A mixed integer linear programming approach for municipal solid waste management

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Combining Geographic Information Systems for Transportation and Mixed Integer Linear Programming in Facility Location-Allocation Problems

In this study, we aimed to assess the solution quality for location-allocation problems from facilities generated by the software TransCAD®?, a Geographic Information System for Transportation (GIS-T). Such facilities were obtained after using two routines together: Facility Location and Transportation Problem, when compared with optimal solutions from exact mathematical models, based on Mixed Integer Linear Programming (MILP), developed externally for the GIS. The models were applied to three simulations: the first one proposes opening factories and customer allocation in the state of Sao Paulo, Brazil;the second involves a wholesaler and a study of location and allocation of distribution centres for retail customers;and the third one involves the location of day-care centers and allocation of demand (0 - 3 years old children). The results showed that when considering facility capacity, the MILP optimising model presents results up to 37% better than the GIS and proposes different locations to open new facilities.

Detection of gross errors using mixed integer optimization approach in process industry

A novel mixed integer linear programming (NMILP) model for detection of gross errors is presented in this paper. Yamamura et al.(1988) designed a model for detection of gross errors and data reconciliation based on Akaike information cri- terion (AIC). But much computational cost is needed due to its combinational nature. A mixed integer linear programming (MILP) approach was performed to reduce the computational cost and enhance the robustness. But it loses the super performance of maximum likelihood estimation. To reduce the computational cost and have the merit of maximum likelihood estimation, the simultaneous data reconciliation method in an MILP framework is decomposed and replaced by an NMILP subproblem and a quadratic programming (QP) or a least squares estimation (LSE) subproblem. Simulation result of an industrial case shows the high efficiency of the method.

A Green Mixed Integer Linear Programming Model for Optimization of Byproduct Gases in Iron and Steel Industry

Byproduct gas is an important secondary energy in iron and steel industry, and its optimization is vital to cost reduction. With the development of iron and steel industry to be more eco-friendly, it is necessary to construct an integrated optimized system, taking economics, energy consumption and environment into consideration. Therefore, the environmental cost caused by pollutants discharge should be factored in total cost when optimizing byproduct gas distribution. A green mixed integer linear programming （MILP） model for the optimization of byproduct gases was established to reduce total cost, including both operation cost and environmental cost. The operation cost included penalty for gas deviation, costs of fuel and water consumption, holder booster trip penalty, and so forth; while the environmental cost consisted of penalties for both direct and indirect pollutants discharge. Case study showed that the proposed model brought an optimum solution and 2.2% of the total cost could be reduced compared with previous one.

A mixed-integer linear programming approach for robust state estimation

In this paper,a mixed integer linear programming(MILP)formulation for robust state estimation(RSE)is proposed.By using the exactly linearized measurement equations instead of the original nonlinear ones,the existingmixed integer nonlinear programming formulation for RSE is converted to a MILP problem.The proposed approach not only guarantees to find the global optimum,but also does not have convergence problems.Simulation results on a rudimentary 3-bus system and several IEEE standard test systems fully illustrate that the proposed methodology is effective with high efficiency.

Reserve Constrained Dynamic Economic Dispatch with Valve-point Effect:A Two-stage Mixed Integer Linear Programming Approach

This paper proposes a deterministic two-stage mixed integer linear programming(TSMILP)approach to solve the reserve constrained dynamic economic dispatch(DED)problem considering valve-point effect(VPE).In stage one,the nonsmooth cost function and the transmission loss are piecewise linearized and consequently the DED problem is formulated as a mixed integer linear programming(MILP)problem,which can be solved by commercial solvers.In stage two,based on the solution obtained in stage one,a range compression technique is proposed to make a further exploitation in the subspace of the whole solution domain.Due to the linear approximation of the transmission loss,the solution obtained in stage two dose not strictly satisfies the power balance constraint.Hence,a forward procedure is employed to eliminate the error.The simulation results on four test systems show that TSMILP makes satisfactory performances,in comparison with the existing methods.

Randomized MILP framework for Securing Virtual Machines from Malware Attacks

Cloud computing involves remote server deployments with public net-work infrastructures that allow clients to access computational resources.Virtual Machines(VMs)are supplied on requests and launched without interactions from service providers.Intruders can target these servers and establish malicious con-nections on VMs for carrying out attacks on other clustered VMs.The existing system has issues with execution time and false-positive rates.Hence,the overall system performance is degraded considerably.The proposed approach is designed to eliminate Cross-VM side attacks and VM escape and hide the server’s position so that the opponent cannot track the target server beyond a certain point.Every request is passed from source to destination via one broadcast domain to confuse the opponent and avoid them from tracking the server’s position.Allocation of SECURITY Resources accepts a safety game in a simple format as input andfinds the best coverage vector for the opponent using a Stackelberg Equilibrium(SSE)technique.A Mixed Integer Linear Programming(MILP)framework is used in the algorithm.The VM challenge is reduced by afirewall-based controlling mechanism combining behavior-based detection and signature-based virus detection.The pro-posed method is focused on detecting malware attacks effectively and providing better security for the VMs.Finally,the experimental results indicate that the pro-posed security method is efficient.It consumes minimum execution time,better false positive rate,accuracy,and memory usage than the conventional approach.

A Linear Mixed Integer Programming Model for N-Vehicle Exploration Problem

Finding the accurate solution for N-vehicle exploration problem is NP-hard in strong sense.In this paper,authors build a linear mixed integer programming model for N-vehicle exploration problem based on its properties.The model is then proved equivalent to the original problem.Given the model,one can apply the already existed methods and algorithms for mixed integer linear programming on N-vehicle exploration problem,which helps to enrich methods for solving N-vehicle exploration problem.

Strategic mining options optimization:Open pit mining, underground mining or both

Near-surface deposits that extend to considerable depths are often amenable to both open pit mining and/or underground mining. This paper investigates the strategy of mining options for an orebody using a Mixed Integer Linear Programming(MILP) optimization framework. The MILP formulation maximizes the Net Present Value(NPV) of the reserve when extracted with(i) open pit mining,(ii) underground mining, and(iii) concurrent open pit and underground mining. Comparatively, implementing open pit mining generates a higher NPV than underground mining. However considering the investment required for these mining options, underground mining generates a better return on investment than open pit mining. Also, in the concurrent open pit and underground mining scenario, the optimizer prefers extracting blocks using open pit mining. Although the underground mine could access ore sooner, the mining cost differential for open pit mining is more than compensated for by the discounting benefits associated with earlier underground mining.

Hybrid Continuous-Time MILP Model for Crude Oil Scheduling under the Influence of High Tides

Vessels,especially very large or ultra large crude carriers(VLCCs or ULCCs),often can only dock and leave the berth during high tide periods to prevent being stranded.Unfortunately,the current crude scheduling models do not take into account tidal conditions,which will seriously affect the feasibility of crude schedule.So we first focus on the docking and leaving operations under the tidal actions,and establish a new hybrid continuous-time mixed integer linear programming(MILP)model which incorporates global event based formulation and unit-specific event based formulation.Upon considering that the multiple blending of crude oil can easily cause the production fluctuating,there are some reasonable assumptions that storage tanks can only store pure crude,and charging tanks just can be refilled after being emptied,which helps us obtain a simple MILP model without composition discrepancy caused by crude blending.Two cases are used to demonstrate the efficacy of proposed scheduling model.The results show that the optimization schedule can minimize the demurrage of the vessels and the number of feeding changeovers of crude oil distillation units(CDUs).

Heuristic Algorithm for Minimizing the Electricity Cost of Smart House

This framework proposes a heuristic algorithm based on LP （linear programming） for optimizing the electricity cost in large residential buildings, in a smart grid environment. Our heuristic tackles large multi-objective energy allocation problem （large number of appliances and high time resolution）. The primary goal is to reduce the electricity bills, and discomfort factor. Also, increase the utilization of domestic renewable energy, and reduce the running time of the optimization algorithm. Our heuristic algorithm uses linear programming relaxation, and two rounding strategies. The first technique, called CR （cumulative rounding）, is designed for thermostatic appliances such as air conditioners and electric heaters, and the second approach, called MCR （minimum cost rounding）, is designed for other interruptible appliances. The results show that the proposed heuristic algorithm can be used to solve large MILP （mixed integer linear programming） problems and gives a decent suboptimal solution in polynomial time.

Optimal dispatch of zero-carbon-emission micro Energy Internet integrated with non-supplementary fired compressed air energy storage system

To utilize heat and electricity in a clean and integrated manner,a zero-carbon-emission micro Energy Internet(ZCE-MEI) architecture is proposed by incorporating non-supplementary fired compressed air energy storage(NSF-CAES) hub.A typical ZCE-MEI combining power distribution network(PDN) and district heating network(DHN) with NSF-CAES is considered in this paper.NSF-CAES hub is formulated to take the thermal dynamic and pressure behavior into account to enhance dispatch flexibility.A modified Dist Flow model is utilized to allow several discrete and continuous reactive power compensators to maintain voltage quality of PDN.Optimal operation of the ZCE-MEI is firstly modeled as a mixed integer nonlinear programming(MINLP).Several transformations and simplifications are taken to convert the problem as a mixed integer linear programming(MILP)which can be effectively solved by CPLEX.A typical test system composed of a NSF-CAES hub,a 33-bus PDN,and an 8-node DHN is adopted to verify the effectiveness of the proposed ZCE-MEI in terms of reducing operation cost and wind curtailment.

Coordinated Planning of Large-Scale Wind Farm Integration System and Transmission Network

Large-scale centralized exploitation of intermittent wind energy resources has become popular in many countries.However,as a result of the frequent occurrence of largescale wind curtailment,expansion of corresponding transmission projects has fallen behind the speed at which installed wind capacity can be developed.In this paper,a coordinated planning approach for a large-scale wind farm integration system and its related regional transmission network is proposed.A bilevel programming model is formulated with the objective of minimizing cost.To reach the global optimum of the bi-level model,this work proposes that the upper-level wind farm integration system planning problem needs to be solved jointly with the lower-level regional transmission planning problem.The bi-level model is expressed in terms of a linearized mathematical problem with equilibrium constraints(MPEC)by Karush-KuhnTucker conditions.It is then solved using mixed integer linear programming solvers.Numerical simulations are conducted to show the validity of the proposed coordinated planning method.

Optimal Scheduling of Distribution Network Incorporating Topology Reconfiguration,Battery Energy System and Load Response

Distributed generation(DG)is becoming increasingly important due to the serious environmental pollution caused by conventional fossil-energy-based generation and the depletion of non-renewable energy.As the flexible resources in the active distribution network(ADN),battery energy system(BES)and responsive load(RL)are all able to assist renewable DG integration in day-ahead dispatch.In addition,the security and economic level can be significantly improved by adjusting network topology.Therefore,in this paper,a coordinated day-ahead scheduling method incorporating topology reconfiguration,BES optimization and load response is presented to minimize the total day-ahead operational costs in the ADN.Linearized current injection models are presented for renewable DG,RL and BES based on the linear power flow model,and an extensible linear switching operations calculation(ELSOC)method is proposed to address the network reconfiguration.Thus,a mixed integer linear programming(MILP)model is proposed for optimal coordinated operation of an ADN.The correctness and effectiveness of the proposed method are demonstrated by simulations on a modified test system.In addition,the combined scenario and Monte-Carlo method is used to handle the uncertainties of loads and DGs,and the results of different uncertainties can further verify the feasibility of the proposed model.

ORDERED WEIGHTED AVERAGING AGGREGATION METHOD FOR PORTFOLIO SELECTION

Portfolio management is a typical decision making problem under incomplete,sometimes unknown, information. This paper considers the portfolio selection problemsunder a general setting of uncertain states without probability. The investor's preferenceis based on his optimum degree about the nature, and his attitude can be described by anOrdered Weighted Averaging Aggregation function. We construct the OWA portfolio selection model, which is a nonlinear programming problem. The problem can be equivalentlytransformed into a mixed integer linear programming. A numerical example is given andthe solutions imply that the investor's strategies depend not only on his optimum degreebut also on his preference weight vector. The general game-theoretical portfolio selectionmethod, max-min method and competitive ratio method are all the special settings of thismodel.

Enhanced Flexible Ramping Product Formulation for Alleviating Capacity Shortage in Look-ahead Commitment

The roll-out of a flexible ramping product provides independent system operators(ISOs)with the ability to address the issues of ramping capacity shortage.ISOs procure flexible ramping capability by committing more generating units or reserving a certain amount of headrooms of committed units.In this paper,we raise the concern of the possibility that the procured flexible ramping capability cannot be deployed in realtime operations due to the unit shut-down in a look-ahead commitment(LAC)procedure.As a solution to the issues of ramping capacity shortage,we provide a modified ramping product formulation designed to improve the reliability and reduce the expected operating cost.The trajectories of start-up and shutdown processes are also considered in determining the ramping capability.A new optimization problem is formulated using mixed integer linear programming(MILP)to be readily applied to the practical power system operation.The performance of this proposed method is verified through simulations using a small-scale system and IEEE 118-bus system.The simulation results demonstrate that the proposed method can improve the generation scheduling by alleviating the ramping capacity shortages.

Vehicle flow formulation for two-echelon time-constrained vehicle routing problem

Two-echelon routing problems,including variants such as the two-echelon vehicle routing problem(2E-VRP)and the two-echelon location routing problem(2E-LRP),involve assignment and location decisions.However,the two-echelon time-constrained vehicle routing problem(2E-TVRP)that caters to from-linehaul-to-delivery practices does not involve assignment decisions.This routing problem variant for networks with two eche-lons has not yet attracted enough research interest.Localized or long-distance services suffer from the lack of the assignment decisions between satellites and customers.Therefore,the 2E-TVRP,rather than using assignment decisions,adopts time constraints to decide the routes on each of the two interacting echelons:large-capacity vehicles trans-port cargoes among satellites on the first echelon,and small-capacity vehicles deliver cargoes from satellites to customers on the second echelon.This study introduces a mixed integer linear programming model for the 2E-TVRP and proposes a heuristic algorithm that incorporates the savings algorithm followed by a variable neighborhood search phase.Illustrative examples are used to test the mathematical formulation and the heuristic and a case study is used to demonstrate that the heuristic can effectively solve realistic-size instances of the 2E-TVRP.

MODELING AND OPTIMIZATION OF CYCLIC HOIST SCHEDULES IN AN ELECTROPLATING LINE

This paper deals with the modeling, analysis and optimization of a specific kind of real industrial problems. This class of problems is known in the literature as Cyclic Hoist Scheduling Problem （CHSP）. In such class of problems, several jobs have to flow through a production line according to an ordered bath sequence. The CHSPs appear in the manufacturing facilities to achieve a mass production and to search a repetitive sequence of moves for the hoist. In this paper, we develop P-Temporal Petri Net models to represent the behavior and validate certain qualitative properties of the basic production line. Afterward, complex configurations of the production line are modeled and their properties such as reachability of desired functioning （cyclic operation）, deadlock-free, resource sharing and management are checked and validated. A mathematical analysis and a simulation study of all proposed Petri net models are carried out using mathematical fundaments of Petri nets and a Visual Object Net ＋＋ tool. The second part of the paper deals with the development of a mixed integer linear programming models to optimize processing of each line configuration. Optimal manufacturing plans of the studied system with cyclic processing sequences are defined and the feasibility of optimal cyclic scheduling of each configuration is proved.

Thermal-Aware Post Layout Voltage-Island Generation for 3D ICs

To reduce the interconnect delay and improve the chip performance, three-dimensional （3D） chip emerged with the rapid increasing of chip integration and chip power density. Therefore, thermal issue is one of the critical challenges in 3D IC design due to the high power density. Multiple Supply Voltages （MSV） technique provides an efficient way to optimize power consumption which in turn may alleviate the hotspots. But the voltage assignment is limited not only by the performance constraints of the design, but also by the physical layout of circuit modules since the modules with the same voltage should be gathered to reduce the power-network routing resource. Especially in 3D designs, the optimization using MSV technique becomes even more complicated since the high temperature also influences the power consumption and delay on paths. In this paper, we address the voltage-island generation problem for MSV designs in 3D ICs based on a mixed integer linear programming （MILP） model. First, we propose a general MILP formulation for voltage-island generation to optimize thermal distribution as well as power-network routing resources while maintaining the whole chip performance. With the thermal^power interdependency, an iterative optimization approach is proposed to obtain the convergence. Experimental results show that our thermal-aware voltage-island generation approach can reduce the maximal on-chip temperature by 23.64% with a reasonable runtime and save the power-network routing resources by 16.71%.

Bus evacuation during no-notice disasters in downtown areas: A case study of the Zhongguancun area, Beijing

This paper focuses on the bus evacuation problem with pedestrians’short-distance walking ability between bus stations during no-notice disasters in downtown areas.A mixed-integer linear programming model is proposed to solve this problem.The objective function is to minimize the evacuation time and number of casualties.The model obtains the flow of evacuees and buses on each arc to the route of buses in the process of evacuation.Furthermore,a real-time bus evacuation demand estimation method is proposed based on smart card data.Finally,the example of Zhongguancun area in Beijing is used to verify the practicality and validity of the model.The results show that pedestrian short-distance walking can effectively reduce casualties and improve the utilization rate of buses.