A possibilistic decision model for new product supply chain design

This paper models supply chain (SC) uncertainties by fuzzy sets and develops a possibilistic SC configuration model for new products with unreliable or unavailable SC statistical data. The supply chain is modeled as a network of stages. Each stage may have one or more options characterized by the cost and lead-time required to fulfill required functions and may hold safety stock to prevent an inventory shortage. The objective is to determine the option and inventory policy for each stage to minimize the total SC cost and maximize the possibility of fulfilling the target service level. A fuzzy SC model is developed to evaluate the performance of the entire SC and a genetic algorithm approach is applied to determine near-optimal solutions. The results obtained show that the proposed approach allows decision makers to perform trade-off analysis among customer service levels, product cost, and inventory investment depending on their risk attitude. It also provides an alternative tool to evaluate and improve SC configuration decisions in an uncertain SC environment.     

A decision support system for wine-making cooperatives

A decision support system has been designed for viticultural cooperatives immerged in a rapid evolution of their economical and legislative environment.To keep or moreover to increase the earnings of their members, cooperatives adopt a policy for ameliorating the production quality. To bring this policy to a successfull issue, they have to suggest their members to adopt a restructuring strategy for their farm estate.This paper presents a vineyard restructure, the simulation of future (DSS) and the decisional impacts of the decision support system.     

Joint decision on production and pricing for online dual channel supply chain system

Online dual channel supply chain system and its joint decision on production and pricing under information asymmetry are investigated. First, optimal production and pricing strategies are depicted according to the centralized system. Next, two kinds of contracts are designed for the decentralized system to coordinate the channel system, and their production and pricing decisions are depicted using a principle-agent method for the asymmetric information on the traditional channel. Finally, some interesting insights are found: the centralized system is not always being better than the decentralized system with a feasible contract if the traditional and professional retailer has lower selling cost. When uncertainty in the traditional channel information is higher, the manufacturer prefers a menu of contracts according to different channel settings. When uncertainty is lower, the manufacturer prefers a single contract. Furthermore, the higher the uncertainty in the traditional channel, the more the information welfare of the traditional retailer will gain. Performance with a menu of contracts cannot outperform that with a single contract integrating optimistic and pessimistic market setting well; their difference in performance is bigger when uncertainty in the traditional channel information is less.     

HADTS: A decision technology system to support Army housing management

The Department of Army must provide its personnel with acceptable housing at minimum cost within the vicinity of military installations. To achieve these housing objectives, the Army often must enter into agreements for the longterm construction of onpost housing or the leasing of existing offpost housing. A decision support system, called HANS, has been developed to project the necessary construction or leasing.HANS had some gaps in supporting the construction and leasing decisions. This paper describes the gaps and shows how a decision technology system, called the Housing Analysis Decision Technology System (HADTS), can help Army managers to overcome the support gaps. It also overviews HADTS's benefits, challenges, and limitations.     

A decision support system for urban waste management

This paper describes a decision support system, for urban waste management in a regional area, to be used for evaluating general policies for service organisation of the collection and for identifying areas suitable for locating waste treatment and disposal plants. The Decision Support System (DSS) is applied to a selected provinces in Sicily. The decision support system allows the generation and evaluation of suitable alternatives with respect to salient features of the problem, especially environmental consequences. The paper describes the identification and collection of relevant information, the structuring of a database, the design of combinatorial optimisation algorithms for solving the core location problem, the study of models for evaluating the different alternatives and their framing in a complete multicriteria decision model. Finally, the solution of the case study, by means of the DSS, is described.     

A logistics deployment decision support system at Pfizer

We constructed a decision-support-system (DSS) to help the distribution network of Pfizer plan the daily deployment of finished goods inventory across its network. Utilizing databases, programming languages, spreadsheets and data inputs from the firm’s ERP; this system guides daily shipments of critical inventory between plants, distribution centers and copackers. This DSS improved the firm’s ability to effectively utilize its storage capacity and meet customer shipping requirements across a widely dispersed network.     

Joint pricing-servicing decision and channel strategies in the supply chain

This paper investigates the equilibrium behavior of a two-echelon supply chain in four channel strategies: (i) vertical integration, (ii) vertical Nash (iii) manufacturer’s Stackelberg and (vi) retailer’s Stackelberg. We examine the price and service level decision for each of the above four channel strategies in two cases: (i) Simultaneous service-level decision: Here, the manufacturer and retailer simultaneously choose a service level. (ii) Sequentially service-level decision: Here, the manufacturer and retailer sequentially choose a service level. We model the demand as a deterministic linear function of retailer’s price and both manufacturer’s and retailer’s service levels. We discuss the optimal configuration from each individual’s perspective for each of the above channel strategies. We show that vertical integration dominates other strategies and leads to the highest service level but lowest retail price among various channel coordination policies considered here. We yield several conclusions about the provision of service level by each supply chain individual to coordinate the channel.     

A prolog-based decision support system for computer capacity planning

This paper describes a project to implement a decision support system for computer capacity planning. The system provides an intelligent interface to the various models needed for this type of planning by assisting the user in model formulation, data manipulation, model execution, interpretation and manipulation of results. The implementation strategy is based on the integration of relational model and database management with logic. A modified version of a Prolog interpreter is utilized as the vehicle for this integrated strategy.This research was supported by Sandia National Laboratories Grant No. 56-3737. Sandia is managed by AT&T Technologies for the U.S. Department of Energy.     

Natural discrete-event process forecasting: A decision support system

A decision support system for the analysis and forecasting of natural discrete-event processes is considered. The corresponding method is based on the sample path analysis using event-to-event operations. An application of the method in the computer-aided decision support system for the long-range weather forecasting is mentioned.     

Scheduling of brand production and shipping within a sugar supply chain

The Australian sugar industry exports about 90% of its five million tonne annual sugar production on the open market to customers in several countries, who request various brands of raw sugar. In planning at this interface of the sugar supply chain, an objective is to schedule the brands of sugar each mill produces throughout the annual harvest season, along with the ports that each ship loads at, to minimize total costs of sugar production and shipping. The complexity of such planning for the sugar industry has been overcome through the development and solution of a mixed-integer programming model to perform the task. The model is used as an annual planning tool to obtain a base schedule, as well as a rescheduling tool to revise the plan during the year as mill production rates and shipments change. Using the Australian sugar industry as a case study, this paper focuses on the mathematical model and solution using two known meta-heuristics based on local search. Using the 2002/2003 and 2003/2004 financial years, a comparison is made between the schedules produced in practice using manual methods and those using the model, which show a total potential cost savings of up to AU$4.0?M for the 2 years.     

A coordination system for seasonal demand problems in the supply chain

This article considers a single product coordination system using a periodic review policy, participants of the system including a supplier and one or more heterogeneous buyers over a discrete time planning horizon in a manufacturing supply chain. In the coordination system, the demand of buyer in each period is deterministic, the supplier replenishes all the buyers, and all participants agree to plan replenishment to minimize total system costs. To achieve the objective of the coordination system, we make use of small lot sizing and frequent delivery policies (JIT philosophy) to transport inventory between supplier and buyers. Moreover, demand variations of buyers are allowed in the coordination system to suit real-world situations, especially for hi-tech industries. Furthermore, according to the mechanisms of minimizing the total relevant costs, the proposed method can obtain the optimal number of deliveries, shipping points and shipping quantities in each order for all participants in the coordination system.     

A hybrid approach combining interior-point and branch-and-bound methods applied to the problem of sugar cane waste

This paper proposes a hybrid approach for solving the multi-objective model related to the minimisation of sugar cane waste collection costs and/or the maximisation of produced energy by this waste, with the aid of strategies for solving multi-objective problems, which transform the problem into a set of single-objective problems. This approach combines the predictor-corrector primal-dual interior-point and branch-and-bound methods in order to solve these single-objective problems. The model consists in identifying the sugar cane varieties with the lowest waste collection costs, while simultaneously it aims to obtain the greatest amount of produced energy by this waste. The hybrid methods are implemented in C++ programming language, and tests are performed to determine the efficient solutions in Pareto optimal sense of the multi-objective model and compare the performance of the hybrid method using the integrality test and without considering it. The mathematical results confirm that the proposed hybrid method for solving the aforementioned models presents good computational performance and reliable solutions.     

Quality investment and price decision in a risk-averse supply chain

In this paper, we investigate quality investment and price decision of a make-to-order (MTO) supply chain with uncertain demand in international trade. Due to volatility of orders from buyers, the supplier and the manufacturer in the supply chain are subject to financial risk. In contrast to the general assumption that players in a supply chain are risk neutral in quality investment and price decision, we consider the risk-averse behavior of the players in three different supply chain strategies: Vertical Integration (VI), Manufacturer’s Stackelberg (MS) and Supplier’s Stackelberg (SS). The study shows that both supply chain strategy and risk-averse behavior have significant impacts on quality investment and pricing. Compared to a risk-neutral supply chain, a risk-averse supply chain has lower, same and higher quality of products in VI, MS and SS, respectively. Also, we derive the conditions under which the supply chain strategy is implemented in a decentralized setting. A numerical study is used to illustrate some related issues.     

A strategic decision support system framework for energy-efficient technology investments

Energy systems optimization under uncertainty is increasing in its importance due to on-going global de-regulation of the energy sector and the setting of environmental and efficiency targets which generate new multi-agent risks requiring a model-based stakeholders dialogue and new systemic regulations. This paper develops an integrated framework for decision support systems (DSS) for the optimal planning and operation of a building infrastructure under appearing systemic de-regulations and risks. The DSS relies on a new two-stage, dynamic stochastic optimization model with moving random time horizons bounded by stopping time moments. This allows to model impacts of potential extreme events and structural changes emerging from a stakeholders dialogue, which may occur at any moment of the decision making process. The stopping time moments induce endogenous risk aversion in strategic decisions in a form of dynamic VaR-type systemic risk measures dependent on the system’s structure. The DSS implementation via an algebraic modeling language (AML) provides an environment that enforces the necessary stakeholders dialogue for robust planning and operation of a building infrastructure. Such a framework allows the representation and solution of building infrastructure systems optimization problems, to be implemented at the building level to confront rising systemic economic and environmental global changes.     

均值-CVaR零售商的供应链服务决策及协调契约

研究零售商具有风险偏好行为下,同时考虑价格、质量和服务水平的供应链联合决策问题。运用均值-CVaR准则来刻画零售商风险偏好行为,它包括风险厌恶、风险中性和风险追求,同时具有损失规避的特性。首先得到供应链集中系统、制造商提供服务（模型$\mbox{I}$）和零售商提供服务（模型$\mbox{II}$）下的最优决策和最优利润（期望效用）。其次,证明了成本共担契约在零售商风险厌恶时可以实现供应链协调.第三,对模型$\mbox{I}$和模型$\mbox{II}$协调后的最优利润（期望效用）进行比较,证明两种模型下制造商利润相同,而与模型$\mbox{I}$相比,模型$\mbox{II}$下零售商获得更多的期望效用。最后,数值例子证明了得到的研究结果。     

A GIS-based decision support system for planning urban transportation policies

A decision support system (DSS) integrated in a geographical information system (GIS) for the analysis and evaluation of different transport policies is presented. The objective of the tool is to assist transport administrators enhance the efficiency of the transportation supply while improving environmental and energy indicators. The DDS works on three levels. The first performs the transport network analysis, the second assesses the energy consumption and pollutant emissions and the third evaluates the several policies selected. Road traffic is simulated using a deterministic, multi-modal traffic assignment model with capacity constraints. The model allows the estimation of traffic flow patterns within each link of the road network starting from the knowledge of the network characteristics and traffic demand. Energy consumption and pollutant emission calculations are based on the methodology developed by the CORINAIR working group. The evaluation of each policy scenario is based on a number of traffic, environmental and energy indicators. A multi-criteria analysis, where decision is based upon judging over appropriate weighted criteria, is adopted. Models are integrated in a GIS environment, which serves as the repository of the data as well as the user interface of the tool. The use of the tool is demonstrated through characteristic case studies on the Greater Athens Area in Greece. Two policy measures, one concerning the extension of the region where half of the private cars are prohibited from entering to the Municipality of Athens and the other the reduction of parking places in the same region by 50% are evaluated.     

A preference disaggregation decision support system for financial classification problems

Classification is one of the most extensively studied problems in the fields of multivariate statistical analysis, operations research and artificial intelligence. Decisions involving a classification of the alternative solutions are of major interest in finance, since several financial decision problems are best studied by classifying a set of alternative solutions (firms, loan applications, investment projects, etc.) in predefined classes. This paper proposes an alternative approach to the classical statistical methodologies that have been extensively used for the study of financial classification problems. The proposed methodology combines the preference disaggregation approach (a multicriteria decision aid method) with decision support systems. More specifically, the FINancial CLASsification (FINCLAS) multicriteria decision support system is presented. The system incorporates a plethora of financial modeling tools, along with powerful preference disaggregation methods that lead to the development of additive utility models for the classification of the considered alternatives into predefined classes. An application in credit granting is used to illustrate the capabilities of the system.