Perishing Goods Transportation Problem

The smart transportation of farming must carry food from agriculture, livestock, and fisheries from one location to another using diverse types of transportation modes to improve the wellbeing of citizens. The mathematical optimization model proposed in this chapter represents the problem of transportation of perishable products which aims to minimize costs and optimize transportation times and costs. Reduction of the time of transportation of perishable products increases the likelihood of delivering fresh products and minimizes economic losses. Transportation time and temperature suitable for the conservation of the product are priorities in this type of problem because the product is transported in time reduces the probability that the product will be exposed to extreme conditions that favor its decomposition. To check the feasibility of the model, this study proposes a set of instances subject to an application obtained a series of solutions that are analyzed to identify if there is a feasible solution.

Implementation of an Intelligent Model for Decision Making Based on CBR for Supply Chain Solution in Retail for a Cluster of Supermarkets

The latter includes customizing the user interface, as well as the way the system retrieves and processes cases afterward. The resulting cases may be shown to the user in different ways, and/or the retrieved cases may be adapted. This chapter is about an intelligent model for decision making based on case-based reasoning to solve the existing problem in the planning of distribution in the supply chain between a distribution center and a chain of supermarkets. First, the authors mentioned the need for intelligent systems in the decision-making processes, where they are necessary due to the limitations associated with conventional human decision-making processes. Among them, human experience is very scarce, and humans get tired of the burden of physical or mental work. In addition, human beings forget the crucial details of a problem, and many of the times are inconsistent in their daily decisions.

Searching for the Optimum Number of Capacitated Materialistic Cars for an Automotive Manufacturing Cell Using a Shuffled Frog Leap Algorithm

Due to the worldwide strengthening of the automotive sector, it presents itself as a challenge for the companies that comprise it to immerse themselves in processes of continuous improvement that contribute to increasing the satisfaction of the needs of its customers, as well as achieving a better positioning in the market. This goal is impossible to reach without proper design and management of the supply chain, consideration of issues related to logistics and inclusion of innovative techniques. In the chapter, the authors considered a manufacturing cell responsible for making the assembly of seats for the automotive industry. Waiting times and blocking of machines are incurred by not using the optimum number of vehicles to be used for the transfer of materials and the capacity with which they should count. The objective of this research is to know near-optimum quantities and capacities of the vehicles, materialistic cars, to avoid this situation. The use of mathematical formulations, simulation, and optimization techniques will be used to solve the problem.

Improvement of the Optimization of an Order Picking Model Associated With the Components of a Classic Volkswagen Beetle Using an Ant Colony Approach

Warehouse operations, specifically order picking process, are receiving close attention of researches due to the need of companies in minimizing operational costs. This chapter explains an ant colony optimization (ACO) approach to improve the order picking process in an auto parts store associated with the components of a classic Volkswagen Beetle car. Order picking represents the most time-consuming task in the warehouse operational expenses and, according to the scientific literature, is becoming a subject matter in operational research. It implements a low-level, picker-to-part order picking using persons as pickers with multiple picks per route. The context of the case study is a discrete picking where users' orders are independent. The authors use mathematical modeling to improve de ACO metaheuristic approach to minimize the order-picking cost.

An Order-Picking Model Associated With Hospital Components and Solved by a Firefly Algorithm

The health industry utilizes critical machines to treat and diagnose illness in patients. Most hospitals cannot afford to have such a machine unavailable; then, they choose to have many components of their critical machines stored inside a warehouse in the hospital. This strategy helps to avoid larger idle times when a machine breaks down. In this chapter, the firefly metaheuristic was selected to solve this problem. This algorithm is related to the behavior of fireflies when they search for potential mating partners. It is also used as a defense against other insects. This behavior consists in the light that they produce. This brightness is the key in this algorithm because, based on this concept, we can use it to attract other fireflies. This characteristic inspired the method to pick components in hospital warehouses.

Use of Compensatory Fuzzy Logic for Knowledge Discovery Applied to the Warehouse Order Picking Problem for Real-Time Order Batching

Order picking is an activity in a warehouse, carried out by a team, where customer orders are prepared. It is also the activity with the highest logistic costs in a company. In this chapter, a proposal is presented aimed at improving the order picking process in a warehouse. Order picking continues to attract the attention of people in charge of optimizing processes in a company for minimizing costs. There are several approaches to order picking such as discrete picking, batch picking, and zone picking. The purpose of this study is mainly oriented toward the reduction of batch picking times to achieve an improvement in the service. In this chapter is proposed the application of compensatory fuzzy logic (CFL), which is a branch of classical logic and is also discussed the possibility of making real-time order batches (dynamic batching) by using compensatory fuzzy rules for the classification of incoming orders.

Intelligent Tool for Decision Making Associated With Hospitalization and Sandstorms for the Optimization of Ambulances

The shortest path problem is a typical problem of optimization. This chapter presents an innovative model associated with the use of case-based reasoning to solve a problem of routing vehicles in a Hospital of El Paso, United States. In this chapter, diverse components are described to characterize this problem through the use of a knowledge system. The algorithm was developed in Java, thus obtaining a tool which determines the best tracks to the vehicles associated with ambulances. An experiment was realized to probe the validations; the results were used to compare it with the Dijkstra algorithm and determine the quality of the results. The future research of this intelligent tool is to determine an innovative perspective related to episodic knowledge applied to resolution of diverse ambulances, and as this topic is determinative to find and remember the best solutions quickly, additionally the authors compare it with a code from other postgraduate students trying to implement an algorithm similar to logistics but using a shuffled frog leap algorithm.

A Fuzzy Logic Classifier for the Three Dimensional Bin Packing Problem Deriving From Package Delivery Companies Application

One of the most common operations in warehouses of package delivery companies (e.g., UPS or FedEx) is to pack the products in trucks in order to locally deliver them to the customers. The products are generally packed in rectangular boxes of different dimensions. An efficient assignment of boxes to delivery trucks must consider the minimization of the number of trucks required. Thus, a considerable amount of fuel is saved, and hence, the delivery time and pollution are reduced. This efficient assignment can be viewed as an application of the well-known three-dimensional bin packing problem (3D-BPP). In this chapter, the authors propose a methodology based on fuzzy logic classification (FLC) to solve 3D-BPP. The FLC assigns a class to each box to be delivered according to their relative proximity. The goal is to contiguously assign the boxes whose destinations are as close as possible.

Predict Energy Charging Points to Electric Vehicles in a Smart City Using a Novel Metaheuristic

The purpose of this chapter is to understand a multivariable optimization associated with the path of a group of vehicles integrated in an ecological community and determine the optimal route involve speed, storage, and travel resources. Time of charge for determining the cost benefit have partnered with a travel plan associated with the charge point in a smart city, which has as principal basis the orography restriction related with the energy consumed. Although this problem has been studied on several occasions, the literature failed to establish ubiquitous computing for interacting with the various values associated with the achievement of the group of vehicles and their cost-benefit of each member of the community, comparing their individual trips for the group and determining the quantity of energy required for each one. There are several factors that can influence in the achievement of a group trip. For this chapter, the authors propose to use bat algorithm, which has proven to be efficient for the convergence of several issues.

Consumer Purchase Preference for the Perception of Quality of Perishable Products in a Smart City

Freshness, flavor, presentation, and nutritional value of fruits and vegetables deteriorate as time passes. That is why the correct implementation of supply chains is a subject of great interest for companies dedicated to the rotation of food marketing. Consumers play a particularly important role: the interaction between the retailer and the consumer determines the waste of food along the supply chain. The consumer's choice behavior and the perception of what is acceptable (or not) affect the management of the offer in the different points of sale dedicated to this line of business, as well as the aesthetic standards to be applied when distributing the products. This chapter explores consumer purchase preference for the perception of perishable products in a smart city.