Optimal Decision Model Research Articles

Sub-optimality in motor planning is retained throughout 9 days practice of 2250 trials.

Optimality in motor planning, as well as accuracy in motor execution, is required to maximize expected gain under risk. In this study, we tested whether humans are able to update their motor planning. Participants performed a coincident timing task with an asymmetric gain function, in which optimal response timing to gain the highest total score depends on response variability. Their behaviours were then compared using a Bayesian optimal decision model. After 9 days of practicing 2250 trials, the total score increased, and temporal variance decreased. On the other hand, the participants showed consistent risk-seeking or risk-averse behaviour, preserving suboptimal motor planning. These results suggest that a human’s computational ability to calculate an optimal motor plan is limited, and it is difficult to improve it through repeated practice with a score feedback.

Business Intelligence System via Group Decision Making

Abstract In the paper a business intelligence tool based on group decision making is proposed. The group decision making uses a combinatorial optimization modeling technique. It takes into account weighted coefficients for evaluation criteria assigned by decision makers together with their scores for the alternatives in respect of these criteria. The proposed optimization model for group decision making considers also the knowledge level of the group members involved as decision makers. This optimization model is implemented in three-layer architecture of Web application for business intelligence by group decision making. Developed Web application is numerically tested for a representative problem for software choice considering six decision makers, three alternatives and 19 evaluation criteria. The obtained results show the practical applicability and effectiveness of the proposed approach.

Optimal Decision Model for Sustainable Hospital Building Renovation-A Case Study of a Vacant School Building Converting into a Community Public Hospital.

Much attention has been paid to hospitals environments since modern pandemics have emerged. The building sector is considered to be the largest world energy consumer, so many global organizations are attempting to create a sustainable environment in building construction by reducing energy consumption. Therefore, maintaining high standards of hygiene while reducing energy consumption has become a major task for hospitals. This study develops a decision model based on genetic algorithms and A* graph search algorithms to evaluate existing hospital environmental conditions and to recommend an optimal scheme of sustainable renovation strategies, considering trade-offs among minimal renovation cost, maximum quality improvement, and low environmental impact. Reusing vacant buildings is a global and sustainable trend. In Taiwan, for example, more and more school space will be unoccupied due to a rapidly declining birth rate. Integrating medical care with local community elder-care efforts becomes important because of the aging population. This research introduces a model that converts a simulated vacant school building into a community public hospital renovation project in order to validate the solutions made by hospital managers and suggested by the system. The result reveals that the system performs well and its solutions are more successful than the actions undertaken by decision-makers. This system can improve traditional hospital building condition assessment while making it more effective and efficient.

The Optimization Decision Model of Sub-contractor Selection in Multiple Subproject and Its Solving Method of Genetic Algorithm

The Optimization Decision Model of Sub-contractor Selection in Multiple Subproject and Its Solving Method of Genetic Algorithm

Impacts of government subsidies on pricing and performance level choice in Energy Performance Contracting: A two-step optimal decision model

Government subsidies generally play a crucial role in pricing and the choice of performance levels in Energy Performance Contracting (EPC). However, the existing studies pay little attention to how the Energy Service Company (ESCO) prices and chooses performance levels for EPC with government subsidies. To fill such a gap, we propose a joint optimal decision model of pricing and performance level in EPC considering government subsidies. The optimization of the model is achieved via a two-stage process. At the first stage, given a performance level, ESCOs choose the best price; and at the second stage, ESCOs choose the optimal performance level for the optimal price. Furthermore, we carry out a numerical analysis to illuminate such an optimal decision mechanism. The results show that both price sensitivity and performance level sensitivity have significant effects on the choice of performance levels with government subsidies. Government subsidies can induce higher performance levels of EPC, the demand for EPC, and the profit of ESCO. We suggest that ESCO’s market strategy should firstly focus on high energy consumption industries with government subsidies and that government subsidies standard adopted in different industries should be differentiated according to the market characteristics and energy efficiency levels of various industries.

Inseparability of Go and Stop in Inhibitory Control: Go Stimulus Discriminability Affects Stopping Behavior.

Inhibitory control, the ability to stop or modify preplanned actions under changing task conditions, is an important component of cognitive functions. Two lines of models of inhibitory control have previously been proposed for human response in the classical stop-signal task, in which subjects must inhibit a default go response upon presentation of an infrequent stop signal: (1) the race model, which posits two independent go and stop processes that race to determine the behavioral outcome, go or stop; and (2) an optimal decision-making model, which posits that observers decides whether and when to go based on continually (Bayesian) updated information about both the go and stop stimuli. In this work, we probe the relationship between go and stop processing by explicitly manipulating the discrimination difficulty of the go stimulus. While the race model assumes the go and stop processes are independent, and therefore go stimulus discriminability should not affect the stop stimulus processing, we simulate the optimal model to show that it predicts harder go discrimination should result in longer go reaction time (RT), lower stop error rate, as well as faster stop-signal RT. We then present novel behavioral data that validate these model predictions. The results thus favor a fundamentally inseparable account of go and stop processing, in a manner consistent with the optimal model, and contradicting the independence assumption of the race model. More broadly, our findings contribute to the growing evidence that the computations underlying inhibitory control are systematically modulated by cognitive influences in a Bayes-optimal manner, thus opening new avenues for interpreting neural responses underlying inhibitory control.

Applying CBR to machine tool product configuration design oriented to customer requirements

Product customization is a trend in the current market-oriented manufacturing environment. However, deduction from customer requirements to design results and evaluation of design alternatives are still heavily reliant on the designer’s experience and knowledge. To solve the problem of fuzziness and uncertainty of customer requirements in product configuration, an analysis method based on the grey rough model is presented. The customer requirements can be converted into technical characteristics effectively. In addition, an optimization decision model for product planning is established to help the enterprises select the key technical characteristics under the constraints of cost and time to serve the customer to maximal satisfaction. A new case retrieval approach that combines the self-organizing map and fuzzy similarity priority ratio method is proposed in case-based design. The self-organizing map can reduce the retrieval range and increase the retrieval efficiency, and the fuzzy similarity priority ratio method can evaluate the similarity of cases comprehensively. To ensure that the final case has the best overall performance, an evaluation method of similar cases based on grey correlation analysis is proposed to evaluate similar cases to select the most suitable case. Furthermore, a computer-aided system is developed using MATLAB GUI to assist the product configuration design. The actual example and result on an ETC series machine tool product show that the proposed method is effective, rapid and accurate in the process of product configuration. The proposed methodology provides a detailed instruction for the product configuration design oriented to customer requirements.

Kernel joint visual tracking and recognition based on structured sparse representation

Visual target tracking and recognition are closely interrelated and can help each other potentially and significantly. The optimal Bayes joint decision and estimation (JDE) model guarantees the general decision (recognition) and estimation (tracking) arriving at the global optimization. In this paper, a kernel joint tracking and recognition (KJTR) algorithm is proposed to apply JDE to the simultaneous visual tracking and recognition problem and use the iteration learning procedure to achieve optimization. Besides, the structured sparse representation (SSR) model shows great efficiency and robustness in exploiting both holistic and local information of the target appearance. We show that constructing the appearance model with SSR can improve the performance of the proposed algorithm. Then, the contribution of each test candidate is considered into the learning procedure by a kernel function. Furthermore, the new joint weights of KJTR provide flexibility with appearance changes and thus robustness to the dynamic scene. The experimental results demonstrate that the KJTR algorithm performs well in terms of accuracy and robustness.

Centralised and decentralised three echelon inventory model for optimal inventory decisions under price dependent demand

In this paper, a three echelon inventory system with a single manufacturer supplying a single kind of a product to a single distributor and then to a single retailer, is considered. Mathematical model is developed for annual total relevant costs of the coordinated and non-coordinated supply chain. The novel idea of the proposed model is that the demand is a function of retailer's unit selling price, which in turn is a function of retailer's replenishment quantity with dependence factor. The objective of the model is to demonstrate the optimality of inventory decisions, shipment frequencies and total relevant costs with respect to dependence factor. Numerical illustration is carried out to demonstrate the optimality of replenishment decisions. Finally, managerial insights are inferred in selecting the optimal replenishment quantity and shipment frequency that minimises the total relevant cost of the supply chain.

Optimal Uncertainty Decision of Innovation Products Output Based on Return Collection

As the length of time between product renewals decreases, the enterprises that make innovative products face new challenges. The production cost structure of innovative products can be changed by collecting product returns, which can be economically valuable for enterprises. Enterprises face both production and demand randomness; hence, it is important to improve the traditional optimal production decision-making model that considers the randomness of demand only. This paper divides innovative products into structural innovation products and improved innovation products. The paper studies the optimal single-period production decision between reused parts and conventional new parts in complementary and substitute relationships. It further gives the analytical expressions for satisfying the optimal production and provides some numerical examples. The research results indicate that when producing innovative products, whether based on structural or improved innovation, with the variance of the quantity of the recycled parts increasing, the optimal purchase quantity of the conventional parts will increase but the expected profits for the innovative products will decrease. With integrated substitution and complementation based on improved innovation, enhancing the fluctuation in the number of recycled parts has a greater impact on the optimal purchase quantity of the substitute conventional parts than that of the complementary parts.

Optimal Decisions and Financing Strategies Selection of Supply Chain with Capital Constraint

Two financing modes can be considered for manufacturer’s production capital constrained: RPFM (retailer’s prepayment financing mode) and PCFM (procurement contract financing mode). Under the RPFM, the retailer places order in advance for a discount price and makes prepayment; manufacturer is able to finance from a bank as production quantity cannot satisfy the second-order quantity of retailer. By contrast, manufacturers make financing from commercial banks based on the procurement contract with upstream supplier under the PCFM. Taking into account the relation between production volumes with the manufacturer’s own capital and retailer’s order quantity, the optimal production and financing decision model for manufacturer under these two financing modes are built. Moreover, the profits of the manufacturer, the retailer, and the supply chain are compared and discussed. Results show that both of the two modes can create new value and profit for the supply chain with capital constraint and achieve optimal production under “newsvendor” mode; the supply chain has the better performance under the RPFM than that achieved under the PCFM. Also, under the RPFM, the manufacturer’s production and the profit of the whole supply chain would be increased when the manufacturer makes the second financing. Similar conclusion is reached under the PCFM. Finally, numerical study was given to demonstrate the conclusions.

基于版权模式的软件产品定价策略

针对永久许可版权的软件销售模式，采用理论经济模型的分析方法，构建两阶段最优决策模型，考察了网络外部性、升级质量不确定对软件厂商定价决策的影响。实验结果表明：升级版本的期望质量较低时应对初级版本采用高价策略；网络外部性具有正反馈效应，当网络外部性强度突破临界点后，厂商可持续降价直至市场完全覆盖。 Aiming at the pattern of selling perpetual license based software, we used analytical approach of theoretical economics to develop an optimal two-period decision model and further discussed the influences on software vendors’ selection of pricing strategy caused by the network externality and quality uncertainty of the new released software. The experiment results showed that high price strategy for initial version would be preferable when the expected quality of software upgrades is low; meanwhile, the network externality presented the effects of positive feedback, and as a consequence, once the critical density value of the network externality was surpassed, software vendors could decrease selling price gradually until the market was completely covered.

A Hybrid Method for Modeling and Solving Supply Chain Optimization Problems with Soft and Logical Constraints

This paper presents a hybrid method for modeling and solving supply chain optimization problems with soft, hard, and logical constraints. Ability to implement soft and logical constraints is a very important functionality for supply chain optimization models. Such constraints are particularly useful for modeling problems resulting from commercial agreements, contracts, competition, technology, safety, and environmental conditions. Two programming and solving environments, mathematical programming (MP) and constraint logic programming (CLP), were combined in the hybrid method. This integration, hybridization, and the adequate multidimensional transformation of the problem (as a presolving method) helped to substantially reduce the search space of combinatorial models for supply chain optimization problems. The operation research MP and declarative CLP, where constraints are modeled in different ways and different solving procedures are implemented, were linked together to use the strengths of both. This approach is particularly important for the decision and combinatorial optimization models with the objective function and constraints, there are many decision variables, and these are summed (common in manufacturing, supply chain management, project management, and logistic problems). TheECLiPSesystem with Eplex library was proposed to implement a hybrid method. Additionally, the proposed hybrid transformed model is compared with the MILP-Mixed Integer Linear Programming model on the same data instances. For illustrative models, its use allowed finding optimal solutions eight to one hundred times faster and reducing the size of the combinatorial problem to a significant extent.

Greening game analysis in supply chains under three decision-making structures

This paper examines a supply chain system consisting of a manufacturer and a retailer, where the two members put in efforts for 'greening' their operations. We consider three different decision-making structures and discuss the optimal decision model from each player's perspective. In model 1, the manufacturer invests in greening and sets the price for the product then the retailer invests in greening and sets the retail price. In model 2, manufacturer-Stackelberg games of greening and pricing are played in sequence. Model 3 outlines the Nash games of greening and pricing, in which the greening Nash game firstly played. Our analysis shows that the manufacturer's profit is the highest in model 1 where the manufacturer has the first mover advantage. However, the retailer's expected profit is the highest in model 3 where the retailer has a good share in the channel leadership. In all models, integrated channel profits are the highest under model 1.

Decision-making model of generation technology under uncertainty based on real option theory

The introduction of market competition and the increased uncertainty factors makes the generators have to decide not only on whether to invest generation capacity or not but also on what kind of generation technology to choose. In this paper, a decision-making model of generation technology investment is proposed. The irreversible investment concept and real option theory is introduced as the fundamental of the model. In order to explain the decision-making process of generator’s investment, the decision-making optimization model was built considering two generation technologies, i.e., the heat-only system and the combined heat and power generation. Also, we discussed the theory deducing process, which explained how to eliminate the overrated economic potential caused by risk hazard, based on economic evaluation of both generation technologies. Finally, practical data from electricity market of Inner Mongolia was used to prove the validity of the model and the impact of uncertainties of electricity and fuel price fluctuation on investment was analyzed according to the simulated results.

Probabilistic Spinning Reserve Model of Power System Containing Wind

In order to reflect the influences of the stochastic uncertain of wind power on the optimal decision-making of the power system spinning reserve, a probabilistic optimization model of the spinning reserve is proposed. Considering uncertain factors such as predicted-deviation of wind, predicted-deviation of load and fault outage of generator, the capacity outage probability table is combined with predicted-deviation of wind and predicted-deviation of load. By introducing the analytic expressed probability reserve constraints into the unit commitment model considering the wind power, the spinning reserve of power system can be optimized to the expectable contingency level. Using a calculation example, the effectiveness of the model is proved, providing a new model for uncertainty analysis and optimal scheduling decisions of power system containing wind.

Optimized Group Replacement Scheduling for Water Pipeline Network

AbstractReplacement of deteriorated water pipes is a capital-intensive activity for utility companies. Replacement planning aims to minimize total costs while maintaining a satisfactory level of service and is usually conducted for individual pipes. Scheduling replacement in groups is seen to be a better method and has the potential to provide benefits such as the reduction of maintenance costs and service interruptions. However, developing group replacement schedules is a complex task and often beyond the ability of a human expert, especially when multiple or conflicting objectives need to be catered for, such as minimization of total costs and service interruptions. This paper describes the development of a novel replacement decision optimization model for group scheduling (RDOM-GS), which enables multiple group-scheduling criteria by integrating new cost functions, a service interruption model, and optimization algorithms into a unified procedure. An industry case study demonstrates that RDOM-GS can im...

A novel medical information management and decision model for uncertain demand optimization.

Accurately planning the procurement volume is an effective measure for controlling the medicine inventory cost. Due to uncertain demand it is difficult to make accurate decision on procurement volume. As to the biomedicine sensitive to time and season demand, the uncertain demand fitted by the fuzzy mathematics method is obviously better than general random distribution functions. To establish a novel medical information management and decision model for uncertain demand optimization. A novel optimal management and decision model under uncertain demand has been presented based on fuzzy mathematics and a new comprehensive improved particle swarm algorithm. The optimal management and decision model can effectively reduce the medicine inventory cost. The proposed improved particle swarm optimization is a simple and effective algorithm to improve the Fuzzy interference and hence effectively reduce the calculation complexity of the optimal management and decision model. Therefore the new model can be used for accurate decision on procurement volume under uncertain demand.

Managing Hospital Inpatient Bed Capacity Through Partitioning Care into Focused Wings

We consider the partitioning of care types into wings from the perspective of a hospital administrator who wishes to optimize the use of a fixed number of beds that provide services for heterogeneous care types. The hospital administrator decides on the number of wings to form, the number of beds to allocate to each wing, and the set of care types to assign to each wing to maximize the total utility to the hospital. The administrator faces an inherent trade-off between forming large wings to pool demand and bed capacity, and forming specialized wings to focus on narrow ranges of care types. Specialized wings not only provide advantages from focused care but also allow the protection of beds for high-utility care types. We provide an optimization model for the wing formation decision and address the advantages of focus endogenously in our model. Using data from a large urban teaching hospital in the United States along with a national database, we report on a number of managerial insights. In particular, as the overall demand increases across all care types, wings are formed to reserve more beds for higher-utility types, which leads to higher overall hospital utility but also some disparity across types, such as increased hospital access for some and decreased access for others. Furthermore, overall bed occupancy decreases as the hospital is split into wings. However, if sufficient focus is attained, shorter lengths-of-stay associated with focused care may increase overall patient throughput. We also observe that when patients are willing to wait longer for admission, the hospital tends to form more wings. This implies that hospitals that garner longer waits can form more specialized wings and thereby benefit from focused care, whereas hospitals that cannot will tend to form fewer, if any, wings, choosing to pool demand and bed capacity.

Group decision making based on multiplicative consistent reciprocal preference relations

The main aim of this paper is to investigate the consistency and consensus of multiplicative consistent reciprocal preference relations. Based on the multiplicative consistency property, a straightforward method to obtain the priority vector for multiplicative consistent reciprocal preference relation is introduced together with some of its desired properties. In addition, some interesting properties on consistency and consensus for reciprocal preference relations are further investigated. A consensus optimization model for group decision making with reciprocal preference relations is proposed, which enables the decision makers to reach consensus with the highest overall consensus level. Numerical examples are provided to make a discussion and comparison with some existing similar methods.