Autonomous control means the decentralized coordination of intelligent logistic objects and the routing through a logistic system by the intelligent parts themselves. This paper shows the application of a pheromone-based autonomous control method to a matrix model of a shop floor and compares the performance to an earlier developed method in different dynamic demand situations. The discrete event simulations are analysed by comparing statistics on throughput time data resulting from the system's behaviour in dynamic order arrival situations.

The dynamic and stochastic vehicle routing problem (DSVRP) can be modelled as a stochastic program (SP). In a two-stage SP with recourse model, the first stage minimizes the a priori routing plan cost and the second stage minimizes the cost of corrective actions, performed to deal with changes in the inputs. To deal with the problem, approaches based either on stochastic modelling or on sampling can be applied. Sampling-based methods incorporate stochastic knowledge by generating scenarios set on realizations drawn from distributions. In this paper we proposed a robust solution approach for the capacitated DSVRP based on sampling strategies. We formulated the problem as a two-stage stochastic program model with recourse. In the first stage the a priori routing plan cost is minimized, whereas in the second stage the average of higher moments for the recourse cost calculated via a set of scenarios is minimized. The idea is to include higher moments in the second stage aiming to compute a robust a priori routing plan that minimizes transportation costs while permitting small changes in the demands without changing solution structure. Additionally, the approach allows managers to choose between optimality and robustness, that is, transportation costs and reconfiguration. The computational results on a generic dynamic benchmark dataset show that the robust routing plan can cover unmet demand while incurring little extra costs as compared to the preplanning. We observed that the plan of routes is more robust; that is, not only the expected real cost, but also the increment within the planned cost is lower.

This article introduces a solution approach for the Stochastic Capacitated Vehicle Routing Problem (SCVRP) with uncertain demands, called Robust Simulation-Based (RoSi) approach. RoSi aims at designing route plans that can be more or less robust based on a decision-maker weight, i.e. solutions that resist demand changes with marginal additional (recourse) cost. For that, RoSi combines simulation with heuristics. It transforms a complex SCVRP into a set of deterministic ones, where well-known heuristics can be applied, computing a set of feasible solutions. These solutions are assessed by Monte Carlo simulation, and the one that deals better with demand fluctuation is selected as the final solution. The efficiency of RoSi is compared with those of three methods in the literature: Integer Linear Programming (ILP) model, Stochastic Programming with Recourse (SPR) model, and Robust Bi-Objective (RoBi) approach through numerical experiments. The results show that RoSi outperforms these methods in most scenarios.

In addition to increasing requirements to adopt more circular economy approaches, Australian municipal waste management systems also face challenges due to the loss of capacity to export waste overseas. Accordingly, these systems will require effective sorting and collecting of waste. Selective Waste Collection (SWC) is the collecting and sorting of household recyclable waste. The study aimed at investigating how SWC systems in an Australian municipality (Wollongong) are organised and their role in the proper management of household plastic waste. For this purpose, we mapped the household plastic waste management system and characterised every SWC system in terms of, e.g., weight and type of plastic collected. We estimated that only about 20% of the household plastic waste generated in 2018–2019 was collected via the SWC systems. Our investigation enabled us to estimate Wollongong’s household plastic waste generation, recycling rate, and final disposal; identify the gaps in knowledge and challenges faced by the systems; and offer recommendations to overcome them. The challenges include contamination, insufficient community awareness, and lack of consistent waste data. Our recommendations include a transition from a commingled to a non-commingled system. The results provide a useful approach and crucial information for performance evaluation, strategy, and planning purposes.