What drives the Rebound Effect in transportation? An evaluation based on a Traveling Purchaser Problem

Limiting climate change is one of the most important challenges of the 21st century. Focusing on the transport sector, encouraging the use of more energy-efficient transport modes, and improving the performance of vehicles are the main targets in the fight for GHG reductions. However, due to Rebound Effect (RE), it is proven that improvements in engine fuel efficiency result in lower cost per kilometer driven and can induce individuals to use vehicles more often or to drive longer distances. As a result, the potential energy savings from improved energy efficiency could be partially or totally offset. Therefore, we decided to examine "What drives the Rebound Effect in transportation". To answer this research question, a Traveling Purchaser Problem was evaluated. This simple real-life business application models a situation in which a company owns one or several vehicles and has to buy specific products. The goal is to select and visit a subset of suppliers to satisfy a given demand for each product while minimizing both purchasing and travel costs. In total, 510 instances of this problem with various characteristics and parameters were generated and solved using the optimization software AIMMS. The impact of five main experimentations was deeply investigated. In addition, the trends obtained from these experiments were confirmed by fitting a logistic regression and a decision tree. The results of the various experiments showed that four variables can influence the occurrence of RE in a transportation network. On the one hand, RE tended to increase with the number of potential suppliers from which the firm can choose and the number of vehicles that the company owns to procure the products. On the other hand, the exclusivity of the products to source, as well as the introduction of a distance-traveled tax, reduced the occurrence of RE. To sum up, significant conclusions could be drawn from the experiments and the results can be easily transferred to real-life business applications. Recommendations for possible future studies were also discussed.nhhma

A Branch-Price-and-Cut Algorithm for the Capacitated Multiple Vehicle Traveling Purchaser Problem with Unitary Demand

The multiple vehicle traveling purchaser problem (MVTPP) consists of simultaneously selecting suppliers and routing a fleet of homogeneous vehicles to purchase different products at the selected suppliers so that all product demands are fulfilled and traveling and purchasing costs are minimized. We consider variants of the MVTPP in which the capacity of the vehicles can become binding and the demand for each product is one unit. Corresponding solution algorithms from the literature are either branch-and-cut or branch-and-price algorithms, where in the latter case the route-generation subproblem is solved on an expanded graph by applying standard dynamic-programming techniques. Our branch-price-and-cut algorithm employs a novel labeling algorithm that works directly on the original network and postpones the purchasing decisions until the route has been completely defined. Moreover, we define a new branching rule generally applicable in case of unitary product demands, introduce a new family of valid inequalities to apply when suppliers can be visited at most once, and show how product incompatibilities can be handled without considering additional resources in the pricing problem. In comprehensive computational experiments with standard benchmark sets we prove that the new branch-price-and-cut approach is highly competitive

Enhancing the Usefulness of Blockchain Technology in Finance Sector

Blockchain technology has become widely popular with the appearance of cryptocurrencies that use the decentralized nature of blockchain in order to exchange funds between their users. In order to verify various needed details during an exchange, consensus mechanisms are used which solve simple but exhaustive calculations. Such operations fulfil their primary goal of verifying, but are a common target of public disapproval due to massive energy consumption and lack of usefulness. This work discusses different approaches and consensus mechanisms with a more useful secondary function, especially focusing on NP-complete problems as mediators in solving complex and resource-heavy problems. A new way of approaching these problems can benefit many areas, like science, healthcare, government and finance, optimizing the current infrastructure and business processes like markets, transactions, insurances, payments and supply chains, or creating more secure, reliable and efficient environment. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

Computational Numerical Solution for Traveling Salesman Problem

This paper examined and analysed the desire of Traveling Salesman Problem (TSP) to find the cheapest way of visiting all given set of cities and returning to the starting point.     We presented a unique decomposition approach model for TSP in which the requirements and features of practical application in communication network, road transportation and supply chains are put into consideration.  We used a Mathematical Modeling solution with the application of Ant Colony Search Algorithm (ACSA) approach for result computation. In our approach, different Agents were created for difference purposes.   Information agent gathered information about best tour and detected the solution agent that arrived at a given point with information message containing details of where the solution agent has come from as well as best tour cost.  The place ant performs local pheromone decay on the relevant links.   This help to avoid random visit to irrelevant edges and allows the place ant to calculate the cost of tour of all place ants including the latest pheromone level on the links to each of the place ants. The solution agent uses available information to decide  which node to visit next and informs the place ant of  its decision to move to a given destination and update better tour  previously sampled while information about where to go next also obtained.       The place ant updates its pheromone value for that link using the equivalent of the algorithm for local pheromone update.    The cycle continues until solution agent arrives at its destination. The main advantage of our approach is that it permits the use of mixed integer programming and combinatorial optimization techniques to compute real optimal routing path, solving the problem in practice by returning actual shortest route with its numerical value and not the best effort result as provided by some previous models and analytical methods. The implementation was carried out using C# programming language.  Data used were generated and the performance evaluation of the model was carried out through simulation using Matlab 7.0.  The result shows that by considering all possible paths between a node as the source and another as the destination, all possible routes for a particular journey with shortest route in each case were generated. Keywords: Ant Colony, Combinatorial Optimization, Mixed Integer Programming, Pheromone, Search Algorithm and Traveling Salesman

Computational Numerical Solution for Traveling Salesman Problem

This paper examined and analyzed the desire of Traveling Salesman Problem (TSP) to find the cheapest way of visiting all given set of cities and returning to the starting point.     We presented a unique decomposition approach model for TSP in which the requirements and features of practical application in communication network, road transportation and supply chains are put into consideration.  We used a Mathematical Modeling solution with the application of Ant Colony Search Algorithm (ACSA) approach for result computation.  In our approach, different Agents were created for difference purposes.   Information agent gathered information about best tour and detected the solution agent that arrived at a given point with information message containing details of where the solution agent has come from as well as best tour cost.  The place ant performs local pheromone decay on the relevant links.   This help to avoid random visit to irrelevant edges and allows the place ant to calculate the cost of tour of all place ants including the latest pheromone level on the links to each of the place ants. The solution agent uses available information to decide  which node to visit next and informs the place ant of  its decision to move to a given destination and update better tour  previously sampled while information about where to go next also obtained.  The place ant updates its pheromone value for that link using the equivalent of the algorithm for local pheromone update.  The cycle continues until solution agent arrives at its destination. The main advantage of our approach is that it permits the use of mixed integer programming and combinatorial optimization techniques to compute real optimal routing path, solving the problem in practice by returning actual shortest route with its numerical value and not the best effort result as provided by some previous models and analytical methods. The implementation was carried out using C# programming language.  Data used were generated and the performance evaluation of the model was carried out through simulation using Matlab 7.0.  The result shows that by considering all possible paths between a node as the source and another as the destination, all possible routes for a particular journey with shortest route in each case were generated. Keywords: Ant Colony, Combinatorial Optimization, Mixed Integer Programming, Pheromone, Search Algorithm and Traveling Salesman

Exact and Heuristic Algorithms for Risk-Aware Stochastic Physical Search

We consider an intelligent agent seeking to obtain an item from one of several physical locations, where the cost to obtain the item at each location is stochastic. We study risk-aware stochastic physical search (RA-SPS), where both the cost to travel and the cost to obtain the item are taken from the same budget and where the objective is to maximize the probability of success while minimizing the required budget. This type of problem models many task-planning scenarios, such as space exploration, shopping, or surveillance. In these types of scenarios, the actual cost of completing an objective at a location may only be revealed when an agent physically arrives at the location, and the agent may need to use a single resource to both search for and acquire the item of interest. We present exact and heuristic algorithms for solving RA-SPS problems on complete metric graphs. We first formulate the problem as mixed integer linear programming problem. We then develop custom branch and bound algorithms that result in a dramatic reduction in computation time. Using these algorithms, we generate empirical insights into the hardness landscape of the RA-SPS problem and compare the performance of several heuristics

The family traveling salesman problem

Consider a depot, a partition of the set of nodes into subsets, called families, and a cost matrix. The objective of the family traveling salesman problem (FTSP) is to find the minimum cost circuit that starts and ends at the depot and visits a given number of nodes per family. The FTSP was motivated by the order picking problem in warehouses where products of the same type are stored in different places and it is a recent problem. Nevertheless, the FTSP is an extension of well-known problems, such as the traveling salesman problem. Since the benchmark instances available are in small number we developed a generator, which given a cost matrix creates an FTSP instance with the same cost matrix. We generated several test instances that are available in a site dedicated to the FTSP. We propose several mixed integer linear programming models for the FTSP. Additionally, we establish a theoretical and a practical comparison between them. Some of the proposed models have exponentially many constraints, therefore we developed a branch-and-cut (B&C) algorithm to solve them. With the B&C algorithm we were able to obtain the optimal value of open benchmark instances and of the majority of the generated instances. As the FTSP is an NP-hard problem we develop three distinct heuristic methods: a genetic algorithm, an iterated local search algorithm and a hybrid algorithm. With all of them we were able to improve the best upper bounds available in the literature for the benchmark instances that still have an unknown optimal value. We created a new variant of the FTSP, called the restricted family traveling salesman problem (RFTSP), in which nodes from the same family must be visited consecutively. We apply to the RFTSP the methods proposed for the FTSP and develop a new formulation based on the interfamily and the intrafamily relationship

A Partial Allocation Local Search Matheuristic for Solving the School Bus Routing Problem with Bus Stop Selection

This paper addresses the school bus routing problem with bus stop selection, which jointly handles the problems of determining the set of bus stops to visit, allocating each student to one of these bus stops and computing the routes that visit the selected bus stops, so that the total routing cost is minimized and the walking distance of the students is limited by a given value. A fast and efficient matheuristic is developed based on an innovative approach that first partially allocates the students to a set of active stops that they can reach, and computes a set of routes that minimizes the routing cost. Then, a refining process is performed to complete the allocation and to adapt the routes until a feasible solution is obtained. The algorithm is tested on a set of benchmark instances. The computational results show the efficiency of the algorithm in terms of the quality of the solutions yielded and the computing time