7663

7135

كارشناسي ارشد

صنايع

اصفهان: دانشگاه صنعتي اصفهان، دانشكده صنايع و سيستم ها

1391

نه،120ص.: مصور،جدول،نمودار

ص.ع.به فارسي و انگليسي

مهدي بيجاري

28/1/92

قاسم مصلحي، ناصر ملاوردي

مهندسي صنايع و سيستم ها

ID7135

به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي

Integrated non cyclical maintenance planning and production planning in PGLSP Mahdi Jafarian m jafarian@in iut ac ir Date of Submission Department of Systems and Industrial Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language Farsi Supervisor Mehdi Bijari m bijary@cc iut ac ir Abstract In many environments product yield is heavily influenced by equipment condition Despite this fact previous research has either focused on the issue of maintenance ignoring the effect of equipment condition on yield or has focused on the issue of production omitting the possibility of actively changing the machine state We formulate two MIP models that use of Markov chain for producing parameters need to process model of a single stage multi parallel machines production system in which demand is supple have up and down bounds We address a general lot sizing problem with pricing The objective is to maximise profit The problem extends the general lot sizing problem GLSP Model one is based on Myer GLSPST model and model two is based on Almada lobo GLSP model We extend almada lobo GLSP model to involve non triangular setup condition Both models are non cyclical and value optimized maintenance planning and have maintenance micro periods Models don t interfere unemployment in depreciation In this study we propose that maintenance has intrinsic value and argue that existing cost centric models ignore an important dimension of maintenance namely its value and in so doing they can lead to sub optimal maintenance strategies The product yield depends on the equipment condition which deteriorates over time The objective is to choose simultaneously the equipment maintenance schedule as well as the demand and quantity to produce in a way that minimizes the sum of expected production backorder and holding costs In this study introduced particle swarm optimization simulated annealing and a heuristic metaheuristic combined method as three meta heuristics to solve the Integrated non cyclical maintenance planning and production planning in sensible time Computational result annonce model two is better than model one in one machine problems But in two or three machines problems model one was better than model two In meta heuristics particle swarm optimization was not succeeded but simulated annealing and heuristic metaheuristic combined method have good results but heuristic metaheuristic combined method have lower variance and solution time in compare by simulated annealing algorithm Keywords non cyclical maintenance production planning value optimized maintenance profite maximization

مهدي بيجاري

قاسم مصلحي، ناصر ملاوردي