Genetic algorithms for studies on AS/RS integrated with machines

This paper proposes the use of genetic algorithms (GAs) for storage and retrieval sequencing in an automated storage and retrieval system that is integrated with machines. Further, it addresses the sequencing when several requests are available and a dual command cycle is performed. The proposed approach is compared with storage and retrieval heuristics such as random, first come first served, and nearest neighbour heuristics. GA rule as machine scheduling is compared with shortest processing time, most work remaining, least operations remaining, least processing time, least work remaining, most operations remaining and random. Case studies demonstrate the effectiveness of the approach.     

Non-identical parallel machine scheduling with fuzzy processing times using genetic algorithm and simulation

This paper addresses non-identical parallel machine scheduling problem with fuzzy processing times (FPMSP). A genetic algorithm (GA) approach embedded in a simulation model to minimize maximum completion time (makespan) is proposed. The results are compared with those obtained by using longest processing time rule, known as the most appropriate dispatching rule for such problems. This application illustrates the need for efficient and effective heuristics to solve FPMSPs. The proposed GA approach yields good results and reaches them fast and several times in one run. Moreover, due to its advantage of being a search algorithm, it can explore alternative schedules providing the same results.     

An ant colony algorithm for scheduling in flowshops with sequence-dependent setup times of jobs

The problem of scheduling in flowshops with sequence-dependent setup times of jobs is considered and solved by making use of ant colony optimization (ACO) algorithms. ACO is an algorithmic approach, inspired by the foraging behavior of real ants, that can be applied to the solution of combinatorial optimization problems. A new ant colony algorithm has been developed in this paper to solve the flowshop scheduling problem with the consideration of sequence-dependent setup times of jobs. The objective is to minimize the makespan. Artificial ants are used to construct solutions for flowshop scheduling problems, and the solutions are subsequently improved by a local search procedure. An existing ant colony algorithm and the proposed ant colony algorithm were compared with two existing heuristics. It was found after extensive computational investigation that the proposed ant colony algorithm gives promising and better results, as compared to those solutions given by the existing ant colony algorithm and the existing heuristics, for the flowshop scheduling problem under study.     

Algorithms for flexible flow shop problems with unrelated parallel machines, setup times, and dual criteria

In textile industries, production facilities are established as multi-stage production flow shop facilities, where a production stage may be made up of parallel machines. This known as a flexible or hybrid flow shop environment. This paper considers the problem of scheduling n independent jobs in such an environment. In addition, we also consider the general case in which parallel machines at each stage may be unrelated. Each job is processed in ordered operations on a machine at each stage. Its release date and due date are given. The preemption of jobs is not permitted. We consider both sequence- and machine-dependent setup times. The problem is to determine a schedule that minimizes a convex combination of makespan and the number of tardy jobs. A 0–1 mixed integer program of the problem is formulated. Since this problem is NP-hard in the strong sense, we develop heuristic algorithms to solve it approximately. Firstly, several basic dispatching rules and well-known constructive heuristics for flow shop makespan scheduling problems are generalized to the problem under consideration. We sketch how, from a job sequence, a complete schedule for the flexible flow shop problem with unrelated parallel machines can be constructed. To improve the solutions, polynomial heuristic improvement methods based on shift moves of jobs are applied. Then, genetic algorithms are suggested. We discuss the components of these algorithms and test their parameters. The performance of the heuristics is compared relative to each other on a set of test problems with up to 50 jobs and 20 stages.     

A novel hybrid genetic algorithm to solve the sequence-dependent permutation flow-shop scheduling problem

Flow-shop scheduling problem (FSP) deals with the scheduling of a set of jobs that visit a set of machines in the same order. The FSP is NP-hard, which means that there is no efficient algorithm to reach the optimal solution of the problem. To minimize the make-span of large permutation flow-shop scheduling problems in which there are sequence-dependent setup times on each machine, this paper develops one novel hybrid genetic algorithms (HGA). Proposed HGA apply a modified approach to generate the population of initial chromosomes and also use an improved heuristic called the iterated swap procedure to improve them. Also the author uses three genetic operators to make good new offspring. The results are compared to some recently developed heuristics and computational experimental results show that the proposed HGA performs very competitively with respect to accuracy and efficiency of the solutions.     

A genetic algorithm and particle swarm optimization for no-wait flow shop problem with separable setup times and makespan criterion

This paper considers the problem of no-wait flow shop scheduling, in which a number of jobs are available for processing on a number of machines in a flow shop context with the added constraint that there should be no waiting time between consecutive operations of a job. Each operation has a separable setup time, meaning that the setup time of an operation is independent on the previous operations; and the machine can be prepared for a specific operation and remain idle before the operation actually starts. The considered objective function in this paper is the makespan. The problem is proven to be NP-hard. In this paper, two frameworks based on genetic algorithm and particle swarm optimization are developed to deal with the problem. For the case of no-wait flow shop problem without setup times, the developed algorithms are applied to a large number of benchmark problems from the literature. Computational results confirm that the proposed algorithms outperform other methods by improving many of the best-known solutions for the test problems. For the problems with setup time, the algorithms are compared against the famous 2-Opt algorithm. Such comparison reveals the efficiency of the proposed method in solving the problem when separable setup times are considered.     

Algorithms for the single machine total weighted completion time scheduling problem with release times and sequence-dependent setups

This paper considers the problem of scheduling n jobs on a single machine to minimize the total weighted completion time in the presence of sequence-dependent setup times and release times. To the best of our knowledge, little research has been devoted to this scheduling problem. Therefore, we developed two exact algorithms, including a constraint programming model and a branch-and-bound method for small problems. The obtained optimal solutions can be used as a benchmark for evaluating the performance of heuristics. With the complexity in mind, two heuristics, including a best index dispatch (BID) and a modified weighted shortest processing time (MWSPT) based on non-delay concepts are also proposed for large problems. The time complexities of the two proposed heuristics are O(n ⁴) and O(n ³), respectively. The computational results showed that the branch-and-bound method could solve most instances with 40 jobs under the time limit of 7,200 s. The BID heuristic is superior to the MWSPT in solution quality, although both can efficiently and effectively obtain near-optimal solutions for large instances.     

A fuzzy dispatching strategy for due-date scheduling of FMSs with information delays

Conventional dispatching strategies for FMSs with routing flexibility have typically employed simple heuristics such as work-in-next-queue (WINQ) and number-in-next-queue (NINQ). The effectiveness of these heuristics, however, deteriorates in FMSs whose operational environment must cope with information delays that are non-negligible in comparison to part processing times. Such delays could arise from planned activities, e.g., acquisition, selection, processing, and transfer of plant-wide system status information as well as from unplanned events such as ERP/IT system malfunctions, mismatch of software interfaces, and erroneous inventory master files, for example. Uncertainties from information delays make a strong case for the introduction of fuzzy controllers for making scheduling decisions. This paper introduces a novel fuzzy logic-based dispatching strategy to cope with a specific manifestation of information delays, called status review delay within FMSs. Status review information delays impact system performance adversely because of the obsolescent nature of the information used in the determination of dispatch decisions. A fuzzy dispatching strategy (FDS), designed specifically for deployment within FMSs where information delays are manifest, provides an appropriate alternative to conventional dispatching strategies such as WINQ and NINQ. In the design of an FDS, relevant system-based parameters are fuzzified and an appropriate rule base is designed. Simulation experiments demonstrate the superiority of an FDS over the conventional WINQ dispatching strategy using the mean tardiness, percent tardy, and mean flowtime performance measures.     

Development and analysis of constructive heuristic algorithms for flow shop scheduling problems with sequence-dependent setup times

This paper focuses on the problem of determining a permutation schedule for n jobs in an m-machine flow shop that operates in a sequence-dependent setup time (SDST) environment. Two constructive heuristic algorithms are developed with the minimisation of makespan as the objective. The first heuristic algorithm termed as setup ranking algorithm obtains the sequence using the setup times of jobs only. The second heuristic algorithm, fictitious job setup ranking algorithm (FJSRA), is developed using the concept of fictitious jobs. Pairs of jobs with minimum setup time between them constitute the fictitious jobs. Both these algorithms are compared with an existing constructive algorithm. For the purpose of experimentation, Taillard benchmark problems are used to develop SDST benchmark problems at eight different levels of sequence-dependent setup times. Graphical analysis, relative performance index analysis and statistical analysis are carried out on the results obtained for all the eight sets of benchmark problems. The analysis reveals that FJSRA emerges as the better algorithm for larger problems and for smaller problems with higher level of setup time. The results of statistical analysis are used to develop setup time dominance matrix for deciding upon the algorithm to be used for a particular size of problem.     

Genetic algorithms for single machine scheduling with quadratic earliness and tardiness costs

In this paper, we consider the single machine scheduling problem with quadratic earliness and tardiness costs, and no machine idle time. We propose a genetic approach based on a random key alphabet and present several algorithms based on this approach. These versions differ on the generation of both the initial population and the individuals added in the migration step, as well as on the use of local search. The proposed procedures are compared with the best existing heuristics, as well as with optimal solutions for the smaller instance sizes. The computational results show that the proposed algorithms clearly outperform the existing procedures and are quite close to the optimum. The improvement over the existing heuristics increases with both the difficulty and the size of the instances. The performance of the proposed genetic approach is improved by the initialization of the initial population, the generation of greedy randomized solutions, and the addition of the local search procedure. Indeed, the more sophisticated versions can obtain similar or better solutions and are much faster. The genetic version that incorporates all the considered features is the new heuristic of choice for small and medium size instances.     

Modeling and scheduling open shops with sequence-dependent setup times to minimize total completion time

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. The problem is formulated 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, a local search engine in form of a fast and simple simulated annealing is incorporated. In order to evaluate the performance of the proposed algorithms, an experiment is designed where 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 the proposed model and EM are effective for the problem.     

A Multi-Objective Genetic Algorithm for Solving Assembly Line Balancing Problem

In this paper, a multi-objective genetic agorithm to solve assembly line balancing problems is proposed. The performance criteria considered are the number of workstations, the line efficiency, the smoothness index before trade and transfer, and the smoothness index after trade and transfer. The developed genetic algorithm is compared with six popular heuristic algorithms, namely, ranked positional weight, Kilbridge and Wester, Moodie and Young, Hoffmann precedence matrix, immediate update first fit, and rank and assign heuristic methods. For comparative evaluation, 20 networks are collected from open literature, and are used with five different cycle times. All the six heuristics and the genetic algorithm are coded in C++ language. It is found that the proposed genetic algorithm performs better in all the performance measures than the heuristics. However, the execution time for the GA is longer, because the GA searches for global optimal solutions with more iterations.     

A discrete PSO for two-stage assembly scheduling problem

In this paper, a discrete particle swarm optimization (PSO) algorithm called DPSO is proposed to solve the two-stage assembly scheduling problem with respect to bicriteria of makespan and mean completion time where setup times are treated as separate from processing times. In DPSO, the particle velocity representation is redefined, and particle movement is modified accordingly. In order to refrain from the shortcoming of premature convergence, individual intensity is defined, which is used to control adaptive mutation of the particle, and mutation mode is decided by the individual fitness. Furthermore, a randomized exchange neighborhood search is introduced to enhance the local search ability of the particle and increase the convergence speed. Finally, the proposed algorithm is tested on different scale problems and compared with the proposed efficient algorithms in the literature recently. The results show that DPSO is an effective and efficient for assembly scheduling problem.     

Due-date scheduling on parallel machines with job splitting and sequence-dependent major/minor setup times

This paper addresses job scheduling problems with parallel machines. To satisfy customers better in a manufacturing company, meeting due dates has been an important performance metric. Besides the numerous other factors affecting due date satisfaction, the splitting of a job through parallel machines can contribute to the reduction of production lead time, resulting in less job tardiness against their due dates. Thus, this paper presents heuristic algorithms for minimizing total tardiness of jobs to meet their due dates in a manufacturing shop with identically functioning machines. The algorithms take into account job splitting and sequence-dependent major/minor setup times. The performance of the proposed heuristics is compared with that of past three algorithms in the literature.     

Scheduling a single machine with multiple preventive maintenance activities and position-based deteriorations using genetic algorithms

In this paper, we study a single machine scheduling problem with deteriorating processing time of jobs and multiple preventive maintenances which reset deteriorated processing time to the original processing time. In this situation, we consider three kinds of problems whose performance measures are makespan, total completion time, and total weighted completion time. First, we formulate integer programming formulations, and using the formulations, one can find optimal solutions for small problems. Since these problems are known to be NP-hard and the size of real problem is very large, we propose a number of heuristics and design genetic algorithms for the problems. Finally, we conduct some computational experiments to evaluate the performance of the proposed algorithms.     

Robust production control policies considering WIP balance and setup time in a semiconductor fabrication line

This paper discusses the problem of determining workload in a semiconductor fabrication line for providing robust production control. A mathematical model is proposed to determine the amount of wafers to be processed on equipment in a photolithography process where the setup time is incurred if the type of wafers is changed on the equipment. The objective of the model is to control the fabrication line by maintaining the target work-in-process (WIP) level as close as possible for the purpose of short cycle time and by minimizing the setup time loss for maximal throughput. The proposed model is formulated using mixed integer programming (MIP) to minimize the weighted sum of two objective functions. A heuristic approach is suggested using linear relaxation and its adjustment. Performances are evaluated for the optimal solution with computational cost and for the heuristics. It is shown that the heuristics give good solutions which are 10% away, on average, from the optimal solution, but which can be obtained in a few seconds.     

Permutation flowshops with transportation times: mathematical models and solution methods

This paper deals with permutation flowshops with considering transportation times of carrying semi-finished jobs from a machine to another one. The transportation between machines can be done using two types of transportation systems: multi-transporter and single-transporter systems. We formulate the problem with both systems as six different mixed integer linear programs. We also provide solution methods including heuristics and metaheuristics in order to solve large-sized problems. The heuristics are the adaptations of well-known heuristics and the proposed metaheuristics are based on artificial immune systems incorporating an effective local search heuristic and simulated annealing. A comprehensive experiment is conducted to compare and evaluate the performance of the models as well as the algorithms. All the results show the effectiveness of the proposed models and algorithms.     

Some single-machine scheduling problems with past-sequence-dependent setup times and a general learning effect

This paper considers a single-machine scheduling model with past-sequence-dependent setup times and a general learning effect. It develops a general model with setup times and learning effect considerations where the actual processing time of a job is not only a function of the total actual processing time of the jobs already processed, but also a function of the job’s scheduled position. The paper shows that the single-machine scheduling problems to minimize the makespan and the sum of the kth power of completion times are polynomially solvable under the proposed model. It further shows that the problems to minimize the total weighted completion time and the maximum lateness are polynomially solvable under certain conditions.     

Bicriteria scheduling of a two-machine flowshop with sequence-dependent setup times

A two-machine flowshop scheduling problem is addressed to minimize setups and makespan where each job is characterized by a pair of attributes that entail setups on each machine. The setup times are sequence-dependent on both machines. It is shown that these objectives conflict, so the Pareto optimization approach is considered. The scheduling problems considering either of these objectives are $ \mathcal{N}{\wp } - {\text{hard}} $ , so exact optimization techniques are impractical for large-sized problems. We propose two multi-objective metaheurisctics based on genetic algorithms (MOGA) and simulated annealing (MOSA) to find approximations of Pareto-optimal sets. The performances of these approaches are compared with lower bounds for small problems. In larger problems, performance of the proposed algorithms are compared with each other. Experimentations revealed that both algorithms perform very similar on small problems. Moreover, it was observed that MOGA outperforms MOSA in terms of the quality of solutions on larger problems.     

Solving a multi-objective overlapping flow-shop scheduling

In flow-shop manufacturing scheduling systems, managers attempt to minimize makespan and manufacturing costs. Job overlaps are typically unavoidable in real-life applications as overlapping production shortens operation throughput times and reduces work-in-process inventories. This study presents an ant colony optimization (ACO) heuristic for establishing a simple and effective mechanism to solve the overlap manufacturing scheduling problem with various ready times and a sequentially dependent setup time. In the proposed approach, the scheduling mechanism and ACO heuristics are developed separately, thereby improving the performance of overlapping manufacturing flow by varying parameters or settings within the ACO heuristics and allowing for flexible application of manufacturing by altering scheduling criteria. Finally, the experimental results of the scheduling problem demonstrate that the ACO heuristics have good performance when searching for answers.