24,581 research outputs found
Decision-based genetic algorithms for solving multi-period project scheduling with dynamically experienced workforce
The importance of the flexibility of resources increased rapidly with the turbulent changes in the industrial context, to meet the customersâ requirements. Among all resources, the most important and considered as the hardest to manage are human resources, in reasons of availability and/or conventions. In this article, we present an approach to solve project scheduling with multi-period human resources allocation taking into account two flexibility levers. The first is the annual hours and working time regulation, and the second is the actorsâ multi-skills. The productivity of each operator was considered as dynamic, developing or degrading depending on the prior allocation decisions. The solving approach mainly uses decision-based genetic algorithms, in which, chromosomes donât represent directly the problem solution; they simply present three decisions: tasksâ priorities for execution, actorsâ priorities for carrying out these tasks, and finally the priority of working time strategy that can be considered during the specified working period. Also the principle of critical skill was taken into account. Based on these decisions and during a serial scheduling generating scheme, one can in a sequential manner introduce the project scheduling and the corresponding workforce allocations
High-Level Object Oriented Genetic Programming in Logistic Warehouse Optimization
DisertaÄnĂ prĂĄce je zamÄĆena na optimalizaci prĆŻbÄhu pracovnĂch operacĂ v logistickĂœch skladech a distribuÄnĂch centrech. HlavnĂm cĂlem je optimalizovat procesy plĂĄnovĂĄnĂ, rozvrhovĂĄnĂ a odbavovĂĄnĂ. JelikoĆŸ jde o problĂ©m patĆĂcĂ do tĆĂdy sloĆŸitosti NP-teĆŸkĂœ, je vĂœpoÄetnÄ velmi nĂĄroÄnĂ© nalĂ©zt optimĂĄlnĂ ĆeĆĄenĂ. MotivacĂ pro ĆeĆĄenĂ tĂ©to prĂĄce je vyplnÄnĂ pomyslnĂ© mezery mezi metodami zkoumanĂœmi na vÄdeckĂ© a akademickĂ© pĆŻdÄ a metodami pouĆŸĂvanĂœmi v produkÄnĂch komerÄnĂch prostĆedĂch. JĂĄdro optimalizaÄnĂho algoritmu je zaloĆŸeno na zĂĄkladÄ genetickĂ©ho programovĂĄnĂ ĆĂzenĂ©ho bezkontextovou gramatikou. HlavnĂm pĆĂnosem tĂ©to prĂĄce je a) navrhnout novĂœ optimalizaÄnĂ algoritmus, kterĂœ respektuje nĂĄsledujĂcĂ optimalizaÄnĂ podmĂnky: celkovĂœ Äas zpracovĂĄnĂ, vyuĆŸitĂ zdrojĆŻ, a zahlcenĂ skladovĂœch uliÄek, kterĂ© mĆŻĆŸe nastat bÄhem zpracovĂĄnĂ ĂșkolĆŻ, b) analyzovat historickĂĄ data z provozu skladu a vyvinout sadu testovacĂch pĆĂkladĆŻ, kterĂ© mohou slouĆŸit jako referenÄnĂ vĂœsledky pro dalĆĄĂ vĂœzkum, a dĂĄle c) pokusit se pĆedÄit stanovenĂ© referenÄnĂ vĂœsledky dosaĆŸenĂ© kvalifikovanĂœm a trĂ©novanĂœm operaÄnĂm manaĆŸerem jednoho z nejvÄtĆĄĂch skladĆŻ ve stĆednĂ EvropÄ.This work is focused on the work-flow optimization in logistic warehouses and distribution centers. The main aim is to optimize process planning, scheduling, and dispatching. The problem is quite accented in recent years. The problem is of NP hard class of problems and where is very computationally demanding to find an optimal solution. The main motivation for solving this problem is to fill the gap between the new optimization methods developed by researchers in academic world and the methods used in business world. The core of the optimization algorithm is built on the genetic programming driven by the context-free grammar. The main contribution of the thesis is a) to propose a new optimization algorithm which respects the makespan, the utilization, and the congestions of aisles which may occur, b) to analyze historical operational data from warehouse and to develop the set of benchmarks which could serve as the reference baseline results for further research, and c) to try outperform the baseline results set by the skilled and trained operational manager of the one of the biggest warehouses in the middle Europe.
Flexible resources allocation techniques: characteristics and modelling
At the interface between engineering, economics, social sciences and humanities, industrial engineering aims to provide answers to various sectors of business problems. One of these problems is the adjustment between the workload needed by the work to be realised and the availability of the company resources. The objective of this work is to help to find a methodology for the allocation of flexible human resources in industrial activities planning and scheduling. This model takes into account two levers of flexibility, one related to the working time modulation, and the other to the varieties of tasks that can be performed by a given resource (multiâskilled actor). On the one hand, multiâskilled actors will help to guide the various choices of the allocation to appreciate the impact of these choices on the tasks durations. On the other hand, the working time modulation that allows actors to have a work planning varying according to the workload which the company has to face
Multiobjective strategies for New Product Development in the pharmaceutical industry
New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems
Multiobjective strategies for New Product Development in the pharmaceutical industry
New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems
Understanding Algorithm Performance on an Oversubscribed Scheduling Application
The best performing algorithms for a particular oversubscribed scheduling
application, Air Force Satellite Control Network (AFSCN) scheduling, appear to
have little in common. Yet, through careful experimentation and modeling of
performance in real problem instances, we can relate characteristics of the
best algorithms to characteristics of the application. In particular, we find
that plateaus dominate the search spaces (thus favoring algorithms that make
larger changes to solutions) and that some randomization in exploration is
critical to good performance (due to the lack of gradient information on the
plateaus). Based on our explanations of algorithm performance, we develop a new
algorithm that combines characteristics of the best performers; the new
algorithms performance is better than the previous best. We show how hypothesis
driven experimentation and search modeling can both explain algorithm
performance and motivate the design of a new algorithm
Project scheduling under undertainty â survey and research potentials.
The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, that is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, stochastic GERT network scheduling, fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling projects under uncertainty.Management; Project management; Robustness; Scheduling; Stability;
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