Improvement of Lagrangian Relaxation Convergence for Production Scheduling

Buil, Roman; Piera, Miquel Àngel; Luh, Peter B.

doi:10.1109/tase.2011.2168817

Cited by 16 publications

(6 citation statements)

References 5 publications

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“…For the FJSP, when the number of jobs is small, some exact algorithms can be employed to solve it, for example, brandand-bound [20][21][22][23], mathematical programming [24][25][26], and Lagrangian relaxation [27][28][29][30]. But when the number of jobs rises, it is difficult to find an optimal solution in a short time.…”

Section: Proposed Improved De For the Efjsp Modelmentioning

confidence: 99%

An Extended Flexible Job Shop Scheduling Model for Flight Deck Scheduling with Priority, Parallel Operations, and Sequence Flexibility

Zhu

Shi

et al. 2017

Scientific Programming

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Efficient scheduling for the supporting operations of aircrafts in flight deck is critical to the aircraft carrier, and even several seconds’ improvement may lead to totally converse outcome of a battle. In the paper, we ameliorate the supporting operations of carrier-based aircrafts and investigate three simultaneous operation relationships during the supporting process, including precedence constraints, parallel operations, and sequence flexibility. Furthermore, multifunctional aircrafts have to take off synergistically and participate in a combat cooperatively. However, their takeoff order must be restrictively prioritized during the scheduling period accorded by certain operational regulations. To efficiently prioritize the takeoff order while minimizing the total time budget on the whole takeoff duration, we propose a novel mixed integer liner programming formulation (MILP) for the flight deck scheduling problem. Motivated by the hardness of MILP, we design an improved differential evolution algorithm combined with typical local search strategies to improve computational efficiency. We numerically compare the performance of our algorithm with the classical genetic algorithm and normal differential evolution algorithm and the results show that our algorithm obtains better scheduling schemes that can meet both the operational relations and the takeoff priority requirements.

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Section: Proposed Improved De For the Efjsp Modelmentioning

confidence: 99%

An Extended Flexible Job Shop Scheduling Model for Flight Deck Scheduling with Priority, Parallel Operations, and Sequence Flexibility

Zhu

Shi

et al. 2017

Scientific Programming

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“…The Lagrangian relaxation decomposition method has been successfully used to determine a near optimal solution with less computational time (e.g., [29] and [30]- [32]). In the CFPSP model, constraint (7) provides a bridge between the decisions of the CF and the parts scheduling in the cells.…”

Section: A Model Features and Two Subproblemsmentioning

confidence: 99%

Using Lagrangian Relaxation Decomposition With Heuristic to Integrate the Decisions of Cell Formation and Parts Scheduling Considering Intercell Moves

Tang

Yan

Wang

et al. 2014

IEEE Trans. Automat. Sci. Eng.

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Cell formation and parts scheduling are two important correlated processes in a cellular manufacturing system; however, the decisions involved in these processes are typically made individually. Determining how to integrate these decisions effectively to pursue a productive and lower cost system has become an important issue. This paper focuses on providing an effective solution to integrate the decisions of cell formation and parts scheduling, while considering intercell moves by using a Lagrangian relaxation decomposition method. A mixed integer nonlinear programming mathematical model (CFPSP) is proposed to determine which part families and machine groups are assigned to cells and in which sequence the parts are processed in the machines to minimize the total tardiness penalty cost. To effectively solve the model, a Lagrangian relaxation decomposition method with a heuristic (LRDH) is developed. Using the LRDH, the CFPSP model is solved by decomposing the model into two subproblems, i.e., the cell formation subproblem (CFPSP-FD) and the parts scheduling subproblem (CFPSP-SD). After linearizing the CFPSP-FD model, the subproblem CFPSP-FD is solved by the MIP optimizer CPLEX. A scatter search approach is developed to solve the subproblem CFPSP-SD. Combined with the Lagrange multipliers, the CFPSP-SD model takes into consideration the assignment of part families and the associated machine groups to each cell, when it sequences the processing of the parts on each machine in cells. An illustration of the application of the CFPSP model in an electronic appliance cellular manufacturing enterprise in China is presented.Note to Practitioners-Cell formation and parts scheduling are two important correlated processes in a cellular manufacturing system; however, the decisions involved in these processes are typically made individually. Determining how to integrate these decisions has become an important issue. This paper proposes an effective solution to integrate the decisions of cell formation and parts scheduling, while considering intercell moves. To effectively solve the model, a Lagrangian relaxation decomposition method with heuristic (LRDH) is developed, which provides a lower bound of the optimal solution. Theoretical analysis and simulation results demonstrate that the LRDH is an effective approach that Manuscript can be easily implemented. This approach facilitates production managers to implement an integrated decision with a near optimal solution, and it has been demonstrated to be effective in an electronic appliance cellular manufacturing enterprise in China.

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“…In manufacturing systems, since the scheduling problems are NP-hard, an efficient approach is required to get best results (Buil et al, 2010;Sridhar et al, 2010). Recently, a fuzzy logic approach has been widely applied to the scheduling problems for both conventional…”

Section: A Review Of the Literaturementioning

confidence: 99%

Development of a Fuzzy-Simulation Model of Scheduling Robotic Flexible Assembly Cells

Abd¹

2013

Journal of Computer Science

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Due to the complexity of scheduling flexible manufacturing systems, the generation of production schedules requires an intelligent technique. Many artificial intelligence techniques such as fuzzy logic, genetic algorithms and neural networks have been successfully applied to the scheduling of advanced manufacturing systems. One such system is Robotic Flexible Assembly Cells (RFACs). Few studies have addressed the problem of scheduling RFACs. The major limitation is that these studies are limited to the assembly of only one product type. The objective of this study is to propose a new intelligent model of scheduling RFACs in a multi-product assembly environment, using fuzzy logic.

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Improvement of Lagrangian Relaxation Convergence for Production Scheduling

Cited by 16 publications

References 5 publications

An Extended Flexible Job Shop Scheduling Model for Flight Deck Scheduling with Priority, Parallel Operations, and Sequence Flexibility

An Extended Flexible Job Shop Scheduling Model for Flight Deck Scheduling with Priority, Parallel Operations, and Sequence Flexibility

Using Lagrangian Relaxation Decomposition With Heuristic to Integrate the Decisions of Cell Formation and Parts Scheduling Considering Intercell Moves

Development of a Fuzzy-Simulation Model of Scheduling Robotic Flexible Assembly Cells

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