ASAP: The After Salesman Problem

The customer contacts taking place after a sales transaction and the services involved are of increasing importance in contemporary business models. The responsiveness to service requests is a key dimension in service quality and therefore an important succes factor in this business domain. This responsiveness is of course highly dependent on the operational scheduling or dispatching decisions made in the often dynamic service settings. We consider the problem of optimizing responsiveness to service requests arriving in real time. We consider three models and formulations and present computational results on exact solution methods. The research is based on practical practical work done with the largest service organization in The Netherlands.operations research and management science;

ANWB automates and improves repair men dispatching

ANWB, the Dutch automobile association, provides assistance, car repair andreplacement services to its nearly 4 million members. ANWB services around 1.3 millionrequests per year in The Netherlands. Historically, the operational planning process ofassigning requests to service men was regionally organized, and human planners solvedthe sometimes large and hectic planning situations in real time. At a national level, some50 planners were required to do the job, and the quality of the planning and operationswere largely unknown. In a large business process reengineering project, ANWBredesigned the planning processes, leveraging state of the art IT and operations researchtechniques. As a result, the 24/7 planning processes are smoothened, can be executed byas few as 14 planners who work at a national level, and the operational planning andperformance have improved. As new competitors entered the market, ANWB has beenable to sustain its extraordinary high customer ratings and market share, while adaptingits proposition to the competitive prices dictated by the market.Economics (Jel: A)

On the Value and Challenge of Real-Time Information in Dynamic Dispatching of Service Vehicles

Ubiquitous computing technologies and information systems pave the way for real-time planning and management. In the process of dynamic vehicle dispatching, the adherent challenge is to develop decision support systems using real-time information in an appropriate quality and at the right moment in order to improve their value creation. As real-time information enables replanning at any point in time, the question arises when replanning should be triggered. Frequent replanning may lead to efficient routing decisions due to vehicles’ diversions from current routes while less frequent replanning may enable effective assignments due to gained information. In this paper, the authors analyze and quantify the impact of the three main triggers from the literature, exogenous customer requests, endogenous vehicle statuses, and replanning in fixed intervals, for a dynamic vehicle routing problem with stochastic service requests. To this end, the authors generalize the Markov-model of an established dynamic routing problem and embed the different replanning triggers in an existing anticipatory assignment and routing policy. They particularly analyze under which conditions each trigger is advantageous. The results indicate that fixed interval triggers are inferior and dispatchers should focus either on the exogenous customer process or the endoge- nous vehicle process. It is further shown that the exogenous trigger is advantageous for widely spread customers with long travel durations and few dynamic requests while the endogenous trigger performs best for many dynamic requests and when customers are accumulated in clusters

Supply chain management: An opportunity for metaheuristics

In today’s highly competitive and global marketplace the pressure on organizations to find new ways to create and deliver value to customers grows ever stronger. In the last two decades, logistics and supply chain has moved to the center stage. There has been a growing recognition that it is through an effective management of the logistics function and the supply chain that the goal of cost reduction and service enhancement can be achieved. The key to success in Supply Chain Management (SCM) require heavy emphasis on integration of activities, cooperation, coordination and information sharing throughout the entire supply chain, from suppliers to customers. To be able to respond to the challenge of integration there is the need of sophisticated decision support systems based on powerful mathematical models and solution techniques, together with the advances in information and communication technologies. The industry and the academia have become increasingly interested in SCM to be able to respond to the problems and issues posed by the changes in the logistics and supply chain. We present a brief discussion on the important issues in SCM. We then argue that metaheuristics can play an important role in solving complex supply chain related problems derived by the importance of designing and managing the entire supply chain as a single entity. We will focus specially on the Iterated Local Search, Tabu Search and Scatter Search as the ones, but not limited to, with great potential to be used on solving the SCM related problems. We will present briefly some successful applications.Supply chain management, metaheuristics, iterated local search, tabu search and scatter search

The Benefits of Information Sharing in Carrier-Client Collaboration

This dissertation includes three related papers to investigate different methods that can help transport providers improve their operational efficiency. The first paper models and measures the profit improvement trucking companies can achieve by collaborating with their clients to obtain advance load information (ALI). The core research method is to formulate a comprehensive and flexible mixed integer mathematical model and implement it in a dynamic rolling horizon context. The findings illustrate that access to the second day ALI can improve the profit by an average of 22%. We also found that the impact of ALI depends on radius of service and trip length but statistically independent of load density and fleet size. The second paper investigates the following question of relevance to truckload dispatchers striving for profitable decisions in the context of dynamic pick-up and delivery problems: since not all future pick-up/delivery requests are known with certainty, how effective are alternative methods for guiding those decisions? We propose an intuitive policy and integrate it into a new two-index mixed integer programming formulation, which we implement using the rolling horizon approach. On average, in one of the practical transportation network settings, the proposed policy can, with just second-day ALI, yield an optimality ratio equal to almost 90% of profits in the static optimal solution. We enhance the proposed policy by adopting the idea of a multiple scenario approach. In comparison to other dispatching methods, our proposed policies were found to be very competitive in terms of solution quality and computational efficiency. Finally, inspired by a real-life third party logistic provider, the third paper addresses a dynamic pickup and delivery problem with full truckload (DPDFL) for local operators. The main purpose of this work is to investigate the impact of potential factors on the carriers’ operational efficiency. These factors, which are usually under managerial influence, are vehicle diversion capability, the DPDFL decision interval, and how far in advance the carrier knows of clients’ shipment requirements; i.e., ALI. Through comprehensive numerical experiments and statistical analysis, we found that the ALI and re-optimization interval significantly influence the total cost, but that diversion capability does not. A major contribution of this work is that we develop an efficient benchmark solution for the DPDFL’s static version by discretization of time windows. We observed that three-day ALI and an appropriate decision interval can reduce deviation from the benchmark solution to less than 8%

The dynamic nearest neighbor policy for the multi-vehicle pick-up and delivery problem

In this paper, a dynamic nearest neighbor (DNN) policy is proposed for operating a fleet of vehicles to serve customers, who place calls in a Euclidean service area according to a Poisson process. Each vehicle serves one customer at a time, who has a distinct origin and destination independently and uniformly distributed within the service area. The new DNN policy is a refined version of the nearest neighbor (NN) policy that is well known to perform sub-optimally when the frequency of customer requests is high. The DNN policy maintains geographically closest customer-to-vehicle assignments, due to its ability to divert/re-assign vehicles that may be already en-route to pick up other customers, when another vehicle becomes available or a new customer call arrives. Two other pertinent issues addressed include: the pro-active deployment of the vehicles by anticipating in which regions of the service area future calls are more likely to arise; and, imposition of limits to avoid prohibitively long customer wait times. The paper also presents accurate approximations for all the policies compared. Extensive simulations, some of which are included herein, clearly show the DNN policy to be tangibly superior to the first-comefirst-served (FCFS) and NN policies

The importance of information flows temporal attributes for the efficient scheduling of dynamic demand responsive transport services

The operation of a demand responsive transport service usually involves the management of dynamic requests. The underlying algorithms are mainly adaptations of procedures carefully designed to solve static versions of the problem, in which all the requests are known in advance. However there is no guarantee that the effectiveness of an algorithm stays unchanged when it is manipulated to work in a dynamic environment. On the other hand, the way the input is revealed to the algorithm has a decisive role on the schedule quality. We analyze three characteristics of the information flow (percentage of real-time requests, interval between call-in and requested pickup time and length of the computational cycle time), assessing their influence on the effectiveness of the scheduling proces

An Assignment-Based Approach to Efficient Real-Time City-Scale Taxi Dispatching

This study proposes and evaluates an efficient real-time taxi dispatching strategy that solves the linear assignment problem to find a globally optimal taxi-to-request assignment at each decision epoch. The authors compare the assignment-based strategy with two popular rule-based strategies. They evaluate dispatching strategies in detail in the city of Berlin and the neighboring region of Brandenburg using the microscopic large-scale MATSim simulator. The assignment-based strategy produced better results for both drivers (less idle driving) and passengers (less waiting). However, computing the assignments for thousands of taxis in a huge road network turned out to be computationally demanding. Certain adaptations pertaining to the cost matrix calculation were necessary to increase the computational efficiency and assure real-time responsiveness

ACTIVE RELOCATION AND DISPATCHING OF HETEROGENEOUS EMERGENCY VEHICLES

An emergency is a situation that causes an immediate risk to the property, health, or lives of civilians and can assume a variety of forms such as traffic accidents, fires, personal medical emergencies, terrorist attacks, robberies, natural disasters, etc. Emergency response services (ERSs) such as police, fire, and medical services play crucial roles in all communities and can minimize the adverse effects of emergency incidents by decreasing the response time. Response time is not only related to the dispatching system, but also has a very close relationship to the coverage of the whole network by emergency vehicles. The goal of this dissertation is to develop a model for an Emergency Management System. This model will dynamically relocate the emergency vehicles to provide better coverage for the whole system. Also, when an emergency happens in the system the model will consider dispatching and relocation problem simultaneously. In addition, it will provide real-time route guidance for emergency vehicles. In summary, this model will consider three problems simultaneously: area coverage, vehicle deployment, and vehicle routing. This model is event-based and will be solved whenever there is an event in the system. These events can be: occurrence of an emergency, change in the status of vehicles, change in the traffic data, and change in the likelihood of an emergency happening in the demand nodes. Three categories of emergency vehicle types are considered in the system: police cars, ambulances, and fire vehicles. The police department is assumed to have a homogeneous fleet, but ambulances and fire vehicles are heterogeneous. Advanced Life Support (ALS) and Basic Life Support (BLS) ambulances are considered, along with Fire Engines, Fire Trucks, and Fire Quints in the fire vehicle category. This research attempts to provide double coverage for demand nodes by non-homogenous fleet while increasing the equity of coverage of different demand nodes. Also, the model is capable of considering the partial coverage in the heterogeneous vehicle categories. Two kinds of demand nodes are considered, ordinary nodes and critical nodes. Node demands may vary over time, so the model is capable of relocating the emergency fleet to cover the points with highest demand. In addition, an attempt is made to maintain work load balance between different vehicles in the system. Real-world issues, such as the fact that vehicles prefer to stay at their home stations instead of being relocated to other stations and should be back at their home depots at the end of the work shift, are taken into account. This is a unique and complex model; so far, no study in the literature has addressed these problems sufficiently. A mathematical formulation is developed for the proposed model, and numerical examples are designed to demonstrate its capabilities. Xpress 7.1 is used to run this model on the numerical examples. Commercial software like Xpress can be used to solve the proposed model on small-size problems, but for large-size and real-world problems, an appropriate heuristic is needed. A heuristic method that can find good solutions in reasonable time for this problem is developed and tested on several cases. Also, the model is applied to a real-world case study to test its performance. To investigate the model's behavior on a real-world problem, a very sophisticated simulation model that can see most of the details in the system has been developed and the real case study data has been used to calibrate the model. The results show that the proposed model is performing very well and efficient and it can greatly improve the performance of emergency management centers