Waste collection

Waste collection is a part of the process of waste management. It is the transfer of solid waste from the point of use and disposal to the point of treatment or landfill. Waste collection also includes the curbside collection of recyclable materials that technically are not waste, as part of a municipal landfill diversion program. Household waste Household waste in economically developed countries will generally be left in waste containers or recycling bins prior to collection by a waste collector using a waste collection vehicle. Waste collection barges are used in some towns, for example in Venice, Italy. However, in many developing countries, such as Mexico and Egypt, waste left in bins or bags at the side of the road will not be removed unless residents interact with the waste collectors. Mexico City residents must haul their trash to a waste collection vehicle which makes frequent stops around each neighborhood. The waste collectors will indicate their readiness by

Integrating a heterogeneous fixed fleet and a flexible assignment of destination depots in the waste collection VRP with intermediate facilities

Michel Bierlaire, Iliya Dimitrov Markov, Sacha Varone

We consider a complex recyclable waste collection problem that extends the class of vehicle routing problems with intermediate facilities by integrating a heterogeneous fixed fleet and a flexible assignment of destination depots. Several additional side constraints, such as a mandated break period contingent on tour start time, multiple vehicle capacities, and site dependencies are also included. This specific problem was inspired by a real-world application and does not appear in the literature. It is modeled as an MILP which is enhanced with several valid inequalities. Due to the rich nature of the problem, state-of-the-art solvers are only able to tackle instances of small to medium size. To solve realistic instances, we propose a multiple neighborhood search heuristic capable of systematically treating all problem features and general enough to respond to the varying characteristics of the case study regions for which it is intended. The results show that the heuristic achieves optimality on small instances, exhibits competitive performance in comparison to state-of-the-art solution methods for special cases of our problem, and leads to important savings in the state of practice. Moreover, it highlights and quantifies the savings from allowing a flexible depot assignment. The data from the state of practice comes from a company in the waste collection industry in Geneva, Switzerland. (C) 2015 Elsevier Ltd. All rights reserved.

Pergamon-Elsevier Science Ltd2016

A general framework for routing problems with stochastic demands

Michel Bierlaire, Mohammad Yousef Maknoon, Iliya Dimitrov Markov, Sacha Varone

We introduce a unified modeling and solution framework for various classes of rich vehicle and inventory routing problems as well as other probability-based routing problems with a time-horizon dimension. Demand is assumed to be stochastic and non-stationary, and is forecast using any forecasting model that provides expected demands over the planning horizon, with error terms from any empirical distribution. We discuss possible applications to various problems from the literature and practice: from health care, waste collection, and maritime inventory routing, to routing problems based on event probabilities, such as facility maintenance where the breakdown probability of a facility increases with time. We provide a detailed discussion on the effects of the stochastic dimension on modeling and the solution methodology. We develop a mixed integer non-linear model, provide examples of how it can be reduced and adapted to specific problem classes, and demonstrate that probability-based routing problems over a planning horizon can be seen through the lens of inventory routing. The optimization methodology is heuristic, based on Adaptive Large Neighborhood Search. The case study is based on waste collection and facility maintenance instances derived from real data. We analyze the cost benefits of open tours and the availability of better forecasting methodologies. We demonstrate that relaxing the distributional assumptions on the error terms and calculating probabilities using simulation information has only a minor impact on computation time. Simulating the error terms on the final solution further allows us to verify the low level of occurrence of undesirable events, such as stock-outs, overflows or breakdowns, with a moderate impact on the routing cost compared to alternative realistic policies. What is more, simulating the objective of the final solution shows that it is an excellent representation of the real cost.

2017

A general framework for routing problems with stochastic demands

Michel Bierlaire, Mohammad Yousef Maknoon, Iliya Dimitrov Markov, Sacha Varone

2017

Integrating a heterogeneous fixed fleet and a flexible assignment of destination depots in the waste collection VRP with intermediate facilities

A general framework for routing problems with stochastic demands

Vehicle routing and demand forecasting in a generalized waste collection problem

A general framework for routing problems with stochastic demands

Vehicle routing and demand forecasting in a generalized waste collection problem

Integrating a heterogeneous fixed fleet and a flexible assignment of destination depots in the waste collection VRP with intermediate facilities