A computational analysis of lower bounds for big bucket production planning problems

In this paper, we analyze a variety of approaches to obtain lower bounds for multi-level production planning problems with big bucket capacities, i.e., problems in which multiple items compete for the same resources. We give an extensive survey of both known and new methods, and also establish relationships between some of these methods that, to our knowledge, have not been presented before. As will be highlighted, understanding the substructures of difficult problems provide crucial insights on why these problems are hard to solve, and this is addressed by a thorough analysis in the paper. We conclude with computational results on a variety of widely used test sets, and a discussion of future research

A Mixed-Integer Linear Programming Model for Transportation Planning in the Full Truck Load Strategy to Supply Products with Unbalanced Demand in the Just in Time Context: A Case Study

[EN] Growing awareness in cutting transport costs and minimizing the environmental impact means that companies are increasingly interested in using the full truck load strategy in their supply tasks. This strategy consists of filling trucks completely with one product type or a mixture of products from the same supplier. This paper aims to propose a mixed-integer linear programming model and procedure to fill trucks which considers limitations of stocks, stock levels and unbalanced demand and minimization of the total number of trucks used in the full truck load strategy. The results obtained from a case study are presented and are exported in a conventional spreadsheet available for a company in the automotive industry.Maheut ., JP.; García Sabater, JP. (2013). A Mixed-Integer Linear Programming Model for Transportation Planning in the Full Truck Load Strategy to Supply Products with Unbalanced Demand in the Just in Time Context: A Case Study. IFIP Advances in Information and Communication Technology. 397:576-583. doi:10.1007/978-3-642-40361-3_73S576583397Bitran, G.R., Haas, E.A., Hax, A.C.: Hierarchical production planning: a single stage system. Operations Research 29, 717–743 (1981)Sun, H., Ding, F.Y.: Extended data envelopment models and a practical tool to analyse product complexity related to product variety for an automobile assembly plant. International Journal of Logistics Systems and Management 6, 99–112 (2010)Boysen, N., Fliedner, M.: Cross dock scheduling: Classification, literature review and research agenda. Omega 38, 413–422 (2010)Garcia-Sabater, J.P., Maheut, J., Garcia-Sabater, J.J.: A two-stage sequential planning scheme for integrated operations planning and scheduling system using MILP: the case of an engine assembler. Flexible Services and Manufacturing Journal 24, 171–209 (2012)Ben-Khedher, N., Yano, C.A.: The Multi-Item Replenishment Problem with Transportation and Container Effects. Transportation Science 28, 37–54 (1994)Cousins, P.D.: Supply base rationalisation: myth or reality? European Journal of Purchasing Supply Management 5, 143–155 (1999)Kiesmüller, G.P.: A multi-item periodic replenishment policy with full truckloads. International Journal of Production Economics 118, 275–281 (2009)Goetschalckx, M.: Transportation Systems Supply Chain Engineering, vol. 161, pp. 127–154. Springer, US (2011)Liu, R., Jiang, Z., Fung, R.Y.K., Chen, F., Liu, X.: Two-phase heuristic algorithms for full truckloads multi-depot capacitated vehicle routing problem in carrier collaboration. Computers Operations Research 37, 950–959 (2010)Arunapuram, S., Mathur, K., Solow, D.: Vehicle Routing and Scheduling with Full Truckloads. Transportation Science 37, 170–182 (2003

An overview of tradeoff curve analysis in the design of manufacturing systems Uma visão geral da análise de curvas de tradeoff no projeto de sistemas de manufatura

Uncertainty in manufacturing systems has long been the source of managerial complexity. In this paper we discuss the impact of different sources of uncertainty and present a methodology to assess their impact on system behavior. We introduce the concept of tradeoff curves as a characteristic of a manufacturing system and illustrate their use to make decisions concerning the amount and type of capacity necessary to manage the system efficiently, to assess the impact of products arrival and processing uncertainties, as well as the consequences of changes in throughput and product mix. The methodology is illustrated with an example derived from an actual application in the semiconductor industry.Incerteza nos sistemas de manufatura tem sido há muito tempo fonte de complexidade gerencial. Neste artigo discutimos o impacto de diferentes fontes de incerteza e apresentamos um metodologia para avaliar tal impacto no comportamento do sistema. Introduzimos o conceito de curvas de tradeoff como uma característica de um sistema de manufatura e ilustramos sua utilização para tomar decisões com respeito à quantidade e tipo de capacidade necessária para gerir o sistema eficientemente, para avaliar o impacto de incerteza na chegada e processamento de produtos, assim como as conseqüências de mudanças na taxa média de produção e no mix de produtos. A metodologia é ilustrada com um exemplo derivado de uma aplicação real numa indústria de semicondutores