Scheduling Operations and SMED: Complementary Ways to Improve Productivity

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.

Minimizing total tardiness in parallel machine scheduling with setup times: An adaptive memory-based GRASP approach

This paper presents a simulated annealing algorithm to minimize makespan of single machine scheduling problem with uniform parallel machines. The single machine scheduling problem with uniform parallel machines consists of n jobs, each with single operation, which are to be scheduled on m parallel machines with different speeds. Since, this scheduling problem is a combinatorial problem; usage of a heuristic is inevitable to obtain the solution in polynomial time. In this paper, simulated annealing algorithm is presented. In the first phase, a seed generation algorithm is given. Then, it is followed by three variations of the simulated annealing algorithms and their comparison using ANOVA in terms of their solutions on makespan.

Dispatching Rule-based Scheduling Algorithms in a Single Machine with Sequence-dependent Setup Times and Energy Requirements

The Unrelated Parallel Machines Scheduling Problem (UPMSP) with sequence-dependent setup times has been widely applied to cloud computing, edge computing and so on. When the setup times are ignored, UPMSP will be a NP problem. Moreover, when considering the sequence related setup times, UPMSP is difficult to solve, and this situation will be more serious in the case of high-dimensional. This work firstly select the maximum completion time as the optimization objective, which establishes a mathematical model of UPMSP with sequence-dependent setup times. In addition, an improved firefly algorithm with courtship learning is proposed. Finally, in order to provide an approximate solution in an acceptable time, the proposed algorithm is applied to solve the UPMSP with sequence-dependent setup times. The experimental results show that the proposed algorithm has competitive performance when dealing with UPMSP with sequence-dependent setup times.

Heuristics for an assembly flow-shop with non-identical assembly machines and sequence dependent setup times to minimize sum of holding and delay costs

This paper considers a production lot sizing and scheduling problem with sequence-dependent setup times that are not triangular. Consider, for example, a product that contaminates some other product unless either a decontamination occurs as part of a substantial setup time or there is a third product that can absorb ’s contamination. When setup times are triangular then and there is always an optimal lot sequence with at most one lot per product per period (AM1L). However, product ’s ability to absorb ’s contamination presents a shortcut opportunity and could result in shorter non-triangular setup times such that . This implies that it can sometimes be optimal for a shortcut product such as to be produced in more than one lot within the same period, breaking the AM1L assumption in much research. This paper formulates and explains a new optimal model that not only permits multiple setups and lots per product in a period (ML), but also prohibits subtours using a polynomial number of constraints rather than an exponential number. Computational tests demonstrate the effectiveness of the ML model, even in the presence of just one decontaminating shortcut product, and its fast speed of solution compared to the equivalent AM1L model.

Metaheuristic algorithms for balancing robotic assembly lines with sequence-dependent robot setup times

To study a scheduling problem of flexible flow shop, where at least one production stage is made up of unrelated parallel machines, and setup times are sequence- and machine-dependent. The objective is to find a schedule that minimizes the makespan and the number of tardy jobs in a static scheduling environment. For this problem, a 0-1 mixed integer programming is formulated. The model is, however, a combinatorial optimization problem which is too difficult to be solved for large-sized problems, and hence, a heuristic is developed to obtain good solutions in reasonable time. The heuristic has three phases. The first phase uses a constructive algorithm. It starts with the generation of the operating time representative for each operation. Then, it uses a dispatching rule or a simple flow shop makespan heuristic to determine an initial solution. The improvement algorithm based on shift moves or pairwise interchanges of jobs is applied to improve the solution in the second phase. After that, a metaheuristic is used to refine the solution in the final phase. Several well-known heuristics are tested in each phase. The performances of the heuristics are compared to one another based on a set of test problems with up to 50 jobs and 20 stages and with an optimal solution for small-sized problems. The computational results indicate that the Nawaz, Enscore, and Ham (NEH) algorithm is most suitable for determining the initial solution, the all-pairwise-interchange approach is good for improving the solution, and the simulated annealing algorithm is best metaheuristic for refining the solution.

Scheduling is an important tool for manufacturing and engineering, where it can have a major impact on the productivity of a process. In manufacturing, the purpose of scheduling is to minimise the production time and costs. Production scheduling aims to maximise the efficiency of the operation and reduce costs. The following approaches are used to solve the job shop problem. We keep all of our machines well-maintained to prevent any problems, but there is no way to completely prevent down-time. We can work to get our batch sizes as small as is reasonably possible while also reducing the setup time of each batch. This allows us to eliminate a sizable portion of each part waiting while the rest of the parts in the batch are being machined. In this paper, we propose the production scheduling problem solving using genetic and greedy algorithms with sequence dependant setup times considering the minimisation of the maximum completion time.

PurposeIn order to achieve excellence in manufacturing, goals like lean, economic and quality production with enhanced productivity play a crucial role in this competitive environment. It also necessitates major improvements in generally three primary technical areas: variation reduction, equipment reliability, and production scheduling. Complexity of the real world scheduling problems also increases with interactive multiple decision‐making criteria. This paper aims to deal with multi‐objective flow shop scheduling problems, including sequence dependent set up time (SDST). The paper also aims to consider the objective of minimizing the weighted sum of total weighted tardiness, total weighted earliness and makespan simultaneously. It proposes a new heuristic‐based hybrid simulated annealing (HSA) for near optimal solutions in a reasonable time.Design/methodology/approachSix modified NEH's based HSA algorithms are proposed for efficient scheduling of jobs in a multi‐objective SDST flow shop. Problems of up to 200 jobs and 20 machines are tested by the proposed HSA and a defined relative percentage improvement index is used for analysis and comparison of different MNEH's based hybrid simulated annealing algorithms.FindingsFrom the results, it has been derived that performance of SA_EWDD (NEH) up to ten machines' problems, and SA_EPWDD (NEH) up to 20 machines' problems, were better over others especially for large sized SDST flow shop scheduling problems for the considered multi‐objective fitness function.Originality/valueHSA and multi‐objective decision making proposed in the present work is a modified approach in the area of SDST flow shop scheduling.

Having analyzed studies on the assembly line balancing problem considering setup times, a few papers are observed. However, researches show that setup time plays an important role in total production time. Besides, in these studies, the setup times are assumed to be deterministic. Considering setup times to be deterministic decreases proximity of problem to real life application. Because the operation and setup times may vary in real applications due to human factors, machine breakdowns, lack of equipment and environmental factors. Therefore, in this study, assembly line balancing problem is investigated considering the sequence-dependent stochastic setup times. A heuristic approach based on COMSOAL is presented for the problem and it is seen that algorithm is effective for the problem.

Scheduling uniform parallel dedicated machines with job splitting, sequence-dependent setup times, and multiple servers

This paper focuses on a make-to-order production system, where rejection of some orders is inevitable due to limited production capacity. In such a system, accepting all orders may cause overloads, order delays, and customer dissatisfaction. For this reason, firms tend to reject some orders. The order acceptance and scheduling problem is defined as deciding simultaneously which orders to be selected and how to schedule these selected orders. An extension of this problem with sequence-dependent setup times and release dates has been rarely studied, and the existing studies suggest that setup activities wait for the release date to be performed. However, in real production environments this may not be the case. Therefore, this paper examines the relationships between setup times and release dates considering the overall scheduling literature. Previous scheduling studies define two different relationships concerning setup times and release dates. One of them considers setup time is completely dependent on release date, and the other one claims that they are completely independent. In this paper, a new relationship is addressed to propound that setup time may be partially dependent on the release date. The paper also proposes a new mixed integer linear programming formulation with O(n2) binary decision variables and O(n2) constraints. It includes a detailed computational analysis by solving available instances in the literature, which suggests that existing formulation can solve the test problems to optimality with up to 10 orders in a given time limit. Our proposed formulation, however, can solve the test problems to optimality with up to 50 orders within the same time limit. According to the findings, our approach seems to be more suitable for real-life applications, and the proposed formulation is extremely faster than the existing one.

Much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. Furthermore, most scheduling problems which have been discussed in the literature are under the assumption that machines are continuously available. Nevertheless, in most real life industries, a machine can be unavailable for many reasons, such as unanticipated breakdowns, i.e., stochastic unavailability, or due to a scheduled preventive maintenance where the periods of unavailability are known in advance, i.e., deterministic unavailability. This paper deals with the hybrid flow shop scheduling problems in which there are sequence-dependent setup times, commonly known as the SDST, and machines which suffer stochastic breakdown to optimize objectives based on expected makespan. This type of production system is found in industries such as chemical, textile, metallurgical, printed circuit board, and automobile manufacture. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. The genetic algorithm can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. This paper describes how we can incorporate simulation into genetic algorithm approach to the scheduling of a SDST hybrid flow shop with machines that suffer stochastic breakdown. An overview of the hybrid flow shops and scheduling under stochastic unavailability of machines are presented. Subsequently, the details of incorporated simulation into genetic algorithm approach are described and implemented. Consequently, the results obtained are analyzed with Taguchi experimental design.