A goal programming model for joint decision making of inventory lot-size,supplier selection and carrier selection

In this paper, we address a problem in which a storage space constrained buyer procures a single product in multiple periods from multiple suppliers. The production capacity constrained suppliers offer all-unit quantity discounts. The late deliveries and rejections are also incorporated in sourcing. In addition, we consider transportation cost explicitly in decision making which may vary because of freight quantity and distance of shipment between the buyer and a supplier. We propose a multi-objective integer linear programming model for joint decision making of inventory lot-sizing, supplier selection and carrier selection problem. In the multi-objective formulation, net rejected items, net costs and net late delivered items are considered as three objectives that have to be minimized simultaneously over the decision horizon. The intent of the model is to determine the timings, lot-size to be procured, and supplier and carrier to be chosen in each replenishment period. We solve the multi-objective optimization problem using three variants of goal programming (GP) approaches: preemptive GP, non-preemptive GP and weighted max–min fuzzy GP. The solution of these models is compared at different service-level requirements using value path approach.     

A multi-objective particle swarm optimization for project selection problem

Selecting the most appropriate projects out of a given set of investment proposals is recognized as a critical issue for which the decision maker takes several aspects into consideration. Since many of these aspects may be conflicting, the problem is rendered as a multi-objective one. Consequently, we consider a multi-objective project selection problem in this study where total benefits are to be maximized while total risk and total coat must be minimized, simultaneously. Since solving an NP-hard problem becomes demanding as the number of projects grows, a multi-objective particle swarm with new selection regimes for global best and personal best for swarm members is designed to find the locally Pareto-optimal frontier and is compared with a salient multi-objective genetic algorithm, i.e. SPEAII, based on some comparison metrics with random instances.     

Fuzzy R&D portfolio selection of interdependent projects

Global competition of markets has forced firms to invest in targeted R&D projects so that resources can be focused on successful outcomes. A number of options are encountered to select the most appropriate projects in an R&D project portfolio selection problem. The selection is complicated by many factors, such as uncertainty, interdependences between projects, risk and long lead time, that are difficult to measure. Our main concern is how to deal with the uncertainty and interdependences in project portfolio selection when evaluating or estimating future cash flows. This paper presents a fuzzy multi-objective programming approach to facilitate decision making in the selection of R&D projects. Here, we present a fuzzy tri-objective R&D portfolio selection problem which maximizes the outcome and minimizes the cost and risk involved in the problem under the constraints on resources, budget, interdependences, outcome, projects occurring only once, and discuss how our methodology can be used to make decision support tools for optimal R&D project selection in a corporate environment. A case study is provided to illustrate the proposed method where the solution is done by genetic algorithm (GA) as well as by multiple objective genetic algorithm (MOGA).     

Interactive balance space approach for solving multi-level multi-objective programming problems

This paper studies a multi-level multi-objective decision-making (ML-MODM) problems with linear or non-linear constraints. The objective functions at each level are non-linear functions, which are to be maximized or minimized.This paper presents a three-level multi-objective decision-making (TL-MODM) model and an interactive algorithm for solving such a model. The algorithm simplifies three-level multi-objective decision-making problems by transforming them into separate multi-objective decision making problems at each level, thereby avoiding the difficulty associated with non-convex mathematical programming. Our algorithm is an extension of the work of Shi and Xia [X. Shi, H. Xia, Interactive bi-level multi-objective decision making, Journal of the Operational Research Society 48 (1997) 943-949], which dealt with interactive bi-level multi-objective decision-making problems, with some modifications in assigning satisfactoriness to each objective function in all the levels of the TL-MODM problem. Also, we solve each separate multi-objective decision making problem of the TL-MODM problem by the balance space approach.A new formula is introduced to interconnect the satisfactoriness and the proportions of deviation needed to reflect the relative importance of each objective function. Thus, we have the proportions of deviation including satisfactoriness.In addition, we present new definitions for the satisfactoriness and the preferred solution in view of singular-level multi-objective decision making problems that corresponds to the η-optimal solution of the balance space approach. Also, new definitions for the feasible solution and the preferred solution (η-optimal point) of the TL-MODM problem are presented. An illustrative numerical example is given to demonstrate the algorithm.     

Designing a hybrid system dynamic model for analyzing the impact of strategic alignment on project portfolio selection

One of the key challenges in project organizations is the alignment of portfolio management with major corporate strategies. Usually, project-based organizations use shared resources to control and plan the project portfolio. Therefore, the exploitation of shared resources and project planning decisions made in this regard can change the progress of projects and affect the success rate of the projects. In this article, the integration of system dynamics with multi-objective decision making is applied to address project portfolio selection. The project portfolio has been modeled using four basic dimensions including technology, complexity, innovation and time sensitivity. The aim is to plan and control the progress of project portfolio while maximizing the strategic adaptation subject to the changes of the human resources. For this purpose, a two-stage MO-PSO with TOPSIS is proposed for portfolio selection problem that can solve real-world instances of the problem in a reasonable time. The result of the sensitivity analysis indicated that the proposed decision support system (DSS) provides insights into the impact of strategic alignment on project portfolio selection. According to the simulation results, the integrated methodology of this research can assist in choosing the suitable projects to achieve a project's strategic goals following the organization strategy.     

Web-based CBR system applied to early cost budgeting for pavement maintenance project

With the growth of transportation networks in developing countries, the cost-efficacy control of maintenance operations has become critical to the infrastructure asset management after highway construction. To effectively manage numerous projects annually with limited resources, it is necessary to accurately estimate costs and leave a trail of project information during the process of making maintenance project selection decisions. This paper outlines the development of a case-based reasoning (CBR) expert prototype system that compares historical data at the work item-level across the case library. This study attempts to determine preliminary project cost with readily available information rapidly based on previous experience of pavement maintenance related construction to assist decision makers in project screening and budget allocation. Various CBR modeling approaches were presented and assessed in terms of their mean absolute prediction error rates. Design and implementation of a web-based CBR system is demonstrated in this study to efficiently handle the attribute and case similarity computation and the results are displayed using browsers. Furthermore, weighting attributes employed in the CBR system were compared via eigenvector and equal weighting methods for estimating aggregate cost and component costs. Historical generic pavement maintenance projects were gathered from the Taiwan transportation agencies and used for model training and testing. Furthermore, k-fold cross-validation was employed to verify the CBR estimating system. The analytical results demonstrate the ability of the system to estimate the item-level cost of pavement maintenance projects with the satisfactory precision during the conceptual project phase. The developed prototype web-based CBR system can efficiently provide timely and accurate information in an efficient way and provide an alternative estimation tool that can be combined with other evaluation criteria, such as indexes of pavement serviceability and structure strength, to improve the decision making in relation to budget allocation.     

Necessity constraint in two plant optimal production problem with imprecise parameters

The purpose of this paper is to present and solve a real-life problem of two plants producing the same item under fuzzy-stochastic environment. Here, an item alongwith random defective units is produced at two different plants situated in different locations under a single management. The rates of demand, production and defectiveness at these places are different. Demands of the item are primarily met locally from the respective plants but if a stock-out situation occurs in a plant, immediately some stock, from the other plant if available, is rushed to the stock-out plant. The demands are known but production rates are unknown, functions of time are taken as control variables. The available budget for the management house is imprecise. The holding, shortage and transportation costs are assumed to be imprecise and represented by fuzzy numbers which are transformed to corresponding interval numbers. Following interval mathematics and nearest interval approximation, the objective function is changed to respective multi-objective functions and thus the single-objective fuzzy problem is reduced to a crisp multi-objective decision making (MODM) problem. The MODM problem is then again transformed to a single crisp objective function with the help of weighted sum method. Using fuzzy relations, the imprecise budget constraint expressed in the form of necessity constraint is transformed into an equivalent crisp one. Then, total cost consisting of production, holding, shortage and transportation (from one plant to another) costs is expressed as an optimal control problem and solved using weighted sum method, the Kuhn-Tucker conditions, Pontryagin’s Optimal Control principle and generalized reduced gradient (GRG) technique. The model has been illustrated by numerical data. The optimum results are presented in both tabular and graphical forms.     

Simulation-based multi-objective model for supply chains with disruptions in transportation

Unpredictable disruptions (e.g., accidents, traffic conditions, among others) in supply chains (SCs) motivate the development of decision tools that allow designing resilient routing strategies. The transportation problem, for which a model is proposed in this paper, consists of minimizing the stochastic transportation time and the deterministic freight rate. This paper extends a stochastic multi-objective minimum cost flow (SMMCF) model by proposing a novel simulation-based multi-objective optimization (SimMOpt) solution procedure. A real case study, consisting of the road transportation of perishable agricultural products from Mexico to the United States, is presented and solved using the proposed SMMCF-Continuous/SimMOpt solution framework. In this case study, time variability is caused by the inspection of products at the U.S.-Mexico border ports of entry. The results demonstrate that this framework is effective and overcomes the limitations of the multi-objective stochastic minimum cost flow problem (which becomes intractable for large-scale instances).     

Is your back-up IT infrastructure in a safe location?

Building redundant capacity into an organization’s information technology (IT) infrastructure is a standard part of business continuity planning (BCP). Traditionally, cost concerns have dominated the decision of where to locate the redundant facilities. However; recently managers are becoming more aware of the fact that the very issues that make the main IT facilities vulnerable to disruption (i.e. man-made or natural disasters) are likely to impact the redundant (back-up) facilities as well. This complicates the process of selecting redundant facility location(s). The problem is essentially a multi-criteria decision problem, and can be addressed using the location analysis techniques that have been used in other domains in the past. Meanwhile, what make this context somewhat unique are the decision criteria and the rather subjective nature of the decision process. This paper provides a simple decision model for the problem, and illustrates the model with a case where relevant decision criteria are identified and the solution is obtained using a mix of objective and subjective decision techniques. We believe the paper is valuable because it presents an actionable methodology for practitioners involved in BCP.     

A knowledge-oriented meta-framework for integrating sensor network infrastructures

In this paper, we describe a meta-framework that helps guide development of sensor network (SN) cyberinfrastructure in a way that enables emerging sensor infrastructures, including advances in sensor hardware, communication, monitoring applications, and knowledge representation, to interoperate. This framework is guided by the DAST principle. That is, the overall goal of any SN infrastructure is essentially the same: to acquire the right Data from the right Area using the right Sensors at the right Time. In conformity with this principle, our meta-framework integrates SN infrastructures along axes related to the answers to five questions: Why has processing been requested? What are the goals of the processing? Where is it carried out? How is it carried out? And, when will the results be provided? The infrastructure components are integrated by using various data standards and technologies currently available from various SN research groups, and mapping them to an overarching knowledge-based meta-framework. In concrete terms, we show in this paper how four distinct sensor technology projects under development in our research lab are used to fit these five axes of SN infrastructure and how they can be indirectly integrated through the use of software agent-based tools, which embody the meta-framework: an ontology-based decision support system that applies models of SN infrastructure to its evaluation techniques; SN configuration tools that enable network configurations to be exported into common geospatial standards; a transformation engine that converts these SN configurations, along with collected data, into a representation that meshes with our infrastructure models so that they may be used within our decision support environment; and a Virtual SN to handle many of the management and control aspects of SNs.     

A volume flexible economic production lot-sizing problem with imperfect quality and random machine failure in fuzzy-stochastic environment

An “economic production lot size” (EPLS) model for an item with imperfect quality is developed by considering random machine failure. Breakdown of the manufacturing machines is taken into account by considering its failure rate to be random (continuous). The production rate is treated as a decision variable. It is assumed that some defective units are produced during the production process. Machine breakdown resulting in idle time of the respective machine which leads to additional cost for loss of manpower is taken into account. It is assumed that the production of the imperfect quality units is a random variable and all these units are treated as scrap items that are completely wasted. The models have been formulated as profit maximization problems in stochastic and fuzzy-stochastic environments by considering some inventory parameters as imprecise in nature. In a fuzzy-stochastic environment, using interval arithmetic technique, the interval objective function has been transformed into an equivalent deterministic multi-objective problem. Finally, multi-objective problem is solved by Global Criteria Method (GCM). Stochastic and fuzzy-stochastic problems and their significant features are illustrated by numerical examples. Using the result of the stochastic model, sensitivity of the nearer optimal solution due to changes of some key parameters are analysed.     

A fuzzy group data envelopment analysis model for high-technology project selection: A case study at NASA

The assessment and selection of high-technology projects is a difficult decision making process at the National Aeronautic and Space Administration (NASA). This difficulty is due to the multiple and often conflicting objectives in addition to the inherent technical complexities and valuation uncertainties involved in the assessment process. As such, a systematic and transparent decision making process is needed to guide the assessment process, shape the decision outcomes and enable confident choices to be made. Various methods have been proposed to assess and select high-technology projects. However, applying these methods has become increasingly difficult in the space industry because there are many emerging risks implying that decisions are subject to significant uncertainty. The source of uncertainty can be vagueness or ambiguity. While vague data are uncertain because they lack detail or precision, ambiguous data are uncertain because they are subject to multiple interpretations. We propose a data envelopment analysis (DEA) model with ambiguity and vagueness. The vagueness of the objective functions is modeled by means of multi-objective fuzzy linear programming. The ambiguity of the input and output data is modeled with fuzzy sets and a new α-cut based method. The proposed models are linear, independent of α-cut variables, and capable of maximizing the satisfaction level of the fuzzy objectives and efficiency scores, simultaneously. Moreover, these models are capable of generating a common set of multipliers for all projects in a single run. A case study involving high-technology project selection at NASA is used to demonstrate the applicability of the proposed models and the efficacy of the procedures and algorithms.     

A hybrid multi-objective PSO–EDA algorithm for reservoir flood control operation

Reservoir flood control operation (RFCO) is a complex multi-objective optimization problem (MOP) with interdependent decision variables. Traditionally, RFCO is modeled as a single optimization problem by using a certain scalar method. Few works have been done for solving multi-objective RFCO (MO-RFCO) problems. In this paper, a hybrid multi-objective optimization approach named MO-PSO–EDA which combines the particle swarm optimization (PSO) algorithm and the estimation of distribution algorithm (EDA) is developed for solving the MO-RFCO problem. MO-PSO–EDA divides the particle population into several sub-populations and builds probability models for each of them. Based on the probability model, each sub-population reproduces new offspring by using PSO based and EDA methods. In the PSO based method, a novel global best position selection method is designed. With the help of the EDA based reproduction, the algorithm can lean linkage between decision variables and hence have a good capability of solving complex multi-objective optimization problems, such as the MO-RFCO problem. Experimental studies on six benchmark problems and two typical multi-objective flood control operation problems of Ankang reservoir have indicated that the proposed MO-PSO–EDA performs as well as or superior to the other three competitive multi-objective optimization algorithms. MO-PSO–EDA is suitable for solving MO-RFCO problems.     

A milp bi-objective model for static portfolio selection of R&;D projects with synergies

This paper presents a multi-objective MILP model for portfolio selection of research and development (R&D) projects with synergies. The proposed model incorporates information about the funds assigned to different activities as well as about synergies between projects at the activity and project level. The latter aspects are predominant in the context of portfolio selection of R&D projects in public organizations. Previous works on portfolio selection of R&D projects considered interdependencies mainly at the project level. In a few works considering activity level information the models and solution techniques were restricted to problems with a few projects. We study a generalization of our previous model and show that incorporating interdependencies and activity funding information is useful for obtaining portfolios with better quality. Numerical results are presented to demonstrate the efficiency of the proposed approach for large models.     

An interactive possibilistic programming approach for a multi-objective hub location problem: Economic and environmental design

This paper presents a new multi-objective mathematical model for a multi-modal hub location problem under a possibilistic-stochastic uncertainty. The presented model aims to minimize the total transportation and traffic noise pollution costs. Furthermore, it aims to minimize the maximum transportation time between origin-destination nodes to ensure a high probability of meeting the service guarantee. In order to cope with the uncertainties and the multi-objective model, we propose a two-phase approach, including fuzzy interactive multi-objective programming approach and an efficient method based on the Me measure. Due to the NP-hardness of the presented model, two meta-heuristic algorithms, namely hybrid differential evolution and hybrid imperialist competitive algorithm, are developed. Furthermore, a number of sensitivity analyses are provided to demonstrate the effectiveness of the presented model. Finally, the foregoing meta-heuristics are compared together through different comparison metrics.     

A multi-objective decision-making model with fuzzy rough coefficients and its application to the inventory problem

In this paper, we concentrate on developing a fuzzy rough multi-objective decision-making model according to uncertainty theory. We present some equivalent models and a traditional algorithm based on an interactive fuzzy satisfying method, which is similar to the interactive fuzzy rough satisfying method, in order to obtain a satisfying solution for the decision maker. In addition, the technique of fuzzy rough simulation is applied to deal with general fuzzy rough objective functions and fuzzy rough constraints which are usually difficult to convert into their equivalents. Furthermore, combined with the techniques of fuzzy rough simulation, a genetic algorithm using the compromise approach is designed for solving a fuzzy rough multi-objective programming problem. Finally, a model is applied to an inventory problem to illustrate the usefulness of the proposed model and algorithm, and then a sensitivity analysis is made.     

基于多时间窗的油料保障模型

针对军用油料(POL)调拨运输优化问题,通过引入保障时间窗,考虑了油料保障过程中复杂的时间窗约束和运力约束,提出了基于多时间窗的油料调拨运输的约束满足问题(CSP)模型及其求解算法。首先,对油料保障点、油料需求点、保障时间窗、油料保障需求及油料保障任务等要素进行了形式化描述;在此基础上,建立了油料保障CSP模型,并采用理想点法,将模型中的多目标转化为单目标规划问题;设计了基于粒子群优化(PSO)算法的模型求解方法和步骤,并通过算例介绍了模型的具体运用。算例中,将利用所提模型求解得到的优化方案与最大化油料保障量为单一目标的模型优化方案进行比较,两种方案下的运力安排已达最大,但对各油料需求保障时间的安排,所提模型求解方案中每个油料需求的开始保障时间都不晚于单目标模型求解方案中的保障时间。通过对不同优化方案的比较,表明所提模型和算法能够有效解决多目标油料保障优化问题。     

连锁门店选址与配送中心选择联合决策的f-MIGP模型

建立了连锁门店选址和配送中心选择联合决策问题的模糊多目标混合整数规划模型．针对该模型的特殊结构。提出一种适用的求解策略：首先确定每个模糊目标的隶属度函数;然后将模糊多目标混合整数规划模型转化为等价的清晰多目标混合整数规划模型,通过最大最小算子求出目标值;最后借助于两阶段算法,求出问题的最优解．通过应用算例进一步说明了该模型的有效性和可行性．     

Vendor selection in outsourcing

In any large organization, millions of dollars are spent on outsourcing. Most large organizations are outsourcing those activities that are either not cost efficient if done in-house or not core to their businesses. One of the most critical steps in outsourcing is vendor selection, which is a strategic decision. We model the vendor selection problem as a multi-objective optimization problem, where one or more buyers order multiple products from different vendors in a multiple sourcing network. Price, lead-time and rejects (quality) are explicitly considered as three conflicting criteria that have to be minimized simultaneously. A pricing model under quantity discounts is used to represent the purchasing cost. We present and compare several multi-objective optimization methods for solving the vendor selection problem. The methods include weighted objective, goal programming and compromise programming. The multicriteria models and the methods are illustrated using a realistic example. Value path approach is used to compare the results of different models.     

虚拟企业伙伴选择的优化算法

虚拟企业伙伴选择及优化是组建虚拟企业的一个关键问题。根据虚拟企业任务之间的关系，该文将虚拟企业伙伴选择问题分解为串行问题、并行问题和混合问题3类，并建立了问题的多目标决策模型，通过候选企业筛选和确定优化组合方案，优化了模型求解过程，并验证了该算法的有效性。