scholarly journals An Algorithm for Efficient Generation of Customized Priority Rules for Production Control in Project Manufacturing with Stochastic Job Processing Times

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 337
Author(s):  
Mathias Kühn ◽  
Michael Völker ◽  
Thorsten Schmidt
Keyword(s):  
Production Control ◽  
Project Planning ◽  
Initial Population ◽  
Optimization Approach ◽  
Scheduling Problems ◽  
Priority Rules ◽  
Processing Times ◽  
Problem Class ◽  
Stochastic Solution ◽  
Computationally Intensive

Project Planning and Control (PPC) problems with stochastic job processing times belong to the problem class of Stochastic Resource-Constrained Multi-Project Scheduling Problems (SRCMPSP). A practical example of this problem class is the industrial domain of customer-specific assembly of complex products. PPC approaches have to compensate stochastic influences and achieve high objective fulfillment. This paper presents an efficient simulation-based optimization approach to generate Combined Priority Rules (CPRs) for determining the next job in short-term production control. The objective is to minimize project-specific objectives such as average and standard deviation of project delay or makespan. For this, we generate project-specific CPRs and evaluate the results with the Pareto dominance concept. However, generating CPRs considering stochastic influences is computationally intensive. To tackle this problem, we developed a 2-phase algorithm by first learning the algorithm with deterministic data and by generating promising starting solutions for the more computationally intensive stochastic phase. Since a good deterministic solution does not always lead to a good stochastic solution, we introduced the parameter Initial Copy Rate (ICR) to generate an initial population of copied and randomized individuals. Evaluating this approach, we conducted various computer-based experiments. Compared to Standard Priority Rules (SPRs) used in practice, the approach shows a higher objective fulfilment. The 2-phase algorithm can reduce the computation effort and increases the efficiency of generating CPRs.

scholarly journals Comparative Simulation Study of Production Scheduling in the Hybrid and the Parallel Flow

2017 ◽  
Vol 8 (2) ◽  
pp. 69-80 ◽  
Author(s):  
Maria L.R. Varela ◽  
Justyna Trojanowska ◽  
Silvio Carmo-Silva ◽  
Natalia M.L. Costa ◽  
Jose Machado
Keyword(s):  
Production Scheduling ◽  
Material Handling ◽  
Manufacturing System ◽  
Production Control ◽  
Flow Shop ◽  
Parallel Flow ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Processing Times ◽  
Transportation Times

AbstractScheduling is one of the most important decisions in production control. An approach is proposed for supporting users to solve scheduling problems, by choosing the combination of physical manufacturing system configuration and the material handling system settings. The approach considers two alternative manufacturing scheduling configurations in a two stage product oriented manufacturing system, exploring the hybrid flow shop (HFS) and the parallel flow shop (PFS) environments. For illustrating the application of the proposed approach an industrial case from the automotive components industry is studied. The main aim of this research to compare results of study of production scheduling in the hybrid and the parallel flow, taking into account the makespan minimization criterion. Thus the HFS and the PFS performance is compared and analyzed, mainly in terms of the makespan, as the transportation times vary. The study shows that the performance HFS is clearly better when the work stations’ processing times are unbalanced, either in nature or as a consequence of the addition of transport times just to one of the work station processing time but loses advantage, becoming worse than the performance of the PFS configuration when the work stations’ processing times are balanced, either in nature or as a consequence of the addition of transport times added on the work stations’ processing times. This means that physical layout configurations along with the way transport time are including the work stations’ processing times should be carefully taken into consideration due to its influence on the performance reached by both HFS and PFS configurations.

scholarly journals Approximate and robust bounded job start scheduling for Royal Mail delivery offices

2021 ◽  
Author(s):  
Dimitrios Letsios ◽  
Jeremy T. Bradley ◽  
Suraj G ◽  
Ruth Misener ◽  
Natasha Page
Keyword(s):  
Processing Time ◽  
Approximation Ratio ◽  
Optimization Approach ◽  
Scheduling Problems ◽  
Processing Times ◽  
Lexicographic Optimization ◽  
Lpt Algorithm ◽  
Scheduling Under Uncertainty ◽  
Recovery Approach ◽  
Delivery Scheduling

AbstractMotivated by mail delivery scheduling problems arising in Royal Mail, we study a generalization of the fundamental makespan scheduling $$P||C_{\max }$$ P | | C max problem which we call the bounded job start scheduling problem. Given a set of jobs, each specified by an integer processing time $$p_j$$ p j , that have to be executed non-preemptively by a set of m parallel identical machines, the objective is to compute a minimum makespan schedule subject to an upper bound $$g\le m$$ g ≤ m on the number of jobs that may simultaneously begin per unit of time. With perfect input knowledge, we show that Longest Processing Time First (LPT) algorithm is tightly 2-approximate. After proving that the problem is strongly $${\mathcal {N}}{\mathcal {P}}$$ N P -hard even when $$g=1$$ g = 1 , we elaborate on improving the 2-approximation ratio for this case. We distinguish the classes of long and short instances satisfying $$p_j\ge m$$ p j ≥ m and $$p_j<m$$ p j < m , respectively, for each job j. We show that LPT is 5/3-approximate for the former and optimal for the latter. Then, we explore the idea of scheduling long jobs in parallel with short jobs to obtain tightly satisfied packing and bounded job start constraints. For a broad family of instances excluding degenerate instances with many very long jobs, we derive a 1.985-approximation ratio. For general instances, we require machine augmentation to obtain better than 2-approximate schedules. In the presence of uncertain job processing times, we exploit machine augmentation and lexicographic optimization, which is useful for $$P||C_{\max }$$ P | | C max under uncertainty, to propose a two-stage robust optimization approach for bounded job start scheduling under uncertainty aiming in a low number of used machines. Given a collection of schedules of makespan $$\le D$$ ≤ D , this approach allows distinguishing which are the more robust. We substantiate both the heuristics and our recovery approach numerically using Royal Mail data. We show that for the Royal Mail application, machine augmentation, i.e., short-term van rental, is especially relevant.

Modeling and scheduling hybrid open shops for makespan minimization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kennedy Anderson Guimarães de Araújo ◽  
Tiberius Oliveira e Bonates ◽  
Bruno de Athayde Prata
Keyword(s):  
Programming Model ◽  
Computing Time ◽  
Iterated Local Search ◽  
Mixed Integer ◽  
Scheduling Problems ◽  
Priority Rules ◽  
Production Flow ◽  
Solution Quality ◽  
Content Type ◽  
Constructive Heuristics

Purpose This study aims to address the hybrid open shop problem (HOSP) with respect to the minimization of the overall finishing time or makespan. In the HOSP, we have to process n jobs in stages without preemption. Each job must be processed once in every stage, there is a set of mk identical machines in stage k and the production flow is immaterial. Design/methodology/approach Computational experiments carried out on a set of randomly generated instances showed that the minimal idleness heuristic (MIH) priority rule outperforms the longest processing time (LPT) rule proposed in the literature and the other proposed constructive methods on most instances. Findings The proposed mathematical model outperformed the existing model in the literature with respect to computing time, for small-sized instances, and solution quality within a time limit, for medium- and large-sized instances. The authors’ hybrid iterated local search (ILS) improved the solutions of the MIH rule, drastically outperforming the models on large-sized instances with respect to solution quality. Originality/value The authors formalize the HOSP, as well as argue its NP-hardness, and propose a mixed integer linear programming model to solve it. The authors propose several priority rules – constructive heuristics based on priority measures – for finding feasible solutions for the problem, consisting of adaptations of classical priority rules for scheduling problems. The authors also propose a hybrid ILS for improving the priority rules solutions.

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