A heuristic algorithm for assembly line balancing with stochastic sequence-dependent setup times

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.

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

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.

Multi-product production systems with sequence-dependent setup times are typical in manufacturing of semiconductor chips and other electronic products. In such systems, the scheduling policies to coordinate the production of multiple product types play an important role. In this paper, we study a multi-product manufacturing system with finite buffers, sequence-dependent setup times and various scheduling policies. Using continuous time Markov chain models, we evaluate the performance of such systems under seven scheduling policies, i.e., cyclic, shortest queue, shortest processing time, shortest overall time (including setup time and processing time), longest queue, longest processing time, and longest overall time. The impact of these policies on systemthroughput are compared, and the conditions characterizing the superiority of each policy are investigated. The results of this work can provide production engineers and supervisors practical guidance to operate multi-product manufacturing systems with sequence-dependent setups.

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.

Dynamic facility location problems consider situations where it is important to decide not only where but also when to open (or close) facilities, given a planning time horizon. Considering explicitly multiple time periods is especially important if assignment costs are expected to change significantly in the future and relocating facilities is expensive. The majority of models presented in the literature considers binary decision variables that define whether facilities are open in a given time period. In addition, if a facility is first opened at some time period then clients can usually be assigned to it from that period forward, without any guarantee that the facility will be operational at the planned time period. In this paper, a new mathematical model is presented where uncertainty in the setup time of facilities is explicitly considered. This means that the decision maker is only capable of deciding where to locate facilities and when to begin all the necessary works to get the facilities operational, but the time elapsed until opening the facility is not known for sure. A primal-dual heuristic is also developed for this problem and its performance is compared with the performance of a commercial exact solver.

In most real cases the problem of scheduling operations are sequence dependent, that is, the setup time depends on the sequencing of tasks/batches in the machine. Given the current industrial manufacturing trends and requirements such as high variability, customization and reduction of product life cycles, organizations seek to implement new methodologies to adapt and respond with more flexibly to new demands of the global markets. Thus, this work sought to analyse, inspired by a real case of the textile industry, how production scheduling can be understood as a complement to SMED in order to achieve productivity improvement. In the case under analysis we presented the problem of scheduling tasks in uniform parallel machines with sequence-dependent setup times. The main objective of this work was to minimize the total production time. To this goal, optimization heuristics were used, in this case, simulated annealing algorithm, that demonstrated how the use of heuristics can be an advantage to reduce setup times when associated with other methodologies such as SMED.

We consider here the lot sizing and scheduling problem in single-level manufacturing systems. The shop floor is composed of unrelated parallel machines with sequence dependent setup times. We propose an integer programming model embedding precise capacity information due to scheduling constraints in a classical lot-sizing model. We also propose an iterative approach to generate a production plan taking into account scheduling constraints due to changeover setup times. The procedure executes two decision modules per iteration: a lot-sizing module and a scheduling module. The capacitated lot-sizing problem is solved to optimality considering estimated data and aggregate information, and the scheduling problem is solved by a GRASP heuristic. In the proposed scheme the information flow connecting the two levels is managed in each iteration. We report a set of computational experiments and discuss future work.

The three-machine flowshop scheduling problem to minimize total completion time is studied where setup times are treated as separate from processing times. Setup and processing times of all jobs on all machines are unknown variables before the actual occurrence of these times. The lower and upper bounds for setup and processing times of each job on each machine is the only information that is available. In such a scheduling environment, there may not exist a unique schedule that remains optimal for all possible realizations of setup and processing times. Therefore, it is desired to obtain a set of dominating schedules (which dominate all other schedules) if possible. The objective for such a scheduling environment is to reduce the size of dominating schedule set. We obtain global and local dominance relations for a three-machine flowshop scheduling problem. Furthermore, we illustrate the use of dominance relations by numerical examples and conduct computational experiments on randomly generated problems to measure the effectiveness of the developed dominance relations. The computational experiments show that the developed dominance relations are quite helpful in reducing the size of dominating schedules.

A survey of scheduling problems with setup times or costs

This paper addresses the flexible Job Shop (FJS) Process Planning and scheduling problem with two types of flexibility in process planning and sequence. Almost all of pervious research about FJS problem ignore setup times between jobs. In many realistic situations setup times depend on sequence of jobs. In these conditions it is not valid to absorb the set up times in processing times. In this study the FJS with sequence dependent setup times is modeled as a nonlinear mixed integer programming . The objective function is minimizing maximum completion time of jobs (makespan). The model is proven to be Np-complete. To solve the model an effective approach based on taboo search is developed. The effectiveness of the proposed algorithm is shown via numerical experiments.

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.

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 that 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 (stochastic unavailability), or due to scheduled preventive maintenance where the periods of unavailability are known in advance (deterministic unavailability). This paper deals with hybrid flow shop scheduling problems in which there are sequence-dependent setup times (SDSTs), and machines suffer stochastic breakdowns, to optimise objectives based on the expected makespan. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. An immune algorithm (IA) can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. In this research, a computational method based on a clonal selection principle and an affinity maturation mechanism of the immune response is used. This paper describes how we can incorporate simulation into an immune algorithm for the scheduling of a SDST hybrid flow shop with machines that suffer stochastic breakdowns. The results obtained are analysed using a Taguchi experimental design.

The Off-Table Technique Increases Operating Room Efficiency in Direct Anterior Hip Replacement

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.