Due to their high dependency on highway transportation, Republic of Korea's (ROK's) military and industry suffer from congestion, shortfall of means (convoys in the military case), high cost and increase in en...
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Due to their high dependency on highway transportation, Republic of Korea's (ROK's) military and industry suffer from congestion, shortfall of means (convoys in the military case), high cost and increase in environmental damage. Our research develops an optimization model to guide ROK's military planning using multi-modal transportation. We apply our Military Logistics Transportation Model (MLTM) to a realistic scenario. MLTM provides guidance on the optimal frequency of transportation services and the optimal routes for the freight. By comparing the solution of MLTM with current practice for Wartime Transportation Planning (represented by a heuristic method), our MLTM can reduce the transportation cost up to 29%. This is enabled by the activation of multi-modal transportation and service sharing by multiple demands. We also analyze scenarios in which either sea- port of debarkation (SPOD) where the supply originates has been shut down by enemy attacks. We find that losing Busan SPOD is more damaging than losing Kwangyang SPOD.
Large-scale multicommodity facility location problems are generally intractable with respect to standard mixed-integerprogramming (MIP) tools such as the direct application of general-purpose Branch & Cut (BC) co...
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Large-scale multicommodity facility location problems are generally intractable with respect to standard mixed-integerprogramming (MIP) tools such as the direct application of general-purpose Branch & Cut (BC) commercial solvers i.e. CPLEX. In this paper, the authors investigate a nested partitions (NP) framework that combines meta-heuristics with MIP tools (including branch-and-cut). We also consider a variety of alternative formulations and decomposition methods for this problem class. Our results show that our NP framework is capable of efficiently producing very high quality solutions to multicommodity facility location problems. For large-scale problems in this class, this approach is significantly faster and generates better feasible solutions than either CPLEX (applied directly to the given MIP) or the iterative Lagrangian-based methods that have generally been regarded as the most effective structure-based techniques for optimization of these problems. We also briefly discuss some other large-scale MIP problem classes for which this approach is expected to be very effective.
This paper deals with complex job shop scheduling problems. A (typically large) number of elementary tasks has to be carried out, according to precedence constraints defined by a task graph. As typical of production e...
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Many studies have pointed out that the integration of product design and supply chain design is an essential factor for manufacturing efficiency. Nevertheless, methods that optimize both product design and supply chai...
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Many studies have pointed out that the integration of product design and supply chain design is an essential factor for manufacturing efficiency. Nevertheless, methods that optimize both product design and supply chain performance for different geographical markets have not been much discussed in existing studies. To tackle this issue, this paper proposes a mixedinteger programing (MIP)-based methodology that identifies the profitability of products for different markets through the integration of the Design for Assembly (DFA) and Design for Profit (DFP) approaches. A case study of the bicycle industry is illustrated for this process, and an MIP model is developed to optimize both product design and supply chain design for three regional markets. This model shows suitable product designs for the different markets under the objective of cost minimization. A sensitivity analysis is also performed with labor cost to identify its impact on supply chain execution. Finally, product prices for the markets are estimated based on the optimization result and the rate of commercial profit.
Logistics Operations Command and Control Capability Concept (LOCCC), developed by Jeff Grelson in 2000, introduces a new distribution principle to combat elements. This concept employs a supporting logistics unit in a...
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Logistics Operations Command and Control Capability Concept (LOCCC), developed by Jeff Grelson in 2000, introduces a new distribution principle to combat elements. This concept employs a supporting logistics unit in a general support role and controls it by a unique command center in order to minimize the footprint left by logistics, improve logistic and tactical responsiveness, and reduce the "iron mountain" on the battlefield. This thesis revisits the mathematical models and algorithms developed by Major Thomas Lenhardt to model LOCCC. We preprocess the network topology in order to convert it into an equivalent, simplified network that is computationally tractable with the existing optimization model by using exact and heuristic algorithms. We show that the simplifications and enhancements we propose help us to obtain much faster and better quality solutions than using the original, non-simplified networks. For example, in a ten-minute run, we can obtain a solution that is 98% better in some cases. We also apply the model to a Turkish Infantry Brigade to evaluate LOCCC with sustainment requirements and transportation assets of the Turkish Army.
We introduce a tri-level defender-attacker-defender optimization model that prescribes how Iraq's oil infrastructure can, over time, be expanded, protected, and operated, even in the face of insurgent attacks. The...
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We introduce a tri-level defender-attacker-defender optimization model that prescribes how Iraq's oil infrastructure can, over time, be expanded, protected, and operated, even in the face of insurgent attacks. The outer-most defender model is a mixed- integer program that, given a set of anticipated insurgent attacks, specifies a quarterly capital expansion, defense, and operation plan to maximize oil exports over a decade-long planning horizon. The intermediate attacker model, observing the outer defender plans, is a mixedinteger program that re-optimizes insurgent attacks to minimize export flow. The inner-most defender model is a linear program that re-directs flow in response to insurgent damage. We use open-source descriptions of current Iraqi oil infrastructure and reasonable estimates of the costs to expand capacity and/or defend operating assets, and reduce vulnerability to attacks. We solve this tri-level model by converting it into an equivalent bi-level one, and applying decomposition. For a range of scenarios, we determine the best allocation of effort between improving oil export infrastructure, and defending it.
An important branch of mathematical programming is concerned with optimization in systems described by networks. This paper describes an integrated suite of advanced techniques for dealing with minimum cost network fl...
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An important branch of mathematical programming is concerned with optimization in systems described by networks. This paper describes an integrated suite of advanced techniques for dealing with minimum cost network flow formulations Written in Pascal and implemented on a microcomputer representative of current small computer technology (the APPLE II), this package places unprecedented modeling versatility and solution capability on the analyst's desktop. Able to solve small to medium size problems (3000 arcs or less) at reasonable speeds, programs to handle capacitated linear, nonlinear (convex separable), mixedinteger and elastic ranged linear models in addition to comprehensive control and data management routines are included. Problem size and solution speed benchmarks are given for a variety of models.
This thesis develops a simple method for evaluating adversarial risk within the transportation portion of the nuclear fuel cycle for commercial electric power generation, and develops models that can guide the reducti...
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This thesis develops a simple method for evaluating adversarial risk within the transportation portion of the nuclear fuel cycle for commercial electric power generation, and develops models that can guide the reduction of that risk by such means as rerouting and decoy shipments. A conceivable, worst-case attack by an intelligent adversary will cause a localized release of radioactive material. A damage function is defined using the population in the vicinity of the attack. Using hypothetical, but realistic, transit routes be- tween fuel fabricators and power plants, we identify the worst-case locations for attack. Then we formulate and solve mixed-integer programs to either (a) redesign the network by changing supply contracts, or (b) optimally allocate a resource-constrained assignment of decoy shipments. We also demonstrate a greedy procedure for simple rerouting of individual shipments. Computational methods exploit standard geographical databases, and optimization software solves the models in seconds on a personal computer. Separate but similar analyses would apply to shipments of uranium hexafluoride, spent fuel being shipped for reprocessing, spent fuel being shipped to a repository, and other materials.
This thesis explores Benders decomposition for solving interdiction problems on electric power grids, with applications to analyzing the vulnerability of such grids to terrorist attacks. We refine and extend some exis...
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This thesis explores Benders decomposition for solving interdiction problems on electric power grids, with applications to analyzing the vulnerability of such grids to terrorist attacks. We refine and extend some existing optimization models and algorithms and demonstrate the value of our techniques using standard reliability test networks from IEEE. Our implementation of Benders decomposition optimally solves any problem instance, in theory. However, run times increase as Benders cuts are added to the master problem, and this has prompted additional research to increase the decomposition s efficiency. We demonstrate empirical speed ups by dropping slack cuts, solving a relaxed master problem in some iterations, and using integer but not necessarily optimal master-problem solutions. These mixed strategies drastically reduce computation times. For example, in one test case, we reduce the optimality gap, and the time that it takes to achieve this gap, from 16% in 75 hours to 5% in 16 minutes.
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