Bi-criteria Flow Shop Research Articles

The solution of bi-criteria flow shop scheduling problem through mathematical modelling and its application in a shoe factory

The solution of bi-criteria flow shop scheduling problem through mathematical modelling and its application in a shoe factory

The Solution of Bi-Criteria Flow Shop Scheduling Problem through Mathematical Modelling and its Application in a Shoe Factory

The Solution of Bi-Criteria Flow Shop Scheduling Problem through Mathematical Modelling and its Application in a Shoe Factory

A multi-machine bicriteria flow shop scheduling with sequence dependent setup time

A multi-machine bicriteria flow shop scheduling with sequence dependent setup time

Bi-criteria two-machine flow shop scheduling problem involving sequence-dependent setup times and pieces multiplicity

Bi-criteria two-machine flow shop scheduling problem involving sequence-dependent setup times and pieces multiplicity

Integrated stochastic bicriteria flow shop scheduling problem with preventive maintenance

In this paper, the concept of flexible and multiple preventive maintenance activities is introduced by reformulating classical bicriteria permutation flow shop scheduling problem into stochastic scheduling with objective to minimise system utilisation time with minimum makespan. The mathematical model is formulated to handle the uncertainties in job processing time and the time interval for machine maintenance. The problem of stochastic flow shop scheduling environment with maintenance activities, is NP-hard. Therefore, the advanced metaheuristic is implemented to solve large scale instances of referred problem. Extensive computational analysis is carried out to depict the superiority of proposed metaheuristic in terms of effectiveness and efficacy.

Desert sparrow optimization algorithm for the bicriteria flow shop scheduling problem with sequence-independent setup time

Desert sparrow optimization algorithm for the bicriteria flow shop scheduling problem with sequence-independent setup time

Integrated Stochastic Bicriteria Flow shop Scheduling Problem with Preventive Maintenance

Integrated Stochastic Bicriteria Flow shop Scheduling Problem with Preventive Maintenance

On a bi-criteria flow shop scheduling problem under constraints of blocking and sequence dependent setup time

In this paper, we propose a bi-criteria optimization model for a flow shop scheduling problem with permutation, blocking and sequence dependent setup time. Indeed, these constraints are the most encountered in the industrial field, which demands high command flexibility. The objective is the minimization of two criteria, in our case the makespan and the total tardiness combined in a single objective function with a weighting coefficient for each criterion. To solve this problem, we propose a mixed integer linear programming method and a set of different metaheuristics. The suggested metaheuristics are; the genetic algorithm, the iterated greedy metaheuristic and the iterative local search algorithm. This last algorithm is proposed in two ways of exploration of the neighborhood. To verify the effectiveness of our resolution algorithms, a set of instances with n jobs and m machines is randomly generated from small instances to relatively large size ones. The analysis of the suggested simulation model allowed us to note that the iterative local search algorithm gives good results compared to the iterative greedy algorithm. Moreover, it was found that the weighting parameter plays an essential role in the problem decision making. However, it was established that it is difficult to find a good solution that minimizes both criteria at once, a suitable compromise will be necessary to be adopted using the weighting coefficient.

A hybrid data envelopment analysis_decision tree approach to evaluate the bi-criteria flow shop with blocking problem

The purpose of this paper is to evaluate the overall performance of a bi-objective scheduling problem using a method based on a combination of data envelopment analysis and decision tree. The hybrid approach is applied to the blocking flow shop scheduling problem with both makespan and total tardiness objectives. For this end, we propose two families of three-phase heuristics. In phase one, several constructive heuristics are used to generate initial solutions and in phase two, two improving heuristics are used to generate non-dominated solutions. One of the improving heuristics is based on the makespan criteria and the other one is based on the total tardiness criteria. The non-dominated solutions are then compared using the free disposal hull (FDH) formulation of data envelopment analysis (DEA). The overall performance of the composed heuristics is measured using a decision tree approach.

A hybrid data envelopment analysis_decision tree approach to evaluate the bi-criteria flow shop with blocking problem

The purpose of this paper is to evaluate the overall performance of a bi-objective scheduling problem using a method based on a combination of data envelopment analysis and decision tree. The hybrid approach is applied to the blocking flow shop scheduling problem with both makespan and total tardiness objectives. For this end, we propose two families of three-phase heuristics. In phase one, several constructive heuristics are used to generate initial solutions and in phase two, two improving heuristics are used to generate non-dominated solutions. One of the improving heuristics is based on the makespan criteria and the other one is based on the total tardiness criteria. The non-dominated solutions are then compared using the free disposal hull (FDH) formulation of data envelopment analysis (DEA). The overall performance of the composed heuristics is measured using a decision tree approach.

Recent advances in hybrid evolutionary algorithms for multiobjective manufacturing scheduling

In real manufacturing systems there are many combinatorial optimization problems (COP) imposing on more complex issues with multiple objectives. However it is very difficult for solving the intractable COP problems by the traditional approaches because of NP-hard problems. For developing effective and efficient algorithms that are in a sense “good,” i.e., whose computational time is small as within 3min, we have to consider three issues: quality of solution, computational time and effectiveness of the nondominated solutions for multiobjective optimization problem (MOP).In this paper, we focus on recent hybrid evolutionary algorithms (HEA) to solve a variety of single or multiobjective scheduling problems in manufacturing systems to get a best solution with a smaller computational time. Firstly we summarize multiobjective hybrid genetic algorithm (Mo-HGA) and hybrid sampling strategy-based multiobjective evolutionary algorithm (HSS-MoEA) and then propose HSS-MoEA combining with differential evolution (HSS-MoEA-DE). We also demonstrate those hybrid evolutionary algorithms to bicriteria automatic guided vehicle (B-AGV) dispatching problem, robot-based assembly line balancing problem (R-ALB), bicriteria flowshop scheduling problem (B-FSP), multiobjective scheduling problem in thin-film transistor-liquid crystal display (TFT-LCD) module assembly and bicriteria process planning and scheduling (B-PPS) problem. Also we demonstrate their effectiveness of the proposed hybrid evolutionary algorithms by several empirical examples.

Bi-criteria multistage flow shop scheduling with sequence-dependent setup times

Flow shop scheduling with sequence-dependent setup time is the most complicated case of scheduling of jobs. The operational cost of machines is related to the setup time required for processing of jobs on different machines. To express the randomness probabilities are associated with processing times of different attributes on given machines. The objective is to schedule n-jobs on m-machines to minimise operational cost of machines subject to minimum total elapsed time of jobs in a flow shop whenever the setup times on machines are sequence-dependent. As the problem is NP-hard, a heuristic algorithm is proposed to find the latest time so as to reduce operating time and hence the operational cost with minimum makespan. A numerical example is also given to substantiate the algorithm.

A heuristic genetic algorithm for Bi-Criteria flow shop scheduling with fuzzy processing time and due time

A heuristic genetic algorithm for Bi-Criteria flow shop scheduling with fuzzy processing time and due time

A new approach for multi-criteria scheduling

In this paper we propose a new approach, recommended for solving certain class of multi-criteria scheduling problems, with the use of cloud exploration of the solution space supported by fine-grained parallel computing. The approach allow us to approximate Pareto front more accurately than other known algorithms in competitive time. To show and check advantageous properties of the proposed approach, the new solution algorithm, called VESA, was implemented for the case of bi-criteria flow shop scheduling problem and tested against a number of high-quality benchmarks known in the literature. Vector processing technologies are used to enhance the efficiency of solution search and acceptance rates for the extended simulated annealing metaheuristic. Results are compared using, among others, the independent Hyper-Volume Indicator (IH) measure. Computer test of VESA confirms excellent approximation of the Pareto front.

Optimizing Bicriteria Flow Shop Scheduling Problem by Simulated Annealing Algorithm

We consider the flow shop scheduling problem with minimizing two criteria simultaneously: the total completion time (makespan) and the sum of tardiness of jobs. The problem is strongly NP-hard, since for each separate criteria the problem is strongly NP-hard. There is a number of heuristic algorithms to solve the flow shop problem with various single objectives, but usage of those heuristics to multi-criteria flow shop problems is rather limited. In this paper we propose a new idea of the use of simulated annealing method to solve certain multi-criteria problem. Especially, we define a new acceptance rules and the mechanism of moving the search in different regions of solution space by using so called drift. To illustrate quality of the proposed approach, we present results of the computational experiment provided on well known benchmarks.

Bi-criteria flow shop scheduling with fuzzy simulated annealing algorithm

A new mathematical model for a flow shop scheduling problem is presented in the fuzzy environment. It proposed bi-criteria to be maximized, that is, minimal satisfaction degree with respect to the earliness of jobs and, minimal satisfaction degree of tardiness of jobs. A simulated annealing algorithm was proposed to solve this model in the fuzzy environment. This method was implemented in MATLAB 7.0 software. Key words: Simulated annealing, bi-criteria, fuzzy, earliness and tardiness penalty, flow shop scheduling.

Multi-objective Local Search Combined with NSGA-II for Bi-criteria Permutation Flow Shop Scheduling Problem

This paper proposed a multi-objective local search procedure (MOLS). It is combined with NSGA-II for solving bi-criteria PFSP with the objectives of minimizing makespan and maximum tardiness. Utilizing the properties of active blocks for flow shop scheduling problem, neighborhood structures MOINS (multi-objective insertion) and MOEXC (multi-objective exchange) are designed in order to improve efficiency of perturbation. Any perturbation based on MOINS and MOEXC takes effect on different criteria simultaneously. The original idea of MOLS is systematic change neighborhoods in the local search procedure. The search direction of MOLS on an individual is naturally guided by interaction of MOINS and MOEXC. Moreover, there is no need to set parameters in MOLS. The MOLS combined with popular multi-objective evolutionary algorithm NSGA-II (Non-dominated Sorting Genetic Algorithm-II) is called as “NSGA-II-MOLS”. To illustrate the efficacy of proposed approach, four different scaled problems are used to test performance of NSGA-II-MOLS. The numerous comparisons show efficacy of NSGA-II-MOLS is better than most of algorithms even with the same number of individual evaluations and parameters setting.

Scheduling a bi-criteria flowshop manufacturing cell with sequence-dependent family setup times

Flowshop manufacturing cell scheduling problems (FMCSPs) with sequence-dependent family setup times (SDFSTs) have become a key area in the field of scheduling. Though the need to consider multiple criteria in real-world scheduling is widely recognised, most of the currently available algorithms for FMCSPs with SDFSTs only deal with the optimisation of a single performance criterion. In this study, a two-level multi-start simulated annealing (TLMSA) heuristic was presented for this problem with the objectives of simultaneously minimising makespan and total flow time (or total tardiness). To validate the performance of the proposed approach, the obtained sets of non-dominated solutions were evaluated by seven multi-objective performance indicators. The computational results showed that the proposed TLMSA significantly outperforms the benchmark algorithm in terms of the seven performance indicators. It is believed that it can serve as a new benchmark approach for future research on the two FMCSPs with SDFSTs addressed in this study.

A greedy heuristic and simulated annealing approach for a bicriteria flowshop scheduling problem with precedence constraints—a practical manufacturing case

This paper considers a flowshop scheduling problem with two criteria, where the primary (dominant) criterion is the minimization of material waste and the secondary criterion is the minimization of the total tardiness time. The decision maker does not authorize trade-offs between the criteria. In view of the nature of this problem, a hierarchical (lexicographical) optimization approach is followed. An effective greedy heuristic is proposed to minimize the material waste and a simulated annealing (SA) algorithm is developed to minimize the total tardiness time, subjective to the constraint computed for the primary criterion. The solution accuracy is compared with the optimal solution obtained by complete enumeration for randomly generated problem sets. From the results, it is observed that the greedy heuristic produces the optimal solution and the SA solution does not differ significantly from the optimal solution.

Comments on “A bicriteria flowshop scheduling problem with setup times”

The aim of this paper is to point out that the integer programming model proposed by Eren and Güner [T. Eren, E. Güner, A bicriteria flowshop scheduling problem with setup times, Appl. Math. Comput. 183 (2006) 1292–1300] is incorrect. We propose a new integer programming model for the same scheduling problem based on their model.