Time-Dependent Tourist Tour Planning with Adjustable Profits

Planning a tourist trip in a foreign city can be a complex undertaking: when selecting the attractions and choosing visit order and visit durations, opening hours as well as the public transit timetable need to be considered. Additionally, when planning trips for multiple days, it is desirable to avoid redundancy. Since the attractiveness of activities such as shopping or sightseeing depends on personal preferences, there is no one-size-fits-all solution to this problem. We propose several realistic extensions to the Time-Dependent Team Orienteering Problem with Time Windows (TDTOPTW) which are relevant in practice and present the first MILP representation of it. Furthermore, we propose a problem-specific preprocessing step which enables fast heuristic (iterated local search) and exact (mixed-integer linear programming) personalized trip-planning for tourists. Experimental results for the city of Berlin show that the approach is feasible in practice

Dynamic Collection Scheduling Using Remote Asset Monitoring: Case Study in the UK Charity Sector

Remote sensing technology is now coming onto the market in the waste collection sector. This technology allows waste and recycling receptacles to report their fill levels at regular intervals. This reporting enables collection schedules to be optimized dynamically to meet true servicing needs in a better way and so reduce transport costs and ensure that visits to clients are made in a timely fashion. This paper describes a real-life logistics problem faced by a leading UK charity that services its textile and book donation banks and its high street stores by using a common fleet of vehicles with various carrying capacities. Use of a common fleet gives rise to a vehicle routing problem in which visits to stores are on fixed days of the week with time window constraints and visits to banks (fitted with remote fill-monitoring technology) are made in a timely fashion so that the banks do not become full before collection. A tabu search algorithm was developed to provide vehicle routes for the next day of operation on the basis of the maximization of profit. A longer look-ahead period was not considered because donation rates to banks are highly variable. The algorithm included parameters that specified the minimum fill level (e.g., 50%) required to allow a visit to a bank and a penalty function used to encourage visits to banks that are becoming full. The results showed that the algorithm significantly reduced visits to banks and increased profit by up to 2.4%, with the best performance obtained when the donation rates were more variable

Efficient neighborhood evaluations for the vehicle routing problem with multiple time windows

In the vehicle routing problem with multiple time windows (VRPMTW), a single time window must be selected for each customer from the multiple time windows provided. Compared with classical vehicle routing problems with only a single time window per customer, multiple time windows increase the complexity of the routing problem. To minimize the duration of any given route, we present an exact polynomial time algorithm to efficiently determine the optimal start time for servicing each customer. The proposed algorithm has a reduced worst-case and average complexity than existing exact algorithms. Furthermore, the proposed exact algorithm can be used to efficiently evaluate neighborhood operations during a local search resulting in significant acceleration. To examine the benefits of exact neighborhood evaluations and to solve the VRPMTW, the proposed algorithm is embedded in a simple metaheuristic framework generating numerous new best known solutions at competitive computation times

Modelo Matemático e Meta-Heurística Simulated Annealing para Elaboração de Roteiros Turísticos com base no Tourist Trip Design Problem

Muito embora existam diversos pacotes de viagens com destinos predefinidos contemplando locais mais populares, nos últimos anos tem crescido a procura por soluções que criem roteiros personalizados voltados às necessidades de cada turista. Para suprir essa nova demanda surge o Problema de Elaboração de Rotas Turísticas (PERT) ou TouristTrip Design Problem (TTDP) o qual Van Oudheusden e Vansteenwegen (2007) sugerem o uso do OrienteeringProblem (OP) e suas extensões para resolução desta classe de problemas. Esta dissertação tem por objetivo o desenvolvimento de um modelo matemático e de uma meta-heurística SimulatedAnnealing (SA) para resolução do TouristTrip Design Problem (TTDP)

Exact Algorithm for the Capacitated Team Orienteering Problem with Time Windows

The capacitated team orienteering problem with time windows (CTOPTW) is a problem to determine players' paths that have the maximum rewards while satisfying the constraints. In this paper, we present the exact solution approach for the CTOPTW which has not been done in previous literature. We show that the branch-and-price (B&P) scheme which was originally developed for the team orienteering problem can be applied to the CTOPTW. To solve pricing problems, we used implicit enumeration acceleration techniques, heuristic algorithms, and ng-route relaxations

The bus sightseeing problem

The basic characteristic of vehicle routing problems with profits (VRPP) is that locations to be visited are not predetermined. On the contrary, they are selected in pursuit of maximizing the profit collected from them. Significant research focus has been directed toward profitable routing variants due to the practical importance of their applications and their interesting structure, which jointly optimizes node selection and routing decisions. Profitable routing applications arise in the tourism industry aiming to maximize the profit score of attractions visited within a limited time period. In this paper, a new VRPP is introduced, referred to as the bus sightseeing problem (BSP). The BSP calls for determining bus tours for transporting different groups of tourists with different preferences on touristic attractions. Two interconnected decision levels have to be jointly tackled: assignment of tourists to buses and routing of buses to the various attractions. A mixed-integer programming formulation for the BSP is provided and solved by a Benders decomposition algorithm. For large-scale instances, an iterated local search based metaheuristic algorithm is developed with some tailored neighborhood operators. The proposed methods are tested on a large family of test instances, and the obtained computational results demonstrate the effectiveness of the proposed solution approaches

Orienteering Problem: A survey of recent variants, solution approaches and applications

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ