In this work we investigate opportunities offered by telematics and analytics to enable better informed, and more integrated, collaborative management decisions on construction sites. We focus on efficient refuelling of assets across construction sites. More specifically, we develop decision support models that, by leveraging data supplied by different assets, schedule refuelling operations by minimising the distance travelled by the bowser truck as well as fuel shortages. Motivated by a practical case study elicited in the context of a project we recently conducted at the C610 Systemwide Crossrail site, we introduce the Dynamic Bowser Routing Problem. In this problem the decision maker aims to dynamically refuel, by dispatching a bowser truck, a set of assets which consume fuel and whose location changes over time; the goal is to ensure that assets do not run out of fuel and that the bowser covers the minimum possible distance. We investigate deterministic and stochastic variants of this problem. To tackle deterministic variants, we introduce and contrast a bilinear programming model and a mixed-integer linear programming model. To tackle stochastic variants, by employing a new general purpose software library for stochastic modeling, we introduce a complete stochastic dynamic programming model as well as a novel heuristic we named "sample waning." We consider two stochastic variants of the problem in which asset locations or asset fuel consumptions are uncertain. We demonstrate the effectiveness of our approaches in the context of an extensive computational study designed around information and data collected at C610 Systemwide as well as supplied by our project partners.
The field of Inventory Routing (IR), whose origins can be traced back to the work of @cite_5 , encompasses problems which combine vehicle routing and inventory management decisions. In IR optimisation is delegated to a central entity that jointly optimises all decisions. Recent surveys in the area include . The first exact approach to the Inventory Routing Problem (IRP) was proposed by @cite_3 , who considered the single-vehicle case. Computationally efficient approaches to this problem were discussed in . Approaches for the multi-vehicle case include . Stochastic IRP problems, in which customer demand is modeled as a random variable, include the seminal work by @cite_4 , and a number of more recent contributions .
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For Air Products and Chemicals, Inc., inventory management of industrial gases at customer locations is integrated with vehicle scheduling and dispatching. Their advanced decision support system includes on-line data entry functions, customer usage forecasting, a time distance network with a shortest path algorithm to compute intercustomer travel times and distances, a mathematical optimization module to produce daily delivery schedules, and an interactive schedule change interface. The optimization module uses a sophisticated Lagrangian relaxation algorithm to solve mixed integer programs with up to 800,000 variables and 200,000 constraints to near optimality. The system, first implemented in October, 1981, has been saving between 6 to 10 of operating costs. Abstract Inventory-routing problems (IRPs) arise in vendor-managed inventory systems. They require jointly solving a vehicle routing problem and an inventory management problem. Whereas the solutions they yield tend to benefit the vendor and customers, solving IRPs solely based on cost considerations may lead to inconveniences to both parties. These are related to the fleet size and vehicle load, to the frequency of the deliveries, and to the quantities delivered. In order to alleviate these problems, we introduce the concept of consistency in IRP solutions, thus increasing quality of service. We formulate the multi-vehicle IRP, with and without consistency requirements, as mixed integer linear programs, and we propose a matheuristic for their solution. This heuristic applies an adaptive large neighborhood search scheme in which some subproblems are solved exactly. The proposed algorithm generates solutions offering a good compromise between cost and quality. We analyze the effect of different inventory policies, routing decisions and delivery sizes. We consider an inventory routing problem in discrete time where a supplier has to serve a set of customers over a multiperiod horizon. A capacity constraint for the inventory is given for each customer, and the service cannot cause any stockout situation. Two different replenishment policies are considered: the order-up-to-level and the maximum-level policies. A single vehicle with a given capacity is available. The transportation cost is proportional to the distance traveled, whereas the inventory holding cost is proportional to the level of the inventory at the customers and at the supplier. The objective is the minimization of the sum of the inventory and transportation costs. We present a heuristic that combines a tabu search scheme with ad hoc designed mixed-integer programming models. The effectiveness of the heuristic is proved over a set of benchmark instances for which the optimal solution is known.