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 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_73 S 576 583 397 Document type: Part of book or chapter of boo

Efficiency of heuristic algorithms in solving waste collection vehicle routing problem: a case study

This paper investigated the efficiency of six heuristic algorithms from prior studies in the attempt to solve issues related to waste collection, namely: (i) Nearest Greedy (NG), (ii) Further from Depot (FFD), (iii) Different Initial Customer (DIC), (iv) Savings Approach, (v) Sweep Algorithm, and (vi) Different Initial Customer based on Sweep Algorithm. In fact, these heuristics have been employed to solve several routing problems in past studies, but the performance of each heuristic has never been compared. Hence, this paper looked into the efficiency of these heuristics by testing them on a real case study of waste collection problem in a district located at the north of Peninsular Malaysia. Several solutions obtained from these heuristics were compared with solutions implemented by the waste collection company, especially in terms of the total distance travelled. As a result, the computational results exhibited that DIC generated the best solutions, when compared to other heuristics, with a 12% reduction of the total travel distance

Solving Min-Max Capacitated Vehicle Routing Problem by Local Search

Vehicle routing is a class of combinatorial optimization problems in transportation and logistics. Min-max capacitated vehicle routing is a problem of this class in which the length of the longest route must be minimized. This paper investigates local search approach for solving the min-max capacitated vehicle routing problem with different neighborhood structures. We also propose a combined function instead of the objective function itself for controlling the local search. Experimental results on different datasets show the efficiency of our proposed algorithms compared to previous techniques

Capacitated vehicle routing problem model for carriers

Background: The Capacitated Vehicle Routing Problem (CVRP) is one of the most important transportation problems in logistics and supply chain management. The standard CVRP considers a fleet of vehicles with homogeneous capacity that depart from a warehouse, collect products from (or deliver products to) a set of customer locations (points) and return to the same warehouse. However, the operation of carrier companies and third-party transportation providers may follow a different network flow for collection and delivery. This may lead to non-optimal route planning through the use of the standard CVRP. Objective: To propose a model for carrier companies to obtain optimal route planning. Method: A Capacitated Vehicle Routing Problem for Carriers (CVRPfC) model is used to consider the distribution scenario where a fleet of vehicles depart from a vehicle storage depot, collect products from a set of customer points and deliver them to a specific warehouse before returning to the vehicle storage depot. Validation of the model’s functionality was performed with adapted CVRP test problems from the Vehicle Routing Problem LIBrary. Following this, an assessment of the model’s economic impact was performed and validated with data from a real carrier (real instance) with the previously described distribution scenario. Results: The route planning obtained through the CVRPfC model accurately described the network flow of the real instance and significantly reduced its distribution costs. Conclusion: The CVRPfC model can thus improve the competitiveness of the carriers by providing better fares to their customers, reducing their distribution costs in the process

A robust solving strategy for the vehicle routing problem with multiple depots and multiple objectives

This document presents the development of a robust solving strategy for the Vehicle Routing Problem with Multiple Depots and Multiple Objectives (MO-MDVRP). The problem tackeled in this work is the problem to minimize the total cost and the load imbalance in vehicle routing plan for distribution of goods. This thesis presents a MILP mathematical model and a solution strategy based on a Hybrid Multi- Objective Scatter Search Algorithm. Several experiments using simulated instances were run proving that the proposed method is quite robust, this is shown in execution times (less than 4 minutes for an instance with 8 depots and 300 customers); also, the proposed method showed good results compared to the results found with the MILP model for small instances (up to 20 clients and 2 depots).MaestríaMagister en Ingeniería Industria

The Benefits of Information Sharing in Carrier-Client Collaboration

This dissertation includes three related papers to investigate different methods that can help transport providers improve their operational efficiency. The first paper models and measures the profit improvement trucking companies can achieve by collaborating with their clients to obtain advance load information (ALI). The core research method is to formulate a comprehensive and flexible mixed integer mathematical model and implement it in a dynamic rolling horizon context. The findings illustrate that access to the second day ALI can improve the profit by an average of 22%. We also found that the impact of ALI depends on radius of service and trip length but statistically independent of load density and fleet size. The second paper investigates the following question of relevance to truckload dispatchers striving for profitable decisions in the context of dynamic pick-up and delivery problems: since not all future pick-up/delivery requests are known with certainty, how effective are alternative methods for guiding those decisions? We propose an intuitive policy and integrate it into a new two-index mixed integer programming formulation, which we implement using the rolling horizon approach. On average, in one of the practical transportation network settings, the proposed policy can, with just second-day ALI, yield an optimality ratio equal to almost 90% of profits in the static optimal solution. We enhance the proposed policy by adopting the idea of a multiple scenario approach. In comparison to other dispatching methods, our proposed policies were found to be very competitive in terms of solution quality and computational efficiency. Finally, inspired by a real-life third party logistic provider, the third paper addresses a dynamic pickup and delivery problem with full truckload (DPDFL) for local operators. The main purpose of this work is to investigate the impact of potential factors on the carriers’ operational efficiency. These factors, which are usually under managerial influence, are vehicle diversion capability, the DPDFL decision interval, and how far in advance the carrier knows of clients’ shipment requirements; i.e., ALI. Through comprehensive numerical experiments and statistical analysis, we found that the ALI and re-optimization interval significantly influence the total cost, but that diversion capability does not. A major contribution of this work is that we develop an efficient benchmark solution for the DPDFL’s static version by discretization of time windows. We observed that three-day ALI and an appropriate decision interval can reduce deviation from the benchmark solution to less than 8%