masoud jenabi | AmirKabir University Of Technology (original) (raw)
Papers by masoud jenabi
It is known that in many real industrial settings, some setup is carried out before the process o... more It is known that in many real industrial settings, some setup is carried out before the process of a job. Usually, the magnitude of this setup depends on the order of two consecutive jobs. In this case, the setup is called sequence-dependent. This paper deals with open shop scheduling with sequence-dependent setup times to minimize the total completion time. We formulate the problem as an effective mixed integer linear programming model that best characterizes and solves to optimality small-sized instances of the problem under consideration. Since the electromagnetism-like metaheuristic (EM) is successfully applied to some NP-hard problems, we have been motivated to employ and assess the effectiveness of EM to solve the open shop with setup times. To further enhance EM, we incorporate a local search engine in form of a fast and simple simulated annealing. In order to evaluate the performance of the proposed algorithms, we design an experiment in which the proposed methods are compared against some algorithms in the literature. The related results are analyzed by statistical tools. The experimental results and statistical analyses demonstrate that our proposed model and EM are effective for the problem.
This paper studies multi-objective no-wait flows hop scheduling problems to minimize both makespa... more This paper studies multi-objective no-wait flows hop scheduling problems to minimize both makespan and total tardiness. This paper mathematically formulates it as two effective multi-objective mixed integer linear programming models. The multi-objective models are then solved using a multiple criteria decision making approach. Moreover, this paper proposes a novel multi-objective iterated local search algorithm incorporating with three types of local
Computers & Operations Research, 2008
This paper investigates the scheduling problem of parallel identical batch processing machines in... more This paper investigates the scheduling problem of parallel identical batch processing machines in which each machine can process a group of jobs simultaneously as a batch. Each job is characterized by its size and processing time. The processing time of a batch is given by the longest processing time among all jobs in the batch. Based on developing heuristic approaches, we proposed a hybrid genetic heuristic (HGH) to minimize makespan objective. To verify the performance of our algorithm, comparisons are made through using a simulated annealing (SA) approach addressed in the literature as a comparator algorithm. Computational experiments reveal that affording the knowledge of problem through using heuristic procedures, gives HGH the ability of finding optimal or near optimal solutions in a reasonable time. ᭧
Since its foundation in 1994, the grouping genetic algorithm (GGA) is the only evolutionary algor... more Since its foundation in 1994, the grouping genetic algorithm (GGA) is the only evolutionary algorithm heavily modified to suit the structure of grouping problems. In this paper we design the grouping version of evolution strategies (ES). It is well-known that ES maintains a Gaussian mutation, recombination and a selection operator for optimizing non-linear continuous functions. Therefore, the development of grouping evolution strategies (GES) for solving grouping problems that are discrete in nature, calls for developing operators having the major characteristics of the original ones and being respondent to the structure of grouping problems. We propose a mutation operator analogous to the original one that works with groups instead of scalars and use it in a two phase procedure to generate the new solution. We implement (1+Lambda)-GES and evaluate its performance versus GGA on some of hard benchmarked instances of the bin packing problem. Computational results testify that our approach is efficient and can be regarded as a promising solver for the wide class of grouping problems.
Applied Mathematics and Computation, 2007
This article addresses the economic lot sizing and scheduling problem in flexible flow lines with... more This article addresses the economic lot sizing and scheduling problem in flexible flow lines with unrelated parallel machines over a finite planning horizon. The objective is determination of a cyclic schedule that minimizes the sum of setup and inventory holding costs per unit time without any stock-out. A new mixed zero-one nonlinear mathematical programming has been developed for the problem. Due to difficulty of obtaining the optimal solution especially for medium and large-sized problems, we have also proposed two algorithms: a hybrid genetic algorithm (HGA) and a simulated annealing (SA). Two efficient constructive heuristic algorithms have been also proposed that provide some initial solutions for the algorithms. Moreover, a new local search approach has been proposed to improve solution quality of algorithms. The two proposed solution methods are compared on 180 randomly generated problems. Computational results indicate the superiority of the proposed HGA in compare to the SA with respect to the solution quality, but the proposed SA outperforms the proposed HGA with respect to the required computations time.
The problem of production and delivery lot-sizing and scheduling of set of items in a two-echelon... more The problem of production and delivery lot-sizing and scheduling of set of items in a two-echelon supply chain over a finite planning horizon is addressed in this paper. A single supplier produces several items on a flexible flow line (FFL) production system and delivers them directly to an assembly facility. Based on the wellknown basic period (BP) policy, a new mixed zero-one nonlinear programming model has been developed to minimize average setup, inventory-holding and delivery costs per unit time in the supply chain without any stock-out. The problem is very complex and it could not be solved to optimality especially in real-sized problems. So, an efficient hybrid genetic algorithm (HGA) using the most applied BP approach (i.e. power-of-two policy) has been proposed. The solution quality of the proposed algorithm called PT-HGA has been evaluated and compared with the common cycle approach in some problem instances. Numerical experiments demonstrate the merit of the PT-HGA and indicate that it is a very promising solution method for the problem.
This chapter addresses integrated production and delivery scheduling of several items in a two-ec... more This chapter addresses integrated production and delivery scheduling of several items in a two-echelon supply chain. A single supplier produces the items on a flexible flow line (FFL) under a cyclic policy and delivers them directly to an assembly facility over a finite planning horizon. A new mixed zero-one nonlinear programming model is developed, based on the basic period (BP) policy to minimize average setup, inventory-holding and delivery costs per unit time where stock-out is prohibited. This problem has not yet been addressed in literature. It is computationally complex and has not been solved optimally especially in real-sized problems. Two efficient hybrid genetic algorithms (HGA) are proposed using the power-of-two (PT-HGA) and non-power-of-two (NPT-HGA) policies. The solution’s quality of the proposed algorithms is evaluated and compared with the common cycle approach in a number of randomly generated problem instances. Numerical experiments demonstrate the merit of the NPT-HGA and indicate that it constitutes a very promising solution method for the problem.
International Journal of Advanced Manufacturing Technology, 2009
This paper addresses the problem of lot sizing, scheduling, and delivery of several items in a tw... more This paper addresses the problem of lot sizing, scheduling, and delivery of several items in a two-echelon supply chain over a finite planning horizon. Single supplier produces the items through a flexible flow line and delivers them directly to an assembly facility where the transfer of sub-lots between adjacent stages of supplier’s production system (i.e., lot streaming) is allowed in order to decrease the manufacturing lead time. At first, a mixed zero-one nonlinear programming model is developed based on the so-called basic period (BP) approach aiming to minimize the average setup, inventory holding, and delivery costs per unit time in the supply chain without any stock-out. The problem is very complex and cannot be solved to optimality especially for real-sized problems. Therefore, two efficient hybrid genetic algorithms (HGA) are proposed based on the power-of-two (PTHGA) and non-power-of-two (NPTHGA) variants of BP approach. The solution qualities of the proposed algorithms are compared with a proposed lower bound. Numerical experiments demonstrate that the NPTHGA outperforms the PTHGA algorithm with respect to the solution quality, but the PTHGA outperforms the NPTHGA with respect to the computation time.
It is known that in many real industrial settings, some setup is carried out before the process o... more It is known that in many real industrial settings, some setup is carried out before the process of a job. Usually, the magnitude of this setup depends on the order of two consecutive jobs. In this case, the setup is called sequence-dependent. This paper deals with open shop scheduling with sequence-dependent setup times to minimize the total completion time. We formulate the problem as an effective mixed integer linear programming model that best characterizes and solves to optimality small-sized instances of the problem under consideration. Since the electromagnetism-like metaheuristic (EM) is successfully applied to some NP-hard problems, we have been motivated to employ and assess the effectiveness of EM to solve the open shop with setup times. To further enhance EM, we incorporate a local search engine in form of a fast and simple simulated annealing. In order to evaluate the performance of the proposed algorithms, we design an experiment in which the proposed methods are compared against some algorithms in the literature. The related results are analyzed by statistical tools. The experimental results and statistical analyses demonstrate that our proposed model and EM are effective for the problem.
This paper studies multi-objective no-wait flows hop scheduling problems to minimize both makespa... more This paper studies multi-objective no-wait flows hop scheduling problems to minimize both makespan and total tardiness. This paper mathematically formulates it as two effective multi-objective mixed integer linear programming models. The multi-objective models are then solved using a multiple criteria decision making approach. Moreover, this paper proposes a novel multi-objective iterated local search algorithm incorporating with three types of local
Computers & Operations Research, 2008
This paper investigates the scheduling problem of parallel identical batch processing machines in... more This paper investigates the scheduling problem of parallel identical batch processing machines in which each machine can process a group of jobs simultaneously as a batch. Each job is characterized by its size and processing time. The processing time of a batch is given by the longest processing time among all jobs in the batch. Based on developing heuristic approaches, we proposed a hybrid genetic heuristic (HGH) to minimize makespan objective. To verify the performance of our algorithm, comparisons are made through using a simulated annealing (SA) approach addressed in the literature as a comparator algorithm. Computational experiments reveal that affording the knowledge of problem through using heuristic procedures, gives HGH the ability of finding optimal or near optimal solutions in a reasonable time. ᭧
Since its foundation in 1994, the grouping genetic algorithm (GGA) is the only evolutionary algor... more Since its foundation in 1994, the grouping genetic algorithm (GGA) is the only evolutionary algorithm heavily modified to suit the structure of grouping problems. In this paper we design the grouping version of evolution strategies (ES). It is well-known that ES maintains a Gaussian mutation, recombination and a selection operator for optimizing non-linear continuous functions. Therefore, the development of grouping evolution strategies (GES) for solving grouping problems that are discrete in nature, calls for developing operators having the major characteristics of the original ones and being respondent to the structure of grouping problems. We propose a mutation operator analogous to the original one that works with groups instead of scalars and use it in a two phase procedure to generate the new solution. We implement (1+Lambda)-GES and evaluate its performance versus GGA on some of hard benchmarked instances of the bin packing problem. Computational results testify that our approach is efficient and can be regarded as a promising solver for the wide class of grouping problems.
Applied Mathematics and Computation, 2007
This article addresses the economic lot sizing and scheduling problem in flexible flow lines with... more This article addresses the economic lot sizing and scheduling problem in flexible flow lines with unrelated parallel machines over a finite planning horizon. The objective is determination of a cyclic schedule that minimizes the sum of setup and inventory holding costs per unit time without any stock-out. A new mixed zero-one nonlinear mathematical programming has been developed for the problem. Due to difficulty of obtaining the optimal solution especially for medium and large-sized problems, we have also proposed two algorithms: a hybrid genetic algorithm (HGA) and a simulated annealing (SA). Two efficient constructive heuristic algorithms have been also proposed that provide some initial solutions for the algorithms. Moreover, a new local search approach has been proposed to improve solution quality of algorithms. The two proposed solution methods are compared on 180 randomly generated problems. Computational results indicate the superiority of the proposed HGA in compare to the SA with respect to the solution quality, but the proposed SA outperforms the proposed HGA with respect to the required computations time.
The problem of production and delivery lot-sizing and scheduling of set of items in a two-echelon... more The problem of production and delivery lot-sizing and scheduling of set of items in a two-echelon supply chain over a finite planning horizon is addressed in this paper. A single supplier produces several items on a flexible flow line (FFL) production system and delivers them directly to an assembly facility. Based on the wellknown basic period (BP) policy, a new mixed zero-one nonlinear programming model has been developed to minimize average setup, inventory-holding and delivery costs per unit time in the supply chain without any stock-out. The problem is very complex and it could not be solved to optimality especially in real-sized problems. So, an efficient hybrid genetic algorithm (HGA) using the most applied BP approach (i.e. power-of-two policy) has been proposed. The solution quality of the proposed algorithm called PT-HGA has been evaluated and compared with the common cycle approach in some problem instances. Numerical experiments demonstrate the merit of the PT-HGA and indicate that it is a very promising solution method for the problem.
This chapter addresses integrated production and delivery scheduling of several items in a two-ec... more This chapter addresses integrated production and delivery scheduling of several items in a two-echelon supply chain. A single supplier produces the items on a flexible flow line (FFL) under a cyclic policy and delivers them directly to an assembly facility over a finite planning horizon. A new mixed zero-one nonlinear programming model is developed, based on the basic period (BP) policy to minimize average setup, inventory-holding and delivery costs per unit time where stock-out is prohibited. This problem has not yet been addressed in literature. It is computationally complex and has not been solved optimally especially in real-sized problems. Two efficient hybrid genetic algorithms (HGA) are proposed using the power-of-two (PT-HGA) and non-power-of-two (NPT-HGA) policies. The solution’s quality of the proposed algorithms is evaluated and compared with the common cycle approach in a number of randomly generated problem instances. Numerical experiments demonstrate the merit of the NPT-HGA and indicate that it constitutes a very promising solution method for the problem.
International Journal of Advanced Manufacturing Technology, 2009
This paper addresses the problem of lot sizing, scheduling, and delivery of several items in a tw... more This paper addresses the problem of lot sizing, scheduling, and delivery of several items in a two-echelon supply chain over a finite planning horizon. Single supplier produces the items through a flexible flow line and delivers them directly to an assembly facility where the transfer of sub-lots between adjacent stages of supplier’s production system (i.e., lot streaming) is allowed in order to decrease the manufacturing lead time. At first, a mixed zero-one nonlinear programming model is developed based on the so-called basic period (BP) approach aiming to minimize the average setup, inventory holding, and delivery costs per unit time in the supply chain without any stock-out. The problem is very complex and cannot be solved to optimality especially for real-sized problems. Therefore, two efficient hybrid genetic algorithms (HGA) are proposed based on the power-of-two (PTHGA) and non-power-of-two (NPTHGA) variants of BP approach. The solution qualities of the proposed algorithms are compared with a proposed lower bound. Numerical experiments demonstrate that the NPTHGA outperforms the PTHGA algorithm with respect to the solution quality, but the PTHGA outperforms the NPTHGA with respect to the computation time.