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A mixed-integer linear programming (MILP) MODEL FOR SHORT-RANGE PRODUCTION SCHEDULING OF CEMENT QUARRY OPERATIONS

ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH 2010年第3期27卷 315-333页

作者： Rehman, Safi Ur Asad, Mohammad Waqar Ali NWFP Univ Engn & Technol Peshawar Pakistan

A limestone quarry is the major source for supplying raw materials for cement manufacturing operations. Depending upon the available reserves, a quarry is divided into thousands of mineable blocks. Hence, raw materials inventory is identified in terms of a block model projecting the quantity and quality of critical chemical constituents desired in the cement manufacturing process. An individual block never satisfies the process quality constraints;therefore, the blending of various quarry blocks with few additives purchased from the market becomes a prerequisite. As each block is represented as an integer (0-1) variable, the objective of an optimal quarry production scheduling model is sequential mining of these blocks such that the plant quantity and quality requirements are satisfied at the lowest possible cost. This paper presents a new mixed-integer linear programming (MILP) based blending optimization model accomplishing the defined objective as a short-range production planning tool. The benefits of the model are established through a case study of an existing cement manufacturing operation in the northern part of Pakistan, ensuring significant cost savings compared to schedules produced manually.

关键词： Operations Research mixed integer-linear programming production planning/scheduling cement/mining industry

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Planning With Multiple Transmission and Storage Investment Options Under Uncertainty: A Nested Decomposition Approach

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IEEE TRANSACTIONS ON POWER SYSTEMS 2018年第4期33卷 3559-3572页

作者： Falugi, Paola Konstantelos, Ioannis Strbac, Goran Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

Achieving the ambitious climate change mitigation objectives set by governments worldwide is bound to lead to unprecedented amounts of network investment to accommodate low-carbon sources of energy. Beyond investing in conventional transmission lines, new technologies, such as energy storage, can improve operational flexibility and assist with the cost-effective integration of renewables. Given the long lifetime of these network assets and their substantial capital cost, it is imperative to decide on their deployment on a long-term cost-benefit basis. However, such an analysis can result in large-scale mixed integer linear programming problems that contain many thousands of continuous and binary variables. Complexity is severely exacerbated by the need to accommodate multiple candidate assets and consider a wide range of exogenous system development scenarios that may occur. In this paper, we propose a novel, efficient, and highly generalizable framework for solving large-scale planning problems under uncertainty by using a temporal decomposition scheme based on the principles of Nested Benders. The challenges that arise due to the presence of nonsequential investment state equations and sub-problem nonconvexity are highlighted and tackled. The substantial computational gains of the proposed method are demonstrated via a case study on the IEEE 118 bus test system that involve planning of multiple transmission and storage assets under long-term uncertainty. The proposed method is shown to substantially outperform the current state of the art.

关键词： Energy storage mixed integer-linear programming nested Benders decomposition stochastic programming transmission planning

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Optimizing expansion strategies for ultrascale cloud computing data centers

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SIMULATION MODELLING PRACTICE AND THEORY 2015年第Part1期58卷 15-29页

作者： Al-Ayyoub, Mahmoud Wardat, Mohammad Jararweh, Yaser Khreishah, Abdallah A. Jordan Univ Sci & Technol Dept Comp Sci Irbid 22110 Jordan New Jersey Inst Technol Newark NJ 07102 USA

With the increasing popularity gained by cloud computing systems over the past few years, cloud providers have built several ultrascale data centers at a variety of geographical locations, each including hundreds of thousands of computing servers. Since cloud providers are facing rapidly increasing traffic loads, they must have proper expansion strategies for their ultrascale data centers. The decision of expanding the capacities of existing data centers or building new ones over a certain period requires considering many factors, such as high power consumption, availability of resources, prices (of power, land, etc.), carbon tax, free cooling options, and availability of local renewable power generation. While a rich volume of recent research works focused on reducing the operational cost (OPEX) of the data centers, there exists no prior work, to the best of our knowledge, on investigating the trade-off between minimizing the OPEX of the data centers and maximizing their revenue from the services they offer while respecting the service level agreement (SLA) with their customers. In this study, we model this optimization problem using mixed integer-linear programming. Our proposed model is unique compared to the published works in many aspects such as its ability to handle realistic scenarios in which both data centers' resources (servers) and user generated traffic are heterogeneous. To evaluate the proposed model and the impact of different parameters on it's performance, several simulation experiments are conducted. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Ultrascale data centers Cloud computing Expansion modeling Traffic loads modeling Optimization problems mixed integer-linear programming

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Long-term planning of an integrated solid waste management system under uncertainty - I. Model development

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ENVIRONMENTAL ENGINEERING SCIENCE 2005年第6期22卷 823-834页

作者： Huang, GH Chi, GF Li, YP Univ Regina Fac Engn Environm Syst Engn Program Regina SK S4S 0A2 Canada N China Elect Power Univ Ctr Energy & Environm Syst Engn Beijing 102206 Peoples R China

In the planning of an integrated solid waste management (ISWM) system, not only complicated interactions among various system components but also uncertain properties of many parameters and their interrelationships need to be considered. In this study, an inexact mixed integer linear programming model for long-term planning of the ISWM system is developed. The model can effectively reflect the complexities and uncertainties of the waste management system, as well as policies of waste diversion to extend useful lives of existing landfills. Economically, the model considers costs related to waste collection, transfer, transportation, processing and disposal, capital investments for developing and expanding waste management facilities, and revenues from recycled materials, finished compost, and residual facility values. Its solutions provide bases for answering questions of siting, timing, and sizing for new and expanded waste management facilities in relation to a variety of waste-diversion targets. Another advantage of the proposed model is that variations of system performance and decision variables can be investigated by solving relatively simple submodels, which makes it applicable to large-scale problems. Decision alternatives can be generated by adjusting values of the variables within the resultant intervals according to projected applicable conditions. Provision of these alternatives will allow decision makers to conveniently review and compare a number of potential schemes and make appropriate adjustment (within the resultant intervals) when necessary. In a companion paper, application of the developed model to a real case study in the City of Regina, Canada, will be reported. Details concerning applicability of the developed model, interpretation of the modeling outputs, and postoptimality analysis for the study system will also be explicated.

关键词： decision making diversion inexact optimization mixed integer-linear programming planning solid waste uncertainty

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JOB-SHOP SCHEDULING BASED ON MODIFIED TANK-HOPFIELD linear-programming NETWORKS

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 1994年第3期7卷 321-327页

作者： FOO, SY TAKEFUJI, Y SZU, H FSU FAMUCOLL ENGNDEPT ELECT ENGNTALLAHASSEEFL 32316 CASE WESTERN RESERVE UNIV CLEVELANDOH 44106 USN CTR SURFACE WARFAREWASHINGTONDC 20350

The Tank-Hopfield linear programming network is modified to solve job-shop scheduling, a classical optimization problem. Using a linear energy function, the approach described in this paper avoids the traditional problems associated with most Hopfield networks using quadratic energy functions. Although this approach requires more hardware (in terms of processing elements and resistive interconnects) than a recent approach by Zhou et al. (IEEE Trans. Neural Networks 2, 175-179, 1991) the neurons in the modified Tank-Hopfield network do not perform extensive calculations, unlike those described by Zhou et al.

关键词： JOB-SHOP SCHEDULING HOPFIELD NEURAL NETWORKS mixed integer-linear programming

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On transforming hybrid nonlinear control problems with model uncertainty and input disturbance to mixed integer-linear programs

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2023年第18期360卷 14406-14415页

作者： Merrikh-Bayat, Farshad Mirhoseini, Parvin Univ Scranton Phys & Engn Dept Scranton PA 18510 USA Univ Zanjan Dept Elect & Comp Engn Zanjan Iran

Control of multi-input multi-output (MIMO) hybrid nonlinear dynamic systems which are affine in control inputs is studied in this paper. It is assumed that each control input of the system can be continuous or discrete with a bound constraint. It is also assumed that the controller is discrete-time and updates the control(s) with a constant frequency. Based on these assumptions, a theorem which represents the necessary and sufficient condition for uniform convergence of the sequence of samples of the state vector of system towards the desired point is presented and proved. As a result of this theorem, a (mixed integer-) linear programming for calculation of control(s) is proposed. Two well-known nonlinear hybrid control problems with multiple inputs are solved by using the proposed method and the results are presented.

关键词： Hybrid control Input-affine hybrid nonlinear system linear programming mixed integer-linear programming Multi-input multi-output

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Irregular packing: MILP model based on a polygonal enclosure

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ANNALS OF OPERATIONS RESEARCH 2015年第1期235卷 693-707页

作者： Santoro, Miguel Cezar Lemos, Felipe Kesrouani Univ Sao Paulo Escola Politecn Sao Paulo SP Brazil

This paper addresses the two-dimensional irregular packing problem, also known as the nesting problem. This is a subset of cutting and packing problems of renowned practical and theoretical relevance. A mixed integer-linear programming formulation is proposed to optimize the packing of particular polygonal shapes, convex forms with 3-8 sides, since their opposite sides are parallel. The model can be used to pack enclosures of general irregular shapes, generating upper bounds to the optimal solutions. The model was tested with 270 mass generated instances of small dimensions.

关键词： Strip packing Polygonal packing Two-dimensional packing mixed integer-linear programming Disjunctive formulation Nesting Cutting and packing

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