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Public transport network optimisation in PTV Visum using selection hyper-heuristics
Despite the progress in the field of automatic public transport route optimisation in recent years, there exists a clear gap between the development...
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Automatically evolving preference-based dispatching rules for multi-objective job shop scheduling
Dispatching rules represent a simple heuristic for finding good solutions for job shop scheduling problems. Due to their fast applicability and easy...
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Learning from Nature and Plastiglomerate: A Challenge Between Re-Factory and Re-Setting
Nature is the form and matter of things conceived in their continuous becoming, with the generative power of transformations, disasters, and complex...
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A hyper-heuristic approach for stochastic parallel assembly line balancing problems with equipment costs
This study addresses the stochastic parallel assembly line balancing problem with equipment costs and presents a hyper-heuristic approach based on...
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Two multi-start heuristics for the k-traveling salesman problem
This paper is concerned with the k -traveling salesman problem ( k -TSP), which is a variation of widely studied traveling salesman problem (TSP). Given...
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Designing an adaptive and deep learning based control framework for modular production systems
In today’s rapidly changing production landscape with increasingly complex manufacturing processes and shortening product life cycles, a company’s...
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A Re-characterization of Hyper-Heuristics
Hyper-heuristics Hyper-heuristics are an optimization methodology which ‘search the space of heuristics’ rather than...
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gym-flp: A Python Package for Training Reinforcement Learning Algorithms on Facility Layout Problems
Reinforcement learning (RL) algorithms have proven to be useful tools for combinatorial optimisation. However, they are still underutilised in...
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An Empirical Analysis of a Set of Hybrid Heuristics for the Solution of the Resource Leveling Problem
Consideration is given to the heuristic solution of the resource leveling problem (RLP) in project scheduling with limited resources. The objective...
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Smart scheduling of dynamic job shop based on discrete event simulation and deep reinforcement learning
In the era of Industry 4.0, production scheduling as a critical part of manufacturing system should be smarter. Smart scheduling agent is required to...
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Heuristics for the shelf space allocation problem
The retailers’ goals to maximize the profit of the products in stores are realized on the planogram shelves. In this paper, we investigated a...
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Multi-echelon inventory optimization using deep reinforcement learning
This paper studies the applicability of a deep reinforcement learning approach to three different multi-echelon inventory systems, with the objective...
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A matheuristic for customized multi-level multi-criteria university timetabling
Course timetables are the organizational foundation of a university’s educational program. While students and lecturers perceive timetable quality...
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Swarm intelligence-based framework for accelerated and optimized assembly line design in the automotive industry
This study proposes a dynamic simulation-based framework that utilizes swarm intelligence algorithms to optimize the design of hybrid assembly lines...
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A decision support system based on an artificial multiple intelligence system for vegetable crop land allocation problem
This research focuses on the development of a novel artificial multiple intelligence system (AMIS), which is more flexible and effective than...
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A Systematic Literature Review on No-Idle Flow Shop Scheduling Problem
Scheduling issues have become a critical problem in the company because they affect manufacturing performance and production continuity. One of the...
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Optimizing inland container ship** through reinforcement learning
In this study, we investigate the container delivery problem and explore ways to optimize the complex and nuanced system of inland container...
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Strategic Optionality: Introducing the Idea
Optionality is the state of having options. It’s a context where you can select from a set of choices without being under any obligation to do so. We...
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The New Era of Hybridisation and Learning in Heuristic Search Design
This chapter aims to extend on the overview of heuristic and metaheuristics described in chapter [51] by focussing on the new developments related to...
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Three-dimensional shelf-space allocation and optimal demand planning
This short paper presents a two-step theoretical framework for three-dimensional shelf-space allocation and optimal demand planning. Step one...
