A two-stage fuzzy-AHP model for risk assessment of implementing green initiatives in the fashion supply chain

Green or environmental concerns are drawing more and more attention both in academia and industry. Careful deployment of green initiatives or policies could not only fulfil the requirements of environmental legislation but also lead to a competitive advantage for firms. Nevertheless, making optimal decisions in this regard is not easy. This is principally due to two reasons: (1) the qualitative nature of, and (2) the uncertainty associated with, the parameters involved in the decision-making process. Analytic hierarchy process could be a useful tool to tackle the first challenge because of its ability to handle both qualitative and quantitative variables (decision criteria). Unfortunately, this approach is inadequate at addressing the uncertainties common in real-life applications. This challenge is particularly noticeable in the fashion industry since demand is very volatile, and there are many uncertain variables associated with the whole supply chain. As a result, this paper blends fuzzy logic, which is a popular method of incorporating uncertain parameters into the decision-making process, with analytic hierarchy process to form a selection (decision-making) model for different green initiatives in the fashion industry. The rationale behind the model is to analyse the associated risk of different alternatives, subject to different factors, be they deterministic or not. A numerical example is included in this paper to demonstrate how the proposed model works.     

Design of a Global Decision Support System for a manufacturing SME: Towards participating in Collaborative Manufacturing

This paper discusses the conceptual design of a Global Decision Support System for a manufacturing Small or Medium Enterprise (SM/E), which actively participates in Collaborative Manufacturing. In order to implement the proposed concept, a Web Services based system architecture is proposed to offer maximum interoperability between all the distributed participants of a Collaborative Manufacturing Network (CMN) and their management information systems. Furthermore, this conceptual design utilises a Collaborative decision-support model that effectively interacts with the decision-makers and the management information systems/tools exist in the network, and provides appropriate support to all necessary decision-making steps towards the attainment of the network's strategic goals, while making full benefits of the network resources.     

供应链可靠度管理中的模糊QFD应用

在供应链管理（SCM）中,人们更关注于寻找一种系统的方法,能够在计划阶段对可靠度进行评估。提出了一种模糊QFD模型,使其能够在供应链管理的过程中,传达用户需求与设计参数间的模糊关系;同时提出一套模糊的决策程序：模糊多准则决策．用来寻找一组CRT设计中可靠度需求性能的最优解。主要目的是找出合适的可靠性测试等级,以便使每一条供应链中模糊需求与产品生产参数之间的关系更加合理,以满足用户的需求。     

Omnichannel Management in B2B. Complexity-based model. Empirical evidence from a panel of experts based on Fuzzy Cognitive Maps

In recent years, academics and professionals have proposed omnichannel management as the best approach to offering multiple channels to end customers. This approach has been reinforced by the recent crisis caused by Covid-19 and the consequent demand for digital channels. In the current literature there is an evident gap in the study of omnichannel management for manufacturing or wholesale companies and their relationships with other companies, which typically use B2B models. This article includes a model that permits the identification of causal characteristics in omnichannel management based on fuzzy cognitive maps (FCM), the simulation of possible scenarios and the impact that changes in the environment or in the organization's internal activities may have on omnichannel management.From the results of a Delphi process based on an international Panel of Experts and using complexity theory, a Fuzzy Cognitive Map (FCM) was built that can serve as a reference for B2B omnichannel management. The main value of the research is provided by the practical model that allows simulating what-if scenarios, that is, with the modification of the input conditions with respect to a base scenario and thus favors directing the omnichannel strategy to be followed in a B2B field.     

The fuzzy front end of Japanese new product development projects: impact on success and differences between incremental and radical projects

This study of Japanese New Product Development (NPD) projects explores the fuzzy front end of innovation. The goal of the paper is twofold: First, we analyse the impact of the fuzzy front end on the success of all the NPD projects. We develop a conceptual model based on the information-processing approach. A structural equation model was fitted to data from 497 NPD projects in Japanese manufacturing firms to test the proposed model. The empirical analysis suggests that an early reduction of market and technical uncertainty as well as an initial planning before development have a positive impact on NPD project success. The model accounts for 17% of the variance of the efficiency and 24% of the variance of the effectiveness-dependent variable. Thus, the front end phase is an important driver of NPD project success. Second, we compare the fuzzy front end of incremental to radical NPD projects. Although these projects differ in many aspects of newness, we found only a few differences with regard to the fuzzy front end. For instance, it was more difficult to estimate the market size and price sensitivity of the customers during the fuzzy front end of the radical NPD projects compared with the incremental projects. Implications of the empirical results and limitations of the study are discussed.     

Analysis of the newsboy problem with fuzzy demands and incremental discounts

This paper proposes an analysis method for the single-period (newsboy) inventory problem with fuzzy demands and incremental quantity discounts. In fuzzy environments, the availability of the quantity discount makes the analysis of the associated model more complex. The proposed analysis method is based on ranking fuzzy number and optimization theory. By applying the Yager ranking method, the fuzzy total cost functions with different unit purchasing costs are transformed into convex piecewise nonlinear functions. To effectively and efficiently find the optimal inventory policy, the proofs of two properties regarding the relative position between the price break and minimums of these nonlinear functions are proposed. The closed-form solutions to the optimal order quantities are also derived. Four cases of a numerical example are solved to demonstrate the validity of the proposed analysis method. It is clear that the proposed methodology is applicable to further cases with different types of quantity discounts and other more complicated cases. More importantly, managerial implications are also provided for decision-makers’ references.     

Metaheuristic-based simulation optimization approach to network revenue management with an improved self-adjusting bid price function

Making accurate accept/reject decisions on dynamically arriving customer requests for different combinations of resources is a challenging task under uncertainty of competitors' pricing strategies. Because customer demand may be affected by a competitor's pricing action, changes in customer interarrival times should also be considered in capacity control procedures. In this article, a simulation model is developed for a bid price–based capacity control problem of an airline network revenue management system by considering the uncertain nature of booking cancellations and competitors' pricing strategy. An improved bid price function is proposed by considering competitors' different pricing scenarios that occur with different probabilities and their effects on the customers' demands. The classical deterministic linear program (DLP) is reformulated to determine the initial base bid prices that are utilized as control parameters in the proposed self-adjusting bid price function. Furthermore, a simulation optimization approach is applied in order to determine the appropriate values of the coefficients in the bid price function. Different evolutionary computation techniques such as differential evolution (DE), particle swarm optimization (PSO), and seeker optimization algorithm (SOA), are utilized to determine these coefficients along with comparisons. The computational experiments show that promising results can be obtained by making use of the proposed metaheuristic-based simulation optimization approach.     

Managing a Portfolio of Interdependent New Product Candidates in the Pharmaceutical Industry

Highly regulated industries such as pharmaceuticals and agrochemicals face the challenge of maintaining a 0continuous stream of new products. This is difficult because of low probabilities of technical success, high development costs, uncertain market impact, a scarcity of good new product ideas, and limited human and capital resources available to develop them. The problem of evaluating and selecting which new products to develop and then of sequencing or of scheduling them is complicated further by the presence of dependencies between products both in the market place and in the development process itself. This study proposes a portfolio management approach that selects a sequence of projects, which maximizes the expected economic returns at an acceptable level of risk for a given level of resources in a new product development pipeline. A probabilistic network model of distinct activities is used to capture all the activities and resources required in the “process” of developing a new drug. A prioritization scheme suggesting sequences for developing new independent drug candidates with unlimited resources is generated with a conventional bubble chart approach. These sequences initiate a genetic algorithm (GA)‐based search for the optimal sequence in the presence of product dependencies and limited resources. By statistically evaluating the sequences generated during the GA search using a discrete event simulation model, it is possible to construct an economic reward‐risk frontier that illustrates the trade‐offs between expected rewards and risks. The model ideally is suited to answer various “what if” questions relative to changes in the resource level on pipeline performance. The methodology is illustrated with an industrially motivated case study, involving nine interdependent new product candidates targeting three diseases. The dramatic results yield a candidate sequence with an expected return 28 percent higher than the sequence suggested by the bubble chart approach at almost the same level of risk. The synergism among the candidate dependencies, pipeline resources, and economic and technical uncertainties demonstrates the necessity of a computationally intensive approach if the best development strategy is to be realized.     

Potential of and constraints to the rice Green Revolution in Mozambique: A case study of the Chokwe irrigation scheme

This paper investigates the potential of and constraints to a lowland rice Green Revolution in Mozambique, using data from an agro-climatically favorable area, the Chokwe irrigation scheme. Our data show that, although the potential is high, the insufficient supply of irrigation water, excessively expensive chemical fertilizer, and moderately costly labor hinder the scheme from achieving that potential. First of all, an improvement in irrigation is crucial as it increases yield not only directly but also indirectly through its complementary relationship to chemical fertilizer. We also find that an improvement in credit access is important as it can increase the application of chemical fertilizer and labor by making the acquisition of these expensive inputs through markets possible. The unavailability of hired and exchange labor during peak seasons is another constraint to the intensive use of labor. The introduction of rice varieties with a shorter maturity duration would relax the timing of transplanting and thus spread out the peak seasons’ labor demand.     

Mapping knowledge management and organizational learning in support of organizational memory

The normative literature within the field of knowledge management has concentrated on techniques and methodologies for allowing knowledge to be codified and made available to individuals and groups within organizations. The literature on organizational learning, however, has tended to focus on aspects of knowledge that are pertinent at the macro-organizational level (i.e. the overall business). The authors attempt in this paper to address a relative void in the literature, aiming to demonstrate the inter-locking factors within an enterprise information system that relate knowledge management and organizational learning, via a model that highlights key factors within such an inter-relationship. This is achieved by extrapolating data from a manufacturing organization using a case study, with these data then modeled using a cognitive mapping technique (fuzzy cognitive mapping, FCM). The empirical enquiry explores an interpretivist view of knowledge, within an information systems evaluation (ISE) process, through the associated classification of structural, interpretive and evaluative knowledge. This is achieved by visualizing inter-relationships within the ISE decision-making approach in the case organization. A number of decision paths within the cognitive map are then identified such that a greater understanding of ISE can be sought. The authors therefore present a model that defines a relationship between knowledge management (KM) and organizational learning (OL), and highlights factors that can lead a firm to develop itself towards a learning organization.     

An integrated distribution routing model in multi-supply center system

A distribution routing problem with time constraint is one of the important problems in distribution and supply center management. This research is concerned with an integrated distribution routing problem for multi-supply centers based on improved genetic algorithm and graphical user interface (GUI)-type programming. In this research, we proposed a method based on a three-step approach: in step 1 a sector clustering model is developed to transfer the multi-supply center problem to single supply center problems which are easier to be solved; in step 2 we developed a vehicle routing model with time constraints and in step 3 we developed a GA-TSP model which can improve the vehicle routing schedules. The objective of the problem is to minimize the logistic cost for a set of customers without being tardy or exceeding the capacity or travel time of the vehicles. For computational purpose, we developed a GUI-type computer program according to the proposed methods and the sample outputs show that the proposed method is very effective on a set of standard test problems, and it could be potentially useful in solving the distribution routing problems.     

BENEFIT/COST ANALYSIS USING FUZZY CONCEPT

Two algorithms are proposed to perform benefit/cost analysts in a fuzzy environment. The first algorithm considers the fuzzy measure of benefit to cost ratio to select the optimal alternative group under the available funds. The second algorithm considers the fuzzy measure of incremental benefit to incremental cost ratio to choose the optimal investment alternative from several mutually exclusive investment alternatives. By using these algorithms, the ambiguities involved in the assessment data can be effectively represented and processed to assure a more convincing and effective decision-making.     

Is direct seeded rice a boon for economic performance? Empirical evidence from India

The direct-seeded rice (DSR) establishment method can improve productivity and labor efficiency while taking into account the soil and hydrologic conditions of the field, the availability of appropriate land equipment, and irrigation-drainage systems. Using plot- and household-level data, we analyze the impacts of DSR adoption in two rice-growing states of India. We account for observed and unobserved heterogeneity using endogenous switching regression. We analyze the yield and costs effects of DSR adoption. Our study shows a small but significant effect of DSR adoption on yield and costs. We find increase in rice yields (by 3.74%) for DSR adopters; an increase in rice yields (by 6.79%) if the DSR method were adopted on puddled transplant rice (PTR) parcels. We find a 7.51% reduction in total costs for DSR adopters; a decrease in total costs (by 3.71%) if the DSR method was adopted on PTR parcels. DSR farmers can significantly reduce their fertilizer and land preparation costs. Hence, the decision to adopt DSR may help households with limited resources to reduce their cost of production without compromising the yield.     

基于SVR-IPSO的高危行业企业安全投入优化模型及其改进——以煤炭生产企业为例

传统的安全投入模型对解决高危行业领域中模糊复杂的安全投入问题具有一定局限性,尤其当建立目标函数时,采用隐含线性关系假设的函数进行拟合会影响模型的推广能力。基于此,本文首先采用支持向量回归机（SVR）建立事故损失模型,与传统C-D函数拟合结果相比,该模型具有更好的预测能力;然后,以实际安全投入要求为约束,以安全总成本最小化为原则建立企业安全投入优化模型;最后,采用基于捕食搜索策略的粒子群算法对模型进行求解,同时,为保证全局收敛性,引入自适应控制策略对算法进行了改进。结果表明：该模型能够更加准确地描述安全投入与安全成本间的非线性作用关系,并通过粒子群寻优得到具备可行性的全局最优解,为高危行业企业安全投入结构优化提供新的决策思路。     

建设工程项目设计管理创新研究

论述了项目管理国际化背景下跨文化管理以及各参与方积极合作的重要性,通过对建设工程项目设计管理面临的挑战和设计决策过程的分析,提出通过确保项目设计的每个阶段及每一步的安全来保证整个项目的工艺安全。通过案例分析提出了设计管理创新的路径选择,一要突出科学的设计控制计划编制;二要通过设计质量保证体系保证设计质量;三是要突出设计和采购、施工及试车的协调管理。这些方法和途径有利于保证质量控制目标的全面实现。     

A framework for key account management and revenue management integration

Key Account Management (KAM) and Revenue Management (RevM) have been widely practiced in the service industries for more than three decades, but the effects of RevM on KAM remain largely unknown. This paper addresses this neglected area of study in the marketing field by presenting a framework for KAM and RevM integration that aligns the potentially conflicting management priorities of the two. The study uses an international hotel company as a research context to investigate, first, how a long-term relational approach to KAM may have been affected by RevM short-term revenue maximization goals, and, second, how KAM could be facilitated by RevM through an integrated approach to yield optimization from perishable products and from key accounts. The proposed framework is the first attempt of its kind to amalgamate KAM and RevM, involving critical analysis to assess comprehensively the revenue and the relationship value of a key account.     

基于活动分割的资源优化研究

在假定活动可分割的基础上,针对资源波动问题,提出了一个基于遗传算法的资源优化方法.首先在已知活动工期的基础上,确定项目工期,继而确定关键工序和非关键工序,并利用基于二元表示的遗传算法,对每个可分的非关键活动在其ES(最早开始时间)到LF(最迟结束时间)的范围内进行调整,以优化资源分配.最后引用一个示例,表明了提出的方法在资源优化方面的优越性.     

The strategic dilemmas of innovative enterprises: proposals for high‐technology sectors

High levels of research and development (R&D) expenditure, pressure for innovation and the creation of new knowledge are features that distinguish high‐technology (high‐tech) enterprises from other, less technologically advanced, firms. Confronted with multiple contemporary approaches to strategy and turbulence in their environment, these enterprises make strategic choices continuously and dynamically. This paper proposes a model and matrix for the classification of high‐tech enterprises’ development strategies (with regard to their specific features), which are then verified. Qualitative research was conducted in 61 medium and large high‐tech companies based in Poland that operate either in Poland or in the global marketplace. The results show that high‐tech firms have the fundamental goal of developing R&D activity as a resource (and its redundancy) rather than product/market goals. The studied firms strive above all for leadership in innovation, creating new technologies based on their own R&D resources, while also using outside sources and mostly applying the personalisation approach in knowledge management. However, they choose different paths for product and market development, depending on the opportunities presented by the environment, and the firm's ability to identify and take advantage of these.     

Decentralized control of cooperative and autonomous agents for solving the distributed resource allocation problem

This paper focuses on the decision-making mechanism of coordination protocol for a multi-agent system in collaborative, distributed environment requiring resource allocation solutions. The model of distributed collaboration network (DCN) for distributed resource allocation is developed. The use of monitored viability of each agent as a feedback for the agent's reflexivity and goal adjustment mechanisms during the coordination process is introduced. Question: Will (can) a multi-agent system, as a whole, achieve efficient resource allocations, when each agent makes independent decisions, adjusted by feedback of its own viability measure to maximize its own goal based on limited information about the entire system? To investigate this question, experiments were conducted by using the parallel simulator TIE/Protocol to model the DCN and multi-agent system behavior.     

油气管道第三方破坏风险管理效能评估方法研究

在油气管道第三方破坏风险管理效能评估中,存在十分复杂的影响因素和关联性。针对第三方破坏风险管理的特点,依据事故过程关联因素建立效能评估指标体系,运用多属性决策效能评估加权的TOPSIS方法,对从5个不同管道公司获取的指标调查结果进行整体性效能综合评估。其中,利用改进的灰靶理论的贡献度算法确定加权TOPSIS方法中的各指标权重。应用案例分析表明,这种方法的评估结果具有一定的准确性和合理性。