We consider the simultaneous design and operation of remnant inventory supply chains. Remnant inventory is generated when demand for various lengths of a product may be satisfied by existing inventory, or by cutting a...
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We consider the simultaneous design and operation of remnant inventory supply chains. Remnant inventory is generated when demand for various lengths of a product may be satisfied by existing inventory, or by cutting a large piece into smaller pieces. We formulate our problem as a two-stage stochastic mixed-integer program. In solving our stochastic program, we enhance the standard L-shaped method in two ways. Our computational experiments demonstrate that these enhancements are effective, dramatically reducing the solution time for large instances. (C) 2011 Elsevier B.V. All rights reserved.
This paper presents an operational model that generates short term planning decisions for the fresh produce industry. In particular, the application developed helps the grower to maximize his revenues by making produc...
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This paper presents an operational model that generates short term planning decisions for the fresh produce industry. In particular, the application developed helps the grower to maximize his revenues by making production and distribution decisions during the harvest season. The main motivation for this model comes from the fact that the profitability of producers is highly dependent on the handling of short term planning in the harvest season. Some of the factors affecting profitability include the management of labor costs, the preservation of the value of perishable crops, and the use transportation modes that provide the best trade-off between time (quality of products) and cost. These issues are interrelated, and their judicious management is fundamental for attaining good financial results. The results of the proposed planning model indicate that significant savings can be obtained by managing the trade-off of the freshness at the delivery of the product with the added labor and transportation cost at the grower's side. Moreover the results also show that dynamic, information based, management practices might be preferred over traditional practices based in fixed labor allocation and distribution practices. (C) 2011 Elsevier B.V. All rights reserved.
BHP Billiton's Illawarra Coal operates several longwall coal extraction systems in the Bulli and Wongawilli coal seams in the Southern Coalfields of the Sydney Basin, Australia. IC has applied a proprietary mixed ...
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BHP Billiton's Illawarra Coal operates several longwall coal extraction systems in the Bulli and Wongawilli coal seams in the Southern Coalfields of the Sydney Basin, Australia. IC has applied a proprietary mixedinteger linear programming open pit strategic mine planning tool called Blasor to rapidly evaluate, for a number of longwall mining domains, the jointly optimal underground development strategy and mining sequences necessary. For each development scenario, the Blasor optimizer maximizes the discounted operating cash flow as the objective function, subject to mining, processing, and transportation capacity constraints and product blend constraints. When applied to the underground longwall domain sequence optimization problem, Blasor evaluates a set of carefully considered scenarios, each describing a feasible underground development and transport strategy. In this application, Blasor plays the role of a fair valuation tool for each major scenario, by determining the most valuable extraction schedule for each development scenario. This establishes a basis for comparing the economic merits of competing scenarios that are structurally different.
In recent years there has been growing interest in generating valid inequalities for mixed-integer programs using sets with two or more constraints. In particular, Andersen et al. (2007) [2] and Borozan and Cornuejols...
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In recent years there has been growing interest in generating valid inequalities for mixed-integer programs using sets with two or more constraints. In particular, Andersen et al. (2007) [2] and Borozan and Cornuejols (2009) [3] have studied sets defined by equations that contain exactly one integer variable per row. The integer variables are not restricted in sign. Cutting planes based on this approach have already been computationally studied by Espinoza (2008) [8] for general mixed-integer problems, and there is ongoing computational research in this area. In this paper, we extend the model studied in the earlier papers and require the integer variables to be non-negative. We extend the results in [2] and [3] to our case, and show that cuts generated by their approach can be strengthened by using the non-negativity of the integer variables. In particular, it is possible to obtain cuts which have negative coefficients for some variables. (C) 2010 Elsevier B.V. All rights reserved.
The planning of energy systems is associated with various uncertainties. Such uncertainties may only be expressed by interval numbers or fuzzy sets rather than probability distributions. In addition, issues of capacit...
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The planning of energy systems is associated with various uncertainties. Such uncertainties may only be expressed by interval numbers or fuzzy sets rather than probability distributions. In addition, issues of capacity expansion related to timing, sizing and siting under such uncertainties needs to be addressed. Therefore, the objective of this research is to develop a dynamic optimization model for energy systems planning under uncertainty (DESPU) through integrating interval-parameter, fuzzy and mixed integer programming techniques within an energy systems management framework. The developed methodology is then applied to a hypothetical regional energy system. The results indicate that DESPU has advantages in reflecting complexities of various uncertainties as well as dealing with problems of capacity expansion within energy systems.
We provide an efficient computational approach to solve the mixed integer programming (MIP) model developed by Tarim and Kingsman [8] for solving a stochastic lot-sizing problem with service level constraints under th...
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We provide an efficient computational approach to solve the mixed integer programming (MIP) model developed by Tarim and Kingsman [8] for solving a stochastic lot-sizing problem with service level constraints under the static-dynamic uncertainty strategy. The effectiveness of the proposed method hinges on three novelties: (i) the proposed relaxation is computationally efficient and provides an optimal solution most of the time, (ii) if the relaxation produces an infeasible solution, then this solution yields a tight lower bound for the optimal cost, and (iii) it can be modified easily to obtain a feasible solution, which yields an upper bound. In case of infeasibility, the relaxation approach is implemented at each node of the search tree in a branch-and-bound procedure to efficiently search for an optimal solution. Extensive numerical tests show that our method dominates the MIP solution approach and can handle real-life size problems in trivial time. (C) 2011 Elsevier B.V. All rights reserved.
In this research, several approaches for the optimisation of batch processes are investigated and compared. The motivation for this is the analysis of an oven work centre in a semiconductor wafer fab. Different object...
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In this research, several approaches for the optimisation of batch processes are investigated and compared. The motivation for this is the analysis of an oven work centre in a semiconductor wafer fab. Different objectives like keeping operational due dates as well as the minimisation of lot completion time will be optimised with regard to existing process constraints. First, the problem is modelled from two different viewpoints: as scheduling problem and with the help of a simulation system. For this, it used mixed integer programming (MIP) and a discrete event simulation system respectively. As a next step, a MIP time window decomposition approach combining simulation and mathematical programming is discussed. This reduces the complexity of the given problem by solving local sub-problems. Experiments on real manufacturing data determine the optimisation potential for the work centre using the different approaches. This allows, among others, a comparison to dispatching strategies as they are used in the manufacturing line. [Received: 01 July 2009;Revised: 09 November 2009;Accepted: 01 February 2010].
Third party logistics service providers (3PLs) have an important role in supply chain management. Increasing cooperation with 3PLs is expanding in today's business environment. Hence, 3PLs need to have an efficien...
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Third party logistics service providers (3PLs) have an important role in supply chain management. Increasing cooperation with 3PLs is expanding in today's business environment. Hence, 3PLs need to have an efficient distribution network to meet customer demands. Nevertheless, few researches have tried to propose a solution for distribution network problems of 3PLs. The optimization problem which is discussing in our study is solved in two stages. At the first stage, the assignment problem which includes assigning the order of the vehicles is solved with mixed integer programming by using GAMS 21.6/CPLEX. The output of the first stage is used as an input in the second stage. In this stage routes are determined for vehicles by developing a genetic algorithm by using C#. (C) 2011 Elsevier Ltd. All rights reserved.
A mixedinteger linear programming formulation is proposed for the simultaneous design of network and fleet deployment of a deep-sea liner service provider. The underlying network design problem is based on a 4-index ...
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A mixedinteger linear programming formulation is proposed for the simultaneous design of network and fleet deployment of a deep-sea liner service provider. The underlying network design problem is based on a 4-index (5-index by considering capacity type) formulation of the hub location problem which are known for their tightness. The demand is elastic in the sense that the service provider can accept any fraction of the origin-destination demand. We then propose a primal decomposition method to solve instances of the problem to optimality. Numerical results confirm superiority of our approach in comparison with a general-purpose mixed integer programming solver. (C) 2011 Elsevier Ltd. All rights reserved.
Nature reserves are often designated ad hoc. Despite increasing conservation efforts, loss of biodiversity is still accelerating. Considering land scarcity and demand for alternative uses, efficiency in conservation s...
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Nature reserves are often designated ad hoc. Despite increasing conservation efforts, loss of biodiversity is still accelerating. Considering land scarcity and demand for alternative uses, efficiency in conservation strongly correlates with efficiency in land allocation. Systematic conservation planning can effectively prioritize conservation activities. Previous studies minimize opportunity costs for given conservation targets. However, these studies assume constant marginal costs of habitat protection. We extend this cost minimization approach by also considering a dynamic representation of marginal costs. The more land is allocated to nature reserves, the higher are opportunity costs, i.e., costs of forgone agricultural production. This increase in costs results from changes in the prices of agricultural commodities. We employ a deterministic, spatially explicit mathematical optimization model to allocate species habitats by minimizing opportunity costs for setting aside land for conservation purposes. The model is designed as a mixed integer programming problem and solved with GAMS/CPLEX. Our results show the need for integrating land market feedbacks into conservation planning. We find that ignoring land rent adjustments can lead to highly cost-ineffective solutions in reserve selection.
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