Assessing the Efficiency of a PSS Solution for Waste Collection: A Simulation Based Approach

Using hybrid modelling to simulate and analyse strategies

Purpose

This paper aims to explore the applicability and strengths of proposing the three-paradigm hybrid simulation (HS) approach to developing and analysing strategies. The objective of the modelling effort is to conceptually illustrate its use in strategic planning by combining with the threat-opportunity-weakness-strength (TOWS) matrix, which builds a bridge between strategic management with the operations research community. The authors also aim to introduce a modelling framework to help model designers to apply HS to their own business issues.

Design/methodology/approach

The paper presents a process to develop a HS model associated with the development of strategies using the TOWS matrix.

Findings

After developing the model and testing four strategies, the best option for the supermarket to increase market share and sales is implementing the strength–opportunity strategy, which involves online shopping to adapt to the digital world.

Research limitations/implications

First, some modelling assumptions are used to simplify the development process, but they need further validation. Second, the real data collection is limited. Third, the personal learning edition of the simulation software is not a comprehensive version and has some limitations.

Practical implications

The hybrid model and the scenario planning introduced, in this study, could allow decision makers to rehearse the potential strategy before actual implementation. The framework is easy to implement to other business and industry.

Originality/value

This study links HS with strategic management, which has not been performed previously and evaluates the capability of HS in strategic planning. The functionality of the modelling platform has been tested for simulating a completely dynamic system.

Designing a sustainable dynamic collection service for WEEE: an economic and environmental analysis through simulation

The integration of economic and environmental objectives is crucial in the waste collection sector, especially for flows characterized by a high economic value like waste from electric and electronic equipment (WEEE). WEEE needs a complex and flexible reverse logistics system to face high uncertainty and variability of waste flows, while keeping a high efficiency. A few efforts in the literature have focused on planning an efficient collection service on a local scale. In this paper, a simulation-based methodology is adopted to compare different alternatives for a WEEE collection service in Italy. A dynamic collection scheme (i.e. with variable collection frequencies based on the actual level of waste flow) is simulated in two different logistics configurations, i.e. one based on direct connection and one based on an "hub-and-spoke" network. The impact of adopting electric vehicles is also evaluated. Alternatives are compared through economic and environmental key performance indicators to assess the level of sustainability. Results show the advantages of a "hub-and-spoke" configuration with the use of electric vehicles.

A stochastic optimization framework for planning of waste collection and value recovery operations in smart and sustainable cities

The concept of City 2.0 or smart city is offering new opportunities for handling waste management practices. The existing studies have started addressing waste management problems in smart cities mainly by focusing on the design of new sensor-based Internet of Things (IoT) technologies, and optimizing the routes for waste collection trucks with the aim of minimizing operational costs, energy consumption and transportation pollution emissions. In this study, the importance of value recovery from trash bins is highlighted. A stochastic optimization model based on chance-constrained programming is developed to optimize the planning of waste collection operations. The objective of the proposed optimization model is to minimize the total transportation cost while maximizing the recovery of value still embedded in waste bins. The value of collected waste is modeled as an uncertain parameter to reflect the uncertain value that can be recovered from each trash bin due to the uncertain condition and quality of waste. The application of the proposed model is shown by using a numerical example. The study opens new venues for incorporating the value recovery aspect into waste collection planning and development of new data acquisition technologies that enable municipalities to monitor the mix of recyclables embedded in individual trash bins.

Improving logistic efficiency of WEEE collection through dynamic scheduling using simulation modeling

The complexity of collection systems for Waste from Electric and Electronic Equipment (WEEE) in the EU is increasing, due to the latest directive that sets new collection targets and modes. The high variability and the uncertainty of reverse flows require innovative logistic approaches. One recent option for increasing efficiency and responsiveness in waste collection services, boosted by new technological solutions for waste level monitoring, is to adopt a dynamic collection scheme, where the collection frequency is not established a priori (based on a fixed plan), but it is based on the actual filling levels of waste bins. This option can allow the service provider to plan the collection service following the actual demand, resulting in a more responsive service, while improving the logistic efficiency. This paper evaluates the implementation of dynamic scheduling schemes for the collection of WEEE. A hybrid simulation model has been developed in order to support researchers and practitioners in assessing quantitative impacts of adopting dynamic scheduling in WEEE collection. Three logistic alternatives (a fixed collection schedule scheme, a pure dynamic scheme and a mixed one) have been compared in a test case based on data of an Italian municipality; collection services for different types of WEEE (i.e. large appliances and small items) have been analyzed. Results show a promising performance of dynamic schedules compared to the fixed one, revealing, for the specific test case, how a mixed solution can combine the advantages of dynamic and fixed scheduling, gaining flexibility towards customer demand while improving truck resource utilization.