Optimization model as a decision support system for participating in public tenders for feeding programs

Communities of small family farmers are among the poorest and most vulnerable segments of Brazilian society, so any increase in their disposable income would make a significant difference in their living standards. In response to this social problem, Brazilian authorities have developed programs to encourage family farming (such as the PAA - Food Acquisition Program and the PNAE - National School Feeding Program, in English), giving family farmers priority to the provision of agricultural products and food to schools and public institutions. However, farmers face a challenge both in deciding which public calls they subscribe to and in distributing their products to schools and public institutions. They struggle also in identifying which areas and contracts to compete for, leading to reduced participation of vulnerable farmers in government programs specifically designed to support them. To this end, a decision support system (DSS) based on an optimization model was developed to address this problem. The DSS allows farmers to identify which bids to attend based on a two-phase-gate process, which evaluates bids based on their individual profitability as well as on a geographical area value concentration criteria

Optimising the product distribution decisions for government feeding programs in developing countries

Institutional markets have evolved as one alternative way for smallholder farmers to access the market and supply their produce at a known in advance price and quantity. This helps planning agricultural operations and provides security of income, which is critical for the farmer livelihoods. One such example is the PNAE government feeding program in Brazil, where schools source raw materials and ingredients from local smallholder farmers for school meals. This work presents a Decision Support System (DSS) supporting farmers decisions on which schools to supply, with which products and how to organise the logistical activities, to maximise the net income from participation in these markets. The DSS is applied after the farmers have knowledge which bids they have been successful in, and therefore they have clarity on the potential supply areas. The decisions at this stage can be quite complex, with several factors to be considered simultaneously, such as product range, quantities and price for each school that a bid was won, distance and logistical costs, and logistical synergies when delivered quantities in the same area are larger. At the same time there are constraints such as the land, transportation and resource availability. The proposed DSS is novel in supporting smallholder farmer decisions on supplying institutional markets. The results of the DSS application for a specific smallholder farmer settlement in Brazil are presented and discussed, to assess its applicability

Near-Optimal Heuristics for Just-In-Time Jobs Maximization in Flow Shop Scheduling

The number of just-in-time jobs maximization in a permutation flow shop scheduling problem is considered. A mixed integer linear programming model to represent the problem as well as solution approaches based on enumeration and constructive heuristics were proposed and computationally implemented. Instances with up to 10 jobs and five machines are solved by the mathematical model in an acceptable running time (3.3 min on average) while the enumeration method consumes, on average, 1.5 s. The 10 constructive heuristics proposed show they are practical especially for large-scale instances (up to 100 jobs and 20 machines), with very good-quality results and efficient running times. The best two heuristics obtain near-optimal solutions, with only 0.6% and 0.8% average relative deviations. They prove to be better than adaptations of the NEH heuristic (well-known for providing very good solutions for makespan minimization in flow shop) for the considered problem

Flexible flow line with setup times: heuristic methods

Este trabalho aborda o problema de programação da produção em um flexible flow line com tempos de setup. De acordo com a literatura, este ambiente pode ser considerado como um caso especial do Flow Shop com múltiplas máquinas, onde as tarefas podem saltar estágios. Neste estudo, foram analisados dois problemas: o primeiro, com tempos de setup independentes da sequência, e o segundo, com setup dependente da sequência de tarefas. Além disso, o setup das máquinas para as tarefas pode ser antecipado ou não. No primeiro caso, as máquinas de um estágio podem ser preparadas para o processamento de uma tarefa antes do seu término no estágio anterior. Se o setup não pode ser antecipado, a tarefa deve esperar o seu término no estágio de produção anterior. Este ambiente produtivo pode ser encontrado em um vasto número de indústrias tais como química, eletrônica, automotiva e têxtil. A medida de desempenho dos problemas é a duração total da programação (makespan). Este é um critério apropriado para sistemas de produção com grandes cargas de trabalho e em que a utilização dos recursos produtivos em longo prazo deve ser otimizada. O exame da literatura mostrou que há poucos estudos abordando a programação em flexible flow line. Considerando este aspecto, este trabalho apresenta heurísticas construtivas originais para a obtenção de programações apropriadas ao problema mencionado. Uma extensiva experimentação computacional foi executada para avaliar o desempenho relativo das heurísticas. Os resultados experimentais foram analisados e discutidos.This work addresses the job scheduling on a flexible flow line with separate setup times. According to the literature, this scheduling problem can be considered as a special case of the Flow Shop with multiple machines, where the jobs may skip stages. Two modeled problems have been studied. In the first scheduling problem the setup times are sequence independent, and in the second one these times are sequence dependent. Moreover, the machine setup task can be either anticipatory or non-anticipatory. In the first case, a k-stage machine may be prepared for a job processing before its completion on the k-1 production stage. Otherwise, the setup task must wait for the job completion on the former production stage. This production environment can be found in a number of industries such as chemicals, electronics, automotive, and textiles. The performance measure of the production schedules is the makespan, that is, the total time to complete the schedule. This is an appropriate performance criterion for production systems with large workloads, and where the utilization of productive resources in the long term should be optimized. The literature examination has shown that there is a small number of studies dealing with flexible flow line scheduling. Having this in mind, this work introduces original constructive heuristics in order to obtain suitable schedules for the aforementioned scheduling problem. An extensive computational experience has been carried out in order to evaluate the relative performance of the heuristics. Experimental results are discussed

Proposição de algoritmo simulated annealing para programação em flow shops paralelos proporcionais com tempos de setup explícitos

This article addresses the problem of minimizing makespan on two parallel flow shops with proportional processing and setup times. The setup times are separated and sequence-independent. The parallel flow shop scheduling problem is a specific case of well-known hybrid flow shop, characterized by a multistage production system with more than one machine working in parallel at each stage. This situation is very common in various kinds of companies like chemical, electronics, automotive, pharmaceutical and food industries. This work aimed to propose six Simulated Annealing algorithms, their perturbation schemes and an algorithm for initial sequence generation. This study can be classified as “applied research” regarding the nature, “exploratory” about the objectives and “experimental” as to procedures, besides the “quantitative” approach. The proposed algorithms were effective regarding the solution and computationally efficient. Results of Analysis of Variance (ANOVA) revealed no significant difference between the schemes in terms of makespan. It’s suggested the use of PS4 scheme, which moves a subsequence of jobs, for providing the best percentage of success. It was also found that there is a significant difference between the results of the algorithms for each value of the proportionality factor of the processing and setup times of flow shops.Este trabalho aborda o problema de otimização da duração total da programação (makespan) em dois flow shops paralelos proporcionais com tempos de setup explícitos e independentes da sequência de tarefas. O problema de programação em flow shops paralelos é um caso específico do conhecido flow shop híbrido, caracterizado por sistemas produtivos multiestágio com mais de uma máquina operando em paralelo em cada estágio, e muito frequente em vários tipos de indústrias como química, eletrônica, automotiva, farmacêutica e alimentícia. Este trabalho objetivou propor seis algoritmos Simulated Annealing e seus respectivos esquemas de perturbação, além de um algoritmo para geração da sequência inicial, para solução do problema descrito. Este estudo pode ser classificado como “pesquisa aplicada” quanto à natureza, “pesquisa exploratória” quanto aos objetivos e “pesquisa experimental” quanto aos procedimentos, além da abordagem “quantitativa”. Os algoritmos propostos mostraram-se eficazes quanto a solução e eficientes computacionalmente. Os resultados da Análise de Variância (ANOVA) revelaram que não existe diferença significativa entre os esquemas de perturbação em termos do makespan. Sugere-se a utilização do esquema PS4, que faz o deslocamento de uma subsequência de tarefas, por ter fornecido a melhor porcentagem de sucesso. Verificou-se também que é significativa a diferença entre os resultados dos algoritmos para cada valor do fator de proporcionalidade dos tempos de processamento e de setup dos flow shops

Efeitos de regras de prioridade para programação da produção em sistemas industriais complexos

This paper discusses the efficiency of priority rules for the flexible flow line scheduling problem. This production environment is characterized by multiple identical machines at each stage and the possibility of jobs skipping one or more stages. Sequence-dependent setup times, which can be anticipatory or non-anticipatory, are also considered. The objective of the problem was the makespan minimization (total time to complete the schedule). This study can be classified as “applied research” regarding the nature, “exploratory” about the objectives and “experimental” as to procedures, besides the “quantitative” approach. The statistics used to evaluate the priority rules’ performances were the percentage of success (in finding the best solution), relative deviation, standard deviation of relative deviation and CPU average time. The priority rules LPT3 and LPT5 reached the best performances, considering both the descending order of the workload in all stages, in other words, on the production system treated, it's more advantageous priorizing the jobs with the biggest workload.Neste artigo foi discutida a eficiência da utilização de regras de prioridade para programação de sistemas produtivos conhecidos como flexible flow line com tempos de setup dependentes da sequência de processamento das tarefas. Este ambiente é caracterizado pela presença de múltiplas máquinas idênticas em cada estágio e a da possibilidade das tarefas saltarem um ou mais estágios de produção. Foram considerados tanto tempos de setup antecipados como não antecipados. O objetivo do problema foi a minimização da duração total da programação (makespan). Este estudo pode ser classificado como “pesquisa aplicada” quanto à natureza, “pesquisa exploratória” quanto aos objetivos e “pesquisa experimental” quanto aos procedimentos, além da abordagem “quantitativa”. Os resultados foram analisados por meio da porcentagem de sucesso, desvio relativo, desvio-padrão do desvio relativo e tempo de computação. As duas regras que obtiveram os melhores desempenhos foram a LPT3 e a LPT5, as únicas que consideram a ordenação decrescente da carga de trabalho em todos os estágios, ou seja, no sistema produtivo tratado é mais vantajoso priorizar as tarefas com as maiores cargas de trabalho

Desempenho relativo de regras de prioridade para programação de flow shop híbrido com tempos de setup

This paper focuses the hybrid flow shop scheduling problem with explicit and sequence-independent setup times. This production environment is a multistage system with unidirectional flow of jobs, wherein each stage may contain multiple machines available for processing. The optimized measure was the total time to complete the schedule (makespan). The aim was to propose new priority rules to support the schedule and to evaluate their relative performance at the production system considered by the percentage of success, relative deviation, standard deviation of relative deviation, and average CPU time. Computational experiments have indicated that the rules using ascending order of the sum of processing and setup times of the first stage (SPT1 and SPT1_ERD) performed better, reaching together more than 56% of success. Este trabalho aborda o problema de programação de flow shop híbridos com tempos de setup explícitos e independentes da sequência de tarefas. Este ambiente de produção é um sistema multiestágio com fluxo unidirecional de tarefas, onde cada estágio pode conter múltiplas máquinas disponíveis para processamento. A medida otimizada foi a duração total da programação (makespan). O objetivo foi propor novas regras de prioridade e avaliar o seu desempenho relativo no sistema produtivo considerado por meio da porcentagem de sucesso, desvio relativo médio, desvio-padrão do desvio relativo e tempo médio de computação. A experimentação computacional indicou que as regras que utilizam a ordenação crescente dos tempos de processamento e setup do primeiro estágio (SPT1 e SPT1_ERD) apresentam melhor desempenho, perfazendo juntas mais de 56% de sucesso

Near-Optimal Heuristics for Just-In-Time Jobs Maximization in Flow Shop Scheduling

The number of just-in-time jobs maximization in a permutation flow shop scheduling problem is considered. A mixed integer linear programming model to represent the problem as well as solution approaches based on enumeration and constructive heuristics were proposed and computationally implemented. Instances with up to 10 jobs and five machines are solved by the mathematical model in an acceptable running time (3.3 min on average) while the enumeration method consumes, on average, 1.5 s. The 10 constructive heuristics proposed show they are practical especially for large-scale instances (up to 100 jobs and 20 machines), with very good-quality results and efficient running times. The best two heuristics obtain near-optimal solutions, with only 0.6% and 0.8% average relative deviations. They prove to be better than adaptations of the NEH heuristic (well-known for providing very good solutions for makespan minimization in flow shop) for the considered problem