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

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

Identifying preferred solutions to multi-objective binary optimisation problems, with an application to the multi-objective knapsack problem

In this paper we present a new framework for identifying preferred solutions to multi-objective binary optimisation problems. We develop the necessary theory which leads to new formulations that integrate the decision space with the space of criterion weights. The advantage of this is that it allows for incorporating preferences directly within a unique binary optimisation problem which identifies efficient solutions and associated weights simultaneously. We discuss how preferences can be incorporated within the formulations and also describe how to accommodate the selection of weights when the identification of a unique solution is required. Our results can be used for designing interactive procedures for the solution of multi-objective binary optimisation problems. We describe one such procedure for the multi-objective multi-dimensional binary knapsack formulation of the portfolio selection problem