In this paper, we study the location of emergency centres considering cooperative and backup coverage while natural disasters occur which can result in facility disruption. In this regard, a reliable version of cooperative covering problem is presented considering two types of candidate sites, i.e., reliable and unreliable. To achieve a fortified system against disaster, reliable candidate sites are selected from areas which are far away from the disaster harms. Furthermore, backup coverage is considered to compensate unsatisfied coverage of the demand zones due to facility disruption. The performance of the model is investigated solving numerical examples with different approaches utilising commercial software. The results confirm accurate performance of the model. They also show that both facility failure and backup coverage considerations lead to a more efficient network by incurring some additional cost.
A cooperative covering problem under disruption considering backup coverage
2017-12-30
oai:zenodo.org:3813534
International Journal of Services and Operations Management 29(2) 273-288
Article (Journal)
Electronic Resource
English
Contract design , FDA , Multi-objective biogeography-based optimization (MOBBO) , Queuing systems , Vendor Managed Inventory , Criteria , Facility disruptions , Graph theory , Routing flexibility , Scheduling , Multi-objective decision making , Market capture , Supply chain design , natural disaster , Vendor-buyer integration , Economic order quantity , Multi-objective dynamic facility layout problem , Fuzzy multi-criteria decision making , Discrete delivery ordering , Genetic Algorithm , Multi-server , Operational research , Simulated Annealing , Meta-heuristic algorithms , Tabu search , Transportation channel , Delivery time dependent demand , Decomposition algorithm , Pipeline scheduling , Maximal covering location problem , Game theory , Reliability , Fuzzy multi objective decision making , Variable neighborhood search , backup coverage , Facility location-protection problem , Dynamic (multi-period) MCLP , Iterative optimisation , Modeling , Paper industry , Reliable , Quota allocation , Bi-level programming , reliabilit , TOPSIS , Response surface methodology (RSM) , Social responsibility , Tree-like structure , Optimization , Transaction costs , Capacitated MCLP , Competitive location , Justice-oriented employment , Stochastic process , Stock out , Location problem , Concessive variable neighborhood search , Discrete demand , Service diversity , Textile industry , Material handling , Non-identical retailers , Hill climbing heuristic , Conditional value-at-risk , Uncertainty modeling , Repair center , Disaster management , Information sharing , Linear regression , SCOR , MOVDO , Multi-echelon , Demand uncertainty , Economic production quantity , Ranking genetic algorithm , Batch-ordering , Cell manufacturing , Fuzzy numbers , Inventory-routing , Stochastic lead-time , Discount , SA , Server , Poisson demand , Cooperative covering problem , Entropy-TOPSIS method , Comparative Particle Swarm Optimization , Intermodal P-hub median problem , Quality degree , NSGA-II , Supply strategy , BWE , Queue , Substitutable product , Price-dependent demand , Sample average approximation , Vehicle multi-tours , Sustainable supply chain network design , Multi-product pipelines , Nonlinear regression , MODM , Equal shipments , Bullwhip effect , Lost sales , Stochastic delivery time , Vendor-buyer cooperation , Congestion , Bi-objective reliable facility location problem , Non-dominated ranking genetic algorithm (NRGA) , Exact algorithm , Taguchi method , cooperative covering , Water flow like algorithm , Heuristic algorithm , Steel industry , disruption , Multi-objective invasive weed optimization , Particle swarm optimization , Manufacturing facility , Production-distribution , Supply chain management , Location-allocation problem , Stochastic demand , Refinery supply chain , Supplier selection , Multi-objective optimization , Fuzzy mathematical programming , Congested systems , Pricing , Frequency domain analysis , VNS , Closed-loop supply chain network design , Mixed-integer linear programming , Shipment consolidation , Network , Portfolio selection , Non-equal shipments , Supply risk , Supply networks , Water consumption , Exogenous uncertainty , Fuzzy logic
Coordination with a backup supplier through buy-back contract under supply disruption
Online Contents | 2010
|Transportation Research Record | 2022
|Transportation disruption risk management: business interruption insurance and backup transportation
Online Contents | 2016
|