A linear programming formulation with integer solutions for solving an equipment replacement problem with multiple assets: the concave demand case

A linear programming formulation is presented for the deterministic equipment replacement problem in which multiple assets are required each period and a number of assets are available for replacement. Under common cost assumptions, the linear programming solutions are shown to be integer for certain demand constraints- This paper considers the case of concave demand over a finite horizon. The integer solutions allow for implementable decisions and the formulation allows for the solution of large replacement problems, such as vehicles in a fleet, without the computational effort of branch-and-bound procedures. Numerical solutions are provided for illustration of the formulation and its efficiency in solving large replacement problems.     

Probability-based multiple-criteria optimization of bridge maintenance using monitoring and expected error in the decision process

In the context of limited financial resources under uncertainty, an important challenge is to introduce monitoring concepts in the general assessment, maintenance and repair frameworks of highway bridges. Structural health monitoring (SHM) provides new information on structural performance. As the monitoring duration increases, additional information becomes available. The knowledge of the structure is more accurate, but the monitoring costs are greater. Based on the accuracy of monitoring, different decisions can be made. These decisions involve uncertainties, and, consequently, are expressed in terms of probabilities. A probability-based approach for multiple criteria optimization of bridge maintenance strategies based on SHM is proposed. A measure of the error in the decision process, based on monitoring occurrence and duration, is proposed. Optimal solutions are obtained considering multiple criteria such as expected failure cost, expected monitoring/maintenance cost, expected accuracy of monitoring results, and ensuring that all constraints are satisfied.     

A Stochastic Optimization Model to Reduce Expected Post-Disaster Response Time Through Pre-Disaster Investment Decisions

This paper seeks to enhance network survivability under a disaster and reduce the expected post-disaster response time for transportation networks through pre-disaster investment decisions. The planning focuses on determining the links of the network to strengthen through investment under two types of uncertainties: the disaster characteristics, and the surviving network under each disaster. A bi-level stochastic optimization model is proposed for this problem, in which link investment decisions are made at the upper level to enhance the network survivability subject to a budget constraint such that the expected post-disaster response time is minimized at the lower level. A two-stage heuristic algorithm is proposed to obtain effective solutions efficiently. The numerical experiments indicate that the proposed heuristic algorithm converges to a fixed point representing a feasible solution, within an acceptable tolerance level, of the bi-level stochastic optimization model which is an effective solution under disasters of moderate severity. Parametric and sensitivity analyses reinforce the need for a holistic approach that integrates multiple relevant considerations to determine the link investment decisions.     

Family scheduling with batch availability in flow shops to minimize makespan

This paper addresses a batch scheduling problem in flow shop production systems, where job families are formed based on setup similarities. In order to improve setup efficiency, we consider batching decisions in our solution procedure. Due to its high practical relevance, the batch availability assumption is also adopted in this study. In the presence of sequence-dependent setup times, it is proved that a permutation flow shop is generally not optimal. Therefore, our objective is to determine solutions with inconsistent batches, which essentially lead to non-permutation schedules, to minimize makespan. After examining structural properties, we develop a tabu search algorithm with multiple neighbourhood functions. Computational results confirm the remarkable benefits of batching decisions. Our algorithm also outperforms some well-known and well-performing approaches.     

Multi-criteria human resource allocation for solving multistage combinatorial optimization problems using multiobjective hybrid genetic algorithm

Multi-criteria human resource allocation involves deciding how to divide human resource of limited availability among multiple demands in a way that optimizes current objectives. In this paper, we focus on multi-criteria human resource allocation for solving multistage combinatorial optimization problem. Hence we tackle this problem via a multistage decision-making model. A multistage decision-making model is similar to a complex problem solving, in which a suitable sequence of decisions is to be found. The task can be interpreted as a series of interactions between a decision maker and an outside world, at each stage of which some decisions are available and their immediate effect can be easily computed. Eventually, goals would be reached due to the found of optimized variables. In order to obtain a set of Pareto solutions efficiently, we propose a multiobjective hybrid genetic algorithm (mohGA) approach based on the multistage decision-making model for solving combinatorial optimization problems. According to the proposed method, we apply the mohGA to seek feasible solutions for all stages. The effectiveness of the proposed algorithm was validated by its application to an illustrative example dealing with multiobjective resource allocation problem.     

Network Selection Decisions for Multiple Calls Based on Consensus Level

Next generation multimode terminals have the capability to support different classes of calls simultaneously as well as the ability to connect to two or more radio access technologies (RATs), at the same time, in a heterogeneous wireless network. For a mobile terminal having multiple classes of simultaneous handoff calls (such as file download and video sessions), RAT selection decisions can be made independently for individual calls in the group or jointly for the entire group of calls. Both independent and group RAT selection decisions for multiple calls have advantages and disadvantages. Existing RAT selection algorithms have focused on RAT selection decisions for single calls. Therefore, this paper investigates independent call and group call RAT selection decisions for multiple calls in heterogeneous wireless networks, and proposes a scheme that makes RAT selection decisions for multiple calls based on a consensus level among the multiple calls to be admitted. When this consensus level is among multiple calls to be admitted into a particular RAT and is equal to or above a certain threshold value, a group decision is used. Otherwise, independent decisions are made. The performance of the proposed RAT selection scheme is evaluated in a three service three RAT heterogeneous network, supporting multihomed terminals. Simulation results are given to show the effectiveness of the proposed scheme.     

Genetic algorithm with iterated local search for solving a location-routing problem

This paper deals with a location routing problem with multiple capacitated depots and one uncapacitated vehicle per depot. We seek for new methods to make location and routing decisions simultaneously and efficiently. For that purpose, we describe a genetic algorithm (GA) combined with an iterative local search (ILS). The main idea behind our hybridization is to improve the solutions generated by the GA using a ILS to intensify the search space. Numerical experiments show that our hybrid algorithm improves, for all instances, the best known solutions previously obtained by the tabu search heuristic.     

Template-based configuration and execution of decision workflows in design of complex engineered systems

The research reported in the paper is from a decision-based design perspective wherein the principal role (but not only) role of a designer is to make decisions. Decision workflows are the processes by which the solutions pertaining to the design of complex systems are generated. Decision workflows are core to design processes, in which a set of decisions are connected (or interconnected) to generate shared and desired design outputs. Careful configuration of decision workflows is very important to ensure the generation of designs using available resources. Configuration of decision workflows is a process that requires a designer to use the basic elements to compose feasible workflows and then select an appropriate one for implementation in designing a product or a system. In this paper, we propose a template-based method for the design and execution of decision workflows associated with designing engineered systems. The value of the method is anchored in that it facilitates designers rapidly planning the processes, namely, the decision workflows, for designing products or systems. Moreover, due to the fact that these decision workflows are modeled in a computational manner, designers are able to execute decision workflows to explore the solution space and identify satisficing design solutions in early design stages. A gearbox with connected gears and shafts is a typical complex engineered system that can be partitioned into multiple levels of interacting subsystems. We illustrate the method and the decision workflows using a gear and shaft (within a gearbox) design example.     

A new multiple decisions fusion rule for targets detection in multiple sensors distributed detection systems with data fusion

Currently, multiple sensors distributed detection systems with data fusion are used extensively in both civilian and military applications. The optimality of most detection fusion rules implemented in these systems relies on the knowledge of probability distributions for all distributed sensors. The overall detection performance of the central processor is often worse than expected due to instabilities of the sensors probability density functions. This paper proposes a new multiple decisions fusion rule for targets detection in distributed multiple sensor systems with data fusion. Unlike the published studies, in which the overall decision is based on single binary decision from each individual sensor and requires the knowledge of the sensors probability distributions, the proposed fusion method derives the overall decision based on multiple decisions from each individual sensor assuming that the probability distributions are not known. Therefore, the proposed fusion rule is insensitive to instabilities of the sensors probability distributions. The proposed multiple decisions fusion rule is derived and its overall performance is evaluated. Comparisons with the performance of single sensor, optimum hard detection, optimum centralized detection, and a multiple thresholds decision fusion, are also provided. The results show that the proposed multiple decisions fusion rule has higher performance than the optimum hard detection and the multiple thresholds detection systems. Thus it reduces the loss in performance between the optimum centralized detection and the optimum hard detection systems. Extension of the proposed method to the case of target detection when some probability density functions are known and applications to binary communication systems are also addressed.     

生物多样性数据的Web可视化

生物多样性数据具有多源、异构、数据量大、信息冗杂不直观等特点,对其可视化可帮助科研工作者轻松直观深入的了解数据,发现知识,辅助其科学决策,具有重要的现实意义。可视化与具体的应用场景密切相关,本文针对三类具有不同类型和结构的生物多样性数据,利用HTML5技术开发了三种不同的Web可视化展示和分析方式,多视角、直观地展现了数据源及其内在规律,为领域科学家和保护区管理人员科学认识生物多样性数据提供了一种便利的手段。     

Integrated marketing communications in markets with uncertainty and competition

Firms frequently utilize multiple communications instruments as part of their marketing campaign. Interactions between these instruments suggest that firms should apply Integrated Marketing Communications (IMC) to benefit from the synergies. We review different IMC models and then present a stochastic IMC model for which explicit closed-loop solutions of the optimal advertising and market share are obtained. This enables us to understand the role of firm and market parameters such as synergy on the optimal advertising budget and allocation. For the proposed and existing IMC models, we show that the budget and allocation decisions can be made independently, greatly simplifying the implementation of IMC. We also show that there is an optimal long-run market share that the firm should try to maintain through appropriate use of IMC. Finally, the model and results are generalized to multiple (>2) instruments and multiple competitors.     

An augmented multi-objective particle swarm optimizer for building cluster operation decisions

It is envisioned that other than the grid-building communication, the smart buildings could potentially treat connected neighborhood buildings as a local buffer thus forming a local area energy network through the smart grid. As the hardware technology is in place, what is needed is an intelligent algorithm that coordinates a cluster of buildings to obtain Pareto decisions on short time scale operations. Research has proposed a memetic algorithm (MA) based framework for building cluster operation decisions and it demonstrated the framework is capable of deriving the Pareto solutions on an 8-h operation horizon and reducing overall energy costs. While successful, the memetic algorithm is computational expensive which limits its application to building operation decisions on an hourly time scale. To address this challenge, we propose a particle swarm framework, termed augmented multi-objective particle swarm optimization (AMOPSO). The performance of the proposed AMOPSO in terms of solution quality and convergence speed is improved via the fusion of multiple search methods. Extensive experiments are conducted to compare the proposed AMOPSO with nine multi-objective PSO algorithms (MOPSOs) and multi-objective evolutionary algorithms (MOEAs) collected from the literature. Results demonstrate that AMOPSO outperforms the nine state-of-the-art MOPSOs and MOEAs in terms of epsilon, spread, and hypervolume indicator. A building cluster case is then studied to show that the AMOPSO based decision framework is able to make hourly based operation decisions which could significantly improve energy efficiency and achieve more energy cost savings for the smart buildings.     

The consistent comparison problem in N-version software

The authors have identified a difficulty in the implementation of N-version programming. The problem, called the consistent comparison problem, arises for applications in which decisions are based on the results of comparing finite-precision numbers. It is shown that when versions make comparisons involving the results of finite-precision calculations, it is impossible to guarantee the consistency of their results. It is therefore possible that correct versions may arrive at completely different outputs for an application that does not apparently have multiple correct solutions. If this problem is not dealt with explicitly, an N-version system may be unable to reach consensus even when none of its component versions falls     

A procedure for optimizing development decisions

This paper addresses the problem of organizing data and formulating questions to be answered for the purpose of making planning and development decisions. The problem is separated into five distinct parts. Each part is discussed in the context of the planning process and each leads logically to the optimization of development decisions. The five parts are goal definition, establishment of criteria, criteria weighting, alternative rating and alternative ranking. The feature of making development decisions which distinguishes them from other optimization problems is what has been called ‘fuzziness’. In any realistic problem formulation, the criteria are not precisely defined; they are fuzzy. The relative importance of each criterion is also fuzzy. Indeed, even when one attempts to rate a particular possible solution, he must deal with fuzzy information.The technique proposed in the paper accounts for this uncertainty in all aspects of the problem and yields probabilistic answers. Thus, when various alternative solutions are proposed for a development problem, the technique yields a probabilistic ranking of the alternatives. Sharper results are obtained if less uncertainty is present in certain parts of the data. However, even in the presence of great uncertainty, realistic problem solutions are obtained. Alternative solutions are rated independent of all others and only after the (fuzzy) ratings are complete are comparative rankings accomplished. Throughout the procedure the realistic uncertainties remain a prominent feature of the procedure.     

A genetic algorithm based heuristic for scheduling of virtual manufacturing cells (VMCs)

We present a genetic algorithm (GA) based heuristic approach for job scheduling in virtual manufacturing cells (VMCs). In a VMC, machines are dedicated to a part as in a regular cell, but machines are not physically relocated in a contiguous area. Cell configurations are therefore temporary, and assignments are made to optimize the scheduling objective under changing demand conditions. We consider the case where there are multiple jobs with different processing routes. There are multiple machine types with several identical machines in each type and are located in different locations in the shop floor. Scheduling objective is weighted makespan and total traveling distance minimization. The scheduling decisions are the (i) assignment of jobs to the machines, and (ii) the job start time at each machine. To evaluate the effectiveness of the GA heuristic we compare it with a mixed integer programming (MIP) solution. This is done on a wide range of benchmark problem. Computational results show that GA is promising in finding good solutions in very shorter times and can be substituted in the place of MIP model.     

Collaborative information acquisition for data-driven decisions

Data-driven predictive models are routinely used by government agencies and industry to improve the efficiency of their decision-making. In many cases, agencies acquire training data over time, incurring both direct and opportunity costs. Active learning can be used to acquire particularly informative training data that improve learning cost-effectively. However, when multiple models are used to inform decisions, prior work on active learning has significant limitations: either it improves the accuracy of predictive models without regard to how accuracy affects decision making or it addresses only decisions informed by a single predictive model. We propose that decisions informed by multiple models warrant a new kind of Collaborative Information Acquisition (CIA) policy that allows multiple learners to reason collaboratively about informative acquisitions. This paper focuses on tax audit decisions, which affect a vital revenue source for governments worldwide. Because audits are costly to conduct, active learning policies can help identify particularly informative audits to improve future decisions. However, existing active learning models are poorly suited to audit decisions, because audits are best informed by multiple predictive models. We develop a CIA policy to improve the decisions the models inform, and we demonstrate that CIA can substantially increase sales tax revenues. We also demonstrate that the CIA policy can improve decisions to target directly individuals in a donation campaign. Finally, we discuss and demonstrate the risks for decision making of the naive use of existing active learning policies.     

On the distribution approach to location problems

While making location decisions, the distribution of travel distances among the service recipients (clients) is an important issue. It is usually tackled with the minimax (center) or the minisum (median) solution concepts. Both concepts minimize only simple scalar characteristics of the distribution: the maximal distance and the average distance, respectively. In this paper, all the distances for the individual clients are considered as a set of multiple uniform criteria to be minimized. This results in a multiple criteria model taking into account the entire distribution of distances. Our analysis of the multiple criteria problem focuses on the symmetrically efficient solutions which comply with minimization of distances as well as with impartial consideration of the clients. Various solution concepts generating symmetrically efficient location patterns are analyzed. Finally, the reference distribution approach is developed as an interactive technique which enables us to identify a satisfactory symmetrically efficient location pattern by evolving a reference (target) distribution of distances.     

Towards a unified multi-source-based optimization framework for multi-label learning

In the era of Big Data, a practical yet challenging task is to make learning techniques more universally applicable in dealing with the complex learning problem, such as multi-source multi-label learning. While some of the early work have developed many effective solutions for multi-label classification and multi-source fusion separately, in this paper we learn the two problems together, and propose a novel method for the joint learning of multiple class labels and data sources, in which an optimization framework is constructed to formulate the learning problem, and the result of multi-label classification is induced by the weighted combination of the decisions from multiple sources. The proposed method is responsive in exploiting the label correlations and fusing multi-source data, especially in the fusion of long-tail data. Experiments on various multi-source multi-label data sets reveal the advantages of the proposed method.     

Engaging multiple perspectives: A value-based decision-making model

In some decision contexts, such as a crisis or when confronted by a new or novel set of circumstances, people may be forced to make decisions with limited information or time available for analysis. In such contexts, the set of alternative solutions developed may be greatly affected by the personal values and the perspective those values precipitate. Being able to view the decision environment from multiple perspectives enhances the decision-maker's ability to make better-informed choices. This article introduces the value-based decision-making model that suggests that multiple perspectives may be achieved by considering a foundation of individual values. Empirical testing indicates that this model provides a viable framework that decision-makers and researchers can use to better understand and facilitate multiple perspectives in decision-making.     

An approximate dynamic programming approach to solving a dynamic, stochastic multiple knapsack problem

We model a multiperiod, single resource capacity reservation problem as a dynamic, stochastic, multiple knapsack problem with stochastic dynamic programming. As the state space grows exponentially in the number of knapsacks and the decision set grows exponentially in the number of order arrivals per period, the recursion is computationally intractable for large-scale problems, including those with long horizons. Our goal is to ensure optimal, or near optimal, decisions at time zero when maximizing the net present value of returns from accepted orders, but solving problems with short horizons introduces end-of-study effects which may prohibit finding good solutions at time zero. Thus, we propose an approximation approach which utilizes simulation and deterministic dynamic programming in order to allow for the solution of longer horizon problems and ensure good time zero decisions. Our computational results illustrate the effectiveness of the approximation scheme.