Solving the multi-objective flexible job shop scheduling problem with a novel parallel branch and bound algorithm

Soto, Carlos; Dorronsoro, Bernabè; Fraire, Héctor; Cruz-Reyes, Laura; Gómez-Santillán, Claudia; Rangel, Nelson

doi:10.1016/j.swevo.2019.100632

Cited by 44 publications

(4 citation statements)

References 35 publications

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“…The first group considers the exact algorithm as a way to generate a schedule [22][23][24]. Research by [22] proposed constraint programming after analyzing the MILP (Mixed Integer Linear Programming) model of FJS, [23] adopted a branch and bound approach, and [24] The position of this paper is in the second group, which considers the implementation of a heuristic algorithm on FJS, in this case, LPT. Our research aims to improve the research done by [2] and [3] by modifying the LPT algorithm to minimize makespan.…”

Section: Introductionmentioning

confidence: 99%

Novel Heuristic Algorithm for Flexible Job Shop Scheduling based on the Longest Processing Time Rules to Minimize Makespan

Pakpahan,

Sumarna

2023

JTI

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This research examined the scheduling of jobs with multiple stages unto identical parallel machines to minimize the makespan. The work is motivated by a Flexible Manufacturing System case that produces various parts and has multiple machining centers. Early research towards this system proposed a stage-by-stage independent scheduling, resulting in a non-optimal solution. This study aimed to create a better solution for the system by developing a novel heuristic algorithm based on the classical longest processing time algorithm and simultaneously considering processing time for all stages when deciding the job sequencing and job-machine allocation. The algorithm is defined as Modified LPT for Multiple Identical Machine with Multi-process Capability (M-LPT MIMMPC). We performed a numerical experiment to assess the algorithm's performance by incorporating various cases. We concluded that the resulting makespans are always better than LPT's theoretical bound for parallel machine scheduling. In some cases, it successfully gave an optimal value. Although the experiment scope was still limited, the algorithm showed promising performance results.

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Section: Introductionmentioning

confidence: 99%

Novel Heuristic Algorithm for Flexible Job Shop Scheduling based on the Longest Processing Time Rules to Minimize Makespan

Pakpahan,

Sumarna

2023

JTI

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“…These solutions come from artificial intelligence techniques, which have been active in planning and scheduling for four decades [15]. Therefore, many heuristics and metaheuristics such as genetic algorithms, honey bee optimization, artificial bee colony, ant colony optimization, particle swarm optimization, simulated annealing, and hybrid approaches have been proposed to solve the FJSSP [16]. Chaudhry and Khan [17] performed a review of techniques addressing the FJSSP, highlighting ant colony optimization (ACO), artificial bee colony (ABC), artificial immune system (AIS), evolutionary algorithms, greedy randomized adaptive search procedure (GRASP), Integer/Linear programming, neighborhood search (NS), particle swarm optimization (PSO), simulated annealing (SA), tabu search (TS), mathematical programming, deterministic heuristics, hybrid techniques, and miscellaneous techniques.…”

Section: Introductionmentioning

confidence: 99%

Flexible Job Shop Scheduling Problem with Fuzzy Times and Due-Windows: Minimizing Weighted Tardiness and Earliness Using Genetic Algorithms

et al. 2022

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The current requirements of many manufacturing companies, such as the fashion, textile, and clothing industries, involve the production of multiple products with different processing routes and products with short life cycles, which prevents obtaining deterministic setup and processing times. Likewise, several industries present restrictions when changing from one reference to another in the production system, incurring variable and sequence-dependent setup times. Therefore, this article aims to solve the flexible job shop scheduling problem (FJSSP) considering due windows, sequence-dependent setup times, and uncertainty in processing and setup times. A genetic algorithm is proposed to solve the FJSSP by integrating fuzzy logic to minimize the weighted penalties for tardiness/earliness. The proposed algorithm is implemented in a real-world case study of a fabric finishing production system, and it is compared with four heuristics adapted to the FJSSP such as earliest due date, critical reason, shortest processing time, and Monte Carlo simulation. Results show that the performance of the proposed algorithm provides efficient and satisfactory solutions concerning the objective function and computing time since it overperforms (more than 30%) the heuristics used as benchmarks.

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“…Li et al 12 combined several heuristic strategies and a TS algorithm, constructed a discrete artificial bee colony (DABC) algorithm, and addressed a scheduling problem that simultaneously minimizes makespan, total work load, and maximal workload. Soto et al 13 studied the multi-objective FJSP with the aim of minimizing the total workload, maximal workload and makespan, and proposed a parallel branch and bound algorithm (B&B). With the consideration of the deterioration effect and environmental pollution problem, Wu et al 14 formulated a multi-objective optimization model based on the energy consumption and step-deterioration effect model.…”

Section: Introductionmentioning

confidence: 99%

Improved artificial immune algorithm for the flexible job shop problem with transportation time

Chen

Han

et al. 2020

Measurement and Control

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The flexible job shop problem (FJSP), as one branch of the job shop scheduling, has been studied during recent years. However, several realistic constraints including the transportation time between machines and energy consumptions are generally ignored. To fill this gap, this study investigated a FJSP considering energy consumption and transportation time constraints. A sequence-based mixed integer linear programming (MILP) model based on the problem is established, and the weighted sum of maximum completion time and energy consumption is optimized. Then, we present a combinational meta-heuristic algorithm based on a simulated annealing (SA) algorithm and an artificial immune algorithm (AIA) for this problem. In the proposed algorithm, the AIA with an information entropy strategy is utilized for global optimization. In addition, the SA algorithm is embedded to enhance the local search abilities. Eventually, the Taguchi method is used to evaluate various parameters. Computational comparison with the other meta-heuristic algorithms shows that the improved artificial immune algorithm (IAIA) is more efficient for solving FJSP with different problem scales.

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Solving the multi-objective flexible job shop scheduling problem with a novel parallel branch and bound algorithm

Cited by 44 publications

References 35 publications

Novel Heuristic Algorithm for Flexible Job Shop Scheduling based on the Longest Processing Time Rules to Minimize Makespan

Novel Heuristic Algorithm for Flexible Job Shop Scheduling based on the Longest Processing Time Rules to Minimize Makespan

Flexible Job Shop Scheduling Problem with Fuzzy Times and Due-Windows: Minimizing Weighted Tardiness and Earliness Using Genetic Algorithms

Improved artificial immune algorithm for the flexible job shop problem with transportation time

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