Integrated production planning and order acceptance under uncertainty: A robust optimization approach

Aouam, Tarik; Brahimi, Nadjib

doi:10.1016/j.ejor.2013.02.010

Cited by 47 publications

(24 citation statements)

References 26 publications

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“…For Charnes and Cooper [25], Chen et al [26], Liang [27], and Chen and Xu [28], in practical situations, the goals and model inputs (parameters) for the decisions are normally imprecise/vague, as some relevant information is incomplete and unavailable. However, the data uncertainty is inherent in most practical situations and should be taken into account in any realistic mathematical model [29]. In sugarcane harvest scheduling models, the uncertainty is inherent in most parameters, including climate condition, cost, demand, yield process, and capacity [30].…”

Section: Goal Programming Models With Uncertaintymentioning

confidence: 99%

“…The first problem, from Liao [8], has three objective functions, see (24)- (26), and three constraints, see (27)- (29). In these objective functions, the coefficient x 1 in the g 1 function may have a value of 3 or 6, the coefficient x 2 in the g 2 function may have a value of 5 or 9, and the coefficient x 3 in the g 3 function may have a value of 7 or 10; the goals for each objective function are, respectively, 115, 80, and 110.…”

Section: The Rmcgp-lhs Model To Deal With Uncertainty In the Schedulimentioning

confidence: 99%

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Addressing uncertainty in sugarcane harvest planning through a revised multi-choice goal programming model

Silva

Marins

Dias

2015

Applied Mathematical Modelling

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a b s t r a c tIn this paper a new revised multi-choice goal programming (RMCGP-LHS) model is proposed to deal with uncertainty in sugar cane harvest scheduling for sugar and ethanol milling companies. The RMCGP-LHS model uses a weekly decision-making horizon and takes into account the time and condition of land management, cane cutting decisions, and agricultural logistics. Its objective is to obtain information in order to harvest sugar cane plots in the period closest to the highest saccharose levels, while also minimizing agro-industrial costs. The RMCGP-LHS model was applied to a real case sugar and ethanol mill, and its optimization has provided harvesting policies that were validated by the company's managers. Besides that the RMCGP-LHS model is a very practical tool for simulating in a fast way different scenarios involving uncertainties on model parameters and helping the managers in decision making process in real time.

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Section: Goal Programming Models With Uncertaintymentioning

confidence: 99%

Section: The Rmcgp-lhs Model To Deal With Uncertainty In the Schedulimentioning

confidence: 99%

Addressing uncertainty in sugarcane harvest planning through a revised multi-choice goal programming model

Silva

Marins

Dias

2015

Applied Mathematical Modelling

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“…Aouam et al [8] formulated a model that integrates order acceptance decisions and production planning while considering demand uncertainty and proposed a robust optimization approach to solve it. Cowling et al [9] used a hybrid network to model the CC-HR planning problem and provided a solution based on mathematical programming and heuristic techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated planning approach based on harmony search algorithm and prediction model for steelmaking-continuous casting-hot rolling process

Lin

Hao

Liu

et al. 2014

Proceeding of the 11th World Congress on Intelligent Control and Automation

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Aiming at making an integrated production plan for steelmaking-continuous casting-hot rolling (SM-CC-HR) process in the iron and steel industry, a novel integrated planning framework based on the harmony search algorithm and the prediction model is proposed. In our approach, we use a crude prediction model to evaluate the performance of a hot-rolling planning (HRP) problem in the process of optimizing the steelmaking-continuous casting planning (SCP) problem, and use a knowledge-based simulation model to obtain a solution of the HRP problem. An Objective-Directed Strategy (ODS) is introduced into the local search process of the harmony search algorithm for balancing the exploitation and exploration of the solution space. Numerical computation experiments on practical production data from an iron and steel enterprise show that more than 10% higher effectiveness and efficiency of the proposed approach are obtained compared with the normal practices followed in the company relying on human expertise.Index Terms -steelmaking-continuous casting-hot rolling; integrated planning; harmony search algorithm; prediction model.

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“…Aouam and Brahimi (2013) present a robust model that integrates production planning with load dependent lead-times and order acceptance decisions, which considers demand uncertainty and where a fraction of the order quantity can be accepted. They show that integrating the two decisions provides the planner with the flexibility to select the orders to be satisfied fully or partially.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, it is often important to distinguish customer orders for several reasons (Pahl et al, 2007;Aouam and Brahimi, 2013). Firstly, even if the finished good is the same, different customers might impose particular conditions on the source of raw materials or on the quality control tests to be carried out during the manufacturing process of their orders.…”

Section: Introductionmentioning

confidence: 99%

Integrating order acceptance decisions with flexible due dates in a production planning model with load-dependent lead times

Brahimi

Aouam

Aghezzaf

2014

International Journal of Production Research

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We consider a tactical planning problem, which integrates production planning decisions together with order acceptance decisions, while taking into account the dependency between workload and lead times. The proposed model determines which orders to accept and in which period they should be produced so that they can be delivered to the customer within the acceptable flexible due dates. When the number of accepted orders increases, the workload and production lead time also increase and this may result in the possibility of missing customer due dates. This problem is formulated as a mixed integer linear program for which two relax-and-fix heuristic solution methods are proposed. The first one decomposes the problem based on time periods while the second decomposes it based on orders. The performances of these heuristics are compared with that of a state-of-the-art commercial solver. Our results show that the time-based relax-and-fix heuristic outperforms the orderbased relax-and-fix heuristic and the solver solution as it yields better integrality gaps for much less CPU effort.

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Integrated production planning and order acceptance under uncertainty: A robust optimization approach

Cited by 47 publications

References 26 publications

Addressing uncertainty in sugarcane harvest planning through a revised multi-choice goal programming model

Addressing uncertainty in sugarcane harvest planning through a revised multi-choice goal programming model

An integrated planning approach based on harmony search algorithm and prediction model for steelmaking-continuous casting-hot rolling process

Integrating order acceptance decisions with flexible due dates in a production planning model with load-dependent lead times

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