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result(s) for
"Production scheduling."
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Integration of process planning and scheduling : approaches and algorithms
\"Both process planning and scheduling are very important functions of manufacturing, which affects together the cost to manufacture a product and the time to deliver it. This book contains various approaches proposed by researchers, to integrate the process planning and scheduling functions of manufacturing under varying configurations of shops. It is useful for both beginners and advanced researchers to understand and formulate the Integration Process Planning and Scheduling (IPPS) problem effectively\"-- Provided by publisher.
BOOK
Master Planning and Scheduling
Discover the practical, real-world advantages of the Oliver Wight master planning and scheduling methodology.The newly revised Fourth Edition of Master Planning and Scheduling: An Essential Guide to Competitive Manufacturing delivers a masterful exploration of today's master planning and scheduling techniques, as well as an insightful discussion.
eBook
A memetic algorithm for multi-objective distributed production scheduling: minimizing the makespan and total energy consumption
The classical distributed production scheduling problem (DPSP) assumes that factories are identical, and each factory is composed of just some machines. Inspired by the fact that manufacturers these days typically work across different factories, and each of these factories normally has some workshops, we study an important extension of the DPSP with different factories and workshops (DPFW), where jobs can be processed and transferred between the factories, workshops and machines. To the best of our knowledge, this is the very first time distributed production scheduling with different factories and workshops is studied. We propose a novel memetic algorithm (MA) to solve this DPFW, aiming to minimize the makespan and total energy consumption. The proposed MA is incorporated with a well-designed chromosome encoding method and a balance-transfer initialization method to generate a good initial population. An effective local search operator is also presented to improve the MA’s convergence speed and fully exploit its solution space. A total of 50 DPFW benchmark instances are used to evaluate the performance of our MA. Computational experiments carried out confirm that the MA is able to easily obtain better solutions for the majority of the tested problem instances compared to three other well-known algorithms, demonstrating its superior performance over these algorithms in terms of solution quality. Our proposed method and the results presented here may be helpful for production managers who work with distributed manufacturing systems in scheduling their production activities by considering different factories and workshops. With this DPFW, imbalanced resource loads and unexpected bottlenecks, which regularly arise in traditional DPSP models, can be easily avoided.
Journal Article
Competitive Two-Agent Scheduling and Its Applications
We consider a scheduling environment with
m (m
≥ 1) identical machines in parallel and two agents. Agent
A
is responsible for
n
1
jobs and has a given objective function with regard to these jobs; agent
B
is responsible for
n
2
jobs and has an objective function that may be either the same or different from the one of agent
A
. The problem is to find a schedule for the
n
1
+
n
2
jobs that minimizes the objective of agent
A
(with regard to his
n
1
jobs) while keeping the objective of agent
B
(with regard to his
n
2
jobs) below or at a fixed level
Q
. The special case with a single machine has recently been considered in the literature, and a variety of results have been obtained for two-agent models with objectives such as
f
max
, ∑ w
j
C
j
, and ∑
U
j
. In this paper, we generalize these results and solve one of the problems that had remained open. Furthermore, we enlarge the framework for the two-agent scheduling problem by including the total tardiness objective, allowing for preemptions, and considering jobs with different release dates; we consider also identical machines in parallel. We furthermore establish the relationships between two-agent scheduling problems and other areas within the scheduling field, namely rescheduling and scheduling subject to availability constraints.
Journal Article
A handbook for construction planning and scheduling
\"A Handbook for Construction Planning & Scheduling presents the key issues of planning and programming in scheduling in a clear, concise and practical way\"-- Provided by publisher.
Book
A New Placement Heuristic for the Orthogonal Stock-Cutting Problem
This paper presents a new best-fit heuristic for the two-dimensional rectangular stock-cutting problem and demonstrates its effectiveness by comparing it against other published approaches. A placement algorithm usually takes a list of shapes, sorted by some property such as increasing height or decreasing area, and then applies a placement rule to each of these shapes in turn. The proposed method is not restricted to the first shape encountered but may dynamically search the list for better candidate shapes for placement. We suggest an efficient implementation of our heuristic and show that it compares favourably to other heuristic and metaheuristic approaches from the literature in terms of both solution quality and execution time. We also present data for new problem instances to encourage further research and greater comparison between this and future methods.
Journal Article
Research on Multi-Objective Low-Carbon Flexible Job Shop Scheduling Based on Improved NSGA-II
To optimize the production scheduling of a flexible job shop, this paper, based on the NSGA-II algorithm, proposes an adaptive simulated annealing non-dominated sorting genetic algorithm II with enhanced elitism (ASA-NSGA-EE) that establishes a multi-objective flexible job shop scheduling model with the objective functions of minimizing the maximum completion time, processing cost, and carbon emissions generated from processing. The ASA-NSGA-EE algorithm adopts an adaptive crossover and mutation genetic strategy, which dynamically adjusts the crossover and mutation rates based on the evolutionary stage of the population, aiming to reduce the loss of optimal solutions. Additionally, it incorporates the simulated annealing algorithm to optimize the selection strategy by leveraging its cooling characteristics. Furthermore, it improves the elite strategy through incorporating elite selection criteria. Finally, by simulation experiments, the effectiveness of the improved NSGA-II algorithm is validated by comparing it with other algorithms.
Journal Article