory model for a spare parts supply chain facing a facility disruption risk and has a restriction for CO2 emission, is developed. The model is later formulated as a non-linear mixed-integer programming problem and is solved using MATLAB.Findings: The model gives optimal decisions about the location of the warehouse facility and the policy of inventory management of each location selected. The sensitivity analysis shows that the very low probability of facility disruption does not influence controlling the average emission level. However, the average emission level certainly decreases with the increment of the disruption probability when the facility disruption probability is significant.Practical implications: Using this model, based on the cost and emission parameters and the likelihood of facility disruption, the spare part’s manufacturer can optimize the total average cost of the spare part’s supply chain through making a trade-off between productions, warehouse selection, inventory warehousing and demand allocation.Originality/value: Previous research focuses only on developing a framework for designing an efficient spare parts planning and control system. The inventory-location model for spare parts is not addressed in the sense of risk of facilities disturbance and emission. This research first time jointly considered the probabilistic facility disruption risk and carbon emission for modeling the spare part’s supply chain network.

Integrating Resilience and Sustainability Criteria in the Supply Chain Network Design. A Systematic Literature Review

“…In terms of social criteria, the social costs of carbon emissions, the economic impact of road transport (noise, congestion, and accidents), and the cost of the externalities in the rail transport are studied in [59]; the social cost of opening a renewable energy generation unit is studied in [64]. In several articles, environmental aspects are included as a cost criteria [52,[57][58][59][60]64,65,73,86,91,95].…”

“…In [79], a newspaper boy problem is used to calculate inventory levels in retailers. In [95], the inventory level in warehouses is modeled by a birth and death process (continuous-time Markov chain). Considering the intermodal transportation problems introduced by [58][59][60], the tactical decisions consist of selecting the mode of transport and the hubs in which the modes of transport are changed.…”

“…Modeling Approach [60] Robust Programming [90] Robust Mixed Integer Linear Programming (ROMILP) [55] Mixed Integer Linear Programming (MILP) [91] Mixed Integer Linear Programming (MILP) [92] Mixed Integer Linear Programming (MILP) [93] Mixed Integer Non-Linear Programming (MINLP) [67] Mixed Integer Linear Programming (MILP) [94] Mixed Integer Linear Programming (MILP) [95] Mixed Integer Non-Linear Programming (MINLP) [68] Mixed Integer Linear Programming (MILP) [96] Stochastic Mixed-Integer Programming [97] Robust Programming [98] Mixed Integer Linear Programming (MILP) Table 7. Modeling approach and objective-Part b.…”

Civil Aircraft Spare Parts Prediction and Configuration Management Techniques: Review and Prospect

“…Gallego-Garcı´a et al 129 analyzed the impact of the distribution network design for spare parts management and presented a conceptual model to improve spare parts support effectiveness. Karim and Nakade 130 developed a location-inventory model for a spare parts supply chain facing disruption risk by applied queuing theory. Although the prediction methods based on spare parts supply chain dealt with some problems, the future work can model user's benefits as another optimization objective.…”

“…Multi-level spare parts inventory and redundant system Spare parts reliability [107][108][109][110][111][112] Redundancy system [116][117][118][119][120][121][122][123][124] Supply chain [126][127][128][129][130] Optimization algorithms Supply volume, supply time, system guarantee rate, maintenance period, cost of spare parts in each warehouse Genetic algorithm [131][132][133][134][135][136][137] Solve fast and can be applied to highdimensional problems…”