Nasser Salmasi

In this research, we consider the flowshop batch processing problem (FBPP) with minimization of makespan, in which the composition of batches can change on different machines. A batch capacity of a machine restricts not only the maximum number of jobs, but also the total attribute size of jobs assigned to the batch processed on the machine. This is the first time that the FBPP is considered for different batch compositions on machines with respect to both the total size and the number of jobs… Show more

In this research, we consider the flowshop batch processing problem (FBPP) with minimization of makespan, in which the composition of batches can change on different machines. A batch capacity of a machine restricts not only the maximum number of jobs, but also the total attribute size of jobs assigned to the batch processed on the machine. This is the first time that the FBPP is considered for different batch compositions on machines with respect to both the total size and the number of jobs assigned to batches. We propose a mixed-integer linear programming model for the research problem. Since this problem is shown to be NP-hard, several meta-heuristic algorithms based on particle swarm optimization (PSO), enhanced with local search structures, are proposed to solve the research problem heuristically. To have more diversity, different rules are implemented to generate the initial population of the PSO algorithms. Two lower bounding mechanisms are also proposed to generate good quality lower bounds for special cases of the research problem and, consequently, evaluate the performance of the proposed PSO algorithms. A data generation mechanism has been developed in a way that it fairly reflects the real industry requirements. The proposed PSO algorithms are examined by different numerical experiments and the results affirm the efficiency of the proposed algorithms. Show less

In this research, the no-wait flowshop sequence dependent group scheduling problem with minimization of total completion time, noted as F_m |nwt,fmls,S_plk | ∑▒C_j is approached. A mixed integer linear mathematical model is proposed for the research problem. Due to the NP-hard nature of the proposed problem, several metaheuristic algorithms based on particle swarm optimization and variable neighbourhood search with different neighbourhood search structures are proposed to solve the problem… Show more

In this research, the no-wait flowshop sequence dependent group scheduling problem with minimization of total completion time, noted as F_m |nwt,fmls,S_plk | ∑▒C_j is approached. A mixed integer linear mathematical model is proposed for the research problem. Due to the NP-hard nature of the proposed problem, several metaheuristic algorithms based on particle swarm optimization and variable neighbourhood search with different neighbourhood search structures are proposed to solve the problem, heuristically. Also, an efficient heuristic algorithm is proposed to generate the initial feasible solutions for the proposed algorithms. The performances of the proposed algorithms are compared by using available test problems in the literature. Based on the results, the proposed metaheuristics with structure oriented initial solutions have a better performance than the algorithms with random-generated initial solutions. Show less

In this research, the flexible flowline scheduling problem with minimization of makespan as the objective by considering constraints for the beginning and terminating times of processing the jobs at stages is investigated for the first time. The process of jobs at some stages cannot be started before a specific time and should be completed before another specific time. Since the process of jobs at each stage should be performed at an interval time, in spite of regular scheduling problems, every… Show more

In this research, the flexible flowline scheduling problem with minimization of makespan as the objective by considering constraints for the beginning and terminating times of processing the jobs at stages is investigated for the first time. The process of jobs at some stages cannot be started before a specific time and should be completed before another specific time. Since the process of jobs at each stage should be performed at an interval time, in spite of regular scheduling problems, every schedule cannot be considered as a feasible solution. A mathematical model is developed to solve the proposed research problem optimally. Since the research problem is shown to be NP_hard, several hybrid meta-heuristic algorithms based on particle swarm optimization (PSO) and simulated annealing (SA) are proposed to heuristically solve large sized problems. In these algorithms, for each renewed particle in PSO algorithm, a local search is performed based on SA. The major difference among the proposed algorithms is the rules used to perform the local search. The performances of the proposed algorithms are compared based on randomly generated test problems. The results show that the best proposed metaheurestic algorithms have a good performance with the average percentage gap of 0.289% compared to the optimal solution for the test problems can be solved optimally. Show less

The nurse scheduling problem (NSP) has received a great amount of attention in recent years. In the NSP, the goal is to assign shifts to the nurses in order to satisfy the hospital’s demand during the planning horizon by considering different objective functions. In this research, we focus on maximizing the nurses’ preferences for working shifts and weekends off by considering several important factors such as hospital’s policies, labor laws, governmental regulations, and the status of nurses… Show more

The nurse scheduling problem (NSP) has received a great amount of attention in recent years. In the NSP, the goal is to assign shifts to the nurses in order to satisfy the hospital’s demand during the planning horizon by considering different objective functions. In this research, we focus on maximizing the nurses’ preferences for working shifts and weekends off by considering several important factors such as hospital’s policies, labor laws, governmental regulations, and the status of nurses at the end of the previous planning horizon in one of the largest hospitals in Iran i.e., Milad Hospital. Due to the shortage of available nurses, at first the minimum total number of required nurses is determined. Then, a mathematical programming model is proposed to solve the problem optimally. Since the proposed research problem is NP-hard, a meta-heuristic algorithm based on simulated annealing (SA) is applied to heuristically solve the problem in a reasonable time. An initial feasible solution generator and several novel neighborhood structures are applied to enhance performance of the SA algorithm. Inspired from our observations in Milad hospital, random test problems are generated to evaluate the performance of the SA algorithm. The results of computational experiments indicate that the applied SA algorithm provides solutions with average percentage gap of 5.49% compared to the upper bounds obtained from the mathematical model. Moreover, the applied SA algorithm provides significantly better solutions in a reasonable time than the schedules provided by the head nurses. Show less

The NP-hard single machine sequence-dependent group scheduling problem with
minimization of total weighted earliness and tardiness is investigated. An arc-time-indexed
formulation is presented and a lagrangian-based branch-and-bound algorithm is proposed. The lagrangian relaxation of the arc-time-indexed formulation can be solved as a shortest path problem. Results of an extensive computational study demonstrate the efficacy of the proposed algorithm and establish the sensitivity of the… Show more

The NP-hard single machine sequence-dependent group scheduling problem with
minimization of total weighted earliness and tardiness is investigated. An arc-time-indexed
formulation is presented and a lagrangian-based branch-and-bound algorithm is proposed. The lagrangian relaxation of the arc-time-indexed formulation can be solved as a shortest path problem. Results of an extensive computational study demonstrate the efficacy of the proposed algorithm and establish the sensitivity of the proposed algorithm to the setting of its parameters. Show less

This research considers a flexible flowshop sequence-dependent group scheduling problem with minimization of total completion time. A mixed integer linear mathematical model for the research problem is developed. Since the research problem is shown to be strongly NP-hard, a metaheuristic algorithm based on memetic algorithm (MA) is proposed. A lower bounding method based on the Branch and Price algorithm is also proposed to evaluate the quality of the MA. In order to evaluate the performance of… Show more

This research considers a flexible flowshop sequence-dependent group scheduling problem with minimization of total completion time. A mixed integer linear mathematical model for the research problem is developed. Since the research problem is shown to be strongly NP-hard, a metaheuristic algorithm based on memetic algorithm (MA) is proposed. A lower bounding method based on the Branch and Price algorithm is also proposed to evaluate the quality of the MA. In order to evaluate the performance of the proposed algorithms, random test problems, ranging in size from small, medium, to large are generated and solved by the MA and the lower bounding method. The results show that the average percentage gap of the MA is 6.03 % compared to the result of the lower bounding method for randomly generated test problems. Show less

In this research, the parallel machine scheduling problem with preemption by considering a constant transportation delay for migrated jobs and minimization of makespan as the criterion i.e., Pm|pmtn(delay)|Cmax is investigated. It is assumed that when a preempted job resumes on another machine, it is required a delay between the processing time of the job on these two machines. This delay is called transportation delay. We criticize an existing mathematical model for the research problem in the… Show more

In this research, the parallel machine scheduling problem with preemption by considering a constant transportation delay for migrated jobs and minimization of makespan as the criterion i.e., Pm|pmtn(delay)|Cmax is investigated. It is assumed that when a preempted job resumes on another machine, it is required a delay between the processing time of the job on these two machines. This delay is called transportation delay. We criticize an existing mathematical model for the research problem in the literature [Boudhar M, Haned A. Preemptive scheduling in the presence of transportation times. Computers & Operations Research 2009; 36(8):2387–93]. Then, we prove that there exists an optimal schedule with at most (m−1) preemptions with transportation among machines for the problem. We also propose a linear programming formulation and an exact algorithm for the research problem in case of equal transportation delay. The experiments show that the proposed exact algorithm performs better than the proposed mathematical model. Show less

In this research, a permutation flowshop sequence-dependent group scheduling problem with minimisation of total completion time is considered. Since the problem is shown to be strongly NP-hard, a hybrid genetic (HG) algorithm is proposed. The only available lower bounding (LB) method for the proposed research problem in the literature based on branch and price (B&P) algorithm is also enhanced by proposing efficient method to solve sub-problems and proposing a better branching rule. A… Show more

In this research, a permutation flowshop sequence-dependent group scheduling problem with minimisation of total completion time is considered. Since the problem is shown to be strongly NP-hard, a hybrid genetic (HG) algorithm is proposed. The only available lower bounding (LB) method for the proposed research problem in the literature based on branch and price (B&P) algorithm is also enhanced by proposing efficient method to solve sub-problems and proposing a better branching rule. A statistical comparison shows that both the proposed HG algorithm and the proposed LB have better performance than the other methods from the literature with an average 5.96% percentage gap. [Received 13 December 2011; Revised 24 May 2012; Revised 15 November 2012; Accepted 16 November 2012] Show less

In this research, the flexible flowshop sequence-dependent group scheduling problem with minimisation of makespan as the criterion () is investigated. A mixed integer linear mathematical model for the research problem is developed. Since the research problem is shown to be NP-hard, a meta-heuristic algorithm based on memetic algorithm (MA) is developed to efficiently solve the problem. Also, a lower bounding technique based on the developed mathematical model is proposed to evaluate the quality… Show more

In this research, the flexible flowshop sequence-dependent group scheduling problem with minimisation of makespan as the criterion () is investigated. A mixed integer linear mathematical model for the research problem is developed. Since the research problem is shown to be NP-hard, a meta-heuristic algorithm based on memetic algorithm (MA) is developed to efficiently solve the problem. Also, a lower bounding technique based on the developed mathematical model is proposed to evaluate the quality of the proposed MA. The performance of the proposed MA is compared with the existing algorithm in the literature, i.e. tabu search (TS), by solving the available test problems in the literature. A comparison based on paired t-test shows that the average makespan of the proposed MA is 3% lower than the average makespan of the TS. The average percentage gap of MA for small-size problems comparing with the optimal solution is 0.8%. Also, the average percentage gap of the proposed MA compared to the proposed lower bound for medium-size test problems (problems up to 65 jobs in all groups) is 5%. Show less

This paper considers two different due date assignment and sequencing problems in single machine where the processing times of jobs are random variables. The first problem is to minimise the maximum due date so that all jobs are stochastically on time. It is shown that sequencing the jobs in decreasing service level (DSL) order optimally solves the problem. The results are then extended for two special cases of flow shop problem. The other problem is to minimise a total cost function which is a… Show more

This paper considers two different due date assignment and sequencing problems in single machine where the processing times of jobs are random variables. The first problem is to minimise the maximum due date so that all jobs are stochastically on time. It is shown that sequencing the jobs in decreasing service level (DSL) order optimally solves the problem. The results are then extended for two special cases of flow shop problem. The other problem is to minimise a total cost function which is a linear combination of three penalties: penalty on job earliness, penalty on job tardiness, and penalty associated with long due date assignment. The assignment of a common due date and distinct due dates are investigated for this problem. It is shown that the optimal sequence for the case of common due date is V-shaped. Show less

In this research, the no-wait flowshop sequence-dependent setup time scheduling problem with minimization of weighted earliness and tardiness penalties as the criterion, typically classified as View the MathML source, is investigated. A mixed integer linear programming model for the research problem is proposed. As the problem is shown to be strongly NP-hard, several metaheuristic algorithms based on tabu search (TS) and particle swarm optimization (PSO) algorithms are developed to… Show more

In this research, the no-wait flowshop sequence-dependent setup time scheduling problem with minimization of weighted earliness and tardiness penalties as the criterion, typically classified as View the MathML source, is investigated. A mixed integer linear programming model for the research problem is proposed. As the problem is shown to be strongly NP-hard, several metaheuristic algorithms based on tabu search (TS) and particle swarm optimization (PSO) algorithms are developed to heuristically solve the problem. A timing algorithm is generated to find the optimal schedule and calculate the objective function value of a given sequence. In order to compare the performance of the proposed algorithms, random test problems are generated and solved by all metaheuristic algorithms. Computational results show that the PSO algorithm has better performance than TS algorithm especially for the large sized problems. Show less

In this research, minimizing the expected number of tardy jobs in a dynamic m machine flow-shop scheduling problem, i.e., Fm∣∣rj∣∣E[∑Uj] is investigated. It is assumed that the jobs with deterministic processing times and stochastic due dates arrive randomly to the flow-shop cell. The due date of each job is assumed to be normally distributed with known mean and variance. A dynamic method is proposed for this problem by which the m machine stochastic flow-shop problem is decomposed into m… Show more

In this research, minimizing the expected number of tardy jobs in a dynamic m machine flow-shop scheduling problem, i.e., Fm∣∣rj∣∣E[∑Uj] is investigated. It is assumed that the jobs with deterministic processing times and stochastic due dates arrive randomly to the flow-shop cell. The due date of each job is assumed to be normally distributed with known mean and variance. A dynamic method is proposed for this problem by which the m machine stochastic flow-shop problem is decomposed into m stochastic single-machine sub-problems. Then, each sub-problem is solved as an independent stochastic single-machine scheduling problem by a mathematical programming model. Comparison of the proposed method with the most effective rule of thumb for the proposed problem, i.e., shortest processing time first rule shows that the proposed method performs 23.9 % better than the SPT rule on average for industry-size scheduling problems. Show less

In this research, two scheduling problems i.e., single machine scheduling problem with minimizing the number of tardy jobs (1∥∑Uj) and two machine flow shop scheduling problem with a common due date and minimizing the number of tardy jobs (F2|dj=d|∑Uj) are investigated in a stochastic setting in the class of non-preemptive static list policies. It is assumed that the processing times of jobs are independent random variables. The stochastic problems are solved based on chance constrained… Show more

In this research, two scheduling problems i.e., single machine scheduling problem with minimizing the number of tardy jobs (1∥∑Uj) and two machine flow shop scheduling problem with a common due date and minimizing the number of tardy jobs (F2|dj=d|∑Uj) are investigated in a stochastic setting in the class of non-preemptive static list policies. It is assumed that the processing times of jobs are independent random variables. The stochastic problems are solved based on chance constrained programming. An equivalent deterministic problem is generated for each stochastic problem by linearization of the chance constraints. Then, the generated deterministic problems are solved using efficient algorithms, which have been developed for the deterministic version of the problems. Several numerical examples are presented to illustrate the solution methods. Show less

This research deals with a flexible flowshop scheduling problem with the arrival and delivery of jobs in groups and processing them individually. Each group of jobs has a specific release time. Due to the special characteristics of each job, only a specific group of machines in each stage are eligible to process that job. All jobs in a group should be delivered at the same time after processing. The objectives of the problem are the minimization of the sum of the completion time of groups on… Show more

This research deals with a flexible flowshop scheduling problem with the arrival and delivery of jobs in groups and processing them individually. Each group of jobs has a specific release time. Due to the special characteristics of each job, only a specific group of machines in each stage are eligible to process that job. All jobs in a group should be delivered at the same time after processing. The objectives of the problem are the minimization of the sum of the completion time of groups on one hand and the minimization of sum of the differences between the completion time of jobs and the delivery time of the group containing that job (waiting period) on the other hand. The problem can be stated as FFc /r j , M j /irreg based on existing scheduling notations. This problem has many applications in the production and service industries such as ceramic tile manufacturing companies and restaurants. A mathematical model has been proposed to solve the problem. Since the research problem is shown to be NP-complete, a particle swarm optimization (PSO) algorithm is applied to solve the problem approximately. Based on the frequency of using local search procedure, four scenarios of PSO have been developed. The algorithms are compared by applying experimental design techniques on random test problems with different sizes. The results show that the PSO algorithm enhanced with local search for all particles has a weaker performance than the other scenarios in solving large-sized problems. Show less

Data Mining, Supplier’s Evaluation, Credit Levels, Neural Networks, Criteria
Importance

Flow shop scheduling problems with sequence-dependent setup times and minimising the number of tardy jobs as the criterion (Fm |prmu, Sijk |ΣUj ) are considered in this research. A mixed-integer linear programming model is developed for the research problem. Since the proposed research problem has been proven to be NP-hard, several meta-heuristic algorithms based on tabu search (TS) and the imperialist competitive algorithm (ICA) are proposed to heuristically solve the problem. In order to find… Show more

Flow shop scheduling problems with sequence-dependent setup times and minimising the number of tardy jobs as the criterion (Fm |prmu, Sijk |ΣUj ) are considered in this research. A mixed-integer linear programming model is developed for the research problem. Since the proposed research problem has been proven to be NP-hard, several meta-heuristic algorithms based on tabu search (TS) and the imperialist competitive algorithm (ICA) are proposed to heuristically solve the problem. In order to find the best meta-heuristic algorithm, random test problems, ranging in size from small, medium, to large, are generated and solved by the meta-heuristic algorithms. Then, a detailed statistical experiment based on the split-plot design is performed to find the best meta-heuristic algorithm. The results of the experiment show that the performance of ICA is worse than the other algorithms for small- and medium-sized problems. The hybrid of ICA and the TS algorithm provides better performance than the other proposed algorithms for large-sized problems. Show less

In this paper, the flexible flow shop sequence-dependent group scheduling problem (FFSDGS) with minimisation of makespan as the criterion (FFm  | fmls, Splk  | C max) is investigated. For the first time a mathematical model for the proposed research problem is developed. Since the problem is shown to be NP-hard, six metaheuristic algorithms based on tabu search (TS) are developed to efficiently solve the problem. The proposed metaheuristics are different to the only available metaheuristic… Show more

In this paper, the flexible flow shop sequence-dependent group scheduling problem (FFSDGS) with minimisation of makespan as the criterion (FFm  | fmls, Splk  | C max) is investigated. For the first time a mathematical model for the proposed research problem is developed. Since the problem is shown to be NP-hard, six metaheuristic algorithms based on tabu search (TS) are developed to efficiently solve the problem. The proposed metaheuristics are different to the only available metaheuristic algorithm in the literature based on TS. By applying randomised complete block design and using available test problems in the literature, the best of the proposed TS algorithms in this research is identified. The performance of the best developed metaheuristic algorithm is then compared with the existing algorithm in the literature by solving the test problems, also available in the literature, ranging in size from small, medium, to large. A comparison based on paired t-test at 95% confidence interval, shows that the best proposed algorithm in this research has a better performance than the existing algorithm in the literature with an average percentage deviation of around 1.0% for medium and large size problems. Show less

Group scheduling within the context of sequence dependent setup times in flexible flow shop with minimizing the makespan as the objective (FFm|fmls, Splr|Cmax) is considered in this research. A mixed integer linear programming model is developed for the research problem. Since the proposed research problem is proved to be NP-hard, two different metaheuristic approaches based on simulated annealing (SA) are developed to solve the problem heuristically. Since generating the initial solution in… Show more

Group scheduling within the context of sequence dependent setup times in flexible flow shop with minimizing the makespan as the objective (FFm|fmls, Splr|Cmax) is considered in this research. A mixed integer linear programming model is developed for the research problem. Since the proposed research problem is proved to be NP-hard, two different metaheuristic approaches based on simulated annealing (SA) are developed to solve the problem heuristically. Since generating the initial solution in the area of flexible flow shop has its own complexities, four different initial solution (IS) finding mechanisms with varying levels of computational difficulty are also developed to aid the search algorithms in identifying an IS. For evaluating the proposed algorithms, the makespan value and the elapsed time to solve the test problems are considered as two response variables representing effectiveness and efficiency of the algorithms. Based on obtained results in the aspect of makespan, the proposed SA algorithm in which the sequence of groups and jobs in the first stage provides the initial solution with initial solution random generate mechanism is recommended for all sizes. For the elapsed time the SA algorithm in which the sequence of groups and jobs in the first stage is used as an initial solution with initial solution random generate mechanism provides a better result than the other proposed algorithms. The performance of the metaheuristic algorithm is compared with the only available metaheuristic algorithm in literature, i.e., tabu search to evaluate the quality of the proposed algorithm. The results show that the proposed SA algorithm in this research has a superior performance than the tabu search based on a paired t-test comparison. Show less

This paper focuses on the flow shop sequence dependent group scheduling (FSDGS) problem with minimisation of total completion time as the criterion (Fm|fmls, prmu, Splk|ΣCJ). The research problem is formulated in form of two different mixed integer linear programming (MILP) models. Comparing with the latest MILP model for the proposed problem in the literature, the complexity size of the proposed models are significantly reduced. One of the proposed mathematical models is so effective that even… Show more

This paper focuses on the flow shop sequence dependent group scheduling (FSDGS) problem with minimisation of total completion time as the criterion (Fm|fmls, prmu, Splk|ΣCJ). The research problem is formulated in form of two different mixed integer linear programming (MILP) models. Comparing with the latest MILP model for the proposed problem in the literature, the complexity size of the proposed models are significantly reduced. One of the proposed mathematical models is so effective that even medium-sized instances (problems up to 60 jobs in all groups) are solved to optimality in a reasonable amount of time. Moreover, a metaheuristic hybridising genetic and simulated annealing algorithm, called GSA, is proposed to solve the problems heuristically. All the results and analyses show the high performance of the proposed mathematical models as well as the proposed metaheuristic algorithm compared to the available ones in literature. Show less

This paper investigates permutation flow shop scheduling problems with sequence-dependent setup times with minimizing the number of tardy jobs as criterion (Fm|prmu, Sijk|∑Uj). Since the proposed research problem has been proven to be NP-hard, three different meta heuristic algorithms based on tabu search (TS) has been proposed to solve the problem. These three algorithms are different based on their tabu list characteristics. In order to evaluate the performance of the proposed TS algorithms… Show more

This paper investigates permutation flow shop scheduling problems with sequence-dependent setup times with minimizing the number of tardy jobs as criterion (Fm|prmu, Sijk|∑Uj). Since the proposed research problem has been proven to be NP-hard, three different meta heuristic algorithms based on tabu search (TS) has been proposed to solve the problem. These three algorithms are different based on their tabu list characteristics. In order to evaluate the performance of the proposed TS algorithms, test problems in different ranges are generated to find the best algorithm. The comparison shows that the TS algorithm which its tabu list keeps track of the slots that the jobs are assigned has a better performance than the other algorithms Show less

The flowshop sequence dependent group scheduling problem with minimization of makespan as the objective (F m |fmls, S plk, prmu|C max ) is considered in this paper. It is assumed that several groups with different number of jobs are assigned to a flow shop cell that has m machines. The goal is to find the best sequence of processing the jobs in each group and the groups themselves with minimization of makespan as the objective. A mathematical model for the research problem is developed in this… Show more

The flowshop sequence dependent group scheduling problem with minimization of makespan as the objective (F m |fmls, S plk, prmu|C max ) is considered in this paper. It is assumed that several groups with different number of jobs are assigned to a flow shop cell that has m machines. The goal is to find the best sequence of processing the jobs in each group and the groups themselves with minimization of makespan as the objective. A mathematical model for the research problem is developed in this paper. As the research problem is shown to be NP-hard, a hybrid ant colony optimization (HACO) algorithm is developed to solve the problem. A lower bounding technique based on relaxing a few constraints of the mathematical model developed for the original problem is proposed to evaluate the quality of the HACO algorithm. Three different problem structures, with two, three, and six machines, are used in the generation of the test problems to test the performance of the algorithm and the lower bounding technique developed. The results obtained from the HACO algorithm and those that have appeared in the published literature are also compared. The comparative results show that the HACO algorithm has a superior performance compared to the best available algorithm based on memetic algorithm with an average percentage deviation of around 1.0% from the lower bound. Show less

Particle Swarm Optimization (PSO) algorithm for a Flow Shop Sequence Dependent Group
Scheduling (FSDGS) problem, Σ with minimization of total flow time as the criterion (Fm|fmls, Splk, prmu| Cj), is proposed in this research. An encoding scheme based on Ranked Order Value (ROV) is developed, which converts the continuous position value of particles in PSO to job and group permutations. A neighborhood search strategy, called Individual Enhancement (IE), is fused to enhance the search and to… Show more

Particle Swarm Optimization (PSO) algorithm for a Flow Shop Sequence Dependent Group
Scheduling (FSDGS) problem, Σ with minimization of total flow time as the criterion (Fm|fmls, Splk, prmu| Cj), is proposed in this research. An encoding scheme based on Ranked Order Value (ROV) is developed, which converts the continuous position value of particles in PSO to job and group permutations. A neighborhood search strategy, called Individual Enhancement (IE), is fused to enhance the search and to balance the exploration and exploitation. The performance of the algorithm is compared with the best available meta-heuristic algorithm in literature, i.e. the Ant Colony Optimization (ACO) algorithm, based on available test problems. The results show that the proposed algorithm has a superior performance to the ACO algorithm. Show less

In this research, we develop an approach to investigate the sensitivity of the parameters of data envelopment analysis technique and determine the stable region of a unit in comparison with others. For this purpose, first we develop a mathematical model to determine the closest boundary on which the specific unit will be projected by changing its data. Then, by applying this model we introduce an algorithm to define the stability region of that unit. This approach requires less calculation and… Show more

In this research, we develop an approach to investigate the sensitivity of the parameters of data envelopment analysis technique and determine the stable region of a unit in comparison with others. For this purpose, first we develop a mathematical model to determine the closest boundary on which the specific unit will be projected by changing its data. Then, by applying this model we introduce an algorithm to define the stability region of that unit. This approach requires less calculation and also relaxes many restrictions which are included in other approaches. Show less

Artificial neural networks (ANNs) have been widely used in modelling and control of many practical industrial nonlinear processes. However, most of them have focused on a general approach for input selection. In this paper, a hierarchical hybrid approach of partial least squared (PLS) analysis and ANNs has been applied for predictive control of a real drying process in ceramic tile manufacturing. This approach is employed in order to promote the reliability of neural network model via reduction… Show more

Artificial neural networks (ANNs) have been widely used in modelling and control of many practical industrial nonlinear processes. However, most of them have focused on a general approach for input selection. In this paper, a hierarchical hybrid approach of partial least squared (PLS) analysis and ANNs has been applied for predictive control of a real drying process in ceramic tile manufacturing. This approach is employed in order to promote the reliability of neural network model via reduction of the input set dimension. First, PLS analysis is done to arrive at the significant factors that influence the spray drying quality the most. Also, the significant factors are used to construct the predictive neural network model so called the Focus-NN model. Next, the reliability of the Focus-NN model is compared with the ANN model. In order to develop a predictive-control strategy using a more reliable model, i.e. the Focus-NN model, several scenarios as an accurate, fast running and inexpensive method are deployed to identify the optimal process settings considering the desired output. Show less

We have developed a mathematical programming model for minimizing total flow time of the flow shop sequence dependent group scheduling (FSDGS) problem, typically classified as Fm|fmls, Splk, prmu|∑Cj. As the problem is shown to be strongly NP-hard, a tabu search (TS) algorithm as well as a hybrid ant colony optimization (HACO) algorithm have been developed to heuristically solve the problem. A lower bounding (LB) method based on the Branch-and-Price algorithm is also developed to evaluate the… Show more

We have developed a mathematical programming model for minimizing total flow time of the flow shop sequence dependent group scheduling (FSDGS) problem, typically classified as Fm|fmls, Splk, prmu|∑Cj. As the problem is shown to be strongly NP-hard, a tabu search (TS) algorithm as well as a hybrid ant colony optimization (HACO) algorithm have been developed to heuristically solve the problem. A lower bounding (LB) method based on the Branch-and-Price algorithm is also developed to evaluate the quality of the metaheuristic algorithms. In order to compare the performance of metaheuristic algorithms, random test problems, ranging in size from small, medium, to large are created and solved by both the TS and the HACO algorithms. A comparison shows that the HACO algorithm has a better performance than the TS algorithm. The results of the heuristic algorithms are also compared with the results of the LB method to evaluate the quality of the solutions. The LB method presented in this paper can be generalized to solve the FSDGS problem with other objective functions. Show less

A two-machine flowshop scheduling problem is addressed to minimize setups and makespan where each job is characterized by a pair of attributes that entail setups on each machine. The setup times are sequence-dependent on both machines. It is shown that these objectives conflict, so the Pareto optimization approach is considered. The scheduling problems considering either of these objectives are N℘−hard, so exact optimization techniques are impractical for large-sized problems. We propose two… Show more

A two-machine flowshop scheduling problem is addressed to minimize setups and makespan where each job is characterized by a pair of attributes that entail setups on each machine. The setup times are sequence-dependent on both machines. It is shown that these objectives conflict, so the Pareto optimization approach is considered. The scheduling problems considering either of these objectives are N℘−hard, so exact optimization techniques are impractical for large-sized problems. We propose two multi-objective metaheurisctics based on genetic algorithms (MOGA) and simulated annealing (MOSA) to find approximations of Pareto-optimal sets. The performances of these approaches are compared with lower bounds for small problems. In larger problems, performance of the proposed algorithms are compared with each other. Experimentations revealed that both algorithms perform very similar on small problems. Moreover, it was observed that MOGA outperforms MOSA in terms of the quality of solutions on larger problems. Show less

We present several heuristic algorithms based on tabu search for solving the multi-stage sequence-dependent group scheduling (SDGS) problem by considering minimization of makespan as the criterion. As the problem is recognized to be strongly NP-hard, several meta (tabu) search-based solution algorithms are developed to efficiently solve industry-size problem instances. Also, two different initial solution generators are developed to aid in the application of the tabu search-based algorithms. A… Show more

We present several heuristic algorithms based on tabu search for solving the multi-stage sequence-dependent group scheduling (SDGS) problem by considering minimization of makespan as the criterion. As the problem is recognized to be strongly NP-hard, several meta (tabu) search-based solution algorithms are developed to efficiently solve industry-size problem instances. Also, two different initial solution generators are developed to aid in the application of the tabu search-based algorithms. A lower bounding technique based on relaxing the mathematical model for the original SDGS problem is applied to estimate the quality of the heuristic algorithms. To find the best heuristic algorithm, random test problems, ranging in size from small, medium, to large are created and solved by the heuristic algorithms. A detailed statistical experiment, based on nested split-plot design, is performed to find the best heuristic algorithm and the best initial solution gener-ator. The results of the experiment show that the tabu search-based algorithms can provide high quality solu-tions for the problems with an average percentage error of only 1.00%. Show less

This paper focuses on minimizing the total completion time in two-machine group scheduling problems with sequence-dependent setups that are typically found in discrete parts manufacturing. As the problem is characterized as strongly NP-hard, three search algorithms based on tabu search are developed for solving industry-size scheduling problems. Four different lower bounding mechanisms are developed to identify a lower bound for all problems attempted, and the largest of the four is aptly used… Show more

This paper focuses on minimizing the total completion time in two-machine group scheduling problems with sequence-dependent setups that are typically found in discrete parts manufacturing. As the problem is characterized as strongly NP-hard, three search algorithms based on tabu search are developed for solving industry-size scheduling problems. Four different lower bounding mechanisms are developed to identify a lower bound for all problems attempted, and the largest of the four is aptly used in the evaluation of the percentage deviation of the search algorithms to assess their efficacy. The problem sizes are classified as small, medium and large, and to accommodate the variability that might exist in the sequence-dependent setup times on both machines, three different scenarios are considered. Such finer levels of classification have resulted in the generation of nine different categories of problem instances, thus facilitating the performance of a very detailed statistical experimental design to assess the efficacy and efficiency of the three search algorithms. The search algorithm based on long-term memory with maximal frequencies either recorded a statistically better makespan or one that is indifferent when compared with the other two with all three scenarios and problem sizes. Show less