AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Project scheduling under uncertainty using fuzzy modelling and solving techniques

In the real world, projects are subject to numerous uncertainties at different levels of planning. Fuzzy project scheduling is one of the approaches that deal with uncertainties in project scheduling problem. In this paper, we provide a new technique that keeps uncertainty at all steps of the modelling and solving procedure by considering a fuzzy modelling of the workload inspired from the fuzzy/possibilistic approach. Based on this modelling, two project scheduling techniques, Resource Constrained Scheduling and Resource Leveling, are considered and generalized to handle fuzzy parameters. We refer to these problems as the Fuzzy Resource Constrained Project Scheduling Problem (FRCPSP) and the Fuzzy Resource Leveling Problem (FRLP). A Greedy Algorithm and a Genetic Algorithm are provided to solve FRCPSP and FRLP respectively, and are applied to civil helicopter maintenance within the framework of a French industrial project called Helimaintenance

Fuzzy uncertainty modelling for project planning; application to helicopter maintenance

Maintenance is an activity of growing interest specially for critical systems. Particularly, aircraft maintenance costs are becoming an important issue in the aeronautical industry. Managing an aircraft maintenance center is a complex activity. One of the difficulties comes from the numerous uncertainties that affect the activity and disturb the plans at short and medium term. Based on a helicopter maintenance planning and scheduling problem, we study in this paper the integration of uncertainties into tactical and operational multiresource, multi-project planning (respectively Rough Cut Capacity Planning and Resource Constraint Project Scheduling Problem). Our main contributions are in modelling the periodic workload on tactical level considering uncertainties in macro-tasks work contents, and modelling the continuous workload on operational level considering uncertainties in tasks durations. We model uncertainties by a fuzzy/possibilistic approach instead of a stochastic approach since very limited data are available. We refer to the problems as the Fuzzy RoughCut Capacity Problem (FRCCP) and the Fuzzy Resource Constraint Project Scheduling Problem (RCPSP).We apply our models to helicopter maintenance activity within the frame of the Helimaintenance project, an industrial project approved by the French Aerospace Valley cluster which aims at building a center for civil helicopter maintenance

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Production/maintenance cooperative scheduling using multi-agents and fuzzy logic

Within companies, production is directly concerned with the manufacturing schedule, but other services like sales, maintenance, purchasing or workforce management should also have an influence on this schedule. These services often have together a hierarchical relationship, i.e. the leading function (most of the time sales or production) generates constraints defining the framework within which the other functions have to satisfy their own objectives. We show how the multi-agent paradigm, often used in scheduling for its ability to distribute decision-making, can also provide a framework for making several functions cooperate in the schedule performance. Production and maintenance have been chosen as an example: having common resources (the machines), their activities are actually often conflicting. We show how to use a fuzzy logic in order to model the temporal degrees of freedom of the two functions, and show that this approach may allow one to obtain a schedule that provides a better compromise between the satisfaction of the respective objectives of the two functions

Scheduling uncertain orders in the customer–subcontractor context

Within the customer–subcontractor negotiation process, the first problem of the subcontractor is to provide the customer with a reliable order lead-time although his workload is partially uncertain. Actually, a part of the subcontractor workload is composed of orders under negotiation which can be either confirmed or cancelled. Fuzzy logic and possibility theory have widely been used in scheduling in order to represent the uncertainty or imprecision of processing times, but the existence of the manufacturing orders is not usually set into question. We suggest a method allowing to take into account the uncertainty of subcontracted orders. This method is consistent with list scheduling: as a consequence, it can be used in many classical schedulers. Its implementation in a scheduler prototype called TAPAS is described. In this article, we focus on the performance of validation tests which show the interest of the method

Fuzzy multi-objective optimisation for master planning in a ceramic supply chain

This is an Accepted Manuscript of an article published in International Journal of Production Research on 2012, available online: http://www.tandfonline.com/10.1080/00207543.2011.588267.In this paper, we consider the master planning problem for a centralised replenishment, production and distribution ceramic tile supply chain. A fuzzy multi-objective linear programming (FMOLP) approach is presented which considers the maximisation of the fuzzy gross margin, the minimisation of the fuzzy idle time and the minimisation of the fuzzy backorder quantities. By using an interactive solution methodology to convert this FMOLP model into an auxiliary crisp single-objective linear model, a preferred compromise solution is obtained. For illustration purposes, an example based on modifications of real-world industrial problems is used.This research has been carried out in the framework of a project funded by the Science and Technology Ministry of the Spanish Government, entitled 'Project of reinforcement of the competitiveness of the Spanish managerial fabric through the logistics as a strategic factor in a global environment' (Ref. PSE-370000-2008-8).Peidro Payá, D.; Mula, J.; Alemany Díaz, MDM.; Lario Esteban, FC. (2012). Fuzzy multi-objective optimisation for master planning in a ceramic supply chain. International Journal of Production Research. 50(11):3011-3020. https://doi.org/10.1080/00207543.2011.588267S301130205011Alemany, M.M.E.et al., 2010. Mathematical programming model for centralized master planning in ceramic tile supply chains.International Journal of Production Research, 48 (17), 5053–5074Beamon, B. M. (1998). Supply chain design and analysis: International Journal of Production Economics, 55(3), 281-294. doi:10.1016/s0925-5273(98)00079-6Chen, C.-L., & Lee, W.-C. (2004). Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers & Chemical Engineering, 28(6-7), 1131-1144. doi:10.1016/j.compchemeng.2003.09.014Chern, C.-C., & Hsieh, J.-S. (2007). A heuristic algorithm for master planning that satisfies multiple objectives. Computers & Operations Research, 34(11), 3491-3513. doi:10.1016/j.cor.2006.02.022Kreipl, S., & Pinedo, M. (2009). Planning and Scheduling in Supply Chains: An Overview of Issues in Practice. Production and Operations Management, 13(1), 77-92. doi:10.1111/j.1937-5956.2004.tb00146.xLai, Y.-J., & Hwang, C.-L. (1993). Possibilistic linear programming for managing interest rate risk. Fuzzy Sets and Systems, 54(2), 135-146. doi:10.1016/0165-0114(93)90271-iLi, X., Zhang, B., & Li, H. (2006). Computing efficient solutions to fuzzy multiple objective linear programming problems. Fuzzy Sets and Systems, 157(10), 1328-1332. doi:10.1016/j.fss.2005.12.003Mula, J., Peidro, D., Díaz-Madroñero, M., & Vicens, E. (2010). Mathematical programming models for supply chain production and transport planning. European Journal of Operational Research, 204(3), 377-390. doi:10.1016/j.ejor.2009.09.008Mula, J., Peidro, D., and Poler, R., 2010b. The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand.International Journal of Production Economics, In pressPark *, Y. B. (2005). An integrated approach for production and distribution planning in supply chain management. International Journal of Production Research, 43(6), 1205-1224. doi:10.1080/00207540412331327718Peidro, D., Mula, J., Poler, R., & Lario, F.-C. (2008). Quantitative models for supply chain planning under uncertainty: a review. The International Journal of Advanced Manufacturing Technology, 43(3-4), 400-420. doi:10.1007/s00170-008-1715-yPeidro, D., Mula, J., Poler, R., & Verdegay, J.-L. (2009). Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Sets and Systems, 160(18), 2640-2657. doi:10.1016/j.fss.2009.02.021Selim, H., Araz, C., & Ozkarahan, I. (2008). Collaborative production–distribution planning in supply chain: A fuzzy goal programming approach. Transportation Research Part E: Logistics and Transportation Review, 44(3), 396-419. doi:10.1016/j.tre.2006.11.001Selim, H., & Ozkarahan, I. (2006). A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach. The International Journal of Advanced Manufacturing Technology, 36(3-4), 401-418. doi:10.1007/s00170-006-0842-6Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2), 193-214. doi:10.1016/j.fss.2007.08.010Haehling von Lanzenauer, C., & Pilz-Glombik, K. (2002). Coordinating supply chain decisions: an optimization model. OR Spectrum, 24(1), 59-78. doi:10.1007/s291-002-8200-3Zimmermann, H.-J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45-55. doi:10.1016/0165-0114(78)90031-

A reusable iterative optimization software library to solve combinatorial problems with approximate reasoning

Real world combinatorial optimization problems such as scheduling are typically too complex to solve with exact methods. Additionally, the problems often have to observe vaguely specified constraints of different importance, the available data may be uncertain, and compromises between antagonistic criteria may be necessary. We present a combination of approximate reasoning based constraints and iterative optimization based heuristics that help to model and solve such problems in a framework of C++ software libraries called StarFLIP++. While initially developed to schedule continuous caster units in steel plants, we present in this paper results from reusing the library components in a shift scheduling system for the workforce of an industrial production plant.Comment: 33 pages, 9 figures; for a project overview see http://www.dbai.tuwien.ac.at/proj/StarFLIP