Resource-constrained unrelated parallel machine scheduling problem with sequence dependent setup times, precedence constraints and machine eligibility restrictions

Afzalirad, Mojtaba; Rezaeian, Javad

doi:10.1016/j.cie.2016.05.020

Cited by 103 publications

(56 citation statements)

References 46 publications

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“…The authors presented a MIP model and a solution procedure based on a novel "aggregate model." Afzalirad and Rezaeian (2016) addressed an unrelated parallel machine scheduling problem with resource constraints, sequence-dependent setup times, different release dates, machine eligibility, and precedence constraints. Two new meta-heuristic algorithms including genetic algorithm (GA) and artificial immune system (AIS) are developed to find optimal or near optimal solutions for this pure integer model.…”

Section: Related Literaturementioning

confidence: 99%

Shop floor lot-sizing and scheduling with a two-stage stochastic programming model considering uncertain demand and workforce efficiency

2017

Computers & Industrial Engineering

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Efficient and flexible production planning is necessary for the manufacturing industry to stay competitive in today's global market. Shop floor lot-sizing and scheduling is one of the most challenging and rewarding subjects for the management. In this study, a two-stage stochastic programming model is proposed to solve a single-machine, multi-product shop floor lot-sizing and scheduling problem. Two sources of uncertainties are considered simultaneously: product demand from the market, and workforce efficiency, which is the major contribution of this study. The workforce efficiency affects the system productivity, and we propose different distributions to model its uncertainty with insufficient information.The model aims to determine optimal lot sizes and the production sequence that minimizes expected total system costs over the planning horizon, including setup, inventory, and production costs. A case study is performed on a supply chain producing brake equipment in the automotive industry. The numerical results illustrate the usefulness of the stochastic model under volatile environment, and the solution quality is analyzed. AbstractEfficient and flexible production planning is necessary for the manufacturing industry to stay competitive in today's global market. Shop floor lot-sizing and scheduling is one of the most challenging and rewarding subjects for the management. In this study, a two-stage stochastic programming model is proposed to solve a single-machine, multi-product shop floor lot-sizing and scheduling problem. Two sources of uncertainties are considered simultaneously: product demand from the market, and workforce efficiency, which is the major contribution of this study.The workforce efficiency affects the system productivity, and we propose different distributions to model its uncertainty with insufficient information. The model aims to determine optimal lot sizes and the production sequence that minimizes expected total system costs over the planning horizon, including setup, inventory, and production costs. A case study is performed on a supply chain producing brake equipment in the automotive industry. The numerical results illustrate the usefulness of the stochastic model under volatile environment, and the solution quality is analyzed. Highlights• A two-stage stochastic programming model for lot-sizing and scheduling is proposed.• Two uncertain factors, product demand and workforce efficiency are considered.• The numerical results of optimal production sequence and quantities are illustrated.• The significance of stochastic model is presented, and solution quality is analyzed.

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Section: Related Literaturementioning

confidence: 99%

Shop floor lot-sizing and scheduling with a two-stage stochastic programming model considering uncertain demand and workforce efficiency

2017

Computers & Industrial Engineering

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“…They handled multiple-batch orders by defining a campaign operating mode, which is common in practice but can produce suboptimal solutions, since a campaign mode consists of scheduling a predefined number (or range) of batches of the same product. Afzalirad & Rezaeian (2016) formulated a discrete model, proposed two metaheuristic algorithms to solve it, and validated them with 14 randomly generated examples. Recently, Hinder & Mason (2017) exploited known order-based properties of optimal solutions for simpler single stage, family setup times and a single machine scheduling problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…They consider sequence-dependent preparation times, unit-dependent preparation times and due dates (Afzalirad & Rezaeian, 2016;Atan & Akturk, 2008;Castro & Novais, 2009;Mendez, Henning, & Cerda, 2000;Xue & Sun, 2010). Novara, Novas, & Henning (2016) performed a review on scheduling problems of resource-constrained multiproduct multistage batch plants.…”

Section: Literature Reviewmentioning

confidence: 99%

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A continuous time model for a short-term multiproduct batch process scheduling

Díaz-Ramírez

Huertas

2018

Ing. Inv.

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In the chemical industry, it is common to find production systems characterized by having a single stage or a previously identified bottleneck stage, with multiple non-identical parallel stations and with setup costs that depend on the production sequence. This paper proposes a mixed integer production-scheduling model that identifies lot size and product sequence that maximize profit. It considers multiple typical industry conditions, such as penalties for noncompliance or out of service periods of the productive units (or stations) for preventive maintenance activities. The model was validated with real data from an oil chemical company. Aiming to analyze its performance, we applied the model to 155 instances of production, which were obtained using Monte Carlo technique on the historical production data of the same company. We obtained an average 12 % reduction in the total cost of production and a 19 % increase in the estimated profit.Keywords: MIP modeling, goal programming, batch process scheduling, short-term scheduling, mathematical formulation, Monte Carlo simulation. RESUMENEn la industria química es común encontrar sistemas de producción caracterizados por tener una sola etapa o una etapa cuello de botella, con múltiples estaciones paralelas, no idénticas, y con costos de preparación o alistamiento dependientes de la secuencia de producción. Este artículo propone un modelo lineal mixto de programación de la producción que busca identificar el tamaño de lote y la secuenciación de productos con el objetivo de maximizar el beneficio. Considera múltiples condiciones típicas de la industria, tales como la penalización por incumplimientos, la programación de mantenimientos preventivos de las estaciones y la disponibilidad temporal de las estaciones. El modelo se validó con datos reales de una empresa de la industria del petróleo. Buscando analizar el desempeño del modelo, se analizaron los resultados de aplicar el modelo a 155 instancias generadas aplicando simulación Montecarlo, a los datos históricos de producción de la misma compañía. Se obtuvo una reducción del 12 % en la reducción total del costo de producción y un incremento del 19 % en la utilidad estimada.Palabras clave: programación entera mixta, programación de la producción por lotes, industria química, modelación matemática, simulación Monte Carlo.

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“…However, in reality, especially in the assemble lines with multivariety production, one machine is only eligible to process specified orders, which is called the machine eligibility constraint. Recently, some scholars have considered this constraint in the unrelated parallel machine scheduling models (see [1][2][3]). Moreover, in the assemble line with machine eligibility constraints, processing technologies used in machines are different; thus production costs of one order on different machines are often different and do not depend on the processing times.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Order Acceptance and Scheduling on Unrelated Parallel Machines with Machine Eligibility Constraints

Wang

2018

Mathematical Problems in Engineering

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This paper studies the order acceptance and scheduling problem on unrelated parallel machines with machine eligibility constraints. Two objectives are considered to maximize total net profit and minimize the makespan, and the mathematical model of this problem is formulated as multiobjective mixed integer linear programming. Some properties with respect to the objectives are analysed, and then a classic list scheduling (LS) rule named the first available machine rule is extended, and three new LS rules are presented, which focus on the maximization of the net profit, the minimization of the makespan, and the trade-off between the two objectives, respectively. Furthermore, a list-scheduling-based multiobjective parthenogenetic algorithm (LS-MPGA) is presented with parthenogenetic operators and Pareto-ranking and selection method. Computational experiments on randomly generated instances are carried out to assess the effectiveness and efficiency of the four LS rules under the framework of LS-MPGA and discuss their application environments. Results demonstrate that the performance of the LS-MPGA developed for trade-off is superior to the other three algorithms.

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Resource-constrained unrelated parallel machine scheduling problem with sequence dependent setup times, precedence constraints and machine eligibility restrictions

Cited by 103 publications

References 46 publications

Shop floor lot-sizing and scheduling with a two-stage stochastic programming model considering uncertain demand and workforce efficiency

Shop floor lot-sizing and scheduling with a two-stage stochastic programming model considering uncertain demand and workforce efficiency

A continuous time model for a short-term multiproduct batch process scheduling

Multiobjective Order Acceptance and Scheduling on Unrelated Parallel Machines with Machine Eligibility Constraints

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