Dynamic job shop scheduling for missed due date performance

In this research, missed due date in terms of mean absolute lateness (MAL) and mean square lateness (MSL) has been considered as a performance criterion and a scheduling study has been performed to improve the missed due date performance in dynamic, stochastic, multi machine job shop environments. In the study, a new due date assignment model was proposed and a new dynamic dispatching rule was developed. The results indicate that the proposed due date assignment model is very successful for improving the missed due date performance and the developed dispatching rule is also very successful for meeting the assigned due dates.     

Using dispatching rules for job shop scheduling with due date-based objectives

This paper addresses the job shop-scheduling problem with due date-based objectives including the tardy rate, mean tardiness and maximum tardiness. The focused approach is the dispatching rules. Eighteen dispatching rules are selected from the literature, and their features and design concepts are discussed. Then a dispatching rule is proposed with the goal of achieving a good and balanced performance when more than one objective is concerned at the same time. First, three good design principles are recognized from the existing rules. Second, it introduces a due date extension procedure to solve a problem of negative allowance time. Third, a job candidate reduction mechanism is developed to make the rule computationally efficient. Lastly, a comprehensive simulation study is conducted with the 18 existing rules as the benchmarks. The experimental results verify the superiority of the proposed rule, especially on the tardy rate and mean tardiness.     

A heuristic model for dynamic flexible job shop scheduling problem considering variable processing times

In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artificial Bee Colony algorithm, is proposed for a dynamic flexible job-shop scheduling (DFJSP) problem. This problem consists of n jobs that should be processed by m machines and the processing time of jobs deviates from estimated times. The objective is near-optimal scheduling after any change in tasks in order to minimise the maximal completion time (Makespan). In the proposed model, first, scheduling is done according to the estimated processing times and then re-scheduling is performed after determining the exact ones considering machine set-up. In order to evaluate the performance of the proposed model, some numerical experiments are designed in small, medium and large sizes in different levels of changes in processing times and statistical results illustrate the efficiency of the proposed algorithm.     

Forecasting policies for scheduling a stochastic due date job shop

This work studies the problem of scheduling a production plant subject to uncertain processing times that may arise, e.g. from the variability of human labour or the possibility of machine breakdowns. The problem is modelled as a job shop with random processing times, where the expected total weighted tardiness must be minimized. A heuristic is proposed that amplifies the expected processing times by a selected factor, which are used as input for a deterministic scheduling algorithm. The quality of a particular solution is measured using a risk averse penalty function combining the expected deviation and the worst case deviation from the optimal schedule. Computational tests show that the technique improves the performance of the deterministic algorithm by 25% when compared with using the unscaled expected processing times as inputs.     

An integrative heuristic method for detailed operations scheduling in assembly job shop systems

In this paper, we describe a new heuristic method for simulating and supporting the operations scheduling process in assembly job shop systems. The method is based on the assumption that the improvement in operations synchronisation at fabrication and assembly stations brings forth better achievement of due dates. The method implements two scheduling approaches: a backward approach satisfying due date completely and a forward approach satisfying capacity restrictions completely. The two approaches work iteratively within two different simulation models of the production system – one deterministic and the other probabilistic – in searching for operations synchronisation improvement and due date achievement. The method intends to be integrative, i.e., to be able to integrate effectively three fundamental enterprise systems: order processing, production scheduling, and manufacturing activity control. An experimental study was conceived to evaluate the suitability of the method to support scheduling decision making. As results demonstrate, the method proves to be suitable for this objective. As a co-product, results show the method is better than the single-pass procedure/rules tested on average and is as good as the best single-pass procedure/rule tested.     

The impact of advancing due dates in a pure job shop

The effect on shop performance of granting customer requests for setting earlier clue dates on jobs already in process in the shop was investigated, via computer simulation, for a five-machine pure job shop. One-replicate, two-way classification (with interaction) and contrasts were used to examine the effect on various measures of shop performance of (1) four levels of mean interarrival time, A, and (2) five levels of the percentage of all jobs with CKEDD (customer requested earlier due date) status, T. Regression equations were developed for each performance measure in terms of the independent variables, A and T.     

A general search sequencing rule for job shop sequencing

This paper sets out, an approach to the job shop sequencing problem by determining the priority of a job from a linear combination of the basic quantities of operation times and due date. This achieves a simple yet unified format. Furthermore, since these basic quantities are used in fixed linear combinations in the majority of well-known simple heuristics, the rule outlined in the paper is capable of representing such heuristics as special cases within its framework. A performance function is used to assess the effectiveness of the rule and no limitations are imposed on its structure. The form of the variable priority rule is then determined by a computer search routine basing its decisions on the values of the performance function. This overall approach to job shop sequencing we term the search sequencing rule (SSR).     

Cultural algorithms,an alternative heuristic to solve the job shop scheduling problem

In this work, an approach for solving the job shop scheduling problem using a cultural algorithm is proposed. Cultural algorithms are evolutionary computation methods that extract domain knowledge during the evolutionary process. Additional to this extracted knowledge, the proposed approach also uses domain knowledge given a priori (based on specific domain knowledge available for the job shop scheduling problem). The proposed approach is compared with respect to a Greedy Randomized Adaptive Search Procedure (GRASP), a Parallel GRASP, a Genetic Algorithm, a Hybrid Genetic Algorithm, and a deterministic method called shifting bottleneck. The cultural algorithm proposed in this article is able to produce competitive results with respect to the two approaches previously indicated at a significantly lower computational cost than at least one of them and without using any sort of parallel processing.     

A shuffled frog-leaping algorithm for job shop scheduling with outsourcing options

In this paper, job shop scheduling problem with outsourcing options is considered and a novel shuffled frog-leaping algorithm (SFLA) is presented to minimise total tardiness under condition that total outsourcing cost does not exceed a given upper bound. In SFLA, a tournament selection-based method is used to decompose the whole population into some memeplexes, the search process in each memeplex is done on the best solution of the memeplex and composed of the global search step and the multiple neighbourhood search step. SFLA is tested on a number of instances and compared with some methods from the literature. Computational results validate the promising performance of SFLA on the considered problem.     

An optimization-based algorithm for job shop scheduling

Scheduling is a key factor for manufacturing productivity. Effective scheduling can improve on-time delivery, reduce inventory, cut lead times, and improve the utilization of bottleneck resources. Because of the combinatorial nature of scheduling problems, it is often difficult to find optimal schedules, especially within a limited amount of computation time. Production schedules therefore are usually generated by using heuristics in practice. However, it is very difficult to evaluate the quality of these schedules, and the consistency of performance may also be an issue. In this paper, near-optimal solution methodologies for job shop scheduling are examined. The problem is formulated as integer optimization with a “separable” structure. The requirement of on-time delivery and low work-in-process inventory is modelled as a goal to minimize a weighted part tardiness and earliness penalty function. Lagrangian relaxation is used to decompose the problem into individual part subproblems with intuitive appeal. By iteratively solving these subproblems and updating the Lagrangian multipliers at the high level, near-optimal schedules are obtained with a lower bound provided as a byproduct. This paper reviews a few selected methods for solving subproblems and for updating multipliers. Based on the insights obtained, a new algorithm is presented that combines backward dynamic programming for solving low level subproblems and interleaved conjugate gradient method for solving the high level problem. The new method significantly improves algorithm convergence and solution quality. Numerical testing shows that the method is practical for job shop scheduling in industries. This work was supported in part by the National Science Foundation under DMI-9500037, and the Advanced Technology Center for Precision Manufacturing, University of Connecticut.     

Optimal scheduling for flexible job shop operation

This paper presents a new integer linear programming (ILP) model to schedule flexible job shop, discrete parts manufacturing industries that operate on a make-to-order basis. The model considers groups of parallel homogeneous machines, limited intermediate buffers and negligible set-up effects. Orders consist of a number of discrete units to be produced and follow one of a given number of processing routes. The model allows re-circulation to take place, an important issue in practice that has received scant treatment in the scheduling literature. Good solution times were obtained using commercial mixed-integer linear programming (MILP) software to solve realistic examples of flexible job shops to optimality. This supports the claim that recent advances in computational power and MILP solution algorithms are making this approach competitive with others traditionally applied in job shop scheduling.     

A genetic algorithm for flexible job shop scheduling with fuzzy processing time

This paper presents a flexible job shop scheduling problem with fuzzy processing time. An efficient decomposition-integration genetic algorithm (DIGA) is developed for the problem to minimise the maximum fuzzy completion time. DIGA uses a two-string representation, an effective decoding method and a main population. In each generation, DIGA decomposes the chromosomes of the main population into a job sequencing part and a machine assigning part and independently evolves the populations of these parts. Some instances are designed and DIGA is tested and compared with other algorithms. Computational results show the effectiveness of DIGA.     

Genetic algorithms for job shop scheduling problems with alternative routings

The purpose of this research is to solve a general job shop problem with alternative machine routings. We consider four performance measures: mean flow time, makespan, maximum lateness, and total absolute deviation from the due dates. We first develop mixed-integer linear programming (MILP) formulations for the problems. The MILP formulations can be used either to compute optimal solutions for small-sized problems or to test the performance of existing heuristic algorithms. In addition, we have developed a genetic algorithm that can be used to generate relatively good solutions quickly. Further, computational experiments have been performed to compare the solution of the MILP formulations with that of existing algorithms.     

Multi-objective genetic algorithm for energy-efficient job shop scheduling

The paper investigates the effects of production scheduling policies aimed towards improving productive and environmental performances in a job shop system. A green genetic algorithm allows the assessment of multi-objective problems related to sustainability. Two main considerations have emerged from the application of the algorithm. First, the algorithm is able to achieve a semi-optimal makespan similar to that obtained by the best of other methods but with a significantly lower total energy consumption. Second, the study demonstrated that the worthless energy consumption can be reduced significantly by employing complex energy-efficient machine behaviour policies.     

Seqential approach in job shop scheduling

Abstract

This study investigats a new approach‐the sequential approach‐ in job shop scheduling. The objective is to minimize the total of lateness cost and set‐up cost in job shops. Whenever a scheduling decision has to be made as to which job should receive the next processing, this approach considers each cost sequentially. One of the two costs is considered first, and every time all waiting jobs must be examined in terms of this cost, and only those jobs qualified would the second cost apply to and from which a job would be selected for processing. This investigation was carried out by using GASP IV simulation under a variety of job shop situations. The effectiveness of this approach and job selection mechanism constitute the main theme of this study.     

Time- and cost-based priorities for job shop scheduling

Competitively, cost- and time-based scheduling should provide a firm with a powerful market advantage. The cost- and time-based priority scheduling concept is driven by profit maximization and quick response in a competitive market. The literature on shop scheduling contains numerous studies reporting on the use of dispatching rules that are based only on the time criterion. Alternatively there have been only a few published articles that specifically consider a composite of cost and time. Aggarwal and McCarl and Scudder and Hoffmann have investigated the use of cost and time information for determining the job priority in random job shops. One result of this study is a reconciliation of the differences between the Aggarwal and McCarl and the Scudder and Hoffmann study. Moreover, a simplistic priority rule based on profit margin and due dates is introduced and tested. The results reported in this research are an attempt to reconcile the issue of time-based versus cost-based priority rule performance.     

Twofold look-ahead search for multi-criterion job shop scheduling

The twofold look-ahead search ((TLAS) was proposed as a general purpose search technique and applied to single-criterion job shop scheduling. In the present study, this method is applied to multi-criterion scheduling problems with some modification and illustrated by bi-criterion scheduling with mean tardiness and mean flowtime. Application circumstances are divided into two situations according to the scheduling objective and TLAS is tested for each situation. From the computational results, the proposed method is found to generate higher performance schedules in comparison with other methods. Its algorithmic features and applicability to other problems are also discussed based on several experimental results.     

A generalized permutation approach to job shop scheduling with genetic algorithms

In order to sequence the tasks of a job shop problem (JSP) on a number of machines related to the technological machine order of jobs, a new representation technique — mathematically known as permutation with repetition is presented. The main advantage of this single chromosome representation is — in analogy to the permutation scheme of the traveling salesman problem (TSP) — that it cannot produce illegal operation sequences. As a consequence of the representation scheme a new crossover operator preserving the initial scheme structure of permutations with repetition will be sketched. Its behavior is similar to the well known Order-Crossover for simple permutation schemes. Actually theGOX operator for permutations with repetition arises from aGeneralisation ofOX. Computational experiments show, that GOX passes the information from a couple of parent solutions efficiently to offspring solutions. Together, the new representation and GOX support the cooperative aspect of genetic search for scheduling problems strongly.Supported by the Deutsche Forschungsgemeinschaft (Project Parnet)     

A divide-and-conquer strategy with particle swarm optimization for the job shop scheduling problem

An optimization algorithm based on the ‘divide-and-conquer’ methodology is proposed for solving large job shop scheduling problems with the objective of minimizing total weighted tardiness. The algorithm adopts a non-iterative framework. It first searches for a promising decomposition policy for the operation set by using a simulated annealing procedure in which the solutions are evaluated with reference to the upper bound and the lower bound of the final objective value. Subproblems are then constructed according to the output decomposition policy and each subproblem is related to a subset of operations from the original operation set. Subsequently, all these subproblems are sequentially solved by a particle swarm optimization algorithm, which leads directly to a feasible solution to the original large-scale scheduling problem. Numerical computational experiments are carried out for both randomly generated test problems and the real-world production data from a large speed-reducer factory in China. Results show that the proposed algorithm can achieve satisfactory solution quality within reasonable computational time for large-scale job shop scheduling problems.     

A hybrid computer simulation-artificial neural network algorithm for optimisation of dispatching rule selection in stochastic job shop scheduling problems

Industrial systems are constantly subject to random events with inevitable uncertainties in production factors, especially in processing times. Due to this stochastic nature, selecting appropriate dispatching rules has become a major issue in practical problems. However, previous research implies that using one dispatching rule does not necessarily yield an optimal schedule. Therefore, a new algorithm is proposed based on computer simulation and artificial neural networks (ANNs) to select the optimal dispatching rule for each machine from a set of rules in order to minimise the makespan in stochastic job shop scheduling problems (SJSSPs). The algorithm contributes to the previous work on job shop scheduling in three significant ways: (1) to the best of our knowledge it is the first time that an approach based on computer simulation and ANNs is proposed to select dispatching rules; (2) non-identical dispatching rules are considered for machines under stochastic environment; and (3) the algorithm is capable of finding the optimal solution of SJSSPs since it evaluates all possible solutions. The performance of the proposed algorithm is compared with computer simulation methods by replicating comprehensive simulation experiments. Extensive computational results for job shops with five and six machines indicate the superiority of the new algorithm compared to previous studies in the literature.