3,423 research outputs found

    A simheuristic algorithm for solving an integrated resource allocation and scheduling problem

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    Modern companies have to face challenging configuration issues in their manufacturing chains. One of these challenges is related to the integrated allocation and scheduling of resources such as machines, workers, energy, etc. These integrated optimization problems are difficult to solve, but they can be even more challenging when real-life uncertainty is considered. In this paper, we study an integrated allocation and scheduling optimization problem with stochastic processing times. A simheuristic algorithm is proposed in order to effectively solve this integrated and stochastic problem. Our approach relies on the hybridization of simulation with a metaheuristic to deal with the stochastic version of the allocation-scheduling problem. A series of numerical experiments contribute to illustrate the efficiency of our methodology as well as their potential applications in real-life enterprise settings

    The robust single machine scheduling problem with uncertain release and processing times

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    In this work, we study the single machine scheduling problem with uncertain release times and processing times of jobs. We adopt a robust scheduling approach, in which the measure of robustness to be minimized for a given sequence of jobs is the worst-case objective function value from the set of all possible realizations of release and processing times. The objective function value is the total flow time of all jobs. We discuss some important properties of robust schedules for zero and non-zero release times, and illustrate the added complexity in robust scheduling given non-zero release times. We propose heuristics based on variable neighborhood search and iterated local search to solve the problem and generate robust schedules. The algorithms are tested and their solution performance is compared with optimal solutions or lower bounds through numerical experiments based on synthetic data

    Resource-constrained project scheduling for timely project completion with stochastic activity durations.

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    We investigate resource-constrained project scheduling with stochastic activity durations. Various objective functions related to timely project completion are examined, as well as the correlation between these objectives. We develop a GRASP-heuristic to produce high-quality solutions, using so-called descriptive sampling. The algorithm outperforms other existing algorithms for expected-makespan minimization. The distribution of the possible makespan realizations for a given scheduling policy is studied, and problem difficulty is explored as a function of problem parameters.GRASP; Project scheduling; Uncertainty;

    Clips: a capacity and lead time integrated procedure for scheduling.

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    We propose a general procedure to address real life job shop scheduling problems. The shop typically produces a variety of products, each with its own arrival stream, its own route through the shop and a given customer due date. The procedure first determines the manufacturing lot sizes for each product. The objective is to minimize the expected lead time and therefore we model the production environment as a queueing network. Given these lead times, release dates are set dynamically. This in turn creates a time window for every manufacturing order in which the various operations have to be sequenced. The sequencing logic is based on a Extended Shifting Bottleneck Procedure. These three major decisions are next incorporated into a four phase hierarchical operational implementation scheme. A small numerical example is used to illustrate the methodology. The final objective however is to develop a procedure that is useful for large, real life shops. We therefore report on a real life application.Model; Models; Applications; Product; Scheduling;

    Scheduling theory since 1981: an annotated bibliography

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    A novel class of scheduling policies for the stochastic resource-constrained project scheduling problem.

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    We study the resource-constrained project scheduling problem with stochastic activity durations. We introduce a new class of scheduling policies for this problem, which make a number of a-priori sequencing decisions in a pre-processing phase, while the remaining decisions are made dynamically during project execution. The pre-processing decisions entail the addition of precedence constraints to the scheduling instance, hereby resolving some potential resource conflicts. We compare the performance of this new class with existing scheduling policies for the stochastic resource-constrained project scheduling problem, and we observe that the new class is significantly better when the variability in the activity durations is medium to high.Project scheduling; Uncertainty; Stochastic activity durations; Scheduling policies;

    BALANCING TRADE-OFFS IN ONE-STAGE PRODUCTION WITH PROCESSING TIME UNCERTAINTY

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    Stochastic production scheduling faces three challenges, first the inconsistencies among key performance indicators (KPIs), second the trade-offs between the expected return and the risk for a portfolio of KPIs, and third the uncertainty in processing times. Based on two inconsistent KPIs of total completion time (TCT) and variance of completion times (VCT), we propose our trade-off balancing (ToB) heuristic for one-stage production scheduling. Through comprehensive case studies, we show that our ToB heuristic with preference =0.0:0.1:1.0 efficiently and effectively addresses the three challenges. Moreover, our trade-off balancing scheme can be generalized to balance a number of inconsistent KPIs more than two. Daniels and Kouvelis (DK) proposed a scheme to optimize the worst-case scenario for stochastic production scheduling and proposed the endpoint product (EP) and endpoint sum (ES) heuristics to hedge against processing time uncertainty. Using 5 levels of coefficients of variation (CVs) to represent processing time uncertainty, we show that our ToB heuristic is robust as well, and even outperforms the EP and ES heuristics on worst-case scenarios at high levels of processing time uncertainty. Moreover, our ToB heuristic generates undominated solution spaces of KPIs, which not only provides a solid base to set up specification limits for statistical process control (SPC) but also facilitates the application of modern portfolio theory and SPC techniques in the industry

    Flow Shop Scheduling Problem: a Computational Study

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    A computational study has been developed to obtain optimal / near optimal solution for the flow shop scheduling problem with make-span minimization as the primary criterion and the minimization of either the mean completion time, total waiting time or total idle time as the secondary criterion. The objective is to determine a sequence of operations in which to process ‘n’ jobs on ‘m’ machines in same order (flow shop environment) where skipping is allowed. The Simulation approach for deterministic and stochastic flow shop scheduling has been developed. It reads and manipulates data for 500 jobs on 500 machines. Different factorial experiments present a comparative study on the performance of different dispatching rules, such as FCFS, SPT, LPT, SRPT and LRPT with respect to the objectives of minimizing makespan, mean flow time, waiting time of jobs, and idle time of machines. The proposed model is evaluated and found to be relatively more effective in finding optimal/ near optimal solutions in many cases. The influence of the problem size in computational time for this model is discussed and recommendations for further research are presented
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