Evolutionary tabu search for flexible due-date satisfaction in fuzzy job shop scheduling

Vela, Camino R.; Afsar, Sezin; Palacios, Juan José; González-Rodríguez, Inés; Puente, Jorge de Hoyos

doi:10.1016/j.cor.2020.104931

Cited by 54 publications

(32 citation statements)

References 57 publications

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“…In Chen et al (2020) , 14 benchmark test problems are used to demonstrate the efficiency of a self-learning GA based on reinforcement learning in the FJSP. A fuzzy version of the FJSP is studied in Vela et al (2020) , where an evolutionary algorithm is proposed, using a TS again for optimizing a due-date cost. Dynamic flexibility in FJSP is analyzed in Baykasoğlu, Madenoğlu & Hamzaday (2020) with a greedy randomized adaptive search.…”

Section: State Of the Art Of Fjspmentioning

confidence: 99%

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

Serna¹,

Seck-Tuoh-Mora²,

Medina-Marín³

et al. 2021

PeerJ Computer Science

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The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), in which the neighborhood concepts of a cellular automaton are used, so that a set of leading solutions called smart-cells generates and shares information that helps to optimize instances of the FJSP. The GLNSA algorithm is accompanied by a tabu search that implements a simplified version of the Nopt1 neighborhood defined in Mastrolilli & Gambardella (2000) to complement the optimization task. The experiments carried out show a satisfactory performance of the proposed algorithm, compared with other results published in recent algorithms, using four benchmark sets and 101 test problems.

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Section: State Of the Art Of Fjspmentioning

confidence: 99%

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

Serna¹,

Seck-Tuoh-Mora²,

Medina-Marín³

et al. 2021

PeerJ Computer Science

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show abstract

“…Even in this situation, the numbers of solutions to be analyzed is considerably less than 2 |I| . For example, for |I| = 25 and Z = 3 we obtain B[z] 25 3 = 8.33, thus, considering the impossible situation B[z] = 9 (for z = 1, 2, 3) we have to explore 10 3 = 1000 solutions instead of the 2 25 = 33554432 possible subsetsĨ. Nevertheless, a worst case analysis of this algorithm is presented in section 5.1.…”

Section: Solving Fuzzy Vcsbpp With Partial Packingmentioning

confidence: 99%

“…From the practical point of view, several fuzzy optimization approaches have been recently presented to deal with the real-world conditions in diverse fields, such as: uncertain payoffs in the context of incomplete-information games [6], epistemic and aleatory uncertainties in the context of topology (structural) optimization [19], uncertainty of device parameters and about the network structure (failure and changes) for clustering optimization of wireless ISSN: 1137-3601 (print), 1988-3064 (on-line) c IBERAMIA and the authors ad-hoc networks [20], and uncertainty associated to durations and flexible due dates in job shop scheduling [25]. Recent fuzzy approaches for logistic optimization problems are: the consideration of the uncertainties associated to the cost of stations, demands, prices and distance capacity of each drone in order to minimize the cost of an aerial (drone-based) delivery system [23], the management of an uncertain radius of coverage in location problems [10] and the use of fuzzy numbers to manage uncertainties associated to service time, energy consumption, travel time and recharge in the context of optimizing electric vehicle routing problem [31].…”

Section: Introductionmentioning

confidence: 99%

fuzzy approach for the variable cost and size bin packing problem allowing incomplete packing

Herrera-Franklin¹,

Rosete

García-Borroto

2021

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The Variable Cost and Size Bin Packing Problem (VCSBPP) is a known NP-Hard problem that consists in minimizing the cost of all bins used to pack a set of items. There are many real-life applications of the VCSBPP where the focus is to improve the efficiency of the solution method. In spite of the existence of fuzzy approaches to adapt other optimization problems to real life conditions, VCSBPP has not been extensively studied in terms of relaxations of the crisp conditions. In this sense, the fuzzy approaches for the VCSBPP varies from relaxing the capacity of the bins to the items weights. In this paper we address a non-explored side consisting in relaxing the set of items to be packed. Therefore, our main contribution is a fuzzy version of VCSBPP that allows incomplete packing. The proposed fuzzy VCSBPP is solved by a parametric approach. Particularly, a fast heuristic algorithm is introduced that allows to obtain a set of solutions with interesting trade-offs between cost and relaxation of the original crisp conditions. An experimental study is presented to explore the proposed fuzzy VCSBPP and its solution.

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“…The aim of RTMRA is to provide an adaptive task allocation strategy so that multiple objectives are optimized simultaneously. Assumptions are given as follows [ 5 , 35 , 36 ]: Jobs arrive randomly, and jobs have a different due date. Each operation may be executed on a set of alternative machines.…”

Section: Problem Description and Mathematical Modelmentioning

confidence: 99%

Real-Time Production and Logistics Self-Adaption Scheduling Based on Information Entropy Theory

Yang

Cao

et al. 2020

Sensors

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In recent years, the individualized demand of customers brings small batches and diversification of orders towards enterprises. The application of enabling technologies in the factory, such as the industrial Internet of things (IIoT) and cloud manufacturing (CMfg), enhances the ability of customer requirement automatic elicitation and the manufacturing process control. The job shop scheduling problem with a random job arrival time dramatically increases the difficulty in process management. Thus, how to collaboratively schedule the production and logistics resources in the shop floor is very challenging, and it has a fundamental and practical significance of achieving the competitiveness for an enterprise. To address this issue, the real-time model of production and logistics resources is built firstly. Then, the task entropy model is built based on the task information. Finally, the real-time self-adaption collaboration of production and logistics resources is realized. The proposed algorithm is carried out based on a practical case to evaluate its effectiveness. Experimental results show that our proposed algorithm outperforms three existing algorithms.

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Evolutionary tabu search for flexible due-date satisfaction in fuzzy job shop scheduling

Cited by 54 publications

References 57 publications

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

fuzzy approach for the variable cost and size bin packing problem allowing incomplete packing

Real-Time Production and Logistics Self-Adaption Scheduling Based on Information Entropy Theory

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