A bilevel programming approach to assembly job shop scheduling

A bilevel programming approach for assembly job shop scheduling isproposed. Two levels of decision makers are identified in the model. The firstlevel is the project manager and the second level is the shop floor manager. Thefirst level aims to minimize the earliness and tardiness ofcompleted jobs. Thesecond level aims to minimize the average shop floor throughput time. Becausetheir aims may conflict, these levels optimize their own objectives based on anon-cooperative game playing process. Their decision variables are denoted byorder release mechanisms and dispatching rules respectively. Using a simulationapproach, this paper identifies the best choice for the project manager underdifferent job shop utilization levels. The research findings can providemanagerial guidance to the project manager as which order release mechanisms touse in order to optimize his objective. © 2009 IEEE.published_or_final_versionThe 39th International Conference on Computers & Industrial Engineering (CIE 2009), Troyes, France, 6-9 July 2009. In Proceedings of the 39th CIE, 2009, p. 182-18

A review of discrete-time optimization models for tactical production planning

This is an Accepted Manuscript of an article published in International Journal of Production Research on 27 Mar 2014, available online: http://doi.org/10.1080/00207543.2014.899721[EN] This study presents a review of optimization models for tactical production planning. The objective of this research is to identify streams and future research directions in this field based on the different classification criteria proposed. The major findings indicate that: (1) the most popular production-planning area is master production scheduling with a big-bucket time-type period; (2) most of the considered limited resources correspond to productive resources and, to a lesser extent, to inventory capacities; (3) the consideration of backlogs, set-up times, parallel machines, overtime capacities and network-type multisite configuration stand out in terms of extensions; (4) the most widely used modelling approach is linear/integer/mixed integer linear programming solved with exact algorithms, such as branch-and-bound, in commercial MIP solvers; (5) CPLEX, C and its variants and Lindo/Lingo are the most popular development tools among solvers, programming languages and modelling languages, respectively; (6) most works perform numerical experiments with random created instances, while a small number of works were validated by real-world data from industrial firms, of which the most popular are sawmills, wood and furniture, automobile and semiconductors and electronic devices.This study has been funded by the Universitat Politècnica de València projects: ‘Material Requirement Planning Fourth Generation (MRPIV)’ (Ref. PAID-05-12) and ‘Quantitative Models for the Design of Socially Responsible Supply Chains under Uncertainty Conditions. Application of Solution Strategies based on Hybrid Metaheuristics’ (PAID-06-12).Díaz-Madroñero Boluda, FM.; Mula, J.; Peidro Payá, D. (2014). A review of discrete-time optimization models for tactical production planning. International Journal of Production Research. 52(17):5171-5205. doi:10.1080/00207543.2014.899721S51715205521

Un algoritmo basado en la búsqueda dispersa para resolver el problema de producción distribución de una cadena de suministro

En este trabajo nosotros consideramos el problema de planeación de producción y distribución de una cadena de suministro en una red, que consiste de un conjunto de centros de distribución que buscan dar servicio a un conjunto de minoristas, y dichos centros de distribución abastecidos por un conjunto de plantas, buscando minimizar los costos de transportación en la red y de operación en las plantas, basado en el problema propuesto por Herminia y Calvete en 2011. El problema es formulado como un programa matemático binivel donde el nivel superior (líder) consiste en fijar las rutas de distribución de productos enviados de los centros de distribución a los minoristas, satisfaciendo sus demandas sin exceder de un tiempo límite de duración de cada ruta. Por otro lado, en el nivel inferior (seguidor) se reciben las órdenes de cada centro de distribución y se deciden cuales plantas producirán estas órdenes satisfaciendo las demandas allí conjuntadas sin sobrepasar las capacidades de producción de las plantas. La función objetivo del nivel superior minimiza los costos incurridos en el envío de los productos desde los centros de distribución hacia los minoristas y los costos asociados al envío desde las plantas hasta los centros de distribución considerando un costo de descarga por artículo. En el nivel inferior se busca minimizar los costos de operación en las plantas. En este trabajo proponemos un algoritmo heurístico basado en el equilibrio de Stackelberg y la Búsqueda Dispersa. El algoritmo propuesto consiste en aplicar la búsqueda dispersa en las variables del nivel superior encontrando la mejor respuesta del nivel inferior para cada solución obtenida por la búsqueda dispersa obteniendo así un equilibrio entre estos dos niveles. Nuestro algoritmo ha mostrado ser competitivo y brinda buenos resultados comparados con los publicados por Herminia y Calvete en 2011

Multilevel decision-making: A survey

© 2016 Elsevier Inc. All rights reserved. Multilevel decision-making techniques aim to deal with decentralized management problems that feature interactive decision entities distributed throughout a multiple level hierarchy. Significant efforts have been devoted to understanding the fundamental concepts and developing diverse solution algorithms associated with multilevel decision-making by researchers in areas of both mathematics/computer science and business areas. Researchers have emphasized the importance of developing a range of multilevel decision-making techniques to handle a wide variety of management and optimization problems in real-world applications, and have successfully gained experience in this area. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but also the practical developments in multilevel decision-making in business. This paper systematically reviews up-to-date multilevel decision-making techniques and clusters related technique developments into four main categories: bi-level decision-making (including multi-objective and multi-follower situations), tri-level decision-making, fuzzy multilevel decision-making, and the applications of these techniques in different domains. By providing state-of-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in theoretical research results and applications in relation to multilevel decision-making techniques

Bi-level optimisation and machine learning in the management of large service-oriented field workforces.

The tactical planning problem for members of the service industry with large multi-skilled workforces is an important process that is often underlooked. It sits between the operational plan - which involves the actual allocation of members of the workforce to tasks - and the strategic plan where long term visions are set. An accurate tactical plan can have great benefits to service organisations and this is something we demonstrate in this work. Sitting where it does, it is made up of a mix of forecast and actual data, which can make effectively solving the problem difficult. In members of the service industry with large multi-skilled workforces it can often become a very large problem very quickly, as the number of decisions scale quickly with the number of elements within the plan. In this study, we first update and define the tactical planning problem to fit the process currently undertaken manually in practice. We then identify properties within the problem that identify it as a new candidate for the application of bi-level optimisation techniques. The tactical plan is defined in the context of a pair of leader-follower linked sub-models, which we show to be solvable to produce automated solutions to the tactical plan. We further identify the need for the use of machine learning techniques to effectively find solutions in practical applications, where limited detail is available in the data due to its forecast nature. We develop neural network models to solve this issue and show that they provide more accurate results than the current planners. Finally, we utilise them as a surrogate for the follower in the bi-level framework to provide real world applicable solutions to the tactical planning problem. The models developed in this work have already begun to be deployed in practice and are providing significant impact. This is along with identifying a new application area for bi-level modelling techniques