Bahman Naderi

The distributed permutation flowshop scheduling problem

This paper studies a new generalization of the regular permutation flowshop scheduling problem (PFSP) referred to as the distributed permutation flowshop scheduling problem or DPFSP. Under this generalization, we assume that there are a total of F identical factories or shops, each one with m machines disposed in series. A set of n available jobs have to be distributed among the F factories and then a processing sequence has to be derived for the jobs assigned to each factory. The optimization criterion is the minimization of the maximum completion time or makespan among the factories. This production setting is necessary in todays decentralized and globalized economy where several production centers might be available for a firm. We characterize the DPFSP and propose six different alternative mixed integer linear programming (MILP) models that are carefully and statistically analyzed for performance. We also propose two simple factory assignment rules together with 14 heuristics based on dispatching rules, effective constructive heuristics and variable neighborhood descent methods. A comprehensive computational and statistical analysis is conducted in order to analyze the performance of the proposed methods.

A scatter search algorithm for the distributed permutation flowshop scheduling problem

The distributed permutation flowshop problem has been recently proposed as a generalization of the regular flowshop setting where more than one factory is available to process jobs. Distributed manufacturing is a common situation for large enterprises that compete in a globalized market. The problem has two dimensions: assigning jobs to factories and scheduling the jobs assigned to each factory. Despite being recently introduced, this interesting scheduling problem has attracted attention and several heuristic and metaheuristic methods have been proposed in the literature. In this paper we present a scatter search (SS) method for this problem to optimize makespan. SS has seldom been explored for flowshop settings. In the proposed algorithm we employ some advanced techniques like a reference set made up of complete and partial solutions along with other features like restarts and local search. A comprehensive computational campaign including 10 existing algorithms, together with statistical analyses, shows that the proposed scatter search algorithm produces better results than existing algorithms by a significant margin. Moreover all 720 known best solutions for this problem are improved.

Algorithms for a realistic variant of flowshop scheduling

This paper deals with a realistic variant of flowshop scheduling, namely the hybrid flexible flowshop. A hybrid flowshop mixes the characteristics of regular flowshops and parallel machine problems by considering stages with parallel machines instead of having one single machine per stage. We also investigate the flexible version where stage skipping might occur, i.e., not all stages must be visited by all jobs. Lastly, we also consider job sequence dependent setup times per stage. The optimization criterion considered is makespan minimization. While many approaches for hybrid flowshops have been proposed, hybrid flexible flowshops have been rarely studied. The situation is even worse with the addition of sequence dependent setups. In this study, we propose two advanced algorithms that specifically deal with the flexible and setup characteristics of this problem. The first algorithm is a dynamic dispatching rule heuristic, and the second is an iterated local search metaheuristic. The proposed algorithms are evaluated by comparison against seven other high performing existing algorithms. The statistically sound results support the idea that the proposed algorithms are very competitive for the studied problem.

Scheduling job shop problems with sequence-dependent setup times

In this work we consider job shop problems where the setup times are sequence dependent under minimisation of the maximum completion time or makespan. We present a genetic algorithm to solve the problem. The genetic algorithm is hybridised with a diversification mechanism, namely the restart phase, and a simple form of local search to enrich the algorithm. Various operators and parameters of the genetic algorithm are reviewed to calibrate the algorithm by means of the Taguchi method. For the evaluation of the proposed hybrid algorithm, it is compared against existing algorithms through a benchmark. All the results demonstrate that our hybrid genetic algorithm is very effective for the problem.

A high performing metaheuristic for job shop scheduling with sequence-dependent setup times

This paper investigates scheduling job shop problems with sequence-dependent setup times under minimization of makespan. We develop an effective metaheuristic, simulated annealing with novel operators, to potentially solve the problem. Simulated annealing is a well-recognized algorithm and historically classified as a local-search-based metaheuristic. The performance of simulated annealing critically depends on its operators and parameters, in particular, its neighborhood search structure. In this paper, we propose an effective neighborhood search structure based on insertion neighborhoods as well as analyzing the behavior of simulated annealing with different types of operators and parameters by the means of Taguchi method. An experiment based on Taillard benchmark is conducted to evaluate the proposed algorithm against some effective algorithms existing in the literature. The results show that the proposed algorithm outperforms the other algorithms.

Scheduling open shops with parallel machines to minimize total completion time

This paper explores scheduling a realistic variant of open shops with parallel machines per working stage. Since real production floors seldom employ a single machine for each operation, the regular open shop problem is very often in practice extended with a set of parallel machines at each stage. The purpose of duplicating machines in parallel is to either eliminate or to reduce the impact of bottleneck stages on the overall shop efficiency. The objective is to find the sequence which minimizes total completion times of jobs. We first formulate the problem as an effective mixed integer linear programming model, and then we employ memetic algorithms to solve the problem. We employ Taguchi method to evaluate the effects of different operators and parameters on the performance of memetic algorithm. To further enhance the memetic algorithm, we hybridize it with a simple form of simulated annealing as its local search engine. To assess the performance of the model and algorithms, we establish two computational experiments. The first one is small-sized instances by which the model and general performance of the algorithms are evaluated. The second one consists of large-sized instances by which we further evaluate the algorithms.

A study on integrating sequence dependent setup time flexible flow lines and preventive maintenance scheduling

This paper investigates flexible flow line problems with sequence dependent setup times and different preventive maintenance policies. The optimization criterion is the minimization of makespan. The contribution of this work could be divided into two parts: (1) Since the proposed integrating methods in the literature are often not only complicated but also problem-specific, we have been thinking of providing a technique simple to implement, yet easily extendible to any other machine scheduling problems to overcome the foregoing drawbacks. (2) In order to tackle the problem, we propose a novel variable neighborhood search (VNS) as well as the adaptations of some existing high performing metaheuristics in the literature. The proposed VNS uses advanced neighborhood search structures. In order to evaluate the algorithms, a benchmark is established with the meticulous care. All the results illustrate that the VNS outperforms the other algorithms.

Permutation flowshops in group scheduling with sequence-dependent setup times

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. [Received 11 February 2010; Revised 24 May 2010; Accepted 30 September 2010]

A novel imperialist competitive algorithm for generalized traveling salesman problems

A hybrid imperialist competitive algorithm is presented.It uses assimilation, destruction/construction and imperialist development schemes.The algorithm is calibrated using Taguchi method.For evaluation, it is compared against two effective existing algorithms. This paper deals with generalized traveling salesman problems. In this problem, all nodes are partitioned into some clusters and each cluster must be visited exactly once in a tour. We present an effective metaheuristic method hybridized with a local search procedure to solve this problem. The proposed algorithm is based on the imperialist competitive algorithm (ICA), which is a new socio-politically motivated global search strategy. ICA is enhanced by a novel encoding scheme, assimilation policy procedure, destruction/construction operator and imperialist development plans. Various parameters of the algorithm are analyzed to calibrate the algorithm by means of the Taguchi method. For the evaluation of the proposed algorithm, it is compared against two effective existing algorithms through a set of available instances. The results demonstrate the superiority of our algorithm in both solution quality and robustness of the solution.

Modeling and solving the project selection and scheduling

This paper studies the integrated project selection and scheduling problem.Regarding shortcomings of available techniques, develops a mathematical model.It proposes an ant colony optimization.The algorithm is compared with two algorithms. This paper considers the integrated bi-objective problem of projects selection and scheduling to optimize both total expected benefit and resource usage variation. The benefit is time-dependent. Although this integrated problem has become a very active field of research, the available model and algorithms suffer from serious shortcomings. This paper analyzes the available methods and develops a novel mathematical model, in form of a mixed integer linear program, for the problem. Then, it proposes an ant colony optimization algorithm employing four features of ant generation, colonial, Pareto front updating, and pheromone updating mechanisms. To evaluate the proposed algorithm, it is compared with two available genetic algorithm and scatter search. Using comprehensive numerical experiments and statistical tools, it is shown that the proposed ant colony optimization outperforms the two available algorithms.