Hany Seidgar | University of Science and Technology of Mazandaran (original) (raw)

Papers by Hany Seidgar

Research paper thumbnail of Bi-objective Optimization for Just in Time Scheduling: Application to the Two-Stage Assembly Flow Shop Problem

This paper considers a two-stage assembly flow shop problem (TAFSP) where m machines are in the f... more This paper considers a two-stage assembly flow shop problem (TAFSP) where m machines are in the first stage and an assembly machine is in the second stage. The objective is to minimize a weighted sum of earliness and tardiness time for n available jobs. JIT seeks to identify and eliminate waste components including over production, waiting time, transportation, inventory, movement and defective products.Two-stage assembly flow shop is a combinational production system in which different parts are manufactured on parallel machines independently. This system can be used as a method to produce a variety of products through assembling and combining different set of parts. We apply e-constraint method as an exact approach to validate the proposed model and to obtain fronts of the solutions in the solution spaceThe goal of the proposed problem is trade off between two objectives, minimization makespan and total weighted tardiness and earliness. To analyze effects of n and m factors on ...

Research paper thumbnail of Scheduling two-stage assembly flow shop with random machines breakdowns: integrated new self-adapted differential evolutionary and simulation approach

Soft Computing

This paper takes random machines breakdowns and the two-stage assembly flow shop problem into con... more This paper takes random machines breakdowns and the two-stage assembly flow shop problem into consideration as a realistic assumption in industrial environments. In practical manufacturing environment, disruptions and unforeseen incidents occur, so a schedule being built based on deterministic information is not practical and may lead to poor performance. In this paper, machines in manufacturing and assembly stages are not always available due to random machines breakdowns which occur during processing of each operation. The goal is to minimize the expected the weighted sum of makespan and mean of completion time. Owning to its problem complexity and since the problem belongs to NP-hard class, use of meta-heuristic algorithms is justified to tackle the potential complexity of the problem considered, and hence, we proposed four meta-heuristics algorithms entitled: genetic algorithm, imperialist competitive algorithm, cloud theory-based simulated annealing and new self-adapted differential evolutionary (NSDE) to solve it. Machine breakdown and dynamic nature of the problem, the structural complexity increases markedly. In this regard, to overcome this form of complexity, simulation techniques are typically employed. Eventually, since the proposed problem has both types of complexities (algorithm complexity and structural complexity), simulation is integrated into the proposed meta-heuristic approaches to handle the complexities. We apply artificial neural network as a tuning tool for predicting the input parameters of each proposed meta-heuristics algorithms in uncertain condition. Also, we suggest Taguchi method as one the most important adjusting approaches for analyzing the effect of input parameters in each algorithm. The computational results show which proposed NSDE statistically is better than other proposed meta-heuristics algorithms according two important indicators: quality of solution and computational time.

Research paper thumbnail of A new mathematical model for non-fixed destination multi-depot multiple travelling salesmen with time window problem

International Journal of Services and Operations Management

Consider the problem of having a team of cooperative and autonomous vehicles to repeatedly visit ... more Consider the problem of having a team of cooperative and autonomous vehicles to repeatedly visit a set of target locations and return back to their initial locations. This problem is known as multi-depot multiple travelling salesman problems (MD-MTSP), which applies to several mobile robots applications. The non-fixed destination multi-depot multiple travelling salesmen with time window problem (MmTSPTW) is a generalisation of a well-known MmTSP with considering time window for each node in a tour. The salesmen also are not forced to return to their starting depot. This problem is of great complexity and belongs to NP-complete class of problems. In this paper, a new mathematical model is proposed for MmTSPTW by considering waiting penalties and time window is defined for each depot (city). The salesmen only can service the customers within these time windows and also some penalties are considered for any deviation of start time. The objective function of problem is to minimise the total costs and penalties of the tours.

Research paper thumbnail of Hybrid scheduling and maintenance problem using artificial neural network based meta-heuristics

Journal of Modelling in Management

Purpose The purpose of this paper is to present a new mathematical model for the unrelated parall... more Purpose The purpose of this paper is to present a new mathematical model for the unrelated parallel machine scheduling problem with aging effects and multi-maintenance activities. Design/methodology/approach The authors assume that each machine may be subject to several maintenance activities over the scheduling horizon and a machine turn into its initial condition after maintenance activity and the aging effects start anew. The objective is to minimize the weighted sum of early/tardy times of jobs and maintenance costs. Findings As this problem is proven to be non-deterministic polynomial-time hard (NP-hard), the authors employed imperialist competitive algorithm (ICA) and genetic algorithm (GA) as solution approaches, and the parameters of the proposed algorithms are calibrated by a novel parameter tuning tool called Artificial Neural Network (ANN). The computational results clarify that GA performs better than ICA in quality of solutions and computational time. Originality/value ...

Research paper thumbnail of Genetic and artificial bee colony algorithms for scheduling of multi-skilled manpower in combined manpower-vehicle routing problem

Production & Manufacturing Research, 2016

Abstract This paper investigates a scheduling combined manpower-vehicle routing problem with a ce... more Abstract This paper investigates a scheduling combined manpower-vehicle routing problem with a central depot in and a set of multi-skilled manpower for serving to customers. Teams are in different range of competencies that it will affect the service time duration. Vehicles are in different moving speeds and costs and not all the vehicles are capable to move toward all the customers’ sites. The objective is to minimize the total cost of servicing, routing, and lateness penalties. This paper presents a mixed integer programming model and two meta-heuristic approaches of genetic algorithm (GA) and artificial bee colony algorithm (ABC) are developed to solve the generated problems. Furthermore, Taguchi experimental design method is applied to set the proper values of parameters. The available results show the higher performance of proposed GA compared with ABC, in quality of solutions.

Research paper thumbnail of An efficient hybrid of genetic and simulated annealing algorithms for multi server vehicle routing problem with multi entry

International Journal of Industrial and Systems Engineering, 2016

Research paper thumbnail of Scheduling multi-skilled manpower with considering teams replacement and site-dependent vehicles routing

International Journal of Mathematics in Operation Research

In this paper, a combined manpower-vehicle routing problem (CMVRP) is presented that a central de... more In this paper, a combined manpower-vehicle routing problem (CMVRP) is presented that a central depot is considered in which a set of vehicles and a set of multi-skilled teams originate from it to move toward each customer’s site for servicing tasks. This problem deals with scheduling of multi-skilled manpower to service a set of tasks with due dates and at the same, routing of the vehicles which are used for moving this manpower. Teams are in different range of competency that it will affect the service time duration. Vehicles are in different moving speeds and costs and not all the vehicles are capable to move toward all the customers’ sites. The objective is to find an efficient schedule for the teams and vehicles movement in order to minimise the total cost of servicing, routing and lateness penalties. In this paper, a mixed integer programming model is presented and two meta-heuristics approaches of genetic algorithm (GA) and particle swarm optimisation (PSO) are developed to so...

Research paper thumbnail of Multi Depot Simultaneous Pickup and Delivery Problem with Balanced Allocation of Routes to Drivers

International Journal of Industrial Engineering Theory Applications and Practice, Oct 20, 2014

In this paper, a new mathematical model is developed for a multi-depot vehicle routing problem wi... more In this paper, a new mathematical model is developed for a multi-depot vehicle routing problem with simultaneous pickup and delivery. A non-homogenous fleet of vehicles and a number of drivers with different levels of capabilities are employed to service customers with pickup and delivery demands. The capability of drivers is considered to have a balanced distribution of travels. The objective is to minimize the total cost of routing, penalties for overworking of drivers and fix costs of drivers’ employment. Due to the problem’s NP-hard nature, two meta-heuristic approaches based on Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) are employed to solve the generated problems. The parameter tuning is conducted by Taguchi experimental design method. The obtained results show the high performance of the proposed ICA in the quality of the solutions and computational time.

Research paper thumbnail of Simulated imperialist competitive algorithm in two-stage assembly flow shop with machine breakdowns and preventive maintenance

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture

This article deals with the two-stage assembly flow shop problem considering preventive maintenan... more This article deals with the two-stage assembly flow shop problem considering preventive maintenance activities and machine breakdowns. The objective is to minimize the makespan. This problem is proven to be NP-hard; thus, we proposed two meta-heuristic algorithms, namely, imperialist competitive algorithm and genetic algorithm, to obtain solutions of the problem. This article describes an approach incorporating simulation with imperialist competitive algorithm for the scheduling purpose having machine breakdowns and preventive maintenance activities. The results obtained are analyzed using Taguchi experimental design, and the parameters of proposed algorithms are calibrated by artificial neural network. The computational results demonstrate that imperialist competitive algorithm is statistically better than genetic algorithm in quality of solution and reaches to better solutions at the same computational time.

Research paper thumbnail of Robust scheduling in two-stage assembly flow shop problem with random machine breakdowns: integrated meta-heuristic algorithms and simulation approach

Assembly Automation

Purpose In real manufacturing systems, schedules are often disrupted with uncertainty factors suc... more Purpose In real manufacturing systems, schedules are often disrupted with uncertainty factors such as random machine breakdown, random process time, random job arrivals or job cancellations. This paper aims to investigate robust scheduling for a two-stage assembly flow shop scheduling with random machine breakdowns and considers two objectives makespan and robustness simultaneously. Design/methodology/approach Owing to its structural and algorithmic complexity, the authors proposed imperialist competitive algorithm (ICA), genetic algorithm (GA) and hybridized with simulation techniques for handling these complexities. For better efficiency of the proposed algorithms, the authors used artificial neural network (ANN) to predict the parameters of the proposed algorithms in uncertain condition. Also Taguchi method is applied for analyzing the effect of the parameters of the problem on each other and quality of solutions. Findings Finally, experimental study and analysis of variance (ANO...

Research paper thumbnail of Bi-objective optimization for integrating production and preventive maintenance scheduling in two-stage assembly flow shop problem

Journal of Industrial and Production Engineering, 2016

Research paper thumbnail of A hybrid particle swarm optimisation for scheduling just-in-time single machine with preemption, machine idle time and unequal release times

International Journal of Production Research, 2014

ABSTRACT This paper addresses preemption in just-in-time (JIT) single–machine-scheduling problem ... more ABSTRACT This paper addresses preemption in just-in-time (JIT) single–machine-scheduling problem with unequal release times and allowable unforced machine idle time as realistic assumptions occur in the manufacturing environments aiming to minimise the total weighted earliness and tardiness costs. Delay in production systems is a vital item to be focussed to counteract lost sale and back order. Thus, JIT concept is targeted including the elements required such as machine preemption, machine idle time and unequal release times. We proposed a new mathematical model and as the problem is proven to be NP-hard, three meta-heuristic approaches namely hybrid particle swarm optimisation (HPSO), genetic algorithm and imperialist competitive algorithm are employed to solve the problem in larger sizes. In HPSO, cloud theory-based simulated annealing is employed with a certain probability to avoid being trapped in a local optimum. Taguchi method is applied to calibrate the parameters of the proposed algorithms. A number of numerical examples are solved to demonstrate the effectiveness of the proposed approach. The performance of the proposed algorithms is evaluated in terms of relative percent deviation and computational time where the computational results clarify better performance of HPSO than other algorithms in quality of solutions and computational time.

Research paper thumbnail of An efficient imperialist competitive algorithm for scheduling in the two-stage assembly flow shop problem

ABSTRACT This paper considers a two-stage assembly flow shop problem where m parallel machines ar... more ABSTRACT This paper considers a two-stage assembly flow shop problem where m parallel machines are in the first stage and an assembly machine is in the second stage. The objective is to minimise a weighted sum of makespan and mean completion time for n available jobs. As this problem is proven to be NP-hard, therefore, we employed an imperialist competitive algorithm (ICA) as solution approach. In the past literature, Torabzadeh and Zandieh (201028. Torabzadeh, E., and M. Zandieh. 2010. “Cloud theory-based Simulated Annealing Approach for Scheduling in the Two-stage Assembly Flow Shop.” Advances in Engineering Software 41: 1238–1243.[CrossRef], [Web of Science ®]View all references) showed that cloud theory-based simulated annealing algorithm (CSA) is an appropriate meta-heuristic to solve the problem. Thus, to justify the claim for ICA capability, we compare our proposed ICA with the reported CSA. A new parameters tuning tool, neural network, for ICA is also introduced. The computational results clarify that ICA performs better than CSA in quality of solutions.

Research paper thumbnail of A Multi Depot Simultaneous Pickup and Delivery Problem with Balanced Allocation of Routes to Drivers

International Journal of Industrial engineering: theory, application and practice

In this paper, a new mathematical model is developed for a multi-depot vehicle routing problem wi... more In this paper, a new mathematical model is developed for a multi-depot vehicle routing problem with simultaneous pickup and delivery. A non-homogenous fleet of vehicles and a number of drivers with different levels of capabilities are employed to service customers with pickup and delivery demands. The capability of drivers is considered to have a balanced distribution of travels. The objective is to minimize the total cost of routing, penalties for overworking of drivers and fix costs of drivers’ employment. Due to the problem’s NP-hard nature, two meta-heuristic approaches based on Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) are employed to solve the generated problems. The parameter tuning is conducted by Taguchi experimental design method. The obtained results show the high performance of the proposed ICA in the quality of the solutions and computational time.

Research paper thumbnail of An efficient meta-heuristic algorithm for scheduling a two-stage assembly flow shop problem with Preventive Maintenance activities and reliability approach

International Journal of Industrial and System Engineering

Research paper thumbnail of Scheduling of assembly flow shop problem and machines with random breakdowns

International Journal of Operation Research

Research paper thumbnail of Simulated ICA in Two-Stage Assembly Flow-shop with Machines' Breakdowns and Preventive Maintenance

Journal of Engineering Manufacture

Research paper thumbnail of A HPSO for scheduling just in time single machine with preemption, machine idle time and unequal release times

International Journal of Production Research

Research paper thumbnail of Bi-objective optimization for scheduling the identical parallel batch processing machines with arbitrary job sizes, unequal job release times and capacity limits

International Journal of Production Research

Research paper thumbnail of Learning and Forgetting Effects of Flexible Flow Shop scheduling

International Journal of Innovation and Applied Studies

In this paper we consider learning and forgetting effect of workers for flexible flowshop schedul... more In this paper we consider learning and forgetting effect of workers for flexible flowshop scheduling problem with sequence dependent setup times. The objective is to minimize the weighted sum of maximum completion time and maximum tardiness. The learning effect occurs when operator's (workers) skill increases after repeating similar job causing the decrease of processing time. On the other hand, forgetting effect occurs when an operator relearns the process after an interruption for a batch setup, machine maintenance or operator condition recovery, causing the increase of processing time.

Research paper thumbnail of Bi-objective Optimization for Just in Time Scheduling: Application to the Two-Stage Assembly Flow Shop Problem

This paper considers a two-stage assembly flow shop problem (TAFSP) where m machines are in the f... more This paper considers a two-stage assembly flow shop problem (TAFSP) where m machines are in the first stage and an assembly machine is in the second stage. The objective is to minimize a weighted sum of earliness and tardiness time for n available jobs. JIT seeks to identify and eliminate waste components including over production, waiting time, transportation, inventory, movement and defective products.Two-stage assembly flow shop is a combinational production system in which different parts are manufactured on parallel machines independently. This system can be used as a method to produce a variety of products through assembling and combining different set of parts. We apply e-constraint method as an exact approach to validate the proposed model and to obtain fronts of the solutions in the solution spaceThe goal of the proposed problem is trade off between two objectives, minimization makespan and total weighted tardiness and earliness. To analyze effects of n and m factors on ...

Research paper thumbnail of Scheduling two-stage assembly flow shop with random machines breakdowns: integrated new self-adapted differential evolutionary and simulation approach

Soft Computing

This paper takes random machines breakdowns and the two-stage assembly flow shop problem into con... more This paper takes random machines breakdowns and the two-stage assembly flow shop problem into consideration as a realistic assumption in industrial environments. In practical manufacturing environment, disruptions and unforeseen incidents occur, so a schedule being built based on deterministic information is not practical and may lead to poor performance. In this paper, machines in manufacturing and assembly stages are not always available due to random machines breakdowns which occur during processing of each operation. The goal is to minimize the expected the weighted sum of makespan and mean of completion time. Owning to its problem complexity and since the problem belongs to NP-hard class, use of meta-heuristic algorithms is justified to tackle the potential complexity of the problem considered, and hence, we proposed four meta-heuristics algorithms entitled: genetic algorithm, imperialist competitive algorithm, cloud theory-based simulated annealing and new self-adapted differential evolutionary (NSDE) to solve it. Machine breakdown and dynamic nature of the problem, the structural complexity increases markedly. In this regard, to overcome this form of complexity, simulation techniques are typically employed. Eventually, since the proposed problem has both types of complexities (algorithm complexity and structural complexity), simulation is integrated into the proposed meta-heuristic approaches to handle the complexities. We apply artificial neural network as a tuning tool for predicting the input parameters of each proposed meta-heuristics algorithms in uncertain condition. Also, we suggest Taguchi method as one the most important adjusting approaches for analyzing the effect of input parameters in each algorithm. The computational results show which proposed NSDE statistically is better than other proposed meta-heuristics algorithms according two important indicators: quality of solution and computational time.

Research paper thumbnail of A new mathematical model for non-fixed destination multi-depot multiple travelling salesmen with time window problem

International Journal of Services and Operations Management

Consider the problem of having a team of cooperative and autonomous vehicles to repeatedly visit ... more Consider the problem of having a team of cooperative and autonomous vehicles to repeatedly visit a set of target locations and return back to their initial locations. This problem is known as multi-depot multiple travelling salesman problems (MD-MTSP), which applies to several mobile robots applications. The non-fixed destination multi-depot multiple travelling salesmen with time window problem (MmTSPTW) is a generalisation of a well-known MmTSP with considering time window for each node in a tour. The salesmen also are not forced to return to their starting depot. This problem is of great complexity and belongs to NP-complete class of problems. In this paper, a new mathematical model is proposed for MmTSPTW by considering waiting penalties and time window is defined for each depot (city). The salesmen only can service the customers within these time windows and also some penalties are considered for any deviation of start time. The objective function of problem is to minimise the total costs and penalties of the tours.

Research paper thumbnail of Hybrid scheduling and maintenance problem using artificial neural network based meta-heuristics

Journal of Modelling in Management

Purpose The purpose of this paper is to present a new mathematical model for the unrelated parall... more Purpose The purpose of this paper is to present a new mathematical model for the unrelated parallel machine scheduling problem with aging effects and multi-maintenance activities. Design/methodology/approach The authors assume that each machine may be subject to several maintenance activities over the scheduling horizon and a machine turn into its initial condition after maintenance activity and the aging effects start anew. The objective is to minimize the weighted sum of early/tardy times of jobs and maintenance costs. Findings As this problem is proven to be non-deterministic polynomial-time hard (NP-hard), the authors employed imperialist competitive algorithm (ICA) and genetic algorithm (GA) as solution approaches, and the parameters of the proposed algorithms are calibrated by a novel parameter tuning tool called Artificial Neural Network (ANN). The computational results clarify that GA performs better than ICA in quality of solutions and computational time. Originality/value ...

Research paper thumbnail of Genetic and artificial bee colony algorithms for scheduling of multi-skilled manpower in combined manpower-vehicle routing problem

Production & Manufacturing Research, 2016

Abstract This paper investigates a scheduling combined manpower-vehicle routing problem with a ce... more Abstract This paper investigates a scheduling combined manpower-vehicle routing problem with a central depot in and a set of multi-skilled manpower for serving to customers. Teams are in different range of competencies that it will affect the service time duration. Vehicles are in different moving speeds and costs and not all the vehicles are capable to move toward all the customers’ sites. The objective is to minimize the total cost of servicing, routing, and lateness penalties. This paper presents a mixed integer programming model and two meta-heuristic approaches of genetic algorithm (GA) and artificial bee colony algorithm (ABC) are developed to solve the generated problems. Furthermore, Taguchi experimental design method is applied to set the proper values of parameters. The available results show the higher performance of proposed GA compared with ABC, in quality of solutions.

Research paper thumbnail of An efficient hybrid of genetic and simulated annealing algorithms for multi server vehicle routing problem with multi entry

International Journal of Industrial and Systems Engineering, 2016

Research paper thumbnail of Scheduling multi-skilled manpower with considering teams replacement and site-dependent vehicles routing

International Journal of Mathematics in Operation Research

In this paper, a combined manpower-vehicle routing problem (CMVRP) is presented that a central de... more In this paper, a combined manpower-vehicle routing problem (CMVRP) is presented that a central depot is considered in which a set of vehicles and a set of multi-skilled teams originate from it to move toward each customer’s site for servicing tasks. This problem deals with scheduling of multi-skilled manpower to service a set of tasks with due dates and at the same, routing of the vehicles which are used for moving this manpower. Teams are in different range of competency that it will affect the service time duration. Vehicles are in different moving speeds and costs and not all the vehicles are capable to move toward all the customers’ sites. The objective is to find an efficient schedule for the teams and vehicles movement in order to minimise the total cost of servicing, routing and lateness penalties. In this paper, a mixed integer programming model is presented and two meta-heuristics approaches of genetic algorithm (GA) and particle swarm optimisation (PSO) are developed to so...

Research paper thumbnail of Multi Depot Simultaneous Pickup and Delivery Problem with Balanced Allocation of Routes to Drivers

International Journal of Industrial Engineering Theory Applications and Practice, Oct 20, 2014

In this paper, a new mathematical model is developed for a multi-depot vehicle routing problem wi... more In this paper, a new mathematical model is developed for a multi-depot vehicle routing problem with simultaneous pickup and delivery. A non-homogenous fleet of vehicles and a number of drivers with different levels of capabilities are employed to service customers with pickup and delivery demands. The capability of drivers is considered to have a balanced distribution of travels. The objective is to minimize the total cost of routing, penalties for overworking of drivers and fix costs of drivers’ employment. Due to the problem’s NP-hard nature, two meta-heuristic approaches based on Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) are employed to solve the generated problems. The parameter tuning is conducted by Taguchi experimental design method. The obtained results show the high performance of the proposed ICA in the quality of the solutions and computational time.

Research paper thumbnail of Simulated imperialist competitive algorithm in two-stage assembly flow shop with machine breakdowns and preventive maintenance

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture

This article deals with the two-stage assembly flow shop problem considering preventive maintenan... more This article deals with the two-stage assembly flow shop problem considering preventive maintenance activities and machine breakdowns. The objective is to minimize the makespan. This problem is proven to be NP-hard; thus, we proposed two meta-heuristic algorithms, namely, imperialist competitive algorithm and genetic algorithm, to obtain solutions of the problem. This article describes an approach incorporating simulation with imperialist competitive algorithm for the scheduling purpose having machine breakdowns and preventive maintenance activities. The results obtained are analyzed using Taguchi experimental design, and the parameters of proposed algorithms are calibrated by artificial neural network. The computational results demonstrate that imperialist competitive algorithm is statistically better than genetic algorithm in quality of solution and reaches to better solutions at the same computational time.

Research paper thumbnail of Robust scheduling in two-stage assembly flow shop problem with random machine breakdowns: integrated meta-heuristic algorithms and simulation approach

Assembly Automation

Purpose In real manufacturing systems, schedules are often disrupted with uncertainty factors suc... more Purpose In real manufacturing systems, schedules are often disrupted with uncertainty factors such as random machine breakdown, random process time, random job arrivals or job cancellations. This paper aims to investigate robust scheduling for a two-stage assembly flow shop scheduling with random machine breakdowns and considers two objectives makespan and robustness simultaneously. Design/methodology/approach Owing to its structural and algorithmic complexity, the authors proposed imperialist competitive algorithm (ICA), genetic algorithm (GA) and hybridized with simulation techniques for handling these complexities. For better efficiency of the proposed algorithms, the authors used artificial neural network (ANN) to predict the parameters of the proposed algorithms in uncertain condition. Also Taguchi method is applied for analyzing the effect of the parameters of the problem on each other and quality of solutions. Findings Finally, experimental study and analysis of variance (ANO...

Research paper thumbnail of Bi-objective optimization for integrating production and preventive maintenance scheduling in two-stage assembly flow shop problem

Journal of Industrial and Production Engineering, 2016

Research paper thumbnail of A hybrid particle swarm optimisation for scheduling just-in-time single machine with preemption, machine idle time and unequal release times

International Journal of Production Research, 2014

ABSTRACT This paper addresses preemption in just-in-time (JIT) single–machine-scheduling problem ... more ABSTRACT This paper addresses preemption in just-in-time (JIT) single–machine-scheduling problem with unequal release times and allowable unforced machine idle time as realistic assumptions occur in the manufacturing environments aiming to minimise the total weighted earliness and tardiness costs. Delay in production systems is a vital item to be focussed to counteract lost sale and back order. Thus, JIT concept is targeted including the elements required such as machine preemption, machine idle time and unequal release times. We proposed a new mathematical model and as the problem is proven to be NP-hard, three meta-heuristic approaches namely hybrid particle swarm optimisation (HPSO), genetic algorithm and imperialist competitive algorithm are employed to solve the problem in larger sizes. In HPSO, cloud theory-based simulated annealing is employed with a certain probability to avoid being trapped in a local optimum. Taguchi method is applied to calibrate the parameters of the proposed algorithms. A number of numerical examples are solved to demonstrate the effectiveness of the proposed approach. The performance of the proposed algorithms is evaluated in terms of relative percent deviation and computational time where the computational results clarify better performance of HPSO than other algorithms in quality of solutions and computational time.

Research paper thumbnail of An efficient imperialist competitive algorithm for scheduling in the two-stage assembly flow shop problem

ABSTRACT This paper considers a two-stage assembly flow shop problem where m parallel machines ar... more ABSTRACT This paper considers a two-stage assembly flow shop problem where m parallel machines are in the first stage and an assembly machine is in the second stage. The objective is to minimise a weighted sum of makespan and mean completion time for n available jobs. As this problem is proven to be NP-hard, therefore, we employed an imperialist competitive algorithm (ICA) as solution approach. In the past literature, Torabzadeh and Zandieh (201028. Torabzadeh, E., and M. Zandieh. 2010. “Cloud theory-based Simulated Annealing Approach for Scheduling in the Two-stage Assembly Flow Shop.” Advances in Engineering Software 41: 1238–1243.[CrossRef], [Web of Science ®]View all references) showed that cloud theory-based simulated annealing algorithm (CSA) is an appropriate meta-heuristic to solve the problem. Thus, to justify the claim for ICA capability, we compare our proposed ICA with the reported CSA. A new parameters tuning tool, neural network, for ICA is also introduced. The computational results clarify that ICA performs better than CSA in quality of solutions.

Research paper thumbnail of A Multi Depot Simultaneous Pickup and Delivery Problem with Balanced Allocation of Routes to Drivers

International Journal of Industrial engineering: theory, application and practice

In this paper, a new mathematical model is developed for a multi-depot vehicle routing problem wi... more In this paper, a new mathematical model is developed for a multi-depot vehicle routing problem with simultaneous pickup and delivery. A non-homogenous fleet of vehicles and a number of drivers with different levels of capabilities are employed to service customers with pickup and delivery demands. The capability of drivers is considered to have a balanced distribution of travels. The objective is to minimize the total cost of routing, penalties for overworking of drivers and fix costs of drivers’ employment. Due to the problem’s NP-hard nature, two meta-heuristic approaches based on Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) are employed to solve the generated problems. The parameter tuning is conducted by Taguchi experimental design method. The obtained results show the high performance of the proposed ICA in the quality of the solutions and computational time.

Research paper thumbnail of An efficient meta-heuristic algorithm for scheduling a two-stage assembly flow shop problem with Preventive Maintenance activities and reliability approach

International Journal of Industrial and System Engineering

Research paper thumbnail of Scheduling of assembly flow shop problem and machines with random breakdowns

International Journal of Operation Research

Research paper thumbnail of Simulated ICA in Two-Stage Assembly Flow-shop with Machines' Breakdowns and Preventive Maintenance

Journal of Engineering Manufacture

Research paper thumbnail of A HPSO for scheduling just in time single machine with preemption, machine idle time and unequal release times

International Journal of Production Research

Research paper thumbnail of Bi-objective optimization for scheduling the identical parallel batch processing machines with arbitrary job sizes, unequal job release times and capacity limits

International Journal of Production Research

Research paper thumbnail of Learning and Forgetting Effects of Flexible Flow Shop scheduling

International Journal of Innovation and Applied Studies

In this paper we consider learning and forgetting effect of workers for flexible flowshop schedul... more In this paper we consider learning and forgetting effect of workers for flexible flowshop scheduling problem with sequence dependent setup times. The objective is to minimize the weighted sum of maximum completion time and maximum tardiness. The learning effect occurs when operator's (workers) skill increases after repeating similar job causing the decrease of processing time. On the other hand, forgetting effect occurs when an operator relearns the process after an interruption for a batch setup, machine maintenance or operator condition recovery, causing the increase of processing time.