Resource Planning

Business & Information Systems Engineering, 2015

Managing the response to natural, man-made and technical disasters becomes increasingly important in the light of climate change, globalization, urbanization and growing conflicts. Sudden onset disasters are typically characterized by high stakes, time pressure and uncertain, conflicting or lacking information. Since the planning and management of response is a complex task, decision makers of aid organizations can thus benefit from decision support methods and tools. A key task is the joint allocation of rescue units and the scheduling of incidents under different conditions of collaboration. We present an approach to support decision makers, who coordinate response units, by (a) suggesting mathematical formulations of decision models, (b) providing heuristic solution procedures and (c) evaluating the heuristics against both current best practice behavior and optimal solutions. Our computational experiments show that, for the generated problem instances, (1) current best practice behavior can be improved substantially by our heuristics, (2) the gap between heuristic and optimal solutions is very low for instances without collaboration and (3) our heuristics are capable of providing solutions for all generated instances in less than a second on a state-of-the-art PC.

2014 IEEE Intl Conf on High Performance Computing and Communications, 2014 IEEE 6th Intl Symp on Cyberspace Safety and Security, 2014 IEEE 11th Intl Conf on Embedded Software and Syst (HPCC,CSS,ICESS), 2014

Crisis management challenges decision support systems designers. One problem in the decision marking is to develop systems able to help the coordination of the different involved teams. Another challenge is to make the system work with a degraded communication infrastructure. Each workstation or embedded application must be able to help to make a decision with a degraded network by taking into account the potential decisions made by other agents. We propose in this article a multiagent model, based on an ant colony optimization, and designed to manage the complexity in the deployment of resources to solve a crisis. This model is able to manage data uncertainty, and its global goal is to optimize, in a stable way, fitness functions, like saving lives, defined by multiple users. Moreover, thanks to a reflexive process, the model is able to manage the effects into the environment of its decisions, in order to take more appropriate decisions. Thanks to our transactional model, the system is also able to take into account a large data amount without exploring all potential solutions. The graphical interface should be able to make the user defining rules database. Then, if the nature of the crisis is deeply unchanged, users should be able to change rules' databases.

This paper proposes a generic human-computer software user interface design, called the Resource Allocation Planning System (RAPS), designed to support a person making resource allocation decisions. Although there are many algorithms for automatically solving resource allocation problems, it is often the case that human judgment is also required. Also, while there are software user interfaces to support decision-making for specific resource allocation problems, most of them serve more as organizational charts than as decision-support systems, and most of them become increasingly difficult to use as the size of the resource allocation problem increases. This paper discusses the design and rationale for RAPS and gives an example of how RAPS can be adapted to a specific resource allocation problem.

Emerging Trends in Electrical, Electronic and Communications Engineering, 2017

Decision support is required for effective planning on all kinds of scheduling scenarios. The stochastic scenarios and uncertainty in demands make the scheduling task complex. Multiple objectives in terms of cost, timing window, priorities and travel routes are the driving factors in the scheduling task. These objectives are often associated with given constraints like time, cost, resource limit etc. To meet all these objectives with the given constraints, it requires effective scheduling methods. Among different application areas of scheduling, emergency relief and staff scheduling are two domains which present major challenges for the scheduling research. These two areas provide analogy with many other areas of scheduling. Issues like finding appropriate locations and establishing them in appropriate group, discovering effective path for routing and making efficient plan for distribution and servicing are major challenges for these two and related scheduling cases. This paper covers a survey study on some of the recent papers of these areas that highlights the problem formulations, technologies, methods and algorithms applied. It provides a literature review on technologies and algorithms applied in the area of emergency case relief scheduling and staff scheduling.

We consider the resource availability cost problem and two extensions through general temporal constraints and calendar constraints. With general temporal constraints minimum and maximum time lags between the activities can be ensured. Calendar constraints are used to model breaks in the availability of a resource, e. g ., weekends or public holidays of resource types that equal staff. Especially if long-term and capital-intensive projects are under consideration, resource availability cost problems should be applied because in such projects it is more important to minimize the cost than, e. g ., the project duration. We present mixed-integer linear programming (MILP) formulations as well as constraint programming (CP) models for the three problems. In a performance study we compare the results of the MILP formulations solved by cplex and the CP models solved by the lazy clause generation solver chuffed on benchmark instances from literature and also introduce new benchmarks. Our CP models close all open instances for resource availability cost problems from the literature.

SICE Journal of Control, Measurement, and System Integration

Resource allocation and scheduling under scarce resources and limited time are always critical and challenging tasks, not only because of the complex situation with diverse needs involved, but also of any unpredictable occurrence during the whole dynamic process. This work proposes an agent-based framework to integrate the resource allocation and scheduling under a set of limitations, which could respond to contingent changes as a dynamic system. We focus on the following research questions and formulate them as a constraint satisfaction problem: how many resources should be assigned and dispatched to which location, in which sequence and under what process scheduling with time, resource availability, and ability-matching limits. We first give the corresponding formal definition, and then combine real-coded genetic algorithm and dynamic scheduling of multi-functional resource assignment to tackle the above proposed research questions. In addition, we experiment the model with a small make-up case to suggest some preliminary scenarios. In future, this framework would be further applied to real life emergency situations with empirical data for training purposes and providing insight for relevant policy makers.

mistaconference.org

In this paper, we study the resource availability cost problem. This problem description minimizes the total cost of the (unlimited) renewable resources required to complete the project by a pre-specified project deadline. We developed a heuristic procedure to solve the RACP starting from a heuristic upper bound solution and searching for iterative gradual improvements in the total resource cost.

Disasters are sudden and calamitous events that can cause severe and pervasive negative impacts on society and huge human losses. Governments and humanitarian organizations have been putting tremendous efforts to avoid and reduce the negative consequences due to disasters. In recent years, information technology and big data have played an important role in disaster management. While there has been much work on disaster information extraction and dissemination, real-time optimization for decision support for disaster response is rarely addressed in big data research. With big data as an enabler, optimization of disaster response decisions from a systems perspective would facilitate the coordination among governments and humanitarian organizations to transport emergency supplies to affected communities in a more effective and efficient way when a disaster strikes. In this paper, we propose a mathematical programming approach, with real-time disaster-related information, to optimize the post-disaster decisions for emergency supplies delivery. Since timeliness is key in a disaster relief setting, we propose a rounding-down heuristic to obtain near-optimal solutions for the provision of rapid and effective response. We also conduct two computational studies. The first one is a case study of Iran that aims to examine the characteristics of the solutions provided by our solution methodology. The second one is to evaluate the computational performance, in terms of effectiveness and efficiency, of the proposed rounding-down heuristic. Computational results show that our proposed approach can obtain near-optimal solutions in a short period of time for large and practical problem sizes. This is an extended work of Kuo et al., 2015, which has been published in the Proceedings of IEEE International Congress on Big Data (Big Data Congress) 2015.

Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services - iiWAS '09, 2009

Resource planning for service-based workflows becomes crucial considering a large amount of workflow execution requesters in a SOA or Grid environment. Especially, business process management and performance management of service-based workflows are of high importance avoiding performance degradation. The need for efficient resource planning techniques forces intermediaries, acting as workflow orchestrators, to use efficient heuristics for the determination of service invocation plans for workflows at short computation times. This paper presents an optimization approach for the resource planning problem and proposes an efficient heuristical solution solving the addressed optimization problem at a high solution quality and at a short computation time.