Integer Linear Programming Approach Research Articles

An Integrated Stochastic Optimization and Simulation Approach to SERU vs. Assembly Line Manufacturing Systems

This research compares SERU manufacturing systems to traditional assembly lines, focusing on the impact of uncertainty in task processing time on production output. The study considers worker skill levels and team identity, using a stochastic mixed integer linear programming approach to model uncertainty and optimize workforce allocation. Discrete event simulation is then integrated to evaluate performance using five key performance indicators (KPIs). Results show that SERU systems outperform traditional lines in terms of throughput when uncertainty is considered. The integrated approach provides more reliable performance data than deterministic optimization alone. The study also highlights the advantages of SERU systems when worker skill levels and team identity are factored in. This research fills a gap in the literature by proposing a stochastic optimization approach that considers uncertainty and worker skill levels, and by integrating stochastic optimization with simulation for comprehensive analysis. This approach provides valuable guidance for production managers in optimizing production systems.

PangeBlocks: customized construction of pangenome graphs via maximal blocks

BackgroundThe construction of a pangenome graph is a fundamental task in pangenomics. A natural theoretical question is how to formalize the computational problem of building an optimal pangenome graph, making explicit the underlying optimization criterion and the set of feasible solutions. Current approaches build a pangenome graph with some heuristics, without assuming some explicit optimization criteria. Thus it is unclear how a specific optimization criterion affects the graph topology and downstream analysis, like read mapping and variant calling.ResultsIn this paper, by leveraging the notion of maximal block in a Multiple Sequence Alignment (MSA), we reframe the pangenome graph construction problem as an exact cover problem on blocks called Minimum Weighted Block Cover (MWBC). Then we propose an Integer Linear Programming (ILP) formulation for the MWBC problem that allows us to study the most natural objective functions for building a graph. We provide an implementation of the ILP approach for solving the MWBC and we evaluate it on SARS-CoV-2 complete genomes, showing how different objective functions lead to pangenome graphs that have different properties, hinting that the specific downstream task can drive the graph construction phase.ConclusionWe show that a customized construction of a pangenome graph based on selecting objective functions has a direct impact on the resulting graphs. In particular, our formalization of the MWBC problem, based on finding an optimal subset of blocks covering an MSA, paves the way to novel practical approaches to graph representations of an MSA where the user can guide the construction.

Reallocation of chemotherapy appointments in a large health system using a mixed integer linear programming approach

ABSTRACT Outpatient chemotherapy scheduling has significant implications for both patients and health systems. Consideration of treatment location preference is important for patient satisfaction and outcomes, and it is a complex decision impacted by travel distance. In health systems with one treatment site that stands out from the rest as a destination medical center (the primary site), there are financial and resource utilization incentives to free up as much space as possible for appointments at that site. In this study, we demonstrate that leveraging the underutilized health system sites allows decompression of appointment volume at the primary site, and it takes full advantage of valuable resources such as oncology nurses and chair availability. A Mixed Integer Linear Programming approach was used to develop a model under four scenarios which reallocates appointments from the primary site to other health system sites based on patient travel distance to the sites. This approach was applied to data from the Mayo Clinic Health System Minnesota region, which demonstrated that the health system has the potential to move approximately 50% of eligible appointments out of the primary site, resulting in an overall volume change of approximately 30%. Implications for scheduling policies and infrastructure are discussed.

Optimization of the electric vehicle charging strategy problem for sustainable intercity travels with multiple refueling stops

Electric vehicle (EV) drivers considering long-distance trips still face range anxiety due to the limited range of EVs and the scarcity of charging stations. Thus, it becomes important to ensure the feasibility of the selected route and determine an optimal charging strategy. As a crucial aspect of decision support for EV drivers, this study proposes a mixed integer linear programming (MILP) approach for the EV charging strategy problem (EVCSP), incorporating a piecewise linear approximation technique to address the challenges posed by nonlinear charging times. The proposed optimization model, namely CSPM determines where, when, and how much to charge an EV for a specified route to minimize travel time and cost. The solution time of large-scale test problems and a case study on Türkiye reveal the robustness and reliability of the CSPM. Furthermore, two multi-objective optimization methods (the weighted sum and the lexicographic method) are applied to the case study, and the results are analyzed. The results indicate that the travel cost is more sensitive to the selected charging strategy, with a range of 46.09 % across the applied charging strategies, whereas travel time remains more resilient, with a maximum fluctuation of 19.77 %. A comparative analysis with a full charging strategy reveals that the CSPM reduces the travel time by 60.1 % and improves the cost efficiency by 105.72 %.

Optimal design of concentrated solar power-based hydrogen refueling station: Mixed integer linear programming approach

This paper developed a mixed integer linear programming model to optimally design hydrogen refueling station coupled with an on-grid concentrated solar power. The model aims to minimize the total life cycle cost of the integrated system addressing various constraints. The utility of the proposed model is validated using hydrogen refueling station under the weather conditions of Dhahran city, Saudi Arabia. The station fulfills the daily hydrogen requirements of taxi-fleet in Dhahran city, estimating a daily demand of 4202 kg of hydrogen. The proposed hydrogen refueling station requires a solar field of 71,721 square meters to generate 50,233 MWh of energy annually. The on-grid concentrated solar power system is determined to produce an excess energy of 10,515 MWh which is exported to the grid, highlighting positive economic impact. The levelized cost of hydrogen of the proposed system is 7.17 $/kg. The achieved results validated the feasibility and efficiency of the proposed system, positioning it as a sustainable solution for meeting the rising demand for clean and green transportation in the region.

Integral techno-economic design & operational optimization for district heating networks with a Mixed Integer Linear Programming strategy

The Netherlands, with over 90% of homes heated by natural gas is currently in the early phases of the heat transition to find alternative solutions towards 2050. According to ambitions of the Dutch government up to 50% of future implemented heating systems in the built environment will use District Heat Networks. The investment-heavy nature for District Heating System (DHS) makes it challenging to establish viable business-cases as supply-side parties require security of demand. However, resident participation is lacking as there is no integral estimate for the cost of heat over the lifetime in the early planning phase. This paper proposes a network integral techno-economic optimization with minimal a-priori assumptions. An integral network cost optimization enables to achieve a considerably more reliable cost of heat in the early planning phase. Both the investments and operational strategy are optimized with a Mixed Integer Linear Programming approach that captures the physics as well as the financial choices. Linearization of the physics are chosen to have a conservative impact on the costs estimates. The end scenario network is designed where the placement and size of sources, storage components and pipes are optimized together with the operational strategy, e.g. thermal allocation time-series, for all assets. The workflow was applied to a greenfield network for the Dutch municipality of Rijswijk. It was shown that a 18% reduction in Total Cost of Ownership in the primary grid could be achieved by introducing decentralized sources, decentralized storages and seasonal storage.

RIBAP: a comprehensive bacterial core genome annotation pipeline for pangenome calculation beyond the species level

Microbial pangenome analysis identifies present or absent genes in prokaryotic genomes. However, current tools are limited when analyzing species with higher sequence diversity or higher taxonomic orders such as genera or families. The Roary ILP Bacterial core Annotation Pipeline (RIBAP) uses an integer linear programming approach to refine gene clusters predicted by Roary for identifying core genes. RIBAP successfully handles the complexity and diversity of Chlamydia, Klebsiella, Brucella, and Enterococcus genomes, outperforming other established and recent pangenome tools for identifying all-encompassing core genes at the genus level. RIBAP is a freely available Nextflow pipeline at github.com/hoelzer-lab/ribap and zenodo.org/doi/10.5281/zenodo.10890871.

A multi-objective mixed integer linear programming approach for simultaneous optimization of cost and resilience of power distribution networks

In recent years, customers have experienced a significant increase in weather-related power outages. The power distribution network (PDN), a subset of the power system, in particular is more susceptible to extreme events. Therefore, ensuring the resilient and cost-effective operation of PDNs following extreme weather conditions poses a significant challenge for distribution network operators. This paper presents an approach for simultaneously optimizing the cost and load restoration for resilience enhancement of power distribution networks while determining the optimal positioning and generation levels of mobile emergency generators. The proposed method, in addition, employs a distribution network reconfiguration to improve the load restoration process, by optimally determining the status of switches. The multi-objective formulation involves the minimization of load shedding to increase the resilience of the system, while the other objective is formulated to minimize the cost of load restoration. A weighted sum method is employed to address the multi-objective mixed-integer linear programming (MILP) model. A set of non-dominated solutions determined using the proposed formulation provides opportunities to the distribution system operator in choosing a resilience improvement strategy according to the availability of the operational budget. The proposed model is implemented on 33-bus distribution system to validate the efficacy of the proposed model.

Optimization of containerized application deployment in virtualized environments: a novel mathematical framework for resource-efficient and energy-aware server infrastructure

This study addresses the critical need for effective resource management through software container migration in cloud data centers. It emphasizes its role in avoiding resource shortages and reducing energy consumption in cloud environments. This study introduces a novel multi-objective integer linear programming (ILP) approach for software container replacements, complemented by a specialized algorithm designed to migrate software containers between over- and underutilized hosts to enhance efficiency compared to traditional optimization methods. The simulation results demonstrate the algorithm's effectiveness, validating its potential for optimizing energy usage and resource allocation in cloud environments. Statistical analyses confirm the proposed model's and algorithm's superiority over benchmark approaches, highlighting their potential for enhancing resource management in cloud computing systems.

Inter-array cabling optimization in offshore wind power plants, a new reliability indicator for radial grids

The inter-array grid relates to a significant share of the investments into an offshore wind power plant (OWPP). Optimizing the cable connections regarding costs and reliability is a mathematically complex task due to the high variety of possible wind and component (wind turbine or cable) failure scenarios. This paper presents a novel mixed integer linear programming approach to support investment decisions into OWPPs by trading off cabling purchase and installation costs with power capacity risk (PCR), which is defined as a length-weighed cumulative power flow summation that reflects the consequences of cable failures. Then, quasi-random Monte Carlo simulations assess the optimized collection grids (CGs) to quantify their levelized cost of energy (LCOE). To construct relevant case studies, this work investigates the real OWPPs Ormonde, Horns Rev 1, Thanet, and London Array, which contain 30, 80, 100, and 175 wind turbines. The results reveal Pearson correlation coefficients around 0.99 between the proposed PCR and the expected energy not supplied. Furthermore, this paper’s findings indicate that minimum-cost CGs do not necessarily present the lowest LCOE.

Polynomial-time server allocation algorithm in delay-sensitive internet-of-things monitoring services

This paper proposes a polynomial-time algorithm for a server allocation problem in delay-sensitive Internet-of-Things (IoT) monitoring services. The server allocation problem determines the appropriate servers to which the database and application are allocated to minimize the maximum delay between the latest update of reference data and the start of application processing for monitoring data. The server allocation problem was previously handled by expressing it as an integer linear programming (ILP) problem. Nevertheless, it fails to meet the computational time complexity needed to solve the problem, and it does not offer a more efficient technique than the ILP approach. The proposed algorithm consists of two components. The first step entails choosing utilization servers for both the database and the application. Next, the second phase entails matching each usage server and its corresponding IoT device. We prove that the proposed algorithm obtains an optimal solution in polynomial time. We compare computation times between the ILP approach and the proposed algorithm. Numerical results show that the proposed algorithm obtains the optimal solution faster than the ILP approach.

Optimally integrated waste heat recovery through combined emerging thermal technologies: Modelling, optimization and assessment for onboard multi-energy systems

This paper investigates waste heat (WH) valorisation on maritime vessels from a systemic perspective. It aims to demonstrate how a set of innovative waste heat recovery (WHR) technologies can synergistically work together to valorise waste heat in multiple ways. It proposes and develops a dedicated techno-economic analysis framework and model, surpassing previous literature that focused on individual technologies or specific combinations. Employing a Mixed Integer Linear Programming (MILP) approach, this study evaluates the dynamic and flexible operation of the WHR system throughout the round-trip journey of a vessel. Identifying the most profitable WHR system layout, determining the capacity of technologies, optimising the interconnections between technologies, and establishing strategic WH dispatching are all among the key objectives of the study.An average-scale vessel with a 36 MW diesel engine, is selected for this study which involves a 17-day journey with multiple stops in Northern Europe. The vessel adequately represents the complexity of onboard energy systems in terms of types and variability of demands, as well as WH availability. The proposed WHR system, which is tailored for the selected vessel, integrates three active technologies: an isobaric expansion engine (IEE) to contribute to mechanical power demand, a sorption system for providing cooling, and an advanced Organic Rankine Cycle (ORC) for trigeneration of power, heating, and cooling. All technologies are supported by the passive WHR technology of Thermal Energy Storage (TES). The results show that deployment of the optimised WHR system onboard the selected vessel enhances energy efficiency by 5 to 7.5 percentage points and reduces fuel consumption by 13%. The study also explores economic key performance indicators (KPIs), such as the Internal Rate of Return (IRR) which is found at about 15%, clearly evidencing a compelling solution to ship owners. Additionally, the discussion includes a detailed analysis of the contribution of individual technologies in covering onboard demands, as well as their synergistic interactions.This work clarifies the role, value, and benefit of WHR technologies onboard, advancing the understanding from individual WH recovery interventions to a system-level approach. This is especially valuable in practice, considering its adaptability across various vessel types within the global fleet.

English

English

Exploiting symmetry for the job sequencing and tool switching problem

The Job Sequencing and Tool Switching Problem (SSP), a well-known combinatorial optimization problem in the domain of Flexible Manufacturing Systems (FMS), is studied in this article. The aim of this research is to improve the currently known exact solution methodology for this NP-hard problem. We propose a new integer linear programming approach with symmetry-breaking and tightening cuts that provably outperformed the existing methodology described in the literature. The computational study conducted on a data set with 1,050 instances showed that the developed method can solve hard instances that were not solvable with the state-of-the-art methods presented in the literature.

A BI-OBJECTIVE COST MINIMIZATION MODEL FOR THE INSULAR TOUR ROUTE PLANNING PROBLEM

This article presents a study on the development of a bi-objective cost problem optimization model in planning tourist routes in the island zone. This problem is a new variant of the tour route plan problem. Bi-objective view of two cost components, namely maritime transportation costs and ground transportation costs. Two models were formulated using a mixed integer linear programming approach. The first model was designed to minimize one of the two cost components separately. The second model was bi-objective cost minimization based on the priority weights of the two costs. It was designed to determine minimum transportation costs based on priority weights. Model testing was carried out through numerical experiments on several cases that often occur in industries in Maluku, Indonesia, especially tourism and goods shipping. Each case has variations in the number of islands and nodes. As a result, the model can demonstrate its adaptability to changes in objectives and parameters. For cases that do not have a single solution, an increase in the network structure on the number of islands and nodes will increase the variety of efficient alternative solutions. The set of efficient solutions also shows an inverse relationship between MTC and GTC. The results also show that MTC minimization cannot be used as a reference for TC minimization in cases with many nodes and islands. Efforts to minimize MTC in the island zone impact reducing total costs but do not mean minimizing total costs. In addition, based on the exponential trend line of computing time, the number of nodes has a more significant influence on computing time compared to the number of islands.

Research on capacity optimization of new energy hybrid energy storage system of high-speed railway

With the fast expansion of electrified railways, the need for energy is growing. Thus, improving railway coupling and interconnection, new energy, and energy storage is critical to support low-carbon and green railway development. Therefore, this paper proposes an optimal configuration method for the access capacity of wind power generation system (WPGS), photovoltaic power system (PVPS), and hybrid energy storage system (HESS) in the traction power supply system (TPSS) of high-speed railways. Firstly, the HESS’s mathematical model is developed, then the high-speed railway TPSS’s economic optimization operating model is built, and a HESS energy management approach is presented, taking the energy management strategy parameters and model capacity configuration parameters as the optimization variables. The mixed integer linear programming approach is used to resolve the actual example with the least daily operational cost of the high-speed rail system as the optimization goal, and the optimal capacity of WPGS, PVPS, and HESS is obtained. Finally, through comparison and analysis, it is verified that the economic benefit is the highest when the distributed complementary energy system covering WPGS, PVPS, and HESS is connected to the traction substation, and 10.47% of the daily expenditure is saved.

Modeling and analysis of crosstalk-avoided and crosstalk-aware approaches in spectrally-spatially elastic optical networks

Inter-core crosstalk (Ic-Xt) and inter-mode crosstalk (Im-Xt) occur during the transmission of optical signals through multi-core multi-mode fibers in spectrally-spatially elastic optical networks (SS-EONs), which degrade the signal quality. Xt-Avoided (Xt-Av) and Xt-Aware (Xt-Aw) approaches, respectively, are used in SS-EONs for dealing with Ic-Xt and Im-Xt. From the network deployment perspective, network operators need to understand which approach, Xt-Av or Xt-Aw, is better in order to deal with Ic-Xt and Im-Xt in SS-EONs. To address the above issue, this paper models and analyzes Xt-Av and Xt-Aw approaches by considering Ic-Xt and Im-Xt simultaneously in SS-EONs. We formulate the optimization problems as the integer linear programming (ILP) problem for both Xt-Av and Xt-Aw approaches. When the ILP problems are intractable, we introduce heuristics for Xt-Av and Xt-Aw approaches. We compare ILP and heuristic approaches using Xt-Av and Xt-Aw in terms of the maximum allocated spectrum slot index, blocking ratio, and computation time. Numerical results indicate that the Xt-Aw approach for all Xt threshold values performs better than the Xt-Av approach in terms of resource utilization. At the same time, the Xt-Av approach has the advantage of using less computation time than the Xt-Aw approach. The analysis reveals that, in the Xt-Aw approach, the behavior of the maximum allocated spectrum slot index and blocking ratio on Xt threshold is reciprocal, and hence a suitable Xt threshold that enhances the network performances is required for network management.

A mixed integer linear programming approach for the design of chemical process families

The need for rapid and widespread deployment of new technologies to address climate change goals (e.g., deep, economy-wide decarbonization) presents new opportunities for advancing modular design strategies. Conventional engineering approaches focus on unique designs for each installation, while missing opportunities for manufacturing standardization. Extending insights from the automotive industry, we optimize a platform of common unit module designs while simultaneously designing an entire family of process variants that make use of that platform. This reduces engineering effort, deployment timelines, and manufacturing costs. We propose a nonlinear generalized disjunctive programming formulation and convert this to an efficient mixed-integer linear programming (MILP) formulation through discretization of the design space. We formulate our optimization in Pyomo with costing from IDAES, and we demonstrate the computational performance and solution quality on a water treatment desalination system from the PARETO framework and a carbon capture system built in Aspen Plus® as part of CCSI2.

A Mixed‐Integer Linear Programming Framework for Optimization of Water Network Operations Problems

AbstractWater distribution systems (WDSs) are critical infrastructure used to convey water from sources to consumers. The mathematical framework governing the distribution of flows and heads in extended period simulations of WDSs lends itself to application in a wide range of optimization problems. Applying the classical mixed integer linear programming (MILP) approach to model WDSs hydraulics within an optimization framework can contribute to higher solution accuracy with lower computational effort. However, adapting WDSs models to conform to a MILP formulation has proven challenging because of the intrinsic non‐linearity of system hydraulics and the complexity associated with modeling hydraulic devices that influence the state of the WDS. This paper introduces MILPNet, an adjustable framework for WDSs that can be used to build and solve an extensive array of MILP optimization problems. MILPNet includes constraints that represent the mass balance and energy conservation equations, hydraulic devices, control rules, and status checks. To conform to MILP structure, MILPNet employs piece‐wise linear approximation and integer programming. MILPNet was implemented and tested using Gurobi Python API. Modeling accuracy was shown to be comparable to EPANET, a public domain software for hydraulic modeling, and sensitivity analyses were conducted to examine the impacts of the modeling assumptions on the performance of MILPNet. Additionally, application of the framework was demonstrated using pump scheduling optimization examples in single and rolling horizon scenarios. Our results show that MILPNet can facilitate the construction and solution of optimization problems for a range of applications in WDSs operations.

Integrating Minimum Spanning Tree and MILP in Urban Planning: A Novel Algorithmic Perspective

This paper presents a novel eight-step iterative algorithm for optimizing the layout of a neighborhood, focusing on the efficient allocation of houses to strategically placed facilities, herein referred to as ’points of interest’. The methodology integrates a mixed integer linear programming (MILP) approach with a heuristic algorithm to address a variant of the facility location problem combined with network design considerations. The algorithm begins by defining a set of geographic coordinates to represent houses within a predefined area. It then identifies key points of interest, forming the basis for subsequent connectivity and allocation analyses. The methodology’s core involves applying the Greedy algorithm to assign houses to the nearest points of interest, subject to capacity constraints. The method is followed by computing a Minimum Spanning Tree (MST) among these points to ensure efficient overall connectivity. The proposed algorithm’s iterative design is a key attribute. The most promising result of this approach is its ability to minimize the distance between houses and points of interest while optimizing the network’s total length. This dual optimization ensures a balanced distribution of houses and an efficient layout, making it particularly suitable for urban planning and infrastructure development. The paper’s findings demonstrate the algorithm’s effectiveness in creating a practical and efficient neighborhood layout, highlighting its potential application in large-scale urban planning and development projects.