No-wait flow shop scheduling using fuzzy multi-objective linear programming

Javadi, Babak; Saidi‐Mehrabad, Mohammad; Haji, Alireza; Mahdavi, Iraj; Jolai, Fariborz; Mahdavi–Amiri, Nezam

doi:10.1016/j.jfranklin.2007.12.003

Cited by 33 publications

(10 citation statements)

References 22 publications

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“…Various theories were developed, and FMOLP methods were applied to parameters, constraints, and objective-function problems in formulated models with uncertainty (Wu and Guu 948 T.-S. Su 2001; Buckley, Feuring, and Hayashi 2001;Karsak and Kuzgunkaya 2002;Kumar, Vrat, and Shankar 2004;Liang 2006;Javadi et al 2008;Arıkan and Güngör 2009). Zimmermann (1978) first applied fuzzy set theory to conventional LP models.…”

Section: Application Of Fuzzy Multi-objective Programmingmentioning

confidence: 99%

Optimal vehicle-dispatch decisions for cement silos using a fuzzy multi-objective approach

2013

International Journal of Production Research

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In real dispatch problems for cement-silo trucks, input data or parameters, such as forecasting demand, resources, costs, and the objective function, are often imprecise or fuzzy because some information is incomplete, unavailable, or unobtainable. This work presents a novel fuzzy multi-objective linear programming (FMOLP) model that solves the cement-silo vehicle-dispatch problem in a fuzzy environment. This model is applied to solve multi-source, multi-product, multi-vehicle, and multi-ready-mixed-concrete (RMC) plant vehicle-dispatch problems with imprecise goals, input data, and parameters. This work elucidates the relationship between dispatch planning and RMC plants with a focus on the allocation of cement-silo trucks. This work uses a real cement study case to demonstrate the feasibility of the proposed model. The main contribution of this work is its fuzzy mathematical programming methodology for solving the cementsilo vehicle-dispatch problem in a fuzzy environment. The analytical results can help dispatchers analyse systematically the cost-effectiveness of vehicle-dispatch planning in practical applications.Keywords: cement silos; vehicle dispatching problem; fuzzy multi-objective linear programming 1. Introduction Cement is cheap and heavy, making its transportation costs relatively high. This means that the transportation cost markedly impacts a cement company's profits. Transportation planning and good use of resources are therefore extremely important (Christiansen et al. 2011). Ships and trucks are typically used to transport cement between production location silos and consumption locations. For the transportation processes for cement produced at cement factories, ships transport the cement to silos at consumption ports, and bulk loading equipment is used to load the trucks, which then transport the cement to ready mixed concrete (RMC) plants.Cement silos are in high demand and the number of cement silos at RMC plants is limited. RMC from the silos is dispatched on trucks. However, the limited number of cement-silo trucks cannot meet the demand of RMC plants. Therefore, cement-silo trucks and outsourced trucks are used to meet demand. Modelling the vehicle-dispatch problem for cement-silo trucks is complex. Vehicles are dispatched manually from cement silos, and only rough estimates of the vehicle type and number of trips made by these trucks are available to RMC plants. To meet the demand of RMC plants, cement-silo trucks must make several trips to minimise the total transportation cost. Thus, the vehicle-dispatch problem of cement-silo trucks is to determine the optimal vehicle type and number of vehicles dispatched to each RMC plant.In real dispatch problems for cement-silo trucks, the input data or parameters, such as forecasted demand, amount of resources, costs, and the objective function, are often imprecise or fuzzy because some information is incomplete, unavailable, or unobtainable. Notably, conventional mathematical programming approaches cannot solve all fuzzy programming problems. The...

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Section: Application Of Fuzzy Multi-objective Programmingmentioning

confidence: 99%

Optimal vehicle-dispatch decisions for cement silos using a fuzzy multi-objective approach

2013

International Journal of Production Research

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“…There are several other studies that have been developed in the area of multi-criteria scheduling problem such as: Tavakkoli-Moghaddam et al (2007), Eren (2007), Javadi et al (2008), Chang et al (2008), Ruiz and Allahverdi (2009) and Qian et al (2009). T'kindt and Billaut (2002) presented a quite complete literature review regarding multi-criteria scheduling theory.…”

Section: Introductionmentioning

confidence: 99%

Multicriteria scheduling problem: a hybrid ant colony algorithm integrating the decision-maker’s preferences

Allouche

2017

Int. j. adv. appl. sci.

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The aim of this paper is to present a new hybrid metaheuristic approach based on Ant colony algorithm and the variable neighborhood search noted by ACS_VNS to solve a permutation flowshop scheduling problem. In this context, several criteria are considered which are: the makespan, the total flowtime and the total tardiness of jobs. The proposed approach uses the compromise programming model and the concept of satisfaction function taking into account, explicitly, the decision-maker preferences (DMP). It has been tested through a computational experiment and the obtained results are compared to others for all criteria and for the makespan criterion. The obtained results show the performance of the proposed approach which can be considered as a good tool for multicriteria scheduling problem, especially since it does not necessitate a long computational time.

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“…Karimi et al (2011) investigated group scheduling in flexible flow shops by considering a hybridized approach of ICA and electromagnetic-like mechanism and Jolai et al (2012) studied a novel hybrid meta-heuristic algorithm for a no-wait flexible flow shop scheduling problem with sequence dependent setup times. Javadi et al (2008) investigated no-wait flow shop scheduling using fuzzy multi-objective linear programming.…”

Section: Introductionmentioning

confidence: 99%

An imperialist competitive algorithm for a bi-objective parallel machine scheduling problem with load balancing consideration

Madani-Isfahani¹,

Ghobadian²,

Tekmehdash³

et al. 2013

ijiec

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In this paper, we present a new Imperialist Competitive Algorithm (ICA) to solve a bi-objective unrelated parallel machine scheduling problem where setup times are sequence dependent. The objectives include mean completion time of jobs and mean squares of deviations from machines workload from their averages. The performance of the proposed ICA (PICA) method is examined using some randomly generated data and they are compared with three alternative methods including particle swarm optimization (PSO), original version of imperialist competitive algorithm (OICA) and genetic algorithm (GA) in terms of the objective function values. The preliminary results indicate that the proposed study outperforms other alternative methods. In addition, while OICA performs the worst as alternative solution strategy, PSO and GA seem to perform better.

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No-wait flow shop scheduling using fuzzy multi-objective linear programming

Cited by 33 publications

References 22 publications

Optimal vehicle-dispatch decisions for cement silos using a fuzzy multi-objective approach

Optimal vehicle-dispatch decisions for cement silos using a fuzzy multi-objective approach

Multicriteria scheduling problem: a hybrid ant colony algorithm integrating the decision-maker’s preferences

An imperialist competitive algorithm for a bi-objective parallel machine scheduling problem with load balancing consideration

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