Logistics network is expected that opened facilities work continuously for a long time horizon without any failure, but in real world problems, facilities may face disruptions. This paper studies a reliable joint inventory location problem to optimize the cost of facility locations, customers assignment, and inventory management decisions when facilities face failure risks and do not work. In our model we assume when a facility is out of work, its customers may be reassigned to other operational facilities otherwise they must endure high penalty costs associated with losing service. For defining the model closer to real world problems, the model is proposed based on pmedian problem and the facilities are considered to have limited capacities. We define a new binary variable for showing that customers are not assigned to any facilities. Our problem involves a biobjective model, the first one minimizes the sum of facility construction costs and expected inventory holding costs, the second one function that mentions for the first one is minimized maximum expected customer costs under normal and failure scenarios. For solving this model we use NSGAII and MOSS algorithms have been applied to find the Pareto archive solution. Also, Response Surface Methodology (RSM) is applied for optimizing the NSGAII Algorithm Parameters. We compare the performance of two algorithms with three metrics and the results show NSGAII is more suitable for our model.
Deep Dive into A Multi Objective Reliable Location-Inventory Capacitated Disruption Facility Problem with Penalty Cost Solve with Efficient Meta Historic Algorithms.
Logistics network is expected that opened facilities work continuously for a long time horizon without any failure, but in real world problems, facilities may face disruptions. This paper studies a reliable joint inventory location problem to optimize the cost of facility locations, customers assignment, and inventory management decisions when facilities face failure risks and do not work. In our model we assume when a facility is out of work, its customers may be reassigned to other operational facilities otherwise they must endure high penalty costs associated with losing service. For defining the model closer to real world problems, the model is proposed based on pmedian problem and the facilities are considered to have limited capacities. We define a new binary variable for showing that customers are not assigned to any facilities. Our problem involves a biobjective model, the first one minimizes the sum of facility construction costs and expected inventory holding costs, the secon
Abstract— logistics network is expected that opened facilities work
continuously for a long time horizon without any failure; but in real world
problems, facilities may face disruptions. This paper studies a reliable joint
inventory location problem to optimize cost of facility locations, customers’
assignment, and inventory management decisions when facilities face failure
risks and doesn’t work. In our model we assume when a facility is out of
work, its customers may be reassigned to other operational facilities
otherwise they must endure high penalty costs associated with losing service.
For defining the model closer to real world problems, the model is proposed
based on p-median problem and the facilities are considered to have limited
capacities. We define a new binary variable (𝑍𝑖𝑠) for showing that customers
are not assigned to any facilities. Our problem involve a bi-objective model;
the first one minimizes the sum of facility construction costs and expected
inventory holding costs, the second one function that mention for the first
one is minimizes maximum expected customer costs under normal and
failure scenarios. For solving this model we use NSGAII and MOSS
algorithms have been applied to find the pareto- archive solution. Also
Response Surface Methodology (RSM) is applied for optimizing the
NSGAII Algorithm Parameters. We compare performance of two algorithms
with three metrics and the results show NSGAII is more suitable for our
model.
Keywords—Joint inventory- location problem, facility location, NSGAII,
MOSS
I. INTRODUCTION
Recently most of the studies focus on facilities location
problems, a large number of studies (e.g., Drezner. 1995;
Owen and Daskin and Owen 1999) focused on the
Uncapacited fixed charge location problem (UFL) that their
goals is finding the optimal number of facilities and their
locations in a supply chain network to balance the trade-off
between facility setup costs and day to day shipment or
transportation costs [1].However, in UFL problem inventory
costs and the other were not usually considered. In many
papers where product safekeeping is expensive, the holding
cost and transportation cost may account for a significant
portion of the total system cost. Utilizing UFL models to cases
with significant inventory costs may yield suboptimal design
and hug system cost estimation. Therefore researchers
introduced joint inventory – location models that optimize
facility locations to minimize the sum of the inventory costs,
conclude the facility setup costs and the customer
transportation costs. Various
solution algorithms like
lagrangian relaxation and column generation were used to
solve the joint inventory-location models. Shu et all [2].
Elham Taghizadeh, Industerial Engineering Department, Khaje Nasir Tossi
University, Iran, Tehran, Vanak sq., Molasadra Avenue,
Number21(Elham_tgh@yahoo.com)
Mostafa Abedzadeh, Industerial Engineering Department, Khaje Nasir Tossi
University, Iran, Tehran, Vanak sq., Molasadra Avenue,
Number21(abedzade@kntu.ac.ir)
Mostafa Setak, Industerial Engineering Department, Khaje Nasir Tossi
University, Iran, Tehran, Vanak sq., Molasadra Avenue,
Number21(setak@kntu.ac.ir)
(2005) further improved these algorithms by exploiting certain
special structures in the models. Meta –heuristics algorithms
have also been used to solve these problems (e.g., Azad and
Davoudpour, 2008) [1].
The facilities can be defined as fire station, emergency
shelter, service center, logistics center and telecommunication
post. The facility may provide service to one or several
customer points. Traditional facility location often assumed
that the facilities are always available and never incapacitate;
they will provide service under any Conditions [3]. Many
facilities are subject to potential operational disruptions from
time to time. The famous ‘‘lean’’ concept is about allows
development of global supply chains problems. From the
terrorist attacks to the catastrophic devastation caused by
Hurricane Katrina [4]. Many people believe that our
international supply chains are strong and reliable. However in
reality, these facilities can be unreliable; they will not provide
service to customers that allocation to them because of
maintenance, ranging from natural disasters to temporary
shortages of capacity, breakdown or shut down for some
unknown or known reasons. It is hence of theoretical and
practical notification has been paid to facility location
problems where facilities may not be completely reliable in
recent years. When some facilities are not available, their
customers either forced to travel excessive distances to access
their demand or entirely give up the service and pay penalty
[1]. Reliability is defined as the probability that a system or
component performs its intended function within a given time
horizon. A supply chain is reliable if it performs well w
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