A dynamic programming heuristic for vehicle routing with time-dependent travel times and required breaks.

For the intensively studied vehicle routing problem (VRP), two real-life restrictions have received only minor attention in the VRP-literature: traffic congestion and driving hours regulations. Traffic congestion causes late arrivals at customers and long travel times resulting in large transport costs. To account for traffic congestion, time-dependent travel times should be considered when constructing vehicle routes. Next, driving hours regulations, which restrict the available driving and working times for truck drivers, must be respected. Since violations are severely fined, also driving hours regulations should be considered when constructing vehicle routes, even more in combination with congestion problems. The objective of this paper is to develop a solution method for the VRP with time windows (VRPTW), time-dependent travel times, and driving hours regulations. The major difficulty of this VRPTW extension is to optimize each vehicle’s departure times to minimize the duty time of each driver. Having compact duty times leads to cost savings. However, obtaining compact duty times is much harder when time-dependent travel times and driving hours regulations are considered. We propose a restricted dynamic programming (DP) heuristic for constructing the vehicle routes, and an efficient heuristic for optimizing the vehicle’s departure times for each (partial) vehicle route, such that the complete solution algorithm runs in polynomial time. Computational experiments demonstrate the trade-off between travel distance minimization and duty time minimization, and illustrate the cost savings of extending the depot opening hours such that traveling before the morning peak and after the evening peak becomes possible

Moving to additive manufacturing for spare parts supply

This study seeks to investigate when and how a transition to additive manufacturing (AM) becomes profitable for the low-volume spare parts business. As a starting point, we conducted a case study at an OEM of radar systems which foresaw various opportunities that become available with the transition to AM. In particular, it is the case company that can perceive the prospects of shortened lead times and the promise of tool-less manufacturing. However, scepticism regarding whether a transition will pay off amid high AM piece prices and uncertain AM technology advancements remains. We employed stochastic dynamic programming to assess the situation encountered at the company. Therefore, we regarded particularities such as a decreasing AM piece price over the course of the service horizon and determined if (and when) AM should be prepared or tooling be discarded. It turned out that an immediate investment in AM technology is the most effective strategy and leads to more than 12% cost savings. Numerical experiments further substantiate the results of the case study and indicate that long lead times, high inventories, and severe backorder costs in the classical situation are all arguments in favor of an early investment in AM technology; this occurs despite an (initially) higher AM piece price and additional setup costs. Moreover, we observed that postponing the investment in AM is often not advisable. Instead, conventional manufacturing and AM are recommended to be used in parallel before making a complete transition to AM

Capacity assignment in repair shops with high material uncertainty

We consider a group of identical systems, each consisting of multiple Line Replaceable Units (LRUs) that fail according to a Poisson process. A failed LRU is replaced by a ready-for-use one from a single stock point and, if not available, a backorder cost is incurred per unit of time. The failed LRU is returned to a repair shop, where it is inspected to identify which Shop Replaceable Units (SRUs) caused the failure, and is repaired by replacing the failed SRUs. After repair the LRU is ready-for-use again. Both the LRUs and SRUs are controlled by base stock policies. The repair shop is modeled as a two-stage service process consisting of an inspection and a repair phase. Inspection and repair are executed by one group of repairmen. The repair times depend on the time that elapses between inspection and the repair of a part. We model the total repair capacity as a single server and we compare policies that, based on the repair workload in the repair shop, give priority to either inspection or repair of parts. We suggest two approaches to set the SRU base stock levels, and simulate the system for multiple combinations of the repair workload threshold and predetermined vectors of SRU base stock levels. Based on the simulation results, the LRU base stock levels are optimized. We study a representative setting in which the repair shop faces a high material uncertainty, under different scenarios. We show that a scenario in which we maximize the SRU job completeness, combined with a repair priority policy (in which repair of jobs takes precedence over inspection of jobs) leads to the lowest total costs

Improving effectiveness of spare parts supply by additive manufacturing as dual sourcing option

The low-volume spare parts business is often identified as a potential beneficiary of additive manufacturing (AM) technologies. Currently, high AM unit costs or low AM part reliabilities deem the application of AM economical inferior to conventional manufacturing (CM) methods in most cases. In this paper, we investigate the potential to overcome these deficiencies by combining AM and CM methods. For that purpose, we develop an approach that is tailored toward the unique characteristics of dual sourcing with two production methods. Opposed to the traditional dual sourcing literature, we consider the different failure behavior of parts produced by AM and CM methods. Using numerical experiments and a case study in the aviation industry, we explore under which conditions dual sourcing with AM performs best. Single sourcing with AM methods typically leads to higher purchasing and maintenance costs while single sourcing with CM methods increases backorder and holding costs. Savings of more than 30% compared to the best single sourcing option are possible even if the reliability or unit costs of a part sourced with AM are three times worse than for a CM part. In conclusion, dual sourcing methods may play an important role to exploit the benefits of AM methods while avoiding its drawbacks in the low-volume spare parts business