Emmanouil E. Zachariadis

This paper introduces the Cyclic Production Routing Problem (CPRP). The CPRP is an extension of the well-known NP-hard Production Routing Problem (PRP), which is a hard-to-solve combinatorial optimisation problem with numerous practical applications in the field of freight transportation, logistics and supply chain management. Under the PRP setting, a manufacturer is responsible for determining production decisions, as well as the timing and quantity of replenishment services offered to a set of geographically dispersed customers over a multi-period time horizon. The problem calls for jointly optimising the production, inventory, distribution and routing decisions. In this paper, the basic PRP model is modified to generate repeatable cyclic production and delivery schedules. A two-commodity flow formulation is proposed along with valid inequalities. Extensive comparisons between the basic PRP and the proposed cyclic variant on well-known benchmark instances are provided. The new variant is significantly harder to solve, especially when the vehicle fleet is limited. From a managerial perspective, the generation of cyclic production-routing schedules significantly increases all costs, whereas the number of vehicle routes required to implement a cyclic schedule is higher.

Wildfire is a significant hazard for sensitive areas, i.e. geographic regions where urban communities, installations for processing or storing hazardous chemical substances, valuable assets and monuments, or other important infrastructure meet or intermix with fire-prone wildlands. To avoid human casualties, organized evacuation of the population should be examined as a measure to be appropriately taken, while the disastrous event is in progress. One of the tools proposed for supporting decisions regarding initiating evacuation is the delineation of “Trigger Buffers”. To this end, in the scope of EVITA project (Wildfire Evacuation Trigger Buffers for Sensitive Areas), we have developed a web-based computational system capable of simulating the propagation of wildfire incidents, according to several topographic and meteorological factors. Based on this system, Trigger Buffers are identified for user-selected sensitive locations. Moreover, “Landmark” locations that if burnt, great risk is incurred for more than two neighboring sensitive locations are identified for wide geographic regions. The web application has been efficiently developed to provide wide geographical coverage. The applicability of the web application has been tested by operational and tactical users (players) during a Table-Top exercise (TTX) that took place in Athens, Greece, on March 28, 2014. The feedback provided by the players is now being implemented in the system.

This article introduces and solves a new transportation problem called the pallet-packing vehicle routing problem (PPVRP). PPVRP belongs to the category of practical routing models integrated with loading constraints, and assumes that customers raise a deterministic demand in the form of three-dimensional rectangular boxes. It is aimed at determining the optimal vehicle routes for satisfying customer demand. Regarding the packing aspects, transported boxes are not directly loaded into the vehicle-loading spaces; instead, they are feasibly stacked into pallets that are then loaded onto the vehicles before initiating their tours. Belonging to the class of combined routing and packing models, PPVRP is very hard to be optimally solved within manageable computational time; thus, we focused on heuristic approaches for both the routing and packing aspects of the problem. More specifically, PPVRP is solved via a local search metaheuristic strategy based on the regional aspiration criteria of tabu search. To determine feasible pallet-packing arrangements, we employ an efficient packing heuristic approach. The algorithm is accelerated by storing collected packing feasibility information into memory components. The proposed solution approach is tested on newly introduced benchmark instances derived from well-studied vehicle routing data sets, as well as real-world problems.