Optimizing emergency preparedness and resource utilization in mass-casualty incidents

This paper presents a response model for the aftermath of a Mass-Casualty Incident (MCI) that can be used to provide operational guidance for regional emergency planning as well as to evaluate strategic preparedness plans. A mixed integer programming (MIP) formulation is proposed for the combined ambulance dispatching, patient-to-hospital assignment, and treatment ordering problem. T he goal is to allocate effectively the limited resources during the response so as to improve patient outcomes, while the objectives are to minimize the overall response time and the total flow time required to treat all patients, in a hierarchical fashion. The model is solved via exact and MIP-based heuristic solution methods. The applicability of the model and the performance of the new methods are challenged on realistic MCI scenarios. We consider the hypothetical case of a terror attack at the New York Stock Exchange in Lower Manhattan with up to 150 trauma patients. We quantify the impact of capacity-based bottlenecks for both ambulances and available hospital beds. We also explore the trade-off between accessing remote hospitals for demand smoothing versus reduced ambulance transportation times

Resource constrained routing and scheduling: Review and research prospects

In the service industry, it is crucial to efficiently allocate scarce resources to perform tasks and meet particular service requirements. What considerably complicates matters is when these resources, for example skilled technicians, nurses, and home carers have to visit different customer locations. This paper provides a comprehensive survey on resource constrained routing and scheduling that unveils the problem characteristics with respect to resource qualifications, service requirements and problem objectives. It also identifies the most effective exact and heuristic algorithms for this class of problems. The paper closes with several research prospects

Production Scheduling with Complex Precedence Constraints in Parallel Machines

Heuristic search is a core area of artificial intelligence and the employment of an efficient search algorithm is critical to the performance of an intelligent system. This paper addresses a production scheduling problem with complex precedence constraints in an identical parallel machines environment. Although this particular problem can be found in several production and other scheduling applications; it is considered to be NP-hard due to its high computational complexity. The solution approach we adopt is based on a comparison among several dispatching rules combined with a diagram analysis methodology. Computational results on large instances provide relatively high quality practical solutions in very short computational times, indicating the applicability of the methodology in real life production scheduling applications

The Vehicle Routing Problem with Profits and Consistency Constraints

This paper models and solves a new transportation problem of practical importance; the Consistent Vehicle Routing Problem with Pro fits. There are two sets of customers, the frequent customers that are mandatory to service and the non-frequent potential customers with known and estimated profit s respectively, both having known demands and service requirements over a planning horizon of multiple days. The objective is to determine the vehicle routes that maximize the net profit, while satisfying vehicle capacity, route duration and consistency constraints. A new mathematical model is proposed that captures the pro fit collecting nature, as well as other features of the problem. For addressing this computationally challenging problem, an Adaptive Tabu Search has been developed, utilizing both short- and long-term memory structures to guide the search process. The proposed metaheuristic algorithm is evaluated on existing, as well as newly generated benchmark problem instances. Our computational experiments demonstrate the effectiveness of our algorithm, as it matches the optimal solutions obtained for small-scale instances and performs well on large-scale instances. Lastly, the trade-off between the acquired pro fits and consistent customer service is examined and various managerial insights are derived

Flexible job shop scheduling problems with arbitrary precedence graphs

A common assumption in the shop scheduling literature is that the processing order of the operations of each job is sequential; however, in practice there can be multiple connections and finish-to-start dependencies among the operations of each job. This paper studies exible job shop scheduling problems with arbitrary precedence graphs. Rigorous mixed integer and constraint programming models are presented, as well as an evolutionary algorithm is proposed to solve large scale problems. The proposed heuristic solution framework is equipped with effcient evolution and local search mechanisms as well as new feasibility detection and makespan estimation methods. To that end, new theorems are derived that extend previous theoretical contributions of the literature. Computational experiments on existing benchmark data sets show that the proposed solution methods outperform the current state-of-the-art. Overall, 59 new best solutions and 61 new lower bounds are produced for a total of 228 benchmark problem instances of the literature. To explore the impact of the arbitrary precedence graphs, lower bounds and heuristic solutions are generated for new large-scale problems. These experiments illustrate that the machine assignment flexibility and density of the precedence graphs, affect not only the makespan, but also the difficulty of producing good upper bounds

Acute Promyelocytic Leukemia: an Experience on 95 Greek Patients Treated in the All-Trans-Retinoic Acid Era

Acute promyelocytic leukemia (APL) is highly curable with the combination of all-transretinoic acid (ATRA) and anthracycline based chemotherapy, but the percentage of early deaths remains high. In the present study, we report the clinical, immunophenotypic, cytogenetic and molecular characteristics and outcome of APL patients diagnosed and treated in various Hospitals of Greece and Cyprus

Μέθοδοι βελτιστοποίησης για πολύπλοκα προβλήματα δρομολόγησης και χρονοπρογραμματισμού στόλου οχημάτων

This PhD dissertation is interested in the development of optimization methods for solving complex and large scale Vehicle Routing and Scheduling Problems (VRSP). The main focus is given on studying the computational efficiency and effectiveness of hybrid metaheuristic algorithms. In an attempt to fill in the gaps of the literature identified earlier, the major effort will focus on the Vehicle Routing Problem with Time Windows (VRPTW) and the proposed optimization methods will be tested on several medium and large scale benchmark data sets. Attention will be also given on other variants of the VRPTW with practical and operational side-constraints namely the Open VRPTW (OVRPTW) and the Heterogeneous Fleet VRPTW (HFVRPTW) and the VRPTW with Multiple Compartments and Commodities (MCCVRPTW). Finally, driven from an industrial application we will examine the integration of the suggested methods within a novel Decision Support System that enables schedulers to tackle VRPTW problems with real life operational constraints. A hybrid Multi Start Local Search and a novel Arc Guided Evolutionary Algorithm are proposed for solving the VRPTW. For both approaches an extensive empirical study is performed and the role of key algorithmic elements is analyzed. Computational experiments demonstrate that the proposed optimization methods obtain the best overall results compared to the current state of the art. Considering the Open VRPTW a formal description is first provided followed by a greedy look-ahead construction heuristic and a hybrid Evolutionary Algorithm. All critical aspects of the problem are captured while experimental results demonstrate the competitiveness of the methods with respect to other approaches. Subsequently, the HFVRPTW is addressed. A novel two phase solution framework is developed called Reactive Variable Neighborhood Tabu Search. Computational experiments show that high quality solutions can be reached via the proposed method illustrating its efficiency and effectiveness. Next a real-life VRPTW is presented that incorporates a number of side constraints such as multiple compartments and commodities, time windows, loading restrictions and limited vehicle fleet size and composition availability. An Adaptive Memory Programming method is proposed to address all critical aspects of the problem. Various computational experiments demonstrate the applicability of the method to real life vehicle routing and scheduling applications. Finally, an industrial Decision Support System is presented that handles the efficient and effective management of waste lube oils collection and recycling operations. The main effort is to examine the integration and cooperation of the vehicle routing and scheduling methods within an Enterprise Resource Planning system. The application of the DSS to an industrial environment improved productivity and competitiveness.Η διδακτορική αυτή διατριβή πραγματεύεται την ανάπτυξη μεθόδων βελτιστοποίησης για την επίλυση πολύπλοκων προβλημάτων δρομολόγησης και χρονοπρογραμματισμού στόλου οχημάτων. Η κύρια προσπάθεια εστιάζεται στη μελέτη της υπολογιστικής δύναμης των υβριδικών μεταευρετικών αλγορίθμων. Σε μια προσπάθεια να καλυφθούν τα κενά της βιβλιογραφίας η διδακτορική αυτή διατριβή έρχεται να προτείνει ιδιαίτερα αποδοτικές και αποτελεσματικές μεθόδους για την επίλυση του Προβλήματος Δρομολόγησης Στόλου Οχημάτων με Χρονικά Παράθυρα (ΠΔΣΟΧΠ) καθώς επίσης και άλλων εκδοχών του. Πιο συγκεκριμένα επιχειρείται για πρώτη φορά η μοντελοποίηση του Ανοικτού ΠΔΣΟΧΠ και η ανάπτυξη νέων μεθόδων για το ΠΔΣΟΧΠ με Ετερογενή Στόλο Οχημάτων. Ακόμη εξετάζεται επισταμένως και επιλύεται μια πραγματική βιομηχανική εφαρμογή με πολλαπλούς επιχειρησιακούς περιορισμούς. Τέλος, οι προτεινόμενες μέθοδοι βελτιστοποίησης ενσωματώνονται σε ένα πραγματικό Σύστημα Υποστήριξης Αποφάσεων. Για το ΠΔΣΟΧΠ προτείνεται μια υβριδική μεταευρετική μέθοδος πολλαπλών εκκινήσεων και ένας πρότυπος και μοντέρνος υβριδικός εξελικτικός αλγόριθμος. Η συγκριτική μέτρηση των επιδόσεων των μεθόδων σε πρότυπα προβλήματα της βιβλιογραφίας κατέδειξε την ανταγωνιστικότητα τους. Ιδιαίτερα δε αναφορικά με τη δεύτερη μέθοδο αξίζει να σημειωθεί ότι παρήγαγε τα καλύτερα αποτελέσματα παγκοσμίως τόσο σε μεσαίας όσο και σε μεγάλης κλίμακας προβλήματα. Η αποτελεσματικότητά τους είναι εμφανής παράγοντας, λύσεις υψηλής ποιότητας σε ανταγωνιστικούς υπολογιστικούς χρόνους σε σύγκριση με άλλες μεθόδους της βιβλιογραφίας. Για το Ανοικτό ΠΔΣΟΧΠ δημιουργείται ένα μαθηματικό μοντέλο το οποίο καλύπτει όλες τις πτυχές του προβλήματος, προτείνεται ένας ιδιαίτερα αποτελεσματικός ευρετικός κατασκευαστικός αλγόριθμος καθώς και ένας υβριδικός εξελικτικός αλγόριθμος. Αποτελέσματα σε πρότυπα προβλήματα της βιβλιογραφίας επαληθεύουν την καταλληλότητα και αποδοτικότητα των αλγορίθμων. Για το ΠΔΣΟΧΠ με Ετερογενή Στόλο Οχημάτων προτείνεται ένας υβριδικός μεταευρετικός αλγόριθμος πολλαπλών εκκινήσεων. Βασισμένος στις βασικές αρχές της Απαγορευμένης Έρευνας, ο αλγόριθμος αυτός συνδυάζει μηχανισμούς μεταβλητών γειτονιών και ευφυείς διαδικασίες εκμάθησης. Η συγκριτική μέτρηση των επιδόσεων του σε πρότυπα προβλήματα της βιβλιογραφίας κατέδειξε την ανταγωνιστικότητά του σε σύγκριση με άλλες μεθόδους. Τέλος, στο πλαίσιο της προσπάθειας μοντελοποίησης προβλημάτων με πραγματικούς επιχειρησιακούς περιορισμούς εξετάστηκε ένα πρόβλημα ζήτησης πολλαπλών προϊόντων τα οποία πρέπει να αποθηκεύονται σε διαφορετικά διαμερίσματα των οχημάτων κατά τη διάρκεια της εξυπηρέτησης των πελατών. Για την επίλυση του προβλήματος αυτού προτείνεται ένας αλγόριθμος Προγραμματισμού Προσαρμόσιμης. Αποτελέσματα σε πρότυπα προβλήματα της βιβλιογραφίας επαληθεύουν την απόδοση και την καταλληλότητα της μεθόδου