Tackling Industrial-Scale Supply Chain Problems by Mixed-Integer Programming

sci, Gerald Gamrath

doi:10.4208/jcm.1905-m2019-0055

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…Since, to the best of our knowledge, there are no general-purpose supply chain heuristics to compare with (see, e.g., [6]), we compare different parameter settings of our algorithm with Gurobi running with default parameter settings except for the parameter MIPFocus, which is set to 1, so that Gurobi is focusing on finding feasible points quickly. To this end, we apply the PADM until a first feasible solution is found and use the time required by PADM as the time limit for Gurobi.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Following this trend, SAP, as one of the leading vendors of business software, started to develop and sell products such as the SAP Advanced Planning and Optimization software. One specific function thereof is called Supply Network Planning Optimization (SNP optimization), which relies on mixed-integer linear optimization (MIP) models and solvers [6]. SNP optimization aims at providing quantitative decision support for typical supply chain processes such as material procurement, production, transportation, demand fulfillment, stock keeping, and resource capacity utilization.…”

Section: Introductionmentioning

confidence: 99%

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A decomposition heuristic for mixed-integer supply chain problems

Schewe

Schmidt

Weninger

2020

Operations Research Letters

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Mixed-integer supply chain models typically are very large but are also very sparse and can be decomposed into loosely coupled blocks. In this paper, we use general-purpose techniques to obtain a block decomposition of supply chain instances and apply a tailored penalty alternating direction method, which exploits the structural properties of the decomposed instances. We further describe problem-specific enhancements of the algorithm and present numerical results on real-world instances that illustrate the applicability of the approach.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A decomposition heuristic for mixed-integer supply chain problems

Schewe

Schmidt

Weninger

2020

Operations Research Letters

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“…The core algorithm usually relies on a linear programming engine to solve a sequence of relaxations, which are tightened using a variety of cuts, while bounding schemes are used to efficiently explore the search space. Such solvers have now reached a level of maturity enabling them to tackle and solve large-scale MIP problems encountered in industrial applications [30], in spite of their unfavourable computational complexity. These methods can be used to directly solve the integer programming formulation (3) or solve a relaxed version of it.…”

Section: Mixed-integer Programming Solversmentioning

confidence: 99%

Siting renewable power generation assets with combinatorial optimisation

et al. 2021

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This paper studies the problem of siting renewable power generation assets using large amounts of climatological data while accounting for their spatiotemporal complementarity. The problem is cast as a combinatorial optimisation problem selecting a pre-specified number of sites so as to minimise the number of simultaneous low electricity production events that they experience relative to a pre-specified reference production level. It is shown that the resulting model is closely related to submodular optimisation and can be interpreted as generalising the well-known maximum coverage problem. Both deterministic and randomised algorithms are discussed, including greedy, local search and relaxation-based heuristics as well as combinations of these algorithms. The usefulness of the model and methods is illustrated by a realistic case study inspired by the problem of siting onshore wind power plants in Europe, resulting in instances featuring over ten thousand candidate locations and ten years of hourly-sampled meteorological data. The proposed solution methods are benchmarked against a state-of-the-art mixed-integer programming solver and several algorithms are found to consistently produce better solutions at a fraction of the computational cost. The physical nature of solutions provided by the model is also investigated, and all deployment patterns are found to be unable to supply a constant share of the electricity demand at all times. Finally, a cross-validation analysis shows that, except for an edge case, the model can successfully and reliably identify deployment patterns that perform well on previously unseen climatological data from historical data spanning a small number of weather years.

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“…Além disso, existem situações em que as variáveis podem assumir valores reais, caracterizando a Programação Linear Inteira Mista (PLIM) (Vanderbei, 2020). Como exemplos da PLIM, podem ser citados: a aplicação para os limites de tempo na cadeia de suprimentos, o suporte ao planejamento da capacidade produtiva, o sequenciamento de produtos e quantidade de pessoal em linhas de montagem paralelas (Gamrath et al, 2019;Huka et al, 2021).…”

Section: Introductionunclassified

Programação linear aplicada na indústria e suas conexões com os objetivos de desenvolvimento sustentável

Hoose,

Kripka

2024

Revista PO: R. Eletr. de Eng. de Produção e Correlatas

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Vários problemas do mundo real podem ser modelados com o uso da Programação Linear, dentre eles, os temas voltados para a sua aplicação na indústria. Neste contexto, este estudo tem como objetivo identificar as aplicações da Programação Linear na Indústria e suas conexões com os Objetivos de Desenvolvimento Sustentável (ODS). A metodologia utilizada é uma análise sistemática e bibliométrica de artigos científicos indexados na base de dados Scopus, considerando a aplicação da programação linear na indústria. Também foram pesquisados os desafios, aspectos positivos e negativos, perspectivas e oportunidades no uso da programação linear e a sua conexão com os objetivos de desenvolvimento sustentável. O estudo foi composto pela análise de 136 artigos, referente ao período de 2000 até julho de 2023. Foram gerados mapas temáticos com o uso do Bibliometrix, para orientar as discussões da pesquisa. Constata-se que é possível a aplicação da formulação de problemas da programação linear em várias atividades na indústria, nos assuntos de mão de obra, redução de estoque e atendimento da demanda. O maior desafio está na formulação de problemas que abrangem os aspectos da sustentabilidade, principalmente no uso da energia renovável e o impacto ambiental, que também são temas-motores neste campo de pesquisa.

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Tackling Industrial-Scale Supply Chain Problems by Mixed-Integer Programming

Cited by 3 publications

References 17 publications

A decomposition heuristic for mixed-integer supply chain problems

A decomposition heuristic for mixed-integer supply chain problems

Siting renewable power generation assets with combinatorial optimisation

Programação linear aplicada na indústria e suas conexões com os objetivos de desenvolvimento sustentável

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