Mixed Integer Liner Programming Research Articles

Low-carbon transformation of ethylene production system through deployment of carbon capture, utilization, storage and renewable energy technologies

Ethylene industry contributes significantly to the world economy, but the conventional steam cracking based production process generates huge amount of CO2 emissions due to massive use of fossil fuels for power and heat supply. Deploying technologies of carbon capture, utilization and storage (CCUS) and renewable energy is urgently necessary to steer the emission-intensive ethylene industry towards carbon neutrality. To attain this goal, this paper proposes a low-carbon ethylene production system with the joint deployment of CCUS, wind turbine, solar heat collector, electric boiler and thermal energy storage technologies. Energy, economic and CO2 generation/reduction performance models of each equipment are developed first. Based on which, a mixed-integer liner programming framework is constructed to find the optimal capacity configuration and coordinated operation scheme of the multiple devices, laying a foundation for the reliable, economical and low-carbon operation of the integrated system. Case studies on a practical ethylene production system demonstrate that the proposed low-carbon retrofit leads to 57.50% CO2 reduction at the expense of 15.92% total annual cost increase. Discussions are then carried out to investigate the effects of different decarbonization routes, the impact of carbon emission trading price, the approach to promote the deployment of CO2 hydrogenation to methanol, and the advantage of taking carbon capture as flexible load, through which, broader insights are provided for the low-carbon transformation of the ethylene industry.

OPTIMIZATION OF ANNUAL OPERATION PLAN OF PV, BT AND FC COOPERATION SYSTEM FOR HOUSES

Photovoltaic power generation has attracted attention as a renewable energy technology for households as societies make efforts toward decarbonization. However, it is difficult for PV to supply stable electric power and energy storage since the power generation varies depending on the weather. Therefore, in this study, annual operation plan of a PV, BT and FC (PEFC) cooperation system is optimized by mixed integer liner programming. As a result, optimization of this cooperation system reduced primary energy consumption and operation costs, and increased electricity self-sufficiency.

Coordinated planning transmission tasks in heterogeneous space networks: A semi-distributed approach

This paper studies the coordinated planning of transmission tasks in the heterogeneous space networks to enable efficient sharing of ground stations cross satellite systems. Specifically, we first formulate the coordinated planning problem into a mixed integer liner programming (MILP) problem based on time expanded graph. Then, the problem is transferred and reformulated into a consensus optimization framework which can be solved by satellite systems parallelly. With alternating direction method of multipliers (ADMM), a semi-distributed coordinated transmission task planning algorithm is proposed, in which each satellite system plans its own tasks based on local information and limited communication with the coordination center. Simulation results demonstrate that compared with the centralized and fully-distributed methods, the proposed semi-distributed coordinated method can strike a better balance among task complete rate, complexity, and the amount of information required to be exchanged.

Minimal Controlled Islanding With Similarity-Based Coherency Identification Using Phasor Measurement Data

Controlled islanding is the last protective scheme to prevent passive islanding or total system blackout caused by cascading failures. In this article, a novel coherency detection approach is proposed, which is based on a density and distance clustering algorithm. The coherency detection approach is parameter free and requires only the frequency measurement data since the approach utilizes the cosine similarity combined by the Gaussian principle. Following the coherency detection, the proposed island reduction algorithm utilizes frequency deviation at center-of-inertia reference and locates the fault-affected generators plus the faulted area to construct the islanding scenarios. To find a practical splitting strategy with better network reconnection, an island reduction algorithm is presented to reduce the number of islands and isolate the faulted area from the rest of the system considering the coherency and system operational constraints. Additionally, an mixed integer liner programming (MILP) optimization model based on the linearized ac power flow is employed to find the optimal island boundaries and guarantee the efficient isolation of critical or most affected islands. The efficiency and performance of the proposed islanding approach are tested on the IEEE 118-bus test system.

Self-adaptive multi-objective evolutionary algorithm for flexible job shop scheduling with fuzzy processing time

With increasing environmental awareness and energy requirement, sustainable manufacturing has attracted growing attention. Meanwhile, there is a high level of uncertainty in practical processing procedure, particularly in flexible manufacturing systems. This study addresses the multi-objective flexible job shop scheduling problem with fuzzy processing time (MOFFJSP) to minimize the makespan and the total workload simultaneously. A mixed integer liner programming model is presented and a hybrid self-adaptive multi-objective evolutionary algorithm based on decomposition (HPEA) is proposed to handle this problem. HPEA has the following features: (i) two problem-specific initial rules considering triangular fuzzy number are presented for hybrid initialization to generate diverse solutions; (ii) five problem-specific local search methods are incorporated to enhance the exploitation; (iii) an effective solution selection method based on Tchebycheff decomposition strategy is utilized to balance the convergence and diversity; and (iv) a parameter selection strategy is proposed to improve the quality of non-dominated solutions. To verify the effectiveness of HPEA, it is compared against other well-known multi-objective optimization algorithms. The results demonstrate that HPEA outperforms these five state-of-the-art multi-objective optimization algorithms in solving MOFFJSP.

Demand Response for Industrial Micro-Grid Considering Photovoltaic Power Uncertainty and Battery Operational Cost

An intelligent demand response (DR) program is developed for multi-energy industrial micro-grid consisting of manufacturing facilities, photovoltaic (PV) panels, and battery energy storage system (BESS). The proposed DR program tackles the practical challenges of components in the micro-grid including industrial process represented by a discrete manufacturing production model, uncertainty of PV generation, and operational cost of the BESS. The proposed DR program optimizes day-ahead production scheduling for manufacturing facilities and operation regime for the BESS in response to time of use electricity price and probabilistic forecasting of PV power. To capture the uncertainty of PV power, a data-driven PV power probabilistic forecasting model is developed and a copula-based approach is deployed for the sampling of temporally correlated scenarios of PV power over the scheduling horizon from probabilistic forecasts. The multi-energy management optimization problem is formulated as a scenario-based stochastic nonconvex mixed integer nonlinear programming (MINLP). A hybrid optimization method integrating the evolutionary algorithm and the branch-and-bound algorithm for mixed integer liner programming is proposed to solve the nonconvex MINLP. Simulation studies illustrate that the proposed DR program efficiently reduces the operational cost for manufacturing production and releases the stress of the main grid by making full use of flexibility of all the components in the micro-grid.

A Mixed-Integer Linear Programming Model for the Optimal Operation and Design of Residential Neighbourhoods

Abstract This paper presents the results of a Mixed-Integer Liner Programming (MILP) model for the optimal design and operation of a distributed energy resource (DER) system in the UK residential sector. The model optimizes the design and utilization of a network with integrated heating/cooling pipelines and micro-grid connections between neighborhoods, in two case studies of five and ten houses, respectively. This work explores the use of feed-in tariffs, renewable heat incentives and the ability to buy and sell from and to the national grid. Depending on the scenario it is shown that DER networks mitigate around 30-40% of the CO2 emissions per household, compared with ‘traditional generation’, in which all the electricity comes from the national grid, the heating and domestic hot water are provided by gas boilers and air-conditioning units provide space cooling. Moreover, money from feed-in tariffs, renewable heat incentives and sales to the grid, as well as reduced grid purchases, make the DER networks more economical, and more profitable (approximately £100 per year per household), compared with the traditional energy system.

The Robotic Assembly Line Design (RALD) problem: Model and case studies with practical extensions

Abstract Spot welding assembly lines are widely present in the automotive manufacturing industry. The procedure of building the vehicle’s body employs several robots equipped with spot welding tools. These robots and tools are a quite costly initial investment, requiring an efficient and conscious line design that meets product demands and minimises implementation expense at the same time. In this paper, the Robotic Assembly Line Design (RALD) problem is proposed and studied based on practical characteristics from an automotive company located in Brazil. A Mixed-Integer Liner Programming (MILP) formulation is developed allowing: (i) station paralleling, (ii) equipment selection, and (iii) multiples robots per workstation. The mathematical model aims at minimising the total cost at the desired production rate, which involves robots, tools and facilities. The proposed model considers dead time during a cycle, space constraints, task assignment restrictions, and parallelism possibilities. Dead time is an unproductive and fixed work-piece handling time included in the capacitated transporter robots’ movement time. Computational experiments were performed in order to evidence the parameters’ influence over the optimal line design solution. In addition, practical case studies were conducted with parameters collected from a real-world robotic welding assembly line located on the outskirts of Curitiba-PR (Brazil), reaching optimality. Compared to the strictly serial lines, the model led to great advantages by allowing parallel stations in the production system, making it possible to evaluate an expected trade-off between the production rate and the total cost; reductions of several hundred thousand dollars on the production layout cost can be achieved by the company, as indicated by the studied cases.

Corrective receding horizon scheduling of flexible distributed multi-energy microgrids

The goal of the paper is to provide a comprehensive operational flexibility evaluation of different Multi-energy Microgrid (MEM) options. This is done by incorporating Mixed Integer Liner Programming (MILP) model for annual simulations and expanding it with Receding Horizon Model Predictive Control (RH-MPC) algorithm for short term daily operational analyses. The model optimizes flows of various energy vectors: heat, fossil fuels (natural gas), cooling and electricity, coordinating different microgrid elements with the goal of serving final consumer needs and actively participating in energy markets.The second novelty of the work is in the approach to multi-energy operational flexibility assessment, capturing different technologies, MEM configurations and different modelling concepts. When MEM is connected to the upstream power system its flexibility manifests as capability to alleviate variability and uncertainty in local production of RES and demand. On the other hand, when operating isolated from the rest of the system, the main flexibility indicator is minimum waste of energy while ensuring the satisfaction of all demand needs (electrical and heating/cooling). Following on this, multiple MEM configurations have been analyzed, showing different levels of available flexibility and capability to follow scheduled day-ahead exchange with the rest of the system, but also different amounts of wasted/curtailed energy in off-grid mode. Additionally, detailed analyses are performed concerning algorithm approximations which are often introduced in MEM modelling, such as efficiency of generation units. While these approximations have smaller impact on annual operational flexibility assessment (the difference is around 2–5% in terms of total cost), the result clearly show their significant impact on daily operational flexibility estimates.

An Extended Flexible Job Shop Scheduling Model for Flight Deck Scheduling with Priority, Parallel Operations, and Sequence Flexibility

Efficient scheduling for the supporting operations of aircrafts in flight deck is critical to the aircraft carrier, and even several seconds’ improvement may lead to totally converse outcome of a battle. In the paper, we ameliorate the supporting operations of carrier-based aircrafts and investigate three simultaneous operation relationships during the supporting process, including precedence constraints, parallel operations, and sequence flexibility. Furthermore, multifunctional aircrafts have to take off synergistically and participate in a combat cooperatively. However, their takeoff order must be restrictively prioritized during the scheduling period accorded by certain operational regulations. To efficiently prioritize the takeoff order while minimizing the total time budget on the whole takeoff duration, we propose a novel mixed integer liner programming formulation (MILP) for the flight deck scheduling problem. Motivated by the hardness of MILP, we design an improved differential evolution algorithm combined with typical local search strategies to improve computational efficiency. We numerically compare the performance of our algorithm with the classical genetic algorithm and normal differential evolution algorithm and the results show that our algorithm obtains better scheduling schemes that can meet both the operational relations and the takeoff priority requirements.

途中折り返し運転を考慮した混合整数計画法による運転整理アルゴリズム

When an accident or a natural disaster occurs in railways, it becomes impossible for trains to operate through the accidental area. Thus, in order to avoid further inconvenience of passengers, it is required to continue operating trains in other areas of the railway. This means we partially cancel trains and turn trains back to the neighboring stations in the accidental area. Although many papers about the rescheduling algorithm are published, none of them deal with partial cancellation and turning back of trains. In this paper, we propose a rescheduling algorithm that enables partial cancellation and turning back of trains. Our algorithm assumes a basic timetable while the duration of the accident is given, and it outputs a rescheduling plan in which the sum of the total delays and the penalties for cancellation is minimized based on mixed integer liner programming formulation.

Large-scale mine production scheduling optimisation with mill blending constraints at Newmont’s Twin Creeks Operation

This paper discusses the strategic mine planning process at Newmont’s Twin Creeks Operation. A multi-period Mixed Integer Liner Programming (MILP) model is utilised to optimise a large scale, complex operation with multiple open pits and underground mine sources under tight process blending constraints with the option to stockpile. In addition to life-of-mine production scheduling optimisation using MILP, the strategic planning process includes cost analysis, phase design validations and sensitivity to constraints such as mining rates, mill blending or throughput. Through this planning approach, Twin Creeks Operation is realising significant value from cost reductions and higher near term free cash flow resulting in increases to life-of-mine net present value.

The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion

The green vehicle routing and scheduling problem (GVRSP) aims to minimize green-house gas emissions in logistics systems through better planning of deliveries/pickups made by a fleet of vehicles. We define a new mixed integer liner programming (MIP) model which considers heterogeneous vehicles, time-varying traffic congestion, customer/vehicle time window constraints, the impact of vehicle loads on emissions, and vehicle capacity/range constraints in the GVRSP. The proposed model allows vehicles to stop on arcs, which is shown to reduce emissions up to additional 8% on simulated data. A hybrid algorithm of MIP and iterated neighborhood search is proposed to solve the problem.

Multiechelon supply chain planning with sequence‐dependent changeovers and price elasticity of demand under uncertainty

AbstractAn optimization framework is proposed for a multiechelon multiproduct process supply chain planning under demand uncertainty considering inventory deviation and price fluctuation. In this problem, the sequence‐dependent changeovers occur at the production plants, and price elasticity of demand is considered at the markets. Based on the classic formulation of travelling salesman problem (TSP), a mixed‐integer liner programming (MILP) is developed, whose objective function considers the profit, inventory deviations from the desired trajectories and price changes simultaneously. Model predictive control (MPC) approach is adopted to tackle the uncertain issues, as well as the inventory and price maintenance. The applicability of the proposed model and approach was illustrated by solving a supply chain example. Some issues, including length of the control horizon, price elasticity of demand, weights, inventory trajectories, and changeovers, are discussed based on the computational results. © 2012 American Institute of Chemical Engineers AIChE J, 2012

Analysis of the Economic Benefit of Used Batteries based on Reverse Logistics

Facing the increasing serious environmental problem, it is imminent to carry out the reverse logistics of used batteries. It is a big challenge for government to promote the enterprises to recovery used batteries effectively. Based on the processes of reverse logistics, the paper creates mixed integer liner programming (SMILP) and designs the best reverse logistics network in different recovery rate which can make the enterprises obtain the best economic benefit. Through analyzing the result, the paper provides an effective basis for government to make the recycling policy and punish policy.

Graph theory augmented math programming approach to identify minimal reaction sets in metabolic networks

Bioprocesses are of growing importance as an avenue to produce chemicals. Microorganisms containing only desired catalytic and replication capabilities in their metabolic pathways are expected to offer efficient processes for chemical production. Realizing such minimal cells is the holy grail of metabolic engineering. In this paper, we propose a new method that combines graph-theoretic approaches with mixed-integer liner programming (MILP) to design metabolic networks with minimal reactions. Existing MILP based computational approaches are computationally complex especially for large networks. The proposed graph-theoretic approach offers an efficient divide-and-conquer strategy using the MILP formulation on sub-networks rather than considering the whole network monolithically. In addition to the resulting improvement in computational complexity, the proposed method also aids in identifying the key reactions to be knocked-out in order to achieve the minimal cell. The efficacy of the proposed approach is demonstrated using three case studies from two organisms, Escherichia coli and Saccharomyces cerevisiae.

A capacity evaluation and trading model for semiconductor foundry manufacturing

This study addresses a capacity-trading problem for semiconductor foundry manufacturing. Because of the geographic proximity of fabs, a semiconductor foundry with insufficient capacity can purchase capacity from partner fabs as a short-term solution if a capacity-trading agreement has been established. Capacity-trading with partners could be considered as a source for enhancing competitiveness of factory clusters. This investigation presents several linear programming and mixed integer liner programming formulations for establishing the capacity-trading platform. This research was implemented via a computer program with real factory data using mathematical programming formulations capable of rapidly generating reliable solutions. The proposed trading model involves three modules. The first module, a factory scheduling module, allows planners to make capacity outsourcing decisions. Secondly, a tradable capacity economic analysis module identifies capacity requirements and evaluates tradable capacity. Thirdly, optimal resource allocation is obtained using a combinatorial auction module. This research makes two main contributions. A capacity-trading model is to integrate several mathematical programming modules in capacity auction. In addition, this model is presented for short-term flexibility of capacity expansion and combinatorial purchase strategies are designed to solve the large-scale problem.

424 家庭用ガスエンジンコージェネレーションシステムの電力融通運転による省エネルギー効果(エネルギー有効利用(2),環境保全型エネルギー技術)

The effect of power interchange operation of multiple household gas engine cogeneration units on the energy-saving is investigated using an optimization approach based on the mixed-integer liner programming. This paper numerically analyzes optimal operational strategies for 20 households and three types of household energy supply configurations: the power interchange operation of the gas engine cogeneration units, stand-alone operation of each gas engine cogeneration unit, and conventional energy supply system without the gas engine cogeneration unit. A numerical result clarifies the effectiveness of the power interchange operation from the energy-saving viewpoint and a dominant parameter for evaluating the energy-saving effect.