2,069 research outputs found
A study on exponential-size neighborhoods for the bin packing problem with conflicts
We propose an iterated local search based on several classes of local and
large neighborhoods for the bin packing problem with conflicts. This problem,
which combines the characteristics of both bin packing and vertex coloring,
arises in various application contexts such as logistics and transportation,
timetabling, and resource allocation for cloud computing. We introduce
evaluation procedures for classical local-search moves, polynomial variants of
ejection chains and assignment neighborhoods, an adaptive set covering-based
neighborhood, and finally a controlled use of 0-cost moves to further diversify
the search. The overall method produces solutions of good quality on the
classical benchmark instances and scales very well with an increase of problem
size. Extensive computational experiments are conducted to measure the
respective contribution of each proposed neighborhood. In particular, the
0-cost moves and the large neighborhood based on set covering contribute very
significantly to the search. Several research perspectives are open in relation
to possible hybridizations with other state-of-the-art mathematical programming
heuristics for this problem.Comment: 26 pages, 8 figure
A Polyhedral Study of Mixed 0-1 Set
We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set
Exact and heuristic approaches for multi-component optimisation problems
Modern real world applications are commonly complex, consisting of multiple subsystems
that may interact with or depend on each other. Our case-study about wave
energy converters (WEC) for the renewable energy industry shows that in such a
multi-component system, optimising each individual component cannot yield global
optimality for the entire system, owing to the influence of their interactions or the
dependence on one another. Moreover, modelling a multi-component problem is
rarely easy due to the complexity of the issues, which leads to a desire for existent
models on which to base, and against which to test, calculations. Recently,
the travelling thief problem (TTP) has attracted significant attention in the Evolutionary
Computation community. It is intended to offer a better model for multicomponent
systems, where researchers can push forward their understanding of
the optimisation of such systems, especially for understanding of the interconnections
between the components. The TTP interconnects with two classic NP-hard
problems, namely the travelling salesman problem and the 0-1 knapsack problem,
via the transportation cost that non-linearly depends on the accumulated weight
of items. This non-linear setting introduces additional complexity. We study this
nonlinearity through a simplified version of the TTP - the packing while travelling
(PWT) problem, which aims to maximise the total reward for a given travelling tour.
Our theoretical and experimental investigations demonstrate that the difficulty of a
given problem instance is significantly influenced by adjusting a single parameter,
the renting rate, which prompted our method of creating relatively hard instances
using simple evolutionary algorithms. Our further investigations into the PWT
problem yield a dynamic programming (DP) approach that can solve the problem in
pseudo polynomial time and a corresponding approximation scheme. The experimental
investigations show that the new approaches outperform the state-of-the-art
ones. We furthermore propose three exact algorithms for the TTP, based on the DP
of the PWT problem. By employing the exact DP for the underlying PWT problem
as a subroutine, we create a novel indicator-based hybrid evolutionary approach for
a new bi-criteria formulation of the TTP. This hybrid design takes advantage of the
DP approach, along with a number of novel indicators and selection mechanisms
to achieve better solutions. The results of computational experiments show that the
approach is capable to outperform the state-of-the-art results.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201
A genetic algorithm for the one-dimensional cutting stock problem with setups
This paper investigates the one-dimensional cutting stock problem considering two conflicting objective functions: minimization of both the number of objects and the number of different cutting patterns used. A new heuristic method based on the concepts of genetic algorithms is proposed to solve the problem. This heuristic is empirically analyzed by solving randomly generated instances and also practical instances from a chemical-fiber company. The computational results show that the method is efficient and obtains positive results when compared to other methods from the literature. © 2014 Brazilian Operations Research Society
Research Trends and Outlooks in Assembly Line Balancing Problems
This paper presents the findings from the survey of articles published on the assembly line balancing problems (ALBPs) during 2014-2018. Before proceeding a comprehensive literature review, the ineffectiveness of the previous ALBP classification structures is discussed and a new classification scheme based on the layout configurations of assembly lines is subsequently proposed. The research trend in each layout of assembly lines is highlighted through the graphical presentations. The challenges in the ALBPs are also pinpointed as a technical guideline for future research works
Development of a hybrid metaheuristic for the efficient solution of strategic supply chain management problems: application to the energy sector
Supply chain management (SCM) addresses the strategic, tactical, and operational
decision making that optimizes the supply chain performance. The
strategic level defines the supply chain configuration: the selection of suppliers,
transportation routes, manufacturing facilities, production levels, technologies.
The tactical level plans and schedules the supply chain to meet
actual demand. The operational level executes plans. Tactical and operational
level decision-making functions are distributed across the supply
chain.
To increase or optimize performance, supply-chain functions must be
perfectly coordinated. But the cycles of the enterprise and the market make
this difficult: raw material does not arrive on time, production facilities
fail, workers are ill, customers change or cancel orders, therefore, causing
deviations from the plan. In some cases, these situations may be dealt
with locally. In other cases, the problem cannot be ”locally contained” and
modifications across many functions are required. Consequently, the supply
chain management system must coordinate the revision of plans or schedules.
The ability to better understand an algorithm is important to focus on the
following variables: tactical and operational levels of the supply chain so that
the timely dissemination of information, accurate coordination of decisions,
and management of actions among people and systems is achieved ultimately determines the efficient, coordinated achievement of enterprise goal
Solution of a Bi-Objective Purchasing Scheduling Problem with Constrained Funds using Pareto Optimization
Abstract. In this paper the Purchasing Scheduling Problem (PSP) with limited funds is presented. PSP is formulated through the optimization of two objectives based on the inventory-supply process: maximization of satisfied demands and minimization of purchasing costs. The problem is solved using two variants of the Ant Colony System algorithm (ACS), designed under Pareto's optimization principle in which elements of multi-objective representation for computing a feasible solution are incorporated to the basic design of ACS. Experimental results reveal that the Pareto approach improves solutions over the ACS in 8%, obtaining an efficiency of 80% solving the set of PSP instances as purchasing plans. This reveals the advantages of developing evolutionary algorithms based on multi-objective approaches, which can be exploited in planning and scheduling systems
Flowshop with additional resources during setups: Mathematical models and a GRASP algorithm
[EN] Machine scheduling problems arise in many production processes, and are something that needs to be consider when optimizing the supply chain. Among them, flowshop scheduling problems happen when a number of jobs have to be sequentially processed by a number of machines. This paper addressees, for the first time, the Permutation Flowshop Scheduling problem with additional Resources during Setups (PFSR-S). In this problem, in addition to the standard permutation flowshop constraints, each machine requires a setup between the processing of two consecutive jobs. A number of additional and scarce resources, e.g. operators, are needed to carry out each setup. Two Mixed Integer Linear Programming formulations and an exact algorithm are proposed to solve the PFSR-S. Due to its complexity, these approaches can only solve instances of small size to optimality. Therefore, a GRASP metaheuristic is also proposed which provides solutions for much larger instances. All the methods designed for the PFSR-S in this paper are computationally tested over a benchmark of instances adapted from the literature. The results obtained show that the GRASP metaheuristic finds good quality solutions in short computational times.Juan C. Yepes-Borrero acknowledges financial support by Colfuturo under program Credito-Beca grant number 201503877 and from ElInstituto Colombiano de Credito Educativo y Estudios Tecnicos en el Exterior - ICETEX under program Pasaporte a la ciencia - Doctor-ado, Foco-reto pais 4.2.3, grant number 3568118. This research hasbeen partially supported by the Agencia Estatal de Investigacion (AEI)and the European Regional Development's fund (ERDF): PID2020-114594GB-C21; Regional Government of Andalusia: projects FEDER-US-1256951, AT 21_00032, and P18-FR-1422; Fundacion BBVA: project Netmeet Data (Ayudas Fundacion BBVA a equipos de investigacioncientifica 2019). The authors are partially supported by Agencia Valenciana de la Innovacion (AVI) under the project ireves (innovacionen vehiculos de emergencia sanitaria): una herramienta inteligente dedecision'' (No. INNACC/2021/26) partially financed with FEDER funds(interested readers can visit http://ireves.upv.es), and by the Spanish Ministry of Science and Innovation under the project OPRES-RealisticOptimization in Problems in Public Health'' (No. PID2021-124975OB-I00), partially financed with FEDER funds. Part of the authors aresupported by the Faculty of Business Administration and Managementat Universitat Politecnica de ValenciaYepes-Borrero, JC.; Perea, F.; Villa Juliá, MF.; Vallada Regalado, E. (2023). Flowshop with additional resources during setups: Mathematical models and a GRASP algorithm. Computers & Operations Research. 154. https://doi.org/10.1016/j.cor.2023.10619215
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