Using Genetic Algorithms and Heuristics for Job Shop Scheduling with Sequence-Dependent Setup Times

Polynomial time approximation schemes for general multiprocessor job shop scheduling

Dehydration plants are broadly characterized by a multi-product nature chiefly attributed to the utilization of different raw materials to be processed sequentially so that demand constraints are met. Processing of raw materials is implemented through a series of preprocessing operations that together with drying constitute the production procedure of a pre-specified programme. The core of the manufacturing system that a typical dehydration plant involves, is scheduling of operations so that demand is fulfilled within a pre-determined time horizon imposed by production planning. The typical scheduling operation that dehydration plants involve can be formulated as a general job shop scheduling problem. The aim of this study is to describe a new metaheuristic method for solving the job shop scheduling problem of dehydration plants, termed as the Backtracking Adaptive Threshold Accepting (BATA) method. Our effort focuses on developing an innovative method, which produces reliable and high quality solutions, requiring reasonable computing effort. The main innovation of this method, towards a typical threshold accepting algorithm, is that during the optimization process the value of the threshold is not only lowered, but also raised or backtracked according to how effective a local search is. BATA is described in detail while a characteristic job shop scheduling case study for dehydration plant operations is presented.

A unidirectional mutation-selection approach for the general job shop scheduling problem is presented. The underlying algorithm uses threshold accepting as an iterative search technique and incorporates a special non-monotonic threshold function (threshold waving). Search is performed in the space of operation sequences and, for the purpose of evaluation, is combined with a simple heuristic transforming operation sequences into schedules. Mutation of solution candidates is based on a critical path analysis and, additionally, on a probabilistically employed local hill-climbing operator. Effectiveness of the approach is demonstrated within a comparison with some well-known heuristic and exact algorithms.

This paper presents a multi-objective mathematical model for a flexible job shop scheduling problem (FJSSP) with fuzzy processing times, which is solved by a hybrid intelligent algorithm (HIA). This problem contains a combination of a classical job shop problem with parallel machines (JSPM) to provide flexibility in the production route. Despite the previous studies, the number of parallel machines is not pre-specified in this paper. This constraint with other ones (e.g., sequence-dependent setup times, reentrant workflows, and fuzzy variables) makes the given problem more complex. To solve such a multi-objective JSPM, Pareto-based optimization algorithms based on multi-objective meta-heuristics and multi-criteria decision making (MCDM) methods are utilized. Then, different comparison metrics (e.g., quality, mean ideal distance, and rate of achievement simultaneously) are used. Also, this paper includes two major phases to provide a new model of the FJSSP and introduce a new proposed HIA for solving the presented model, respectively. This algorithm is a hybrid genetic algorithm with the SAW/TOPSIS method, namely HGASAW/HGATOPSIS. The comparative results indicate that HGASAW and HGATOPSIS outperform the non-dominated sorting genetic algorithm (NSGA-II) to tackle the fuzzy multi-objective JSPM.

In many industries including metallurgy, petrochemicals, paper, aerospace, pharmaceutical, electronics, ceramic and automotive manufacturing, the production system can contain a machine bottleneck which processes, in some cases, all the jobs. The management of this machine is crucial for the enterprise because it is responsible for delays in customers' orders delivery. The jobs scheduling on this machine is a way of approaching the problem and treats the whole production system. The single machine model provides a basis for more complicated environment such those seen in many real cases. Also, several studies have shown that several jobs, if not all, have time-dependent setup times. So, decision makers must therefore organize the jobs scheduling on this single machine by trying to minimize downtime while respecting the different deadlines. On the other hand, several resolution methods have been developed in literature to solve this kind of problem using approaching methods like metaheuristics or hybrid methods integrating exact methods. This thesis is in this direction. In this research, we propose several effective approaches to solve the single machine scheduling problem with sequence-dependent setup times with the objective to minimize total tardiness of the jobs. First, a new crossover operator in a genetic algorithm is proposed. This algorithm shows its ability to be competitive with the best approaches found in the literature on benchmark problems. We thus highlight the importance of incorporating specific problem knowledge into genetic operators, even if classical genetic operators could be used. However, the results obtained show that the proposed genetic algorithm is less efficient in comparison to the best resolution method found in the literature. Indeed, we noticed that the proposed algorithm lacks an intensification process for enhancing its. To overcome this shortcoming, we explore resolution class methods that showed promising prospects in the last decade. Even, the hybrid algorithms have achieved very good results in a wide variety of problems. In recent years, several researchers have introduced the hybridization of metaheuristics with exact methods. In fact, this kind of hybridization can be an attractive alternative because both methods have very different characteristics that can be combined to produce better results. Thus we explore the design of hybrid genetic algorithms improving the intensification process of the proposed genetic algorithm by integrating an exact method and mechanisms from other resolution methods to improve the performance. However, we find few approaches hybridizing metaheuristics and constraint programming in the literature to solve scheduling problems. In a second step, we propose a modeling for the total tardiness single machine with setup-dependent times scheduling problem using the constraint-based scheduling and the ILOG C++ API. The related experiments show that algorithm efficiency improves with specific tree search strategies and heuristic scheduling variables. Under prohibitive time computing, the proposed approach results are generally far from the best known solutions. However, we got interesting performance on small instances to consider allowing its hybridization with other resolution methods. In a third step, we introduce a hybrid approach based on the integration between genetic algorithm and constraint based scheduling approaches. This latter approach is integrated in the reproduction and the intensification processes of a genetic algorithm autonomously. Finally, we propose a hybrid algorithm which integrates several concepts from constraint programming, multi-objective optimization and ant colonies optimization in a crossover operator into a genetic algorithm. The proposed hybrid algorithms show excellent performance on benchmark problems from the literature by improving many of the best known solutions for some of them.

Using a family of critical ratio-based approaches to minimize the number of tardy jobs in the job shop with sequence dependent setup times

This paper investigates a more general flexible job shop scheduling problem with multiple process plans which is common in the modern manufacturing system. As an extension of the traditional flexible job shop scheduling problem, various realistic flexibility such as processing flexibility, machine flexibility and sequencing flexibility are considered in this problem. Due to the high complexity and the real-time requirement of this problem, a genetic programming-based evolutionary approach is proposed to automatically generate effective dispatching rules for this problem, and an evaluation method is developed to evaluate the generated dispatching rules. Three experiments are conducted to evaluate the performance of the proposed approach for real cases with large-scale test problems. Numerical results show that the proposed approach outperforms the classical dispatching rules and the state-of-theart algorithms, and is able to provide higher-quality solutions with less computational time.

The Job shop scheduling problem with generic time lags is a generalization of the job shop problem. It is defined as a job shop problem with minimal and maximal delays between starting times of operations of different jobs. In this paper, we propose a combination of genetic algorithm and tabu search to solve Job Shop Problem with Generic Time Lags. Benchmark instances are used to investigate the performance of the proposed metaheuristic. The results show that the proposed combination of metaheuristics improves the efficiency.

In job shop scheduling problems, the previously used nonpreemptive schedules are semiactive, active, and non-delay schedules, and they are initially designed for traditional scheduling performances and do not take energy efficiency into consideration. To fill the gap, this article presents a novel class of nonpreemptive schedules by investigating the special properties of job shop scheduling problems. Then, an energy-efficient generation procedure of the novel nonpreemptive schedules and the standby policy are proposed. An improved genetic algorithm is introduced with three superior features, including the elitist selection, the improved generation alternation model, and the operator decision mechanism. The Friedman test and the Holm multiple comparison test are conducted on experimental results to determine the appropriate parameter for the proposed genetic algorithm. Finally, the computation results and two-sample t-tests show that both energy-efficient generation procedure and standby policy help to reduce energy consumption without worsening production efficiency.

Scheduling for the flexible job shop is very important in both fields of production management and combinatorial optimization. However, when we attempt to formulate job shop scheduling problems which closely describe and represent the real world problem, various factors involved are often imprecisely or ambiguously known to the analyst. Also, in the real situations, machines are not continuously available due to preventive maintenance activity. Besides, in the production system, there are many users and machines in various priorities. Often, user satisfactions, machine stability and schedule itself are in conflict. By considering the imprecise or fuzzy nature of the data in real world problem, job shop scheduling with uncertain processing time and machine available constraint is introduced. Under the fuzzy circumstance, we attempt to make a feasible solution for flexible job shop scheduling problem (FJSSP) based on a hybrid optimization approach with multi-objective which not only to minimize the makespan, but also maximize the user satisfaction and machine stability. In the experiment, we make the comparisons with some other algorithms, such as genetic algorithm, to demonstrate the hybrid approach is much better on efficient search and stability with three benchmark examples. Sensitivity analysis is conducted to study the impact, in term of total objective, when the proportion of machine stability is varied.

A survey of scheduling problems with setup times or costs

Scheduling problems in real systems often require sequence-dependent setup times. The topic of sequence-dependent setup times has not been addressed adequately, and improved competitiveness is thus not achieved. This study proposes a hybrid approach that takes advantage of simulated annealing (SA) and tabu search (TS) to solve single-machine tardiness problems with sequence-dependent setup times. To verify the proposed approach, experiments were conducted on benchmark problem sets that included both the weighted and un-weighted tardiness problems. The results show that the performance of the hybrid approach is superior to that of the SA, genetic algorithm, TS and ant colony optimization approaches, and is comparable with the Tabu-VNS approach. And the proposed approach found new upper bound values for many benchmark problems with an acceptable computational time.

This article concerns the resolution of a hybrid flow shop with dedicated machines, sequence dependent setup and time lags. Such a configuration is encountered in the pasta production industry. The objective is to minimize the maximum completion time of all the jobs (makespan). We first propose a mathematical model for the problem. Given the complexity of the problem, we also developed an upper bound using genetic algorithm and a lower bound in order to evaluate the GA. Computational experiments are applied to a number of randomly generated test problems. The results show that the proposed algorithm can produce results that are very close to optimal schedules if they are known or very close to the lower bounds.

Order review and release (ORR) in a job shop considering sequence-dependent setup time (SDST) is among the most challenging job shop problems. This study investigates the effect of varying setup times and workload trigger levels on ORR policies performance in a stochastic dynamic job shop considering SDST. It is essential to examine the effect of varying setup times and workload trigger levels on ORR policies, because, for different ORR policies, workload trigger level plays a major role to control the work-in-process on the shop floor. Further, in manufacturing industries, less setup time is required in some cases, but it can also be equal to or greater than the processing time in others. Promodel[Formula: see text] simulation software is used to develop the simulation model for conducting experiments. The performance of the system is evaluated in terms of mean lead time (MLT), mean throughput time (MTT), number of tardy jobs (NOTJ), and total setups (TS) performance measures. From results, it is found that for a given ORR policy and at a given setup time level, lower workload trigger level provides minimum MTT and maximum MLT, TS, and NOTJ performance measures. As the workload trigger level increases, MTT performance measure increases, whereas MLT, TS, and NOTJ performance measures decrease. Further, for a given ORR policy and workload trigger level, as the setup time level increases, MTT and MLT performance measures increase. In contrast, TS and NOTJ performance measures decrease. A stochastic dynamic job shop considering ORR and SDST where setup time is either less than, equal to or more than operation processing time is the novelty of the work.

Sequence-dependent setup times are one of the most important factors for the optimization of scheduling the production targets. Usually, they include the changing of tools, fixtures, cutting tools and the cleaning of production equipments. Some of them are relevant not only to the sequence requirement of the products to be processed on the equipment, but also to the processing requirement of the adjoining sequence. In this paper, a job shop scheduling problem with sequence-dependent setup times is described. A mixed integer program model is adopted to deal with this type of problem, and a scheduling algorithm based on biologic immunity mechanism is introduced. The result shows that the antibody encoding method and the mechanism of antibody proliferation and suppression can not only ensure the diversity of the antibody, but can also greatly improve the effectiveness of dealing with complex problems. Finally, a scheduling problem of finishing processing for a woollen mill is analyzed with its result described.