'Enhanced Direct and Indirect Genetic Algorithm Approaches for a Mall Layout and Tenant Selection Problem'

During our earlier research, it was recognised that in order to be successful with an indirect genetic algorithm approach using a decoder, the decoder has to strike a balance between being an optimiser in its own right and finding feasible solutions. Previously this balance was achieved manually. Here we extend this by presenting an automated approach where the genetic algorithm itself, simultaneously to solving the problem, sets weights to balance the components out. Subsequently we were able to solve a complex and non-linear scheduling problem better than with a standard direct genetic algorithm implementation.

Exact solutions to the short sea shipping distribution problem

Short sea shipping has several advantages over other means of transportation, recognized by EU members. The maritime transportation could be dealt like a combination of two well-known problems: the container stowage problem and routing planning problem. The integration of these two well-known problems results in a new problem CSSRP (Container stowage and ship routing problem) that is also an hard combinatorial optimization problem. The aim of this work is to solve the CSSRP using a mixed integer programming model. It is proved that regardless the complexity of this problem, optimal solutions could be achieved in a reduced computational time. For testing the mathematical model some problems based on real data were generated and a sensibility analysis was performed.

The suppliers selection problem: a case study

The effective supplier evaluation and purchasing processes are of vital importance to business organizations, making the suppliers selection problem a fundamental key issue to their success. We consider a complex supplier selection problem with multiple products where minimum package quantities, minimum order values related to delivery costs, and discounted pricing schemes are taken into account. Our main contribution is to present a mixed integer linear programming (MILP) model for this supplier selection problem. The model is used to solve several examples including three real case studies from an electronic equipment assembly company.

Nested Rollout Policy Adaptation for optimizing vehicle selection in complex VRPs

The goal of Vehicle Routing Problems (VRP) and their variations is to transport a set of orders with the minimum number of vehicles at least cost. Most approaches are designed to solve specific problem variations independently, whereas in real world applications, different constraints are handled concurrently. This research extends solutions obtained for the traveling salesman problem with time windows to a much wider class of route planning problems in logistics. The work describes a novel approach that:  supports a heterogeneous fleet of vehicles  dynamically reduces the number of vehicles  respects individual capacity restrictions  satisfies pickup and delivery constraints  takes Hamiltonian paths (rather than cycles) The proposed approach uses Monte-Carlo Tree Search and in particular Nested Rollout Policy Adaptation. For the evaluation of the work, real data from the industry was obtained and tested and the results are reported.

Tabu search for the cyclic bandwidth problem

International audience

Boosting the performance of metaheuristics for the MinLA problem using a more discriminating evaluation function

International audience

Climbing Combinatorial Fitness Landscapes

International audience

Model predictive control applied to a supply chain management problem

Supply chains are ubiquitous in any commercial delivery systems. The exchange of goods and services, from different supply points to distinct destinations scattered along a given geographical area, requires the management of stocks and vehicles fleets in order to minimize costs while maintaining good quality services. Even if the operating conditions remain constant over a given time horizon, managing a supply chain is a very complex task. Its complexity increases exponentially with both the number of network nodes and the dynamical operational changes. Moreover, the management system must be adaptive in order to easily cope with several disturbances such as machinery and vehicles breakdowns or changes in demand. This work proposes the use of a model predictive control paradigm in order to tackle the above referred issues. The obtained simulation results suggest that this strategy promotes an easy tasks rescheduling in case of disturbances or anticipated changes in operating conditions. © Springer International Publishing Switzerland 2017

Iterated responsive threshold search for the quadratic multiple knapsack problem

International audience

Lot production size problem and simulation of urban transport

OBJECTIVES AND STUDY METHOD: There are two subjects in this thesis: “Lot production size for a parallel machine scheduling problem with auxiliary equipment” and “Bus holding for a simulated traffic network”. Although these two themes seem unrelated, the main idea is the optimization of complex systems. The “Lot production size for a parallel machine scheduling problem with auxiliary equipment” deals with a manufacturing setting where sets of pieces form finished products. The aim is to maximize the profit of the finished products. Each piece may be processed in more than one mold. Molds must be mounted on machines with their corresponding installation setup times. The key point of our methodology is to solve the single period lot-sizing decisions for the finished products together with the piece-mold and the mold-machine assignments, relaxing the constraint that a single mold may not be used in two machines at the same time. For the “Bus holding for a simulated traffic network” we deal with One of the most annoying problems in urban bus operations is bus bunching, which happens when two or more buses arrive at a stop nose to tail. Bus bunching reflects an unreliable service that affects transit operations by increasing passenger-waiting times. This work proposes a linear mathematical programming model that establishes bus holding times at certain stops along a transit corridor to avoid bus bunching. Our approach needs real-time input, so we simulate a transit corridor and apply our mathematical model to the data generated. Thus, the inherent variability of a transit system is considered by the simulation, while the optimization model takes into account the key variables and constraints of the bus operation. CONTRIBUTIONS AND CONCLUSIONS: For the “Lot production size for a parallel machine scheduling problem with auxiliary equipment” the relaxation we propose able to find solutions more efficiently, moreover our experimental results show that most of the solutions verify that molds are non-overlapping even if they are installed on several machines. We propose an exact integer linear programming, a Relax&Fix heuristic, and a multistart greedy algorithm to solve this problem. Experimental results on instances based on real-world data show the efficiency of our approaches. The mathematical model and the algorithm for the lot production size problem, showed in this research, can be used for production planners to help in the scheduling of the manufacturing. For the “Bus holding for a simulated traffic network” most of the literature considers quadratic models that minimize passenger-waiting times, but they are harder to solve and therefore difficult to operate by real-time systems. On the other hand, our methodology reduces passenger-waiting times efficiently given our linear programming model, with the characteristic of applying control intervals just every 5 minutes.

Chebanov law and Vakar formula in mathematical models of complex systems

Ecological models written in a mathematical language L(M) or model language, with a given style or methodology can be considered as a text. It is possible to apply statistical linguistic laws and the experimental results demonstrate that the behaviour of a mathematical model is the same of any literary text of any natural language. A text has the following characteristics: (a) the variables, its transformed functions and parameters are the lexic units or LUN of ecological models; (b) the syllables are constituted by a LUN, or a chain of them, separated by operating or ordering LUNs; (c) the flow equations are words; and (d) the distribution of words (LUM and CLUN) according to their lengths is based on a Poisson distribution, the Chebanov's law. It is founded on Vakar's formula, that is calculated likewise the linguistic entropy for L(M). We will apply these ideas over practical examples using MARIOLA model. In this paper it will be studied the problem of the lengths of the simple lexic units composed lexic units and words of text models, expressing these lengths in number of the primitive symbols, and syllables. The use of these linguistic laws renders it possible to indicate the degree of information given by an ecological model.

Development of the Andalusian Registry of Patients Receiving Community Case Management, for the follow-up of people with complex chronic diseases.

Background: Complex chronic diseases are a challenge for the current configuration of Health services. Case management is a service frequently provided for people with chronic conditions and despite its effectiveness in many outcomes, such as mortality or readmissions, uncertainty remains about the most effective form of team organization, structures, and the nature of the interventions. Many processes and outcomes of case management for people with complex chronic conditions cannot be addressed with the information provided by electronic clinical records. Registries are frequently used to deal with this weakness. The aim of this study was to generate a registry-based information system of patients receiving case management to identify their clinical characteristics, their context of care, events identified during their follow-up, interventions developed by case managers, and services used. Methods and design: The study was divided into three phases, covering the detection of information needs, the design and its implementation in the healthcare system, using literature review and expert consensus methods to select variables that would be included in the registry. Objective: To describe the essential characteristics of the provision of ca re lo people who receive case management (structure, process and outcomes), with special emphasis on those with complex chronic diseases. Study population: Patients from any District of Primary Care, who initiate the utilization of case management services, to avoid information bias that may occur when including subjects who have already been received the service, and whose outcomes and characteristics could not be properly collected. Results: A total of 102 variables representing structure, processes and outcomes of case management were selected for their inclusion in the registry after the consensus phase. Total sample was composed of 427 patients, of which 211 (49.4%) were women and 216 (50.6%) were men. The average functional level (Barthel lndex) was 36.18 (SD 29.02), cognitive function (Pfeiffer) showed an average of 4.37 {SD 6.57), Chat1son Comorbidity lndex, obtained a mean of 3.03 (SD 2.7) and Social Support (Duke lndex) was 34.2 % (SD 17.57). More than half of patients include in the Registry, correspond lo immobilized or transitional care for patients discharged from hospital (66.5 %). The patient's educational level was low or very low (50.4%). Caregivers overstrain (Caregiver stress index), obtained an average value of 6.09% (SD 3.53). Only 1.2 % of patients had declared their advanced directives, 58.6 had not defined the tutelage and the vast majority lived at home 98.8 %. Regarding the major events recorded at RANGE Registry, 25.8 % of the selected patients died in the first three months, 8.2 % suffered a hospital admission at least once time, 2.3%, two times, and 1.2% three times, 7.5% suffered a fall, 8.7% had pressure ulcer, 4.7% had problems with medication, and 3.3 % were institutionalized. Stroke is the more prevalent health problem recorded (25.1%), followed by hypertension (11.1%) and COPD (11.1%). Patients registered by NCMs had as main processes diabetes (16.8%) and dementia (11.3 %). The most frequent nursing diagnoses referred to the self-care deficit in various activities of daily living. Regarding to nursing interventions, described by the Nursing Intervention Classification (NIC), dementia management is the most used intervention, followed by mutual goal setting, caregiver and emotional support. Conclusions: The patient profile who receive case management services is a chronic complex patient with severe dependence, cognitive impairment, normal social support, low educational level, health problems such as stroke, hypertension or COPD, diabetes or dementia, and has an informal caregiver. At the first follow up, mortality was 19.2%, and a discrete rate of readmissions and falls.

Strategy for succession in family owned small businesses as a wicked problem to be tamed

Contemporary strategic-planning processes don’t help family businesses cope with some of the big problems they face. Owner managers admit that they are confronted with issues, such as those associated with succession and inter-generational transfer that cannot be resolved merely by gathering additional data, defining issues more clearly, or breaking them down into small problems. Preparing for succession is often put off or ignored, many planning techniques don’t generate fresh ideas and implementing solutions is often fraught with political peril. This paper presents a framework to explore the idea of wicked problems, its relevance to succession planning in family businesses and its implications for practice and policy. A wicked problem has many and varied elements, and is complex as well as challenging. These problems are different to hard but ordinary problems, which people can solve in a finite time period by applying standard techniques. In this paper the authors argue that the wicked problem of family business succession requires a different approach to strategy, founded on social planning processes to engage multiple stakeholders and reconcile family/business interests to foster a joint commitment to possible ways of resolution. This requires academics and practitioners to re-frame traditional business strategic planning processes to achieve more sustainable family business futures.

Strategic Optimization Techniques For FRTU Deployment and Chip Physical Design

Combinatorial optimization is a complex engineering subject. Although formulation often depends on the nature of problems that differs from their setup, design, constraints, and implications, establishing a unifying framework is essential. This dissertation investigates the unique features of three important optimization problems that can span from small-scale design automation to large-scale power system planning: (1) Feeder remote terminal unit (FRTU) planning strategy by considering the cybersecurity of secondary distribution network in electrical distribution grid, (2) physical-level synthesis for microfluidic lab-on-a-chip, and (3) discrete gate sizing in very-large-scale integration (VLSI) circuit. First, an optimization technique by cross entropy is proposed to handle FRTU deployment in primary network considering cybersecurity of secondary distribution network. While it is constrained by monetary budget on the number of deployed FRTUs, the proposed algorithm identi?es pivotal locations of a distribution feeder to install the FRTUs in different time horizons. Then, multi-scale optimization techniques are proposed for digital micro?uidic lab-on-a-chip physical level synthesis. The proposed techniques handle the variation-aware lab-on-a-chip placement and routing co-design while satisfying all constraints, and considering contamination and defect. Last, the first fully polynomial time approximation scheme (FPTAS) is proposed for the delay driven discrete gate sizing problem, which explores the theoretical view since the existing works are heuristics with no performance guarantee. The intellectual contribution of the proposed methods establishes a novel paradigm bridging the gaps between professional communities.

Physicochemical complexity in complex chemical systems

Self-replication and compartmentalization are two central properties thought to be essential for minimal life, and understanding how such processes interact in the emergence of complex reaction networks is crucial to exploring the development of complexity in chemistry and biology. Autocatalysis can emerge from multiple different mechanisms such as formation of an initiator, template self-replication and physical autocatalysis (where micelles formed from the reaction product solubilize the reactants, leading to higher local concentrations and therefore higher rates). Amphiphiles are also used in artificial life studies to create protocell models such as micelles, vesicles and oil-in-water droplets, and can increase reaction rates by encapsulation of reactants. So far, no template self-replicator exists which is capable of compartmentalization, or transferring this molecular scale phenomenon to micro or macro-scale assemblies. Here a system is demonstrated where an amphiphilic imine catalyses its own formation by joining a non-polar alkyl tail group with a polar carboxylic acid head group to form a template, which was shown to form reverse micelles by Dynamic Light Scattering (DLS). The kinetics of this system were investigated by 1H NMR spectroscopy, showing clearly that a template self-replication mechanism operates, though there was no evidence that the reverse micelles participated in physical autocatalysis. Active oil droplets, composed from a mixture of insoluble organic compounds in an aqueous sub-phase, can undergo processes such as division, self-propulsion and chemotaxis, and are studied as models for minimal cells, or protocells. Although in most cases the Marangoni effect is responsible for the forces on the droplet, the behaviour of the droplet depends heavily on the exact composition. Though theoretical models are able to calculate the forces on a droplet, to model a mixture of oils on an aqueous surface where compounds from the oil phase are dissolving and diffusing through the aqueous phase is beyond current computational capability. The behaviour of a droplet in an aqueous phase can only be discovered through experiment, though it is determined by the droplet's composition. By using an evolutionary algorithm and a liquid handling robot to conduct droplet experiments and decide which compositions to test next, entirely autonomously, the composition of the droplet becomes a chemical genome capable of evolution. The selection is carried out according to a fitness function, which ranks the formulation based on how well it conforms to the chosen fitness criteria (e.g. movement or division). Over successive generations, significant increases in fitness are achieved, and this increase is higher with more components (i.e. greater complexity). Other chemical processes such as chemiluminescence and gelation were investigated in active oil droplets, demonstrating the possibility of controlling chemical reactions by selective droplet fusion. Potential future applications for this might include combinatorial chemistry, or additional fitness goals for the genetic algorithm. Combining the self-replication and the droplet protocells research, it was demonstrated that the presence of the amphiphilic replicator lowers the interfacial tension between droplets of a reaction mixture in organic solution and the alkaline aqueous phase, causing them to divide. Periodic sampling by a liquid handling robot revealed that the extent of droplet fission increased as the reaction progressed, producing more individual protocells with increased self-replication. This demonstrates coupling of the molecular scale phenomenon of template self-replication to a macroscale physicochemical effect.