Mathematical programming modelling tools for resource-poor countries and organisations

In recent years, powerful mathematical modelling languages have enabled Operational Research practitioners to rapidly develop prototype tools capable of modelling complex managerial decisions such as staff shift scheduling, or production and supply chain planning. However, such tools have often required expensive commercial optimisation solvers that are sometimes beyond the financial reach of small companies and organisations, particularly in the low-income and emerging economies. Fortunately, the worldwide scope of the internet has put powerful free optimisation tools within the reach of anyone with a modest PC and even a slow internet connection. This article will present examples showing just how beneficial such an approach can be for resource-poor organisations

A reduced integer programming model for the ferry scheduling problem

We present an integer programming model for the ferry scheduling problem, improving existing models in various ways. In particular, our model has reduced size in terms of the number of variables and constraints compared to existing models by a factor of approximately O(n), where n being the number of ports. The model also handles efficiently load/unload time constraints, crew scheduling and passenger transfers. Experiments using real world data produced high quality solutions in 12 hours using CPLEX 12.4 with a performance guarantee of within 15% of optimality, on average. This establishes that using a general purpose integer programming solver is a viable alternative in solving the ferry scheduling problem of moderate size.Comment: To appear in Public Transpor

Branching on multi-aggregated variables

open5siopenGamrath, Gerald; Melchiori, Anna; Berthold, Timo; Gleixner, Ambros M.; Salvagnin, DomenicoGamrath, Gerald; Melchiori, Anna; Berthold, Timo; Gleixner, Ambros M.; Salvagnin, Domenic

An overview of treatment approaches for chronic pain management

Pain which persists after healing is expected to have taken place, or which exists in the absence of tissue damage, is termed chronic pain. By definition chronic pain cannot be treated and cured in the conventional biomedical sense; rather, the patient who is suffering from the pain must be given the tools with which their long-term pain can be managed to an acceptable level. This article will provide an overview of treatment approaches available for the management of persistent non-malignant pain. As well as attempting to provide relief from the physical aspects of pain through the judicious use of analgesics, interventions, stimulations, and irritations, it is important to pay equal attention to the psychosocial complaints which almost always accompany long-term pain. The pain clinic offers a biopsychosocial approach to treatment with the multidisciplinary pain management programme; encouraging patients to take control of their pain problem and lead a fulfilling life in spite of the pain. © 2016 Springer-Verlag Berlin Heidelber

Dynamic fleet scheduling with uncertain demand and customer flexibility

We develop a dynamic fleet scheduling model that demonstrates how a carrier can improve fleet utilization. The fleet scheduling model presented by Lee et al. (Eur J Oper Res 218(1):261-269, 2012) minimizes (1) a carrier's fleet size and (2) the penalty associated with the alternative delivery times selected. The model is static since requests are collected over time and processed together. In this paper we present a stochastic, dynamic version of the fleet reduction model. As demand is revealed throughout an order horizon, decisions are made in stages by sampling anticipated demand to avoid recourse penalties in later stages. Based on computational experiments we find the following:1. Modeling stochasticity improves the quality of solutions relative to the analogous model that does not include stochasticity. Counter-intuitively, an order lead-time distribution in which most loads are requested early can negatively impact optimal solution costs.2. The stochastic model produces good results without requiring prohibitively large numbers of demand scenarios.3. Consignees that place orders early in the order horizon are more often assigned their requested delivery times than those who place orders late.open