Results 51 to 60 of about 2,111 (188)

Setup‐Optimized Sequencing in Job Shops: Modeling Workstation Productivity and Lateness Behavior

Advanced Engineering Materials, Volume 28, Issue 12, 17 June 2026.
Setup‐optimized sequencing in job‐shop production creates a trade‐off between productivity improvement and schedule reliability. A WIP‐explicit modeling framework links sequencing‐induced productivity gains and lateness dispersion through the production operating curve.
Friederike Stefanowski, Matthias Schmidt, Peter Nyhuis   +2 more
wiley   +1 more source

Behavior of the main parameters of the Genetic Algorithm for Flow Shop Scheduling Problems

Revista Cubana de Ciencias Informáticas, 2014
There are different suggested values to adapt the basic parameters of a Genetic Algorithm, however, these values may not be the optimal for all kinds of applications.
Yunior César Fonseca Reyna, Yailen Martínez Jiménez, Ángel Enrique Figueredo León, Luis Alberto Pernía Nieves   +3 more
doaj  

Modeling and generating user‐centered contrastive explanations for the workforce scheduling and routing problem

International Transactions in Operational Research, Volume 33, Issue 3, Page 1525-1558, May 2026.
Abstract In the last decade, explainability has been attracting much attention in the machine learning community. However, this research topic extends beyond this field to encompass others such as operations research and combinatorial optimization (CO).
Mathieu Lerouge, Céline Gicquel, Vincent Mousseau, Wassila Ouerdane   +3 more
wiley   +1 more source

A Hybrid Task Scheduling Technique in Fog Computing Using Fuzzy Logic and Deep Reinforcement Learning

IEEE Access
This work presents an innovative method for scheduling tasks in a fog computing environments by combining the fuzzy logic with deep reinforcement learning.
Prashanth Choppara, S. Sudheer Mangalampalli   +1 more
doaj   +1 more source

PENJADWALAN FLOW SHOP DENGAN PENERAPAN CROSS ENTROPY-GENETIC ALGORITHM (CEGA) UNTUK MEMINIMASI MAKESPAN

Journal of Engineering and Management in Industrial System, 2015
In this study done on leather shoe company that uses flow shop scheduling strategies. Where the purpose of this study is to minimize makespan is the total time needed to complete the entire job.
Hasan Bashori, Pratikto Pratikto, Sugiono Sugiono   +2 more
doaj   +1 more source

A Novel Hybrid Recruit Simulated Annealing and Rat Swarm Optimization Algorithm for Permutation Flow Shop Scheduling

Engineering Reports, Volume 8, Issue 4, April 2026.
Rat swarm position update mechanism in HRSA‐RSO, illustrating the collective exploration behavior around the prey target (A*, B*). ABSTRACT The permutation flow shop scheduling problem (PFSSP) is a classical NP‐hard problem that aims to determine an optimal job sequence across machines to minimize makespan.
Mourad Mzili, Mouna Torki, Toufik Mzili, Mohammed Benzakour Amine, Nebojsa Bacanin, Momina Shaheen   +5 more
wiley   +1 more source

MINIMIZING THE MAKESPAN FOR UNRELATED PARALLEL MACHINES [PDF]

International Journal on Artificial Intelligence Tools, 2007
In this paper, we study the unrelated parallel machine problem for minimizing the makespan, which is NP-hard. We used Simulated Annealing (SA) and Tabu Search (TS) with Neighborhood Search (NS) based on the structure of the problem. We also used a modified SA algorithm, which gives better results than the traditional SA and developed an effective ...
Yunsong Guo, Andrew Lim 0001, Brian Rodrigues, Liang Yang   +3 more
openaire   +2 more sources

Anytime Lexicographic Enumeration of the Pareto Front in Multi‐Objective Combinatorial Optimisation

Journal of Multi-Criteria Decision Analysis, Volume 33, Issue 1, April 2026.
ABSTRACT Multi‐objective combinatorial optimisation problems are widespread in real‐world scenarios, including resource allocation, scheduling and logistics, where multiple competing objectives need to be optimised simultaneously. In industrial contexts, lexicographic optimisation is often used to solve these problems, requiring the decision‐maker (DM)
Marco Foschini, Dimos Tsouros, Bistra Dilkina, Tias Guns   +3 more
wiley   +1 more source

Efficient Task Scheduling and Load Balancing in Fog Computing for Crucial Healthcare Through Deep Reinforcement Learning

IEEE Access
In healthcare, real-time decision making is crucial for ensuring timely and accurate patient care. However, traditional computing infrastructures, with their wide ranging capabilities, suffer from inherent latency, which compromises the efficiency of ...
Prashanth Choppara, Bommareddy Lokesh
doaj   +1 more source

Energy‐aware flexible job shop scheduling problem with nonlinear routes and position‐based learning effect

International Transactions in Operational Research, Volume 33, Issue 2, Page 860-891, March 2026.
Abstract Sustainability has become one of the main objectives in all human activities and, in particular, in manufacturing environments. In this paper, we consider the flexible job shop scheduling problem with the objective of minimizing energy consumption.
Ernesto G. Birgin, José Angel Riveaux, Débora P. Ronconi   +2 more
wiley   +1 more source