An economic lot and delivery scheduling problem with the fuzzy shelf life in a flexible job shop with unrelated parallel machines

This paper considers an economic lot and delivery scheduling problem (ELDSP) in a fuzzy environment with the fuzzy shelf life for each product. This problem is formulated in a flexible job shop with unrelated parallel machines, when the planning horizon is finite and it determines lot sizing, scheduling and sequencing, simultaneously. The proposed model of this paper is based on the basic period (BP) approach. In this paper, a mixed-integer nonlinear programming (MINLP) model is presented and then it is changed into two models in the fuzzy shelf life. The main model is dependent to the multiple basic periods and it is difficult to solve the resulted proposed model for large-scale problems in reasonable amount of time; thus, an efficient heuristic method is proposed to solve the problem. The performance of the proposed model is demonstrated using some numerical examples

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

Determining cycle time for a multi-product FPR model with rework and an improved delivery policy by alternative approach

The present study determines the common cycle time for a multi-product finite production rate (FPR) model with rework and an improved delivery policy [1] by an alternative approach. Conventional method to the multi-product FPR problem employs the differential calculus to first prove convexity of the system cost function, then to derive the optimal common production cycle time that minimizes the long-run average system cost per unit time; whereas the proposed approach obtains the optimal cycle time without the need to reference the differential calculus. Such a simplified method may help those practitioners who have insufficient knowledge of calculus to effectively manage the real-life multi-product FPR problem

Integrated Production and Distribution planning of perishable goods

Tese de doutoramento. Programa Doutoral em Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

An optimization model for material supply scheduling at mixed-model assembly lines

This study is motivated by a real case study and addresses the material supply problem at assembly lines. The aim of the study is to optimally schedule the delivery of raw material at assembly lines while using the minimum number of vehicles. To cope with the problem an original mixed integer linear programming model has been proposed based on the assumptions and constraints observed in the case study. The validity of the model has been examined by solving several real cases and analysing different scenarios. The results of the study show the efficiency and effectiveness of the model.CC BY-NC-ND 4.0</p

Enfoques para la Resolución del Problema ELSP

[ES] En este trabajo se pretende realizar una recopilación de los enfoques planteados en la literatura para la resolución del problema de Programación del Lote Económico, esto es, ELSP. Estos métodos son: Solución Independiente, Ciclo Común, Periodo Básico, Periodo Básico Extendido y Variación del Tamaño de Lote. Para cada una de las aproximaciones de solución se plantea a quien son atribuidas, el correspondiente modelo, así como una serie de referencias que lo han empleado.Este trabajo ha sido realizado gracias a la financiación de la Universidad Politécnica de Valencia, a través del proyecto PAID-05-09-4335 "Coordinación de flujos de materiales e información en sistemas distribuidos de producción".Vidal Carreras, PI. (2010). Enfoques para la Resolución del Problema ELSP. Working Papers on Operations Management. 1(2):31-43. doi:10.4995/wpom.v1i2.787SWORD314312Ballou, R. H. (2004). Logística: Administración de la cadena de suministro. Pearson Educación.Ben-Daya, M., & Hariga, M. (2000). Economic lot scheduling problem with imperfect production processes. Journal of the Operational Research Society, 51(7), 875-881. doi:10.1057/palgrave.jors.2600974Bomberger, E. E. (1966). A Dynamic Programming Approach to a Lot Size Scheduling Problem. Management Science, 12(11), 778-784. doi:10.1287/mnsc.12.11.778Brander, P.; Forsberg, R. (2004). Determination of safety stocks for cyclic schedules with stochastic demands. International Journal of Production Economics, Vol. In Press, Corrected Proof.Brander, P., Levén, E., & Segerstedt, A. (2005). Lot sizes in a capacity constrained facility—a simulation study of stationary stochastic demand. International Journal of Production Economics, 93-94, 375-386. doi:10.1016/j.ijpe.2004.06.034Carstensen, P. (1999). Das Economic Lot Scheduling Problem - Überblick und LP-basiertes Verfahren. OR Spectrum, 21(4), 429-460. doi:10.1007/s002910050097Chandrasekaran, C., Rajendran, C., Chetty, O. V. K., & Hanumanna, D. (2007). Metaheuristics for solving economic lot scheduling problems (ELSP) using time-varying lot-sizes approach. European J. of Industrial Engineering, 1(2), 152. doi:10.1504/ejie.2007.014107Davis, S. G. (1990). Scheduling Economic Lot Size Production Runs. Management Science, 36(8), 985-998. doi:10.1287/mnsc.36.8.985Delporte, C. M., & Thomas, L. J. (1977). Lot Sizing and Sequencing forNProducts on One Facility. Management Science, 23(10), 1070-1079. doi:10.1287/mnsc.23.10.1070Dobson, G. (1987). The Economic Lot-Scheduling Problem: Achieving Feasibility Using Time-Varying Lot Sizes. Operations Research, 35(5), 764-771. doi:10.1287/opre.35.5.764Doll, C. L., & Whybark, D. C. (1973). An Iterative Procedure for the Single-Machine Multi-Product Lot Scheduling Problem. Management Science, 20(1), 50-55. doi:10.1287/mnsc.20.1.50Elmaghraby, S. E. (1978). The Economic Lot Scheduling Problem (ELSP): Review and Extensions. Management Science, 24(6), 587-598. doi:10.1287/mnsc.24.6.587Erlenkotter, D. (1990). Ford Whitman Harris and the Economic Order Quantity Model. Operations Research, 38(6), 937-946. doi:10.1287/opre.38.6.937Eynan, A. (2003). The Benefits of Flexible Production Rates in the Economic Lot Scheduling Problem. IIE Transactions, 35(11), 1057-1064. doi:10.1080/07408170304400Gallego, G. (1990). Scheduling the Production of Several Items with Random Demands in a Single Facility. Management Science, 36(12), 1579-1592. doi:10.1287/mnsc.36.12.1579Gallego, G., & Moon, I. (1992). The Effect of Externalizing Setups in the Economic Lot Scheduling Problem. Operations Research, 40(3), 614-619. doi:10.1287/opre.40.3.614Gallego, G., & Roundy, R. (1992). The economic lot scheduling problem with finite backorder costs. Naval Research Logistics, 39(5), 729-739. doi:10.1002/1520-6750(199208)39:53.0.co;2-nGALLEGO, G., & SHAW, D. X. (1997). Complexity of the ELSP with general cyclic schedules. IIE Transactions, 29(2), 109-113. doi:10.1080/07408179708966318GASCON, A., LEACHMAN, R. C., & LEFRANÇOIS, P. (1994). Multi-item, single-machine scheduling problem with stochastic demands: a comparison of heuristics. International Journal of Production Research, 32(3), 583-596. doi:10.1080/00207549408956954Giri, B. C., Moon, I., & Yun, W. Y. (2003). Scheduling economic lot sizes in deteriorating production systems. Naval Research Logistics, 50(6), 650-661. doi:10.1002/nav.10082Goyal, S. . (1997). Observation on the economic lot scheduling problem: Theory and practice. International Journal of Production Economics, 50(1), 61. doi:10.1016/s0925-5273(97)00025-xHaessler, R. W. (1979). An Improved Extended Basic Period Procedure for Solving the Economic Lot Scheduling Problem. A I I E Transactions, 11(4), 336-340. doi:10.1080/05695557908974480Haessler, R. W., & Hogue, S. L. (1976). Note—A Note on the Single-Machine Multi-Product Lot Scheduling Problem. Management Science, 22(8), 909-912. doi:10.1287/mnsc.22.8.909Hahm, J., & Yano, C. A. (1995). The Economic Lot and Delivery Scheduling Problem: Powers of Two Policies. Transportation Science, 29(3), 222-241. doi:10.1287/trsc.29.3.222Hanssmann, F. (1962). Operations-Research in Production and Inventory Control. J. Wiley.Harris, F. W. (1913). How many parts to make an once. Factory, The Magazine of Management, Vol. 10, nº. 2, pp. 135-6-152.Hsu, W.-L. (1983). On the General Feasibility Test of Scheduling Lot Sizes for Several Products on One Machine. Management Science, 29(1), 93-105. doi:10.1287/mnsc.29.1.93HWANG, H., KIM, D. B., & KIM, Y. D. (1993). Multiproduct economic lot size models with investment costs for setup reduction and quality improvement. International Journal of Production Research, 31(3), 691-703. doi:10.1080/00207549308956751JONES, P. C., & INMAN, R. R. (1989). When Is The Economic Lot Scheduling Problem Easy? IIE Transactions, 21(1), 11-20. doi:10.1080/07408178908966202Khouja, M., Michalewicz, Z., & Wilmot, M. (1998). The use of genetic algorithms to solve the economic lot size scheduling problem. European Journal of Operational Research, 110(3), 509-524. doi:10.1016/s0377-2217(97)00270-1Khoury, B. N., Abboud, N. E., & Tannous, M. M. (2001). The common cycle approach to the ELSP problem with insufficient capacity. International Journal of Production Economics, 73(2), 189-199. doi:10.1016/s0925-5273(00)00175-4Larrañeta, J., & Onieva, L. (1988). The Economic Lot-Scheduling Problem: A Simple Approach. Journal of the Operational Research Society, 39(4), 373-379. doi:10.1057/jors.1988.65Leachman, R. C., & Gascon, A. (1988). A Heuristic Scheduling Policy for Multi-Item, Single-Machine Production Systems with Time-Varying, Stochastic Demands. Management Science, 34(3), 377-390. doi:10.1287/mnsc.34.3.377Madigan, J. G. (1968). Scheduling a Multi-Product Single Machine System for an Infinite Planning Period. Management Science, 14(11), 713-719. doi:10.1287/mnsc.14.11.713Maxwell, W. L. (1964). The scheduling of economic lot sizes. Naval Research Logistics Quarterly, 11(2), 89-124. doi:10.1002/nav.3800110202Moon, I., Giri, B. C., & Choi, K. (2002). Economic lot scheduling problem with imperfect production processes and setup times. Journal of the Operational Research Society, 53(6), 620-629. doi:10.1057/palgrave.jors.2601350Moon, I., Silver, E. A., & Choi, S. (2002). Hybrid genetic algorithm for the economic lot-scheduling problem. International Journal of Production Research, 40(4), 809-824. doi:10.1080/00207540110095222MOON, I., HAHM, J., & LEE, C. (1998). The effect of the stabilization period on the economic lot scheduling problem. IIE Transactions, 30(11), 1009-1017. doi:10.1080/07408179808966557Öner, S., & Bilgiç, T. (2008). Economic lot scheduling with uncontrolled co-production. European Journal of Operational Research, 188(3), 793-810. doi:10.1016/j.ejor.2007.05.016Schweitzer, P. J., & Silver, E. A. (1983). Technical Note—Mathematical Pitfalls in the One Machine Multiproduct Economic Lot Scheduling Problem. Operations Research, 31(2), 401-405. doi:10.1287/opre.31.2.401Segerstedt, A. (1999). Lot sizes in a capacity constrained facility with available initial inventories. International Journal of Production Economics, 59(1-3), 469-475. doi:10.1016/s0925-5273(98)00111-xSoman, C. A., Pieter van Donk, D., & Gaalman, G. (2006). Comparison of dynamic scheduling policies for hybrid make-to-order and make-to-stock production systems with stochastic demand. International Journal of Production Economics, 104(2), 441-453. doi:10.1016/j.ijpe.2004.08.002Soman, C. A., van Donk, D. P., & Gaalman, G. (2004). Combined make-to-order and make-to-stock in a food production system. International Journal of Production Economics, 90(2), 223-235. doi:10.1016/s0925-5273(02)00376-6Soman, C. A., van Donk, D. P., & Gaalman, G. J. C. (2007). 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A scientific routine for stock control. Harvard Business Review, Vol. 13, nº. 1, pp. 116-128.Yao, M. J. & Chang, Y. J. (2009). Solving the economic lot scheduling problem with multiple facilities in parallel using the time-varying lot sizes approach, in Eighth International Conference on Information and Management Sciences, p. F224.Zipkin, P. H. (1991). Computing Optimal Lot Sizes in the Economic Lot Scheduling Problem. Operations Research, 39(1), 56-63. doi:10.1287/opre.39.1.5

An investigation of production and transportation policies for multi-item and multi-stage production systems

Die vorliegende kumulative Dissertation besteht aus fünf Artikeln, einem Arbeitspapier und vier Artikeln, die in wissenschaftlichen Zeitschriften veröffentlicht wurden. Alle fünf Artikel beschäftigen sich mit der Losgrößenplanung, jedoch mit unterschiedlichen Schwerpunkten. Artikel 1 bis 4 untersuchen das Economic Lot Scheduling Problem (ELSP), während sich der fünfte Artikel mit einer Variante des Joint Economic Lot Size (JELS) Problems beschäftigt. Die Struktur dieser Dissertation trägt diesen beiden Forschungsrichtungen Rechnung und ordnet die ersten vier Artikel dem Teil A und den fünften Artikel dem Teil B zu