7 research outputs found
Emergency response network design for hazardous materials transportation with uncertain demand
Transportation of hazardous materials play an essential role on keeping a friendly environment. Every day, a substantial amount of hazardous materials (hazmats), such as flammable liquids and poisonous gases, need to be transferred prior to consumption or disposal. Such transportation may result in unsuitable events for people and environment. Emergency response network is designed for this reason where specialist responding teams resolve any issue as quickly as possible. This study proposes a new multi-objective model to locate emergency response centers for transporting the hazardous materials. Since many real-world applications are faced with uncertainty in input parameters, the proposed model of this paper also assumes that reference and demand to such centre is subject to uncertainty, where demand is fuzzy random. The resulted problem formulation is modelled as nonlinear non-convex mixed integer programming and we used NSGAII method to solve the resulted problem. The performance of the proposed model is examined with several examples using various probability distribution and they are compared with the performance of other existing method
Stochastic facility location with general long-run costs and convex short-run costs.
This paper addresses the problem of minimizing the expected cost of locating a number of single product facilities and allocating uncertain customer demand to these facilities. The total costs consist of two components: firstly linear transportation cost and secondly the costs of investing in a facility as well as maintaining and operating it. These facility costs are general and non-linear in shape and could express both changing economies of scale and diseconomies of scale. We formulate the problem as a two-stage stochastic programming model where both demand and short-run costs may be uncertain at the investment time. We use a solution method based on Lagrangean relaxation, and show computational results for a slaughterhouse location case from the Norwegian meat industry
Supply Chain Network Design Under Uncertain and Dynamic Demand
Supply chain network design (SCND) identifies the production and distribution
resources essential to maximizing a networkâs profit. Once implemented, a SCND
impacts a networkâs performance for the long-term. This dissertation extends the
SCND literature both in terms of model scope and solution approach.
The SCND problem can be more realistically modeled to improve design decisions
by including: the location, capacity, and technology attributes of a resource;
the effect of the economies of scale on the cost structure; multiple products and
multiple levels of supply chain hierarchy; stochastic, dynamic, and correlated demand;
and the gradually unfolding uncertainty. The resulting multistage stochastic
mixed-integer program (MSMIP) has no known general purpose solution methodology.
Two decomposition approachesâend-of-horizon (EoH) decomposition and
nodal decompositionâare applied.
The developed EoH decomposition exploits the traditional treatment of the end-of-horizon effect. It rests on independently optimizing the SCND of every node of the
last level of the scenario-tree. Imposing these optimal configurations before optimizing
the design decisions of the remaining nodes produces a smaller and thus easier to
solve MSMIP. An optimal solution results when the discount rate is 0 percent. Otherwise,
this decomposition deduces a bound on the optimality-gap. This decomposition is neither SCND nor MSMIP specific; it pertains to any application sensitive to the
EoH-effect and to special cases of MSMIP. To demonstrate this versatility, additional
computational experiments for a two-stage mixed-integer stochastic program
(SMIP) are included.
This dissertation also presents the first application of nodal decomposition in
both SCND and MSMIP. The developed column generation heuristic optimizes the
nodal sub-problems using an iterative procedure that provides a restricted master
problemâs columns. The heuristicâs computational efficiency rests on solving
the sub-problems independently and on its novel handling of the master problem.
Conceptually, it reformulates the master problem to avoid the duality-gap. Technologically,
it provides the first application of Leontief substitution flow problems
in MSMIP and thereby shows that hypergraphs lend themselves to loosely coupled
MSMIPs. Computational results demonstrate superior performance of the heuristic
approach and also show how this heuristic still applies when the SCND problem is
modeled as a SMIP where the restricted master problem is a shortest-path problem
Current Topics on Risk Analysis: ICRA6 and RISK2015 Conference
Peer ReviewedPostprint (published version
Current Topics on Risk Analysis: ICRA6 and RISK2015 Conference
ArtĂculos presentados en la International Conference on Risk Analysis ICRA 6/RISK
2015, celebrada en Barcelona del 26 al 29 de mayo de 2015.Peer ReviewedPostprint (published version