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
backup coverage , cooperative covering , disruption , natural disaster , reliabilit , Batch-ordering , Bi-level programming , Bi-objective reliable facility location problem , Bullwhip effect , BWE , Capacitated MCLP , Cell manufacturing , Closed-loop supply chain network design , Comparative Particle Swarm Optimization , Competitive location , Concessive variable neighborhood search , Conditional value-at-risk , Congested systems , Congestion , Contract design , Cooperative covering problem , Criteria , Decomposition algorithm , Delivery time dependent demand , Demand uncertainty , Disaster management , Discount , Discrete delivery ordering , Discrete demand , Dynamic (multi-period) MCLP , Economic order quantity , Economic production quantity , Entropy-TOPSIS method , Equal shipments , Exact algorithm , Exogenous uncertainty , Facility disruptions , Facility location-protection problem , FDA , Frequency domain analysis , Fuzzy logic , Fuzzy mathematical programming , Fuzzy multi objective decision making , Fuzzy multi-criteria decision making , Fuzzy numbers , Game theory , Genetic Algorithm , Graph theory , Heuristic algorithm , Hill climbing heuristic , Information sharing , Intermodal P-hub median problem , Inventory-routing , Iterative optimisation , Justice-oriented employment , Linear regression , Location problem , Location-allocation problem , Lost sales , Manufacturing facility , Market capture , Material handling , Maximal covering location problem , Meta-heuristic algorithms , Mixed-integer linear programming , Modeling , MODM , MOVDO , Multi-echelon , Multi-objective biogeography-based optimization (MOBBO) , Multi-objective decision making , Multi-objective dynamic facility layout problem , Multi-objective invasive weed optimization , Multi-objective optimization , Multi-product pipelines , Multi-server , Network , Non-dominated ranking genetic algorithm (NRGA) , Non-equal shipments , Non-identical retailers , Nonlinear regression , NSGA-II , Operational research , Optimization , Paper industry , Particle swarm optimization , Pipeline scheduling , Poisson demand , Portfolio selection , Price-dependent demand , Pricing , Production-distribution , Quality degree , Queue , Queuing systems , Quota allocation , Ranking genetic algorithm , Refinery supply chain , Reliability , Reliable , Repair center , Response surface methodology (RSM) , Routing flexibility , SA , Sample average approximation , Scheduling , SCOR , Server , Service diversity , Shipment consolidation , Simulated Annealing , Social responsibility , Steel industry , Stochastic delivery time , Stochastic demand , Stochastic lead-time , Stochastic process , Stock out , Substitutable product , Supplier selection , Supply chain design , Supply chain management , Supply networks , Supply risk , Supply strategy , Sustainable supply chain network design , Tabu search , Taguchi method , Textile industry , TOPSIS , Transaction costs , Transportation channel , Tree-like structure , Uncertainty modeling , Variable neighborhood search , Vehicle multi-tours , Vendor Managed Inventory , Vendor-buyer cooperation , Vendor-buyer integration , VNS , Water consumption , Water flow like algorithm
Coordination with a backup supplier through buy-back contract under supply disruption
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