Market demand often exhibits ambiguity due to insufficient historical data (e.g., new product launches), complicating joint decision-making in dual-channel supply chains (DCSC), especially with free-riding on sales effort. To address this, we construct a game-theoretic model and an analytical framework, based on generalized possibility distribution theory, for joint pricing and inventory decisions under such fuzzy demand. Closed-form solutions for optimal pricing decisions, sales effort, and inventory factor are derived under both decentralization and centralization. These solutions are applicable under the decentralized mechanism with fewer or simpler constraints. When the demand distribution is unknown or not second-order differentiable, a distributionally robust fuzzy optimization (DRFO) model is developed. Furthermore, the study investigates coordination mechanisms of the wholesale-price discount and revenue-sharing contract. The research finds that: (1) Under fuzzy demand, retailers respond to risk by increasing sales effort and moderately raising prices. This finding contrasts with the conventional result in stochastic models, where uncertainty typically leads to optimal prices lower than the risk-free price. (2) To maintain competitiveness, the demand-dependent retail price and online price move in tandem. However, sales effort boosts selling prices but is moderated by free-riding and the cost of sales effort. Furthermore, ignoring demand ambiguity leads to a biased estimation of the inter-channel price gap. (3) A higher degree of free-riding harms the retail channel under decentralization, and its impact on the overall supply chain’s expected profit depends on the channel price difference. Numerical studies also reveal that the optimal price decreases as uncertainty increases; price sensitivity suppresses own-channel demand while promoting cross-channel demand; and higher free-riding does not always benefit the manufacturer and may even reduce its profits under decentralization.

This article aims to develop a novel hybrid multi-attribute group decision-making approach under interval-valued intuitionistic fuzzy sets (IVIFS) by integrating variable weight, correlation coefficient, and technique for order performance by similarity to an ideal solution (TOPSIS). First, experts give their evaluation in IVIFS, and then the weighting evaluation matrix is computed based on interval-valued intuitionistic fuzzy weighted averaging operator with the subjective attribute weights given in advance. Second, a simple and useful weighting approach on the basis of correlation coefficient is put forward to obtain the experts weights. Third, we treat the attribute weights as a varying vector, and then propose a variable weighting approach for its acquisition. Fourth, an individual decision can be converted to an alternative decision by considering the experts and attributes weights together. At last, the integrated assessment value of each alternative is computed by TOPSIS, and then the most appropriate alternative is chosen. Two illustrative examples dealt with the problem by the method presented in this article demonstrate the usefulness of this approach, compared with those by the other methods.

Cyber-physical systems and data-driven techniques have potentials to facilitate the prediction and control of product quality, which is one of the two most important issues in modern industries. In this article, we integrate random forest (RF) with Bayesian optimization for quality prediction with large-scale dimensions data, selecting crucial production elements by information gain, and then utilizing sensitivity analysis to maintain product quality. Horizontal empirical experiments are performed to verify the superiorities of RF embedded within Bayesian optimization over classical RF, support vector machine, logistic regression, decision tree, and even background propagation neural network. Besides, we find fewer but critical features handled by RF-Bayesian optimization can realize satisfactory forecast accuracy as well as cost-effective computing time, where we interpret it with Herbert A. Simon's management decision theory and Pareto principle. Consequently, the results could provide managerial insights and operational guidance for product quality prediction and control at the real-life process industry.

This paper examines how open e-logistic standards (OELS) affect firm performance through developing supply chain ambidexterity. We conceptualize supply chain process ambidexterity as the ability of a firm to simultaneously develop alignment and process adaptability capabilities. A questionnaire was designed to collect data from Mainland China. To test the hypotheses, three-stage least squares estimation was employed. The results show that OELS adoption can enhance supply chain process ambidexterity and positively impact operational and financial performance. The influence of ambidexterity on operational performance is constrained by relationship duration while its influence on financial performance is enhanced by the number of suppliers. Our study suggests that OELS, through balancing the contradictory requirements of integration and flexibility, can lead to ambidexterity in the supply chain. This study contributes to the ambidexterity literature and interorganizational systems literature by empirically confirming that OELS are effective boundary-spanning mechanisms to leverage organizational ambidexterity.

In the era of Internet of people and things, big data are merging. Conventional computation algorithms including correlation measures become inefficient to deal with big data problems. Motivated by this observation, we present three fuzzy correlation measurement algorithms, that is, the centroid-based measure, the integral-based measure, and the α-cut-based measure using fuzzy techniques. Data of Shanghai stock price index (SSI) and exchange rates of main foreign currencies over China Yuan from 22 January 2013 to 17 May 2018 are used to check the effectiveness of our algorithms, and, more importantly, to observe the causality relationship between SSI and these main exchange rates. We have observed some findings as follows. First, the usage of the highest, lowest, or closing values in daily exchange rates and stock prices has impact on the significant Granger causes of exchange rates over SSI, but does not produce any opposite cause from SSI to exchange rates. Second, no matter which of our fuzzy measurement algorithms is used, Hongkong Dollar over China Yuan and U.S. Dollar over China Yuan are positively related with SSI, and Euro over China Yuan negatively correlated with SSI is always recognized as a Granger cause to SSI with the significance level being 1%. Finally, both the optimism level and the uncertainty level are observed having impact on the correlation coefficients, but the later brings more significant changes to results of the Granger causality tests.

This article discusses the problem of how to efficiently build aircraft routes that better withstand potential disruptions, such as bad weather, technical problems, and passenger delays. This optimization problem is called robust aircraft maintenance routing problem (RAMRP). There are three approaches in the literature to deal with the RAMRP, such as the buffer time allocation approach (BT), the departure retiming approach (DR), and the scenario-based stochastic programming approach (SSP). Most of the previous approaches have some shortcomings in terms of fleet productivity and delay absorption. In addition, the majority of the RAMRP models overlook maintenance regulations, which result in the generation of infeasible routes. In this article, RAMRP is investigated with two main objectives. First, a novel robustness approach, called the turn-around time reduction approach (TRTR), that avoids the shortcomings of the existing approaches, is incorporated into RAMRP. The second objective is to develop an RAMRP model that simultaneously considers all maintenance regulations. The effectiveness of the proposed RAMRP model along with the TRTR is demonstrated using real data from a major Middle Eastern airline. The results reveal an improved performance of the TRTR over the BT by about 3.43%-12.20% and 2.5%-13.58%, while handling the expected propagated delay costs and fleet productivity, respectively. In addition, the results show that the TRTR is better than the SSP by about 2.07%-18.82%, while minimizing the propagated delay costs. Therefore, the TRTR has a great potential to be implemented in the actual industry.

The connection of physical agriculture with corresponding cyber systems is helpful to achieve precision agriculture. Real-time data from agriculture sensors can provide decision supports to improve the yields and quality of agricultural products, but also bring about challenges one of which is how to mine useful information from these vast amounts of data at acceptable computation costs. To deal with the dimension disaster problem faced by most conventional mining algorithms, in this paper we combine granulation techniques and genetic algorithm (GA) with a support vector machine (SVM) to propose a granular GA-SVM. In the integrated predictor, three granulation methods, that is, Min-Median-Max granulation, Quartile-Median granulation, and fuzzy granulation, are introduced to break down big data in agricultural cyber-physical systems into small-scale granules, and GA is used to find the optimal values of SVM penalty parameter and kernel parameter from the reduced granules. Internet of Things (IoT) data from Luochuan Apple Experimental Demonstration Station in Shaanxi Province, China, verified that the proposed granular GA-SVM predictor is effective to make big data prediction with reduced computation time and equivalent accuracy. Moreover, the predicted environment information could provide guidance for growers achieving precise management of apple planting.

Dempster-Shafer evidence theory (DST) is widely used in intelligent information processing, especially for information fusion. Recently, measuring the information volume in the framework of DST draws a lot of attention. Many theories and tools have been proposed to model the uncertain degree in DST, including Deng entropy. However, Deng entropy and the other uncertainty measures in DST pay no attention to the uncertainty in the frame of discernment (FOD) in the open world, which is the reason of this paper. To address this issue, Deng entropy is extended to the open world in DST framework. With the extended Deng entropy (EDE) in the open world, the uncertain information represented by FOD and the mass function of the empty set now can be properly modelled while measuring the uncertain degree in DST. EDE can be regarded as a generalization of Deng entropy in the open world and it can be degenerated to Deng entropy in the closed world if the mass value of the empty set is zero. A few numerical examples are presented to verify the applicable and useful of the new measure.

Capturing specific interrelationship among input arguments has great importance in the process of aggregation as they may change the aggregation result significantly, which can lead viable changes in the overall decision outcome. In this study, we attempt to aggregate a set of inputs with certain heterogeneous interrelationship pattern among them. To do this, we introduce a new aggregation operator, which we call the extended geometric Bonferroni mean. We investigate its properties and develop an algorithm to learn its associated parameters based on decision maker's perceived view toward the aggregation process. Moreover, to learn such heterogeneous relationship among the inputs from the data set, we provide a learning algorithm. Examples are given to illustrate the realization of algorithm and to show certain advantages over the existing aggregation operators.

The existing economic production quantity (EPQ) models in imperfect production systems normally assume either deteriorating production processes or deteriorating inventory. In this paper, the two aspects are combined together in a single approach to solve single machine, single product EPQ problem. There are assumed to be two states: 1) in-control and 2) out-of-control state. At the beginning of each production cycle, the production process is in control with a low defective rate. After a certain time period, the production process switches from an in-control to an out-of-control state. As a result, the defect rate increases to another level and more defective items are produced. Defective products are reworked after the normal production process all together but before that they are stored in an inventory, so a corresponding inventory holding cost is generated. Backordering is also considered in this model and products are first used to satisfy the backlog demand in each production run. Three different cases are considered and discussed in which the switch of state occurs in the backlog period, the inventory surplus period, and after the normal production period, respectively. By finding the optimal total production and backlog quantity, the expected total cost per unit product is minimized.

The inventory routing problem (IRP) arises in vendor-managed inventory systems, which is a combination of vehicle routing and inventory management. Differing from the traditional IRPs in which the time consumption in transportation is often ignored, in this paper, we take the time consumption into consideration, which brings the lead-time to replenishment. In this situation, the lead-time for replenishment depends on the routing decisions made by the vendor. Consequently, IRPs become more interesting because decisions on the replenishment quantity and routing depend more tightly on each other. The general case of the IRP with replenishment lead-time (i.e., one vendor and N retailers) is modeled mathematically. To solve the problem, first a simple case (i.e., one vendor and two retailers) is analyzed to obtain the theoretical optimal policy. By proving the K-convexity of the objective function, we confirm that the structure of the optimal replenishment and routing policy is of switching curve type. In this policy, the state space, which is composed of the inventory positions of the two retailers, is divided into several domains; and for inventory positions in each domain, there exists an optimal order-up-to level. This structure reveals, for managerial insight, that when lead-time is considered, the current routing decision is not independent of the previous one. Second, since the optimal policy is difficult to realize in practice, a myopic policy that is easier to implement is proposed and numerical experiments are conducted to examine the near-optimal performance of the myopic policy. Finally, the myopic policy is extended to a realistic-size IRP with replenishment lead-time (i.e., the IRP for the case of one vendor and multiple retailers) and a numerical example is provided to indicate the feasibility of the policy.

Inaccurate inventory data are often found in production/inventory systems, and have a significant negative impact on the performance of raw materials replenishment and production control. In this paper, robust replenishment and production control problem for a single-stage production/inventory system with inventory inaccuracy is investigated, with the objective of minimizing the average inventory and production cost. In addition to the inventory inaccuracy, the lead-time of raw materials ordering and machine unreliability are also taken into consideration. The problem is solved in two steps. First, for a single-stage production/inventory system with ordering lead-time and uncertain production capacity, but without inventory inaccuracy, an approximate optimal replenishment, and production control policy is constructed. Second, we reveal that it is the discontinuity of the control variables in different domains of the state space that makes the policy sensitive to inventory data error. Consequently, we develop a robust replenishment and production control policy to eliminate this sensitivity. This approach maintains the structural characteristics of the optimal policy, but allows the control variables to change smoothly when the state variables cross boundaries between different domains in the state space. Numerical experiments are conducted to examine the performance of the proposed robust policy for hedging against inventory inaccuracy, and a sensitivity analysis is used to study how the parameters of the production/inventory system and the robust policy impact on the average inventory and production cost.

In this plethora of increased competitiveness and globalization the effective management of the warehouse system is a challenging task. Realizing that proper scheduling of the warehouses is necessary to outperform the competitors on cost, lead time, and customer service basis (Koster, 1998); the proposed research focuses on optimization of warehouse scheduling problems. This research aims to minimize the total tardiness so that the overall time involved in managing the inventory inside the warehouse could be effectively reduced. This research also deals with the vehicle routing issues in the warehousing scenario and considers various constraints, and decision variables, directly influencing the undertaken objective so as to make the model more realistic to the real world environment. The authors have also proposed a hybrid tabu sample-sort simulated annealing (TSSA) algorithm to reduce the tardiness as well as to enhance the performance of the warehousing system. The proposed TSSA algorithm inherits the merits of the tabu search and sample-sort annealing algorithm. The comparative analysis of the results of the TSSA algorithm with other algorithms such as simulated annealing (SA), tabu search (TS), and hybrid tabu search algorithms indicates its superiority over others, both in terms of computational time as well as total tardiness reduction.

Lot splitting is defined as the process of splitting lots into smaller sub-lots such that successive operations of the same lot can be overlapped on distinct machines. Hence, the lead time of the lot can be possibly shortened. In this paper, a genetic algorithm-based approach is proposed to examine the lot splitting effect under different job shop conditions as defined by three parameters: processing time range, setup time and system congestion index. The experimental results suggest that lot splitting technique has a significant impact on job shop system with longer processing time and less due date tightness.

Flexibility of flexible manufacturing systems (FMSs) has been considered as an effective tool to compete in the present manufacturing environment. Enormous research efforts have been made to harness the benefits of flexibility through superior control strategies. While modeling flexibility and control strategies, researchers have mostly assumed an information system that can provide real-time control. Literature qualitatively reports that the real-time control can be highly capital intensive and difficult to achieve. This paper focuses on FMS operating under review-period (RP)-based control and presents a combined study of routing flexibility (RF), control strategies, and information system under Taguchi's method using simulation. RP-based control for FMS has been compared with real-time control. This paper contributes an approach for the decision maker to study the performance of an FMS operating under RP control and to identify the periodicity (time interval) of RP that will not deteriorate its performance in comparison to real-time control. It also helps the decision maker to reach a tradeoff between RP-based control and real-time control. The results show that RP-based control can be effectively implemented on an FMS having lower RF level. RP-based control can outperform real-time control with a superior control strategy and smaller RP size. The results under Taguchi's method suggest that the RF and control strategy should have maximum relative percentage contributions in FMS performance, whereas contribution of the RP (information system) should be minimum. Increasing the relative percentage contribution of the information system may deteriorate the performance of FMS. The information system is needed as a catalyst to facilitate the contributions of other factors in improving the FMS performance and not its own contribution

In the global competitive world swift, reliable and cost effective production subject to uncertain situations, through an appropriate management of the available resources, has turned out to be the necessity for surviving in the market. This inspired the development of the more efficient and robust methods to counteract the existing complexities prevailing in the market. The present paper proposes a hybrid CFGTSA algorithm inheriting the salient features of GA, TS, SA, and chaotic theory to solve the complex scheduling problems commonly faced by most of the manufacturing industries. The proposed CFGTSA algorithm has been tested on a scheduling problem of an automobile industry, and its efficacy has been shown by comparing the results with GA, SA, TS, GTS, and hybrid TSA algorithms.

Many optimization techniques have been proposed over the years to improve the performance of supply chains. Although these approaches have been shown to be effective, most of them were developed without considering uncertainties in supply chains to simplify the analysis. In fact, uncertainties can deteriorate the performance of supply chains, such as increase in total cost, or drop in fill rate, of the whole system. In distributed supply chains, participating members are not under a sole control by a central authority, the problem is even more stringent due to incomplete information sharing, or so called asymmetrical information sharing. One way to improve the system performance is to achieve coordination among participating parties through establishment of contracts. The objectives of this paper are i) to evaluate the effects of demand uncertainty in a distributed supply chain, which is modelled as an agent-based system; ii) to propose a coordination mechanism with early order completion contract to minimize the negative impacts of demand uncertainty; and iii) to present associated simulation results. Performance of the system is measured in terms of costs and fill rate. Simulation results indicate that the proposed contract approach is able to improve the performance measures of the system.

Abstract: Logistics service providers are professionals who serve clients such as manufacturers, raw material suppliers, distributors, retailers and shippers within the supply chain. Traditionally, the formulation of logistics strategies to execute various logistics services has been based solely on experience. In this paper, an intelligent case-based logistics strategy system (CLSS) is designed to help logistics service providers to develop strategies under various customers' demands. The proposed system, which is suitable for use in warehouse operations in the Hong Kong/Pearl River Delta region, enhances the effectiveness of logistics strategies formulations by integrating case-based reasoning technology seamlessly with various data management software such as a data warehouse, on-line analytical processing and a multidimensional database management system. Through applying CLSS in Eastern Worldwide Company, the resource utilization is maximized while work efficiency is greatly enhanced. As a result, the overall logistics operations are smoothened significantly.

Abstract: To cope with the requirements of high dimensional accuracy for injection molding components, it is important to optimize the process parameters in order to sustain the high level dimensional quality of the molded parts. In this respect, a study in the domain of process optimization is of paramount importance in terms of determining the optimal set of injection molding parameters. To this end, a methodology to establish an integrated model which consists of both fuzzy logic reasoning and a genetic algorithm is proposed. These two artificial intelligence techniques can complement each other to form an integrated model which capitalizes on the merits and at the same time offsets the pitfalls of the involved technologies. To validate the feasibility of the proposed model, a case study related to injection molding optimization is also covered in this paper.

Supply chain management has become one of the most important sources of competitive advantage in different industries. For years, researchers have investigated the various processes within supply chain. There has been an increasing attention placed on performance, design and analysis of supply chain. However, the supply chain is a complex model so it is difficult to analyze the performance of a supply chain philosophically. To evaluate the control mechanism for a supply chain, simulation is one of the effective methods. There are five common supply chain models built up in this paper. From the simulation results, various performance measures such as transportation cost, resources utilization, inventory level, and order cycle time can be calculated.

Fuzzy logic principles and neural networks, both being computational intelligence technologies, can be combined to produce synergetic effects through the formation of a unified approach which takes advantage of the benefits and at the same time counterbalances the flaws of the two technologies. In this paper, a fuzzy neural approach, which is characterized by its ability to suggest the appropriate adjustment of product quantity from various suppliers with different quality standards in a supply chain network, is presented. This approach is particularly useful in situations where multiple supply chain partners are involved to achieve the common objective of producing products to the best satisfaction of customer demands at the lowest possible cost. To validate the feasibility of this approach, a test has been conducted based on the proposed fuzzy neural approach with the objective of suggesting the appropriate selection of suppliers and the optimal quantity allocated to them to meet the required quality standards. This paper describes the methodology for the deployment of this proposed hybrid approach to enhance the machine intelligence of a supply chain network with the description of a case study to exemplify its underlying principles.