Ali Allahverdi

A review of scheduling research involving setup considerations

The majority of scheduling research assumes setup as negligible or part of the processing time. While this assumption simplifies the analysis and/or reflects certain applications, it adversely affects the solution quality for many applications which require explicit treatment of setup. Such applications, coupled with the emergence of production concepts like time-based competition and group technology, have motivated increasing interest to include setup considerations in scheduling problems. This paper provides a comprehensive review of the literature on scheduling problems involving setup times (costs). It classifies scheduling problems into batch and non-batch, sequence-independent and sequence-dependent setup, and categorizes the literature according to the shop environments of single machine, parallel machines, flowshops, and job shops. The suggested classification scheme organizes the scheduling literature involving setup considerations, summarizes the current research results for different problem types, and finally provides guidelines for future research.

The significance of reducing setup times/setup costs

Abstract Scheduling with setup times or setup costs plays a crucial role in todays modern manufacturing and service environments where reliable products/services are to be delivered on time. Scheduling activities profoundly depend on the times/costs required to prepare the facility for performing the activities. However, the vast majority of existing scheduling literature ignores this fact. We define and emphasize the importance, applications, and benefits of explicitly considering setup times/costs in scheduling research. Moreover, a review of the latest research on scheduling problems with setup times/costs is provided.

A PSO and a Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application

The assembly flowshop scheduling problem has been addressed recently in the literature. There are many problems that can be modeled as assembly flowshop scheduling problems including queries scheduling on distributed database systems and computer manufacturing. The problem has been addressed with respect to either makespan or total completion time criterion in the literature. In this paper, we address the problem with respect to a due date-based performance measure, i.e., maximum lateness. We formulate the problem and obtain a dominance relation. Moreover, we propose three heuristics for the problem: particle swarm optimization (PSO), Tabu search, and EDD. PSO has been used in the areas of function optimization, artificial neural network training, and fuzzy system control in the literature. In this paper, we show how it can be used for scheduling problems. We have conducted extensive computational experiments to compare the three heuristics along with a random solution. The computational analysis indicates that Tabu outperforms the others for the case when the due dates range is relatively wide. It also indicates that the PSO significantly outperforms the others for difficult problems, i.e., tight due dates. Moreover, for difficult problems, the developed dominance relation helps reduce error by 65%.

New heuristics for no-wait flowshops to minimize makespan

This paper addresses the m-machine no-wait flowshop scheduling problem to minimize makespan. We propose two heuristics that are based on simulated annealing and Genetic Algorithm techniques. We also propose improvement procedures to these heuristics. Extensive computational experiments show that the simulated annealing heuristic outperforms the best two existing heuristics. They also show that the improvement procedures, applied to the Simulated Annealing and Genetic Algorithm, result in significant reduction in error, about 750% error reduction to Simulated Annealing and about 1960% to Genetic Algorithm.

The third comprehensive survey on scheduling problems with setup times/costs

Scheduling involving setup times/costs plays an important role in todays modern manufacturing and service environments for the delivery of reliable products on time. The setup process is not a value added factor, and hence, setup times/costs need to be explicitly considered while scheduling decisions are made in order to increase productivity, eliminate waste, improve resource utilization, and meet deadlines. However, the vast majority of existing scheduling literature, more than 90 percent, ignores this fact. The interest in scheduling problems where setup times/costs are explicitly considered began in the mid-1960s and the interest has been increasing even though not at an anticipated level. The first comprehensive review paper (Allahverdi et al., 1999) on scheduling problems with setup times/costs was in 1999 covering about 200 papers, from mid-1960s to mid-1988, while the second comprehensive review paper (Allahverdi et al., 2008) covered about 300 papers which were published from mid-1998 to mid-2006. This paper is the third comprehensive survey paper which provides an extensive review of about 500 papers that have appeared since the mid-2006 to the end of 2014, including static, dynamic, deterministic, and stochastic environments. This review paper classifies scheduling problems based on shop environments as single machine, parallel machine, flowshop, job shop, or open shop. It further classifies the problems as family and non-family as well as sequence-dependent and sequence-independent setup times/costs. Given that so many papers have been published in a relatively short period of time, different researchers have addressed the same problem independently, by even using the same methodology. Throughout the survey paper, the independently addressed problems are identified, and need for comparing these results is emphasized. Moreover, based on performance measures, shop and setup times/costs environments, the less studied problems have been identified and the need to address these problems is specified. The current survey paper, along with those of Allahverdi et al. (1999, 2008), is an up to date survey of scheduling problems involving static, dynamic, deterministic, and stochastic problems for different shop environments with setup times/costs since the first research on the topic appeared in the mid-1960s.

Total flowtime in no-wait flowshops with separated setup times

Abstract An important class of scheduling problems is characterized by a no-wait constraint where the jobs have to be processed continuously without waiting between or on consecutive machines. This constraint of no-waiting arises from the characteristics of the processing technology itself. Considering setup times separate from processing times of the jobs forms another important class of scheduling problems. This is particularly important when the ratio of the setup time to the processing time is non-negligible. This paper addresses a scheduling problem which falls in the combined category of no-wait and separate setup times. The performance measure considered is the total flowtime. This paper addresses the two-machine no-wait flowshop problem where the setup time of a job is separated from its processing time. The performance measure considered is the total flowtime. An elimination criterion is developed and optimal solutions are obtained for two special cases. For the generic case, a heuristic algorithm is provided. Computational experience shows that the algorithm yields good solutions.

Optimizing modular product design for reconfigurable manufacturing

The problem of optimizing modular products in a reconfigurable manufacturing system is addressed. The problem is first posed as a generalized subset selection problem where the best subsets of module instances of unknown sizes are determined by minimizing an objective function that represents a trade-off between “the quality loss due to modularization” and the cost of reconfiguration while satisfying the problem constraints. The problem is then formulated and solved as an integer nonlinear programming problem with binary variables. The proposed method is applied to the production of a modular drive system composed of a DC motor and a ball screw. The study is a first attempt toward developing a systematic methodology for manufacturing modular products in a reconfigurable manufacturing system.

The two-stage assembly scheduling problem to minimize total completion time with setup times

We address the two-stage assembly scheduling problem where there are m machines at the first stage and an assembly machine at the second stage. The objective is to schedule the available n jobs so that total completion time of all n jobs is minimized. Setup times are treated as separate from processing times. This problem is NP-hard, and therefore we present a dominance relation and propose three heuristics. The heuristics are evaluated based on randomly generated data. One of the proposed heuristics is known to be the best heuristic for the case of zero setup times while another heuristic is known to perform well for such problems. A new version of the latter heuristic, which utilizes the dominance relation, is proposed and shown to perform much better than the other two heuristics.

Evolutionary heuristics and an algorithm for the two-stage assembly scheduling problem to minimize makespan with setup times

In this paper we address the two-stage assembly flowshop scheduling problem with respect to the makespan criterion where setup times are considered as separate from processing times. We formulate the problem and obtain a dominance relation. Moreover, we propose two evolutionary heuristics: a Particle Swarm Optimization (PSO) and a Tabu search. We also propose a simple and yet efficient algorithm with negligible computational time. We have conducted extensive computational experiments to compare the two heuristics and the algorithm along with a random solution. The computational analysis indicates that both heuristics and the algorithm perform significantly well. The computational analysis also indicates that PSO is the best and that the difference between the average errors of PSO and the algorithm becomes small as the number of jobs increases, while the computational time of PSO becomes much larger. Moreover, the difference between the two errors becomes even smaller as the number of machines (at the first stage) and the ratio of setup times to processing times becomes smaller. Therefore, PSO is recommended for a number of jobs up to 50, whereas the algorithm is suggested for larger numbers of jobs and larger numbers of machines at the first stage.

The two- and m-machine flowshop scheduling problems with bicriteria of makespan and mean flowtime

Abstract This paper considers the flowshop scheduling problem with the objective of minimizing the weighted sum of makespan and mean flowtime. Even though the two-machine problem has been addressed and several heuristics have been established in the literature, these heuristics have not been compared. In this paper, a comparison of these available heuristics in the literature is conducted. Moreover, three new heuristic algorithms are proposed, which can be utilized for both the two-machine and m-machine problems. Computational experiments indicate that the proposed heuristics are superior to all the existing heuristics in the literature including a genetic algorithm. Two dominance relations are also developed; one for the two-machine and the other for the three-machine problem. Experimental results show that both relations are efficient.