the runway configur manag rcm problem govern what combin airport runway use given time what capac runway configur group runway oper under runway configur capac envelop rcces which limit arriv departur capac the rcce identifi uniqu capac constraint base which tarmac use arriv departur both direct travel when switch between rcces some decrement arriv departur capac incur transit previous rcm model frankovich 2009 account cost through requir period inact paper instead focus introduct assess model capabl margin decreas rcce capac dure configur transit transit penalti matrix introduc specifi relat cost term accept arriv departur capac switch between rcces the new model benefit from customiz transit penalti which more close repres realworld condit reason comput cost

Christopher Weld

Michael Duarte

Rex Kincaid

A Runway Configuration Management Model with Marginally Decreasing Transition Capacities

demand season product persist fix period time normal finit time horizon inventori control problem formul type demand realiti difficult predict end season precis thus repres uncertain variabl known random plan horizon paper present productioninventori model deterior item imprecis environ characteris inflat time valu money consid constant demand assum time horizon busi period random natur follow exponenti distribut known mean here consid result effect inflat time valu money both crisp fuzzi for crisp inflat effect total expect profit from plan horizon maxim use genet algorithm deriv optim decis this develop use roulett wheel select arithmet crossov random mutat other hand when inflat effect fuzzi can expect profit fuzzi too fuzzi object optimist pessimist return expect total profit obtain use respect necess possibl measur fuzzi event the have develop use fuzzi simul maxim optimist pessimist return get optim decis have provid some numer exampl some sensit analys illustr model

Debasis Das

Arindam Roy

Samarjit Kar

A Production-Inventory Model for a Deteriorating Item Incorporating Learning Effect Using Genetic Algorithm

classic method intract solv markov decis process mdps requir larg state space decomposit aggreg techniqu veri use cope larg problem these techniqu general special case classic divideandconqu framework split larg unwieldi problem smaller compon solv part order construct global solut this paper review most decomposit approach encount associ literatur over past two decad weigh pros con consid sever categori mdps averag discount weight mdps present briefli varieti methodolog find approxim optim strategi

Cherki Daoui

Mohamed Abbad

Mohamed Tkiouat

Exact Decomposition Approaches for Markov Decision Processes: A Survey

paknejad model consid paper itemwis multiobject model both exponenti uniform leadtim demand taken result compar numer both fuzzi optim intuitionist fuzzi optim techniqu objectiv paper establish intuitionist fuzzi optimizaion method better than usual fuzzi optim techniqu expect annual cost inventori model more minim case intuitionist fuzzi optim method singl object stochast inventori model where leadtim demand follow normal distribut vari defect rate expect annual cost also measur final model consid fuzzi cost compon which make model more realist numer valu uniform exponenti normal leadtim demand compar necessari graphic present also given besid numer illustr

S. Banerjee

T. K. Roy

Solution of Single and Multiobjective Stochastic Inventory Models with Fuzzy Cost Components by Intuitionistic Fuzzy Optimization Technique

this paper propos cluster partit techniqu calcul improv upper bound optim solut maxim cover locat problem given cover distanc graph built consid vertic potenti facil locat edg connect each pair facil attend same client coupl constraint correspond some edg graph identifi relax lagrangean way result disconnect subgraph repres smaller subproblem comput easier solv exact method the propos techniqu compar classic approach use real data instanc from avail literatur

Edson Luiz França Senne

Marcos Antonio Pereira

Luiz Antonio Nogueira Lorena

A Decomposition Heuristic for the Maximal Covering Location Problem

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