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.     

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.     

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.     

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.     

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.     

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.     

Clinical decision support system for dental treatment

BackgroundIn this research, a decision making system, based on fuzzy inference mechanism as proposed by Mamdani, is presented. Literature suggests that there is a lack of consistency among dentists in choosing treatment plan(s). So, this research work aims to facilitate the dentist decide the treatment plan(s) of the broken tooth.MethodsAn expert system based on fuzzy logic has been designed to accept inaccurate and vague values of dental signs and symptoms associated with the broken tooth. We designed a knowledge base with 60 rules and used Mamdani inference algorithm to decide the possible one or more treatment(s) and suggest the same to the dentist.ResultsThe results proposed by the system are compared with the dentists’ suggestions. The Chi-square test of homogeneity is conducted on 100 randomly generated sample cases with the help of three professional dentists. It is found that the results produced by the system are consistent with the treatment plan(s) proposed by the dentists. Chi-square value of the test is 3.843565 which is less than the critical value which is 12.592. Hence, we are unable to reject the null hypothesis that assumes the two populations are homogeneous with respect to treatments.ConclusionsThe accuracy of the proposed decision support system for the treatment of broken tooth enhances the confidence level of the dentists while making decision regarding the treatment plan(s). Simple and interactive GUI makes it easy to use.     

HRSOS: a decision support system to facilitate the privatization of military housing

The armed services must provide their personnel with acceptable housing at minimum cost within the vicinity of military installations. To achieve these housing objectives, the Department of Defense (DoD) has entered into experimental joint ventures with private developers to construct attractive housing projects on military installation property, with some of the projects reserved for military personnel. To support the analysis of the joint ventures, DoD needed a methodology that would help officials evaluate the financial feasibility and cost implications of the housing projects. A decision support system, called the Housing Revitalization Support Office System (HRSOS), was developed to provide the necessary support. This paper describes the decision support system and its underlying methodology. It outlines the process used to determine financially feasible privatized military housing initiatives, reviews the decision support system created to support the process, and describes its application at the US Department of Defense. The paper also discusses the implications for military housing management, public finance, and operations research.     

A fuzzy newsvendor approach to supply chain coordination

In the absence of a clear command and control structure, a key challenge in supply chain management is the coordination and alignment of supply chain members who pursue divergent and often conflicting goals. The newsvendor model is typically used as a framework to quantify the cost of misalignment and to assess the impact of various coordination initiatives. The application of the newsvendor framework, however, requires the specification of some probability distribution for the sources of uncertainty, and in particular, for the market demand. The specification of an adequate demand distribution becomes difficult in the absence of statistical data. We therefore consider a fuzzy approach to the newsvendor problem. We use several fuzzy parameters in the model for the demand, the wholesale price, and the market sales price. We solve the fuzzy newsvendor problem to study three coordination policies: quantity discounts, profit sharing, and buyback. For each coordination policy, the optimal order quantity of the retailer is computed. The possible profits of the members in the supply chain are calculated with minimum sharing of private information. We further extend the fuzzy newsvendor model to a setting with a single manufacturer and multiple retailers under the assumption of ample capacity for the manufacturer. Detailed numerical examples are also provided.     

A quantity discount approach to supply chain coordination

Quantity discounts provide a practical foundation for inventory coordination in supply chains. However, typical supply chain participants may encounter difficulties in implementing the coordination policy simply because (1) specified lot size adjustments may deviate from the economic lot sizes and (2) the buying firm may face amplified overstocking risks related to increased order quantities. The main objective of this study is to develop a quantity discount model that resolves the practical challenges associated with implementing quantity discount policies for supply chain coordination between a supplier and a buyer. The proposed Buyer’s Risk Adjustment (B-RA) model allows the supplier to offer discounts that capitalize on the original economic lot sizes and share the buyer’s risk of temporary overstocking under uncertain demand. The analytical results suggest that the proposed B-RA discount approach is a feasible alternative for supply chain coordination under uncertain demand conditions.     

Make-or-buy service capacity decision in a supply chain providing after-sales service

We consider a supply chain comprising a manufacturer and a retailer. The manufacturer supplies a product to the retailer, while the retailer sells the product bundled with after-sales service to consumers in a fully competitive market. The sales volume is affected by the retailer’s service-level commitment. The retailer can build service capacity in-house at a deterministic price before service demand is realized, or buy the service from an outsourcing market at an uncertain price after service demand realization. We find that the outsourcing market encourages the retailer to make a higher level of service commitment, while prompting the manufacturer to reduce the wholesale price, resulting in more demand realization. We analyze how the expected cost of the service in the outsourcing market and the retailer’s risk attitude affect the decisions of both parties. We derive the conditions under which the retailer is willing to build service capacity in-house and under which it will buy the service from the outsourcing market. Moreover, we find that the manufacturer’s sharing with the retailer the cost to build service capacity improves the profits of both parties.     

A linguistic multi-criteria group decision support system for fabric hand evaluation

Fabric hand evaluation (FHE) is the main measure in textile material selection for fashion design and development. Fabric hand evaluation requires considering multiple evaluation aspects/criteria by a group of evaluators. Some fabric features can also be measured using instruments. The evaluation often uses linguistic terms in the weights of criteria, and the weights and judgments of evaluators. To support a FHE-based material selection, this study first develops a fabric hand-based textile material evaluation model. It then proposes a human-machine measure integrated fuzzy multi-criteria group decision-making method. A software tool is also developed, which implements the proposed method and is applied in fabric hand-based textile material evaluation.