An unsupervised fuzzy clustering approach to the capacitated vehicle routing problem

Capacitated Clustering via Majorization-Minimization and Collaborative Neurodynamic Optimization

This paper addresses capacitated clustering based on majorization-minimization and collaborative neurodynamic optimization (CNO). Capacitated clustering is formulated as a combinatorial optimization problem. Its objective function consists of fractional terms with intra-cluster similarities in their numerators and cluster cardinalities in their denominators as normalized cluster compactness measures. To obviate the difficulty in optimizing the objective function with factional terms, the combinatorial optimization problem is reformulated as an iteratively reweighted quadratic unconstrained binary optimization problem with a surrogate function and a penalty function in a majorization-minimization framework. A clustering algorithm is developed based on CNO for solving the reformulated problem. It employs multiple Boltzmann machines operating concurrently for local searches and a particle swarm optimization rule for repositioning neuronal states upon their local convergence. Experimental results on ten benchmark datasets are elaborated to demonstrate the superior clustering performance of the proposed approaches against seven baseline algorithms in terms of 21 internal cluster validity criteria.

A global satisfaction degree method for fuzzy capacitated vehicle routing problems

There are several uncertain capacitated vehicle routing problems whose delivery costs and demands cannot be estimated using deterministic/statistical methods due to a lack of available and/or reliable data. To overcome this lack of data, third–party information coming from experts can be used to represent those uncertain costs/demands as fuzzy numbers which combined to an iterative–integer programming method and a global satisfaction degree is able to find a global optimal solution. The proposed method uses two auxiliary variables α,λ and the cumulative membership function of a fuzzy set to obtain real–valued costs and demands prior to find a deterministic solution and then iteratively find an equilibrium between fuzzy costs/demands via α and λ. The performed experiments allow us to verify the convergence of the proposed algorithm no matter the initial selection of parameters and the size of the problem/instance.

Optimization and profit distribution in a two-echelon collaborative waste collection routing problem from economic and environmental perspective

In order to reduce waste collection costs and realize sustainable urban development, this paper investigates a two-echelon collaborative waste collection vehicle routing problem (2E-CWCVRP), considering the cooperation and profit distribution between participants in the collection network. An optimization model for 2E-CWCVRP with the aim to minimize total costs and carbon emissions is constructed. Then, a three-stage solution approach is developed to solve this model, including a k-means clustering for simplifying the problem, and a hybrid heuristic for searching the optimal vehicle routes based on Clarke & Wright algorithm and an adaptive large neighborhood search algorithm (CW-ALNS). Finally, an improved Shapley value model is constructed for determining the costs and carbon emissions reduction amount and the best alliance sequence of each participant. The experiment results indicate that: (1) the effectiveness of CW-ALNS algorithm is verified through the benchmark instances; (2) the costs and carbon emissions of the collection network could be reduced simultaneously after the implementation of cooperation; (3) constructing a large collection and transfer network is more efficient than dividing the network into several individual parts. Finally, different alliance sequences are analyzed from the economics and environment perspective and the best alliance sequences are determined.

Multi-shift Single-Vehicle Routing Problem Under Fuzzy Uncertainty

This research considers the single-vehicle routing problem (VRP) with multi-shift and fuzzy uncertainty. In such a problem, a company constantly uses one vehicle to fulfill demand over a scheduling period of several work shifts. In our case, a crew executes maintenance jobs in different sites. The working team runs during different work shifts, but recurrently returns to the depot by the end of the shift (overtime avoidance). The goal consists in minimizing the number of work shifts (makespan). We observe the impact of uncertainty in travel and maintenance processing time on the overtime avoidance constraint. We realize an Artificial Immune Heuristic to get optimal solutions considering both makespan and overtime avoidance. First, we present a Pareto-based framework to evaluate the uncertainty influence. Then, we show a numerical real case study to survey the problem. In particular, a case study scenario has been created on the basis of the environmental changes in travel and processing times observed in Italy during the Covid-19 lockdown period (started on March 9, 2020). Results present important improvements are obtained with the proposed approach.

Sustainable resource management in a supply chain: a methodological proposal combining zero-inflated fuzzy time series and clustering techniques

Purpose

The purpose of this paper is to analyze demand forecast strategies to support a more sustainable management in a pallet supply chain, and thus avoid environmental impacts, such as reducing the consumption of forest resources.

Design/methodology/approach

Since the producer presents several uncertainties regarding its demand logs, a methodology that embed zero-inflated intelligence is proposed combining fuzzy time series with clustering techniques, in order to deal with an excessive count of zeros.

Findings

A comparison with other models from literature is performed. As a result, the strategy that considered at the same time the excess of zeros and low demands provided the best performance, and thus it can be considered a promising approach, particularly for sustainable supply chains where resources consumption is significant and exist a huge variation in demand over time.

Originality/value

The findings of the study contribute to the knowledge of the managers and policymakers in achieving sustainable supply chain management. The results provide the important concepts regarding the sustainability of supply chain using fuzzy time series and clustering techniques.

Modelo de ruteo de vehículos como alternativa de transporte para la UMNG sede campus

El crecimiento acelerado de la población académica en la Universidad Militar Nueva Granada Sede Campus y las condiciones de movilidad poco favorables, motivaron el desarrollo de un proyecto de investigación en el que se aplican metodologías de solución para un problema de ruteo de vehículos CVRP-HF. Como aporte principal, en este artículo se propone un procedimiento eficiente para la caracterización del caso de estudio y el planteamiento de un sistema como alternativa de transporte, donde se maximizan los beneficios y se agrega valor a los servicios ofrecidos por la Universidad. Haciendo uso de herramientas de software y adaptación de principios de programación matemática y procesos heurísticos, se hace seguimiento a una metodología donde se lleva a cabo un proceso inicial de recolección de información y caracterización de variables, seguido por el diagnostico del escenario y el diseño del modelo, hasta llegar finalmente a la validación del modelo y verificación por medio de comparaciones en la simulación del sistema real.

A Two-Stage Fuzzy Approach on the Socio-Economic Drivers of Global Energy Efficiency

Fuzzy models have been increasingly used in decision-making in the energy sector to deal with many uncertainties such as lack of data and climate change. This paper presents a global energetic efficiency analysis based on the time series data of 91 countries from 1960 to 2010, using an integrated two-stage fuzzy approach. More precisely, Fuzzy DEA models for traditional constant and varying returns to scale assumptions are employed in a first stage to assess the relative efficiency of these countries over the course of time. In the second stage, fuzzy regressions based on different rule-based systems are used to predict the impact of a set of demographic and socio-economic variables on energy efficiency. Energetic efficiency appears to be explained by the countervailing forces of urbanization, wealth inequality, and social development. Thus, a transition to a more energetic efficient lower carbon society will depend on how we address certain socio-political factors, such as pursuing a more sustainable urbanization, reducing inequalities and taking into consideration socio-environmental aspects in trade agreements.