Showing papers on "Tardiness published in 2011"

Due-date setting and priority sequencing in a multiclass M/G/1 queue

Abstract: The problem of simultaneous due-date setting and priority sequencing is analyzed in the setting of a multiclass M/G/1 queueing system. The objective is to minimize the weighted average due-date lead time (due-date minus arrival date) of jobs subject to a constraint on either the fraction of tardy jobs or the average job tardiness. Several parametric and nonparametric due-date setting policies are proposed that depend on the class of arriving job, the state of the queueing system at the time of the job's arrival, and the sequencing policy (the weighted shortest expected processing time rule) that is used. In a simulation experiment performed on a two-class M/M/1 system, these policies outperformed traditional due-date setting policies, and due-date setting had a larger impact on performance than priority sequencing.

A new hybrid multi-objective Pareto archive PSO algorithm for a bi-objective job shop scheduling problem

Abstract: This paper presents a new mathematical model for a bi-objective job shop scheduling problem with sequence-dependent setup times and ready times that minimizes the weighted mean flow time ([email protected]?"w) and total penalties of tardiness and earliness (E/T). Obtaining an optimal solution for this complex problem especially in large-sized problem instances within reasonable computational time is cumbersome. Thus, we propose a new multi-objective Pareto archive particle swarm optimization (PSO) algorithm combined with genetic operators as variable neighborhood search (VNS). Furthermore, we use a character of scatter search (SS) to select new swarm in each iteration in order to find Pareto optimal solutions for the given problem. Some test problems are examined to validate the performance of the proposed Pareto archive PSO in terms of the solution quality and diversity level. In addition, the efficiency of the proposed Pareto archive PSO, based on various metrics, is compared with two prominent multi-objective evolutionary algorithms, namely NSGA-II and SPEA-II. Our computational results show the superiority of our proposed algorithm to the foregoing algorithms, especially for the large-sized problems.

A discrete colonial competitive algorithm for hybrid flowshop scheduling to minimize earliness and quadratic tardiness penalties

Abstract: Recently introduced colonial competitive algorithm (CCA) has shown its excellent capability on different optimization problems. The aim of this paper is to propose a discrete version of this method to determine a schedule that minimizes sum of the linear earliness and quadratic tardiness in the hybrid flowshops scheduling problem with simultaneously considering effects of sequence-dependent setup times and limited waiting time. In other word we assume that the waiting time for each job between two consecutive stages cannot be greater than a given upper bound. Also for this problem, a mixed integer program is formulated. Computational results are presented to evaluate the performance of the proposed algorithms for problems with different structures.

A local-best harmony search algorithm with dynamic sub-harmony memories for lot-streaming flow shop scheduling problem

Abstract: In this paper, a local-best harmony search (HS) algorithm with dynamic sub-harmony memories (HM), namely DLHS algorithm, is proposed to minimize the total weighted earliness and tardiness penalties for a lot-streaming flow shop scheduling problem with equal-size sub-lots. First of all, to make the HS algorithm suitable for solving the problem considered, a rank-of-value (ROV) rule is applied to convert the continuous harmony vectors to discrete job sequences, and a net benefit of movement (NBM) heuristic is utilized to yield the optimal sub-lot allocations for the obtained job sequences. Secondly, an efficient initialization scheme based on the NEH variants is presented to construct an initial HM with certain quality and diversity. Thirdly, during the evolution process, the HM is dynamically divided into many small-sized sub-HMs which evolve independently so as to balance the fast convergence and large diversity. Fourthly, a new improvisation scheme is developed to well inherit good structures from the local-best harmony vector in the sub-HM. Meanwhile, a chaotic sequence to produce decision variables for harmony vectors and a mutation scheme are utilized to enhance the diversity of the HM. In addition, a simple but effective local search approach is presented and embedded in the DLHS algorithm to enhance the local searching ability. Computational experiments and comparisons show that the proposed DLHS algorithm generates better or competitive results than the existing hybrid genetic algorithm (HGA) and hybrid discrete particle swarm optimization (HDPSO) for the lot-streaming flow shop scheduling problem with total weighted earliness and tardiness criterion.

On the n-job, one-machine, sequence-independent scheduling problem with tardiness penalties: a branch-bound solution

Abstract: An efficient branch-bound algorithm is presented for solving the n-job, sequence-independent, single machine scheduling problem where the goal is to minimize the total penalty costs resulting from tardiness of jobs. The algorithm and computational results are given for the case of linear penalty functions. The modifications needed to handle the case of nonlinear penalty functions are also presented.

Precast production scheduling using multi-objective genetic algorithms

Abstract: The goal of production scheduling is to achieve a profitable balance among on-time delivery, short customer lead time, and maximum utilization of resources. However, current practices in precast production scheduling are fairly basic, depending heavily on experience, thereby resulting in inefficient resource utilization and late delivery. Moreover, previous methods ignoring buffer size between stations typically induce unfeasible schedules. Certain computational techniques have been proven effective in scheduling. To enhance precast production scheduling, this research develops a multi-objective precast production scheduling model (MOPPSM). In the model, production resources and buffer size between stations are considered. A multi-objective genetic algorithm is then developed to search for optimum solutions with minimum makespan and tardiness penalties. The performance of the proposed model is validated by using five case studies. The experimental results show that the MOPPSM can successfully search for optimum precast production schedules. Furthermore, considering buffer sizes between stations is crucial for acquiring reasonable and feasible precast production schedules.

An adaptive multi-population genetic algorithm to solve the multi-objective group scheduling problem in hybrid flexible flowshop with sequence-dependent setup times

Abstract: In this paper we consider a multi-objective group scheduling problem in hybrid flexible flowshop with sequence-dependent setup times by minimizing total weighted tardiness and maximum completion time simultaneously. Whereas these kinds of problems are NP-hard, thus we proposed a multi-population genetic algorithm (MPGA) to search Pareto optimal solution for it. This algorithm comprises two stages. First stage applies combined objective of mentioned objectives and second stage uses previous stage's results as an initial solution. In the second stage sub-population will be generated by re-arrangement of solutions of first stage. To evaluate performance of the proposed MPGA, it is compared with two distinguished benchmarks, multi-objective genetic algorithm (MOGA) and non-dominated sorting genetic algorithm II (NSGA-II), in three sizes of test problems: small, medium and large. The computational results show that this algorithm performs better than them.

A differential evolution algorithm for the no-idle flowshop scheduling problem with total tardiness criterion

Abstract: In this paper, we investigate the use of a continuous algorithm for the no-idle permutation flowshop scheduling (NIPFS) problem with tardiness criterion. For this purpose, a differential evolution algorithm with variable parameter search (vpsDE) is developed to be compared to a well-known random key genetic algorithm (RKGA) from the literature. The motivation is due to the fact that a continuous DE can be very competitive for the problems where RKGAs are well suited. As an application area, we choose the NIPFS problem with the total tardiness criterion in which there is no literature on it to the best of our knowledge. The NIPFS problem is a variant of the well-known permutation flowshop (PFSP) scheduling problem where idle time is not allowed on machines. In other words, the start time of processing the first job on a given machine must be delayed in order to satisfy the no-idle constraint. The paper presents the following contributions. First of all, a continuous optimisation algorithm is used to solve ...

Simplified multi-objective genetic algorithms for stochastic job shop scheduling

Abstract: Job shop scheduling with multi-objective has been extensively investigated; however, multi-objective stochastic job shop scheduling problem is seldom considered. In this paper, a simplified multi-objective genetic algorithm (SMGA) is proposed for the problem with exponential processing time. The objective is to minimize makespan and total tardiness ratio simultaneously. In SMGA, the chromosome of the problem is ordered operations list, an effective schedule building procedure is proposed, a novel crossover is used, and a simplified binary tournament selection and a simple external archive updating strategy are adopted. SMGA is finally tested on some benchmark problems and compared with some methods from literature. Computational results demonstrate that the good performance of SMGA on the problem.

A two-stage genetic algorithm for multi-objective job shop scheduling problems

Abstract: This paper presents a two-stage genetic algorithm (2S-GA) for multi-objective Job Shop scheduling problems The 2S-GA is proposed with three criteria: Minimize makespan, Minimize total weighted earliness, and Minimize total weighted tardiness The proposed algorithm is composed of two Stages: Stage 1 applies parallel GA to find the best solution of each individual objective function with migration among populations In Stage 2 the populations are combined The evolution process of Stage 2 is based on Steady-State GA using the weighted aggregating objective function The algorithm developed can be used with one or two objectives without modification The genetic algorithm is designed and implemented with the GALIB object library The random keys representation is applied to the problem The schedules are constructed using a permutation with m-repetitions of job numbers Performance of the proposed algorithm is tested on published benchmark instances and compared with results from other published approaches for both the single objective and multi-objective cases The experimental results show that 2S-GA is effective and efficient to solve job shop scheduling problem in term of solution quality

Minimization of total tardiness on unrelated parallel machines with sequence- and machine-dependent setup times under due date constraints

Abstract: The unrelated parallel machine scheduling problem with sequence- and machine-dependent setup times in the presence of due date constraints represents an important but relatively less-studied scheduling problem in the literature. In this study, a simple iterated greedy (IG) heuristic is presented to minimize the total tardiness of this scheduling problem. The effectiveness and efficiency of the proposed IG heuristic are compared with existing algorithms on a benchmark problem dataset used in earlier studies. Extensive computational results indicate that the proposed IG heuristic is capable of obtaining significantly better solutions than the state-of-the-art algorithms on the same benchmark problem dataset with similar computational resources.

Solving a multi-objective job shop scheduling problem with sequence-dependent setup times by a Pareto archive PSO combined with genetic operators and VNS

Abstract: In this paper, we present a combination of particle swarm optimization (PSO) and genetic operators for a multi-objective job shop scheduling problem that minimizes the mean weighted completion time and the sum of the weighted tardiness/earliness costs, simultaneously. At first, we propose a new integer linear programming for the given problem. Then, we redefine and modify PSO by introducing genetic operators, such as crossover and mutation operators, to update particles and improve particles by variable neighborhood search. Furthermore, we consider sequence-dependent setup times. We then design a Pareto archive PSO, where the global best position selection is combined with the crowding measure-based archive updating method. To prove the efficiency of our proposed PSO, a number of test problems are solved. Its reliability based on some comparison metrics is compared with a prominent multi-objective genetic algorithm (MOGA), namely non-dominated sorting genetic algorithm II (NSGA-II). The computational results show that the proposed PSO outperforms the above MOGA, especially for large-sized problems.

Some single-machine scheduling problems with general effects of learning and deterioration

Abstract: Learning and job deterioration co-exist in many realistic scheduling situations. This paper introduces a general scheduling model with the effects of learning and deterioration simultaneously which is a significant generalization of some existing models in the literature. By the effects of learning and deterioration, we mean that job processing times are defined by functions of their start times and positions in the sequence. This paper shows that the single-machine scheduling problems to minimize the makespan, sum of the kth power of completion times, total lateness and sum of earliness penalties (with a common due date) are polynomially solvable under the proposed model. It further shows that the problems to minimize the total weighted completion time, discounted total weighted completion time, maximum lateness, maximum tardiness, total tardiness and total weighted earliness penalties (with a common due date) are polynomially solvable under certain conditions.

A tabu search algorithm for solving a multicriteria flexible job shop scheduling problem

Abstract: The problem that we consider in this article is a flexible job shop scheduling problem issued from the printing and boarding industry. Two criteria have to be minimised, the makespan and the maximum lateness. Two tabu search algorithms are proposed for finding a set of non-dominated solutions: the first is based on the minimisation of one criterion subject to a bound on the second criterion (e-constraint approach) and the second is based on the minimisation of a linear combination of criteria. These algorithms are tested on benchmark instances from the literature and the results are discussed. The total tardiness is considered as a third criterion for the second tabu search and results are presented and discussed.

Solving a multi-objective open shop scheduling problem by a novel hybrid ant colony optimization

Abstract: This paper considers an open shop scheduling problem that minimizes bi-objectives, namely makespan and total tardiness. This problem, due to its complexity, is ranked in the class of NP-hard problems. In this case, traditional approaches cannot reach to an optimal solution in a reasonable time. Thus, we propose an efficient method based on multi-objective simulated annealing and ant colony optimization, in order to solve the given problem. Furthermore a decoding operator is applied in order to improve the quality of generated schedules. Finally, we compare our computational results with a well-known multi-objective genetic algorithm, namely NSGA II. In addition, comparisons are made in single objective case. The outputs show encouraging results in the form of the solution quality.