Integrated scheduling of rake and stockyard management with ship berthing: a block based evolutionary algorithm

Pratap, Saurabh; B, Manoj Kumar; Saxena, Divyanshu; Tiwari, Manoj Kumar

doi:10.1080/00207543.2015.1111535

Cited by 29 publications

(14 citation statements)

References 24 publications

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“…Besides, it is of upmost importance to think about real world problems in this context (cf. [37][38][39][40][41]), to see that we have problems with large dimensions where it is not possible to apply the algorithms presented here. In this regard, a line of research that we intend to explore in the future is the application of meta-heuristic algorithms to solve such problems.…”

Section: Conclusion and Future Studymentioning

confidence: 99%

New approach for solving intuitionistic fuzzy multi-objective transportation problem

et al. 2018

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Multi-objective transportation problem (MOTP) under intuitionistic fuzzy (IF) environment is analysed in this paper. Due to the fluctuation of market scenario, we assume that the transportation cost, the supply and the demand parameters are not always precise. Hence, the parameters are imprecise, i.e., they are IF numbers. Considering the specific cut interval, the IF transportation cost matrix is converted to interval cost matrix in our proposed problem. Again, using the same concept, the IF supply and the IF demand of the MOTP are reduced to the interval form. Then the proposed MOTP is changed into the deterministic MOTP, which includes interval form of the objective functions. Two approaches, namely intuitionistic fuzzy programming and goal programming, are used to derive the optimal solutions of our proposed problem, and then the optimal solutions are compared. A numerical example is included to illustrate the feasibility and the applicability of the proposed problem. Finally, we present the conclusions with the future scopes of our study.

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Section: Conclusion and Future Studymentioning

confidence: 99%

New approach for solving intuitionistic fuzzy multi-objective transportation problem

et al. 2018

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“…In particular, complex hybrid logical and terminal constraints, non-stationarity in job process execution and complex interrelations between process design, capacity utilisation and machine set-ups need to be pointed out. In many cases, nature-inspired evolutionary algorithms have been suggested to solve such complex scheduling problems as documented (Borisovsky, Dolgui, and Eremeev 2009;Chang et al 2013;Ciro et al 2016;Pratap et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Scheduling in production, supply chain and Industry 4.0 systems by optimal control: fundamentals, state-of-the-art and applications

Dolgui

Ivanov

Sethi

et al. 2018

International Journal of Production Research

234

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This paper presents a survey on the applications of optimal control to scheduling in production, supply chain and Industry 4.0 systems with a focus on the deterministic maximum principle. The first objective is to derive major contributions, application areas, limitations, as well as research and application recommendations for the future research. The second objective is to explain control engineering models in terms of industrial engineering and production management. To achieve these objectives, optimal control models, qualitative methods of performance analysis and computational methods for optimal control are considered. We provide a brief historic overview and clarify major mathematical fundamentals whereby the control engineering terms are brought into correspondence with industrial engineering and management. The survey allows the grouping of models with only terminal constraints with application to master production scheduling, models with hybrid terminal-logical constraints with applications to short term job and flow shop scheduling, and hybrid structural-terminal-logical constraints with applications to customised assembly systems such as Industry 4.0. Computational algorithms in state, control and adjoint variable spaces are discussed.

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“…A scheduling solution is bounded by constraints such as conflicting access to operations at a given time and limited resource capacities which contribute to the NP-hardness of the problem. Heuristic methods are often used for such problem solving and involve a model that takes inputs, constraints and decision variables [3].…”

Section: Introductionmentioning

confidence: 99%

Agent Cooperation Mechanism for Decentralized Manufacturing Scheduling

Jules

Saadat

2017

IEEE Trans. Syst. Man Cybern, Syst.

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Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.When citing, please reference the published version. Take down policyWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

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Integrated scheduling of rake and stockyard management with ship berthing: a block based evolutionary algorithm

Cited by 29 publications

References 24 publications

New approach for solving intuitionistic fuzzy multi-objective transportation problem

New approach for solving intuitionistic fuzzy multi-objective transportation problem

Scheduling in production, supply chain and Industry 4.0 systems by optimal control: fundamentals, state-of-the-art and applications

Agent Cooperation Mechanism for Decentralized Manufacturing Scheduling

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