In this manuscript, the mean of the study and the auxiliary variable, as well as the rank of the auxiliary variable, were needed to develop a new, improved ratio-in-regression type estimator for population mean. Up to the first order of approximation, expressions for the bias and mean square error of the existing and proposed estimators are computed. The effectiveness and stability of our new, enhanced estimator are evaluated using simulation and two actual data sets. The suggested estimator's superior performance to all other considered estimators is shown both conceptually and numerically. The mean square error is the lowest, and PREs out-performs other known estimators by a factor of more than one hundred. Overall, we draw the conclusion that the suggested new improved estimator outperforms all its predecessors.

The widespread use of knowledge management in a wide range of applications aids in professionally and wisely streamlining the procedure to produce improved outcomes and deliverables. Knowledge management is essential in the construction sector due to the considerable investment, extended deadlines and the need for higher performance efficiency and quality. The usage of BIM in construction projects has simplified the procedures required in building construction. The fundamental motivation behind the creation of BIM was the need to apply knowledge management approaches to make projects more sustainable and compliant with green building requirements. There are various limitations and risks associated with using BIM in construction projects, such as issues with file sharing and data security. This research examines numerous BIM benefits and drawbacks from a variety of angles, providing information and various measures for building the program. BIM adoption in construction projects includes a wide range of benefits as well as drawbacks. The relationship between the variety of information and outcomes was explored using different findings from numerous scientific studies that looked at the use of BIM in the construction sector. Due to the wide range of data that is available in the BIM sector, numerous areas were also found in this study. A thorough analysis was undertaken to present a variety of author viewpoints from various articles. This report's strengths and weaknesses have been noted. It has been demonstrated that BIM has additional benefits, including meeting client expectations, reducing design errors, and achieving project sustainability. However, there are also drawbacks to BIM, including user skill gaps, ambiguous standards and protocols, and transfer data problems. A few studies have addressed some of the research gaps and limitations in some of the domains. Additionally, these regions were thoroughly addressed from many authors' points of view.

This paper investigates an identical parallel machine scheduling problem with flexible maintenance and job release times and attempts to optimize two objectives: the minimization of the makespan and total tardiness simultaneously. A mixed-integer programming (MIP) model for solving small-scale instances is presented first, and then a modified NSGA-Ⅱ (M-NSGA-Ⅱ) algorithm is constructed for solving medium- and large-scale instances by incorporating several strategies. These strategies include: (ⅰ) the proposal of a decoding method based on dynamic programming, (ⅱ) the design of dynamic probability crossover and mutation operators, and (ⅲ) the presentation of neighborhood search method. The parameters of the proposed algorithm are optimized by the Taguchi method. Three scales of problems, including 52 instances, are generated to compare the performance of different optimization methods. The computational results demonstrate that the M-NSGA-Ⅱ algorithm obviously outperforms the original NSGA-II algorithm when solving medium- and large-scale instances, although the time taken to solve the instances is slightly longer.

Aiming at distribution logistics planning in green manufacturing, heterogeneous-vehicle vehicle routing problems are identified for the first time with multiple time windows that meet load constraints, arrival time window constraints, material demand, etc. This problem is expressed by a mathematical model with the characteristics of the vehicle routing problem with split deliveries by order. A hybrid ant colony optimization algorithm based on tabu search is designed to solve the problem. The search time is reduced by a peripheral search strategy and an improved probability transfer rule. Parameter adaptive design is used to avoid premature convergence, and the local search is enhanced through a variety of neighborhood structures. Based on the problem that the time window cannot be violated, the time relaxation rule is designed to update the minimum wait time. The algorithm has the best performance that meets the constraints by comparing with other methods.

In this paper, we present a two-dimensional irregular bin packing problem (2DIBPP) that takes into account the slit distance and allows the pieces to rotate freely. The target is to arrange a specified collection of pieces with irregular shapes into a minimal number of bins. Firstly, we develop a mathematical model for the 2DIBPP that considers slit distance and free rotation of the pieces, and an equidistant edge expansion approach is then proposed to handle the slit distance. Secondly, a two-stage method is implemented to get a finite collection of promising rotation angles, effectively decreasing the search neighbourhood. Thirdly, we decompose the 2DIBPP into two sub-problems: piece assignment and packing. The Partial Bin Packing (PBP) strategy is employed in the allocation stage, and we adopt an overlap minimization method to pack the pieces into an individual bin. Finally, we use a local search (LS) algorithm to advance the quality of the solutions by adjusting the piece assignment across bins. Experimental evidence exhibits that our approach is competitive in most instances of the literature, with four better results in five benchmark instances.

The inventory routing problem (IRP) arises in the joint practices of vendor-managed inventory (VMI) and vehicle routing problem (VRP), aiming to simultaneously optimize the distribution, inventory and vehicle routes. This paper studies the multi-vehicle multi-compartment inventory routing problem with stochastic demands (MCIRPSD) in the context of fuel delivery. The problem with maximum-to-level (ML) replenishment policy is modeled as a two-stage stochastic programming model with the purpose of minimizing the total cost, in which the inventory management and routing decisions are made in the first stage while the corresponding resource actions are implemented in the second stage. An acceleration strategy is incorporated into the exact single-cut Benders decomposition algorithm and its multi-cut version respectively to solve the MCIRPSD on the small instances. Two-phase heuristic approaches based on the single-cut decomposition algorithm and its multi-cut version are developed to deal with the MCIRPSD on the medium and large-scale instances. Comparing the performance of the proposed algorithms with the Gurobi solver within limited time, the average objective value obtained by the proposed algorithm has decreased more than 7.30% for the medium and large instances, which demonstrates the effectiveness of our algorithms. The impacts of the instance features on the results are further analyzed, and some managerial insights are concluded for the manager.

For a serial delivery system, the latter 3PL needs to be prepared at the transshipment node in advance to reduce the total delivery time. In this paper, we propose the concept of Connecting Time Spot (CTS) to help 4PL schedule the latter 3PL when to wait at the transshipment node. We study a serial delivery system with a 4PL and two 3PLs, where 4PL designs optimal contracts with two types of CTS (GCTS is derived by system parameter and DCTS is determined by 4PL’s optimization) to induce 3PLs to exert the optimal effort levels. We analyze the effects of CTS on the system profit in the centralized system. For the decentralized system, we particularly investigate the optimal contracts in three penalty modes which are according to the occupancy of the warehouses. The results show that CTS can avoid 3PLs’ idle resources and enhance the system profit for serial delivery both in the centralized system and the decentralized system. Compared with GCTS, DCTS has a better performance in enhancing the system profits. Also, the optimal incentive contracts achieve Pareto improvement for system profits. Interestingly, one 3PL’s delivery penalty mode will not affect the other 3PL’s optimal contracts.

We address a manufacturing environment with the no-wait constraint which is common in industries such as metal, plastic, and semiconductor. Setup times are modelled as uncertain with the objective of minimizing maximum lateness which is an important performance measure for customer satisfaction. This problem has been addressed in scheduling literature for the two-machine no-wait flowshop where dominance relations were presented. Recently, another dominance relation was presented and shown to be about 90% more efficient than the earlier ones. In the current paper, we propose two new dominance relations, which are less restrictive than the earlier ones in the literature. The new dominance relations are shown to be 140% more efficient than the most recent one in the literature. As the level of uncertainty increases, the newly proposed dominance relation performs better, which is another strength of the newly proposed dominance relation. Moreover, we also propose constructive heuristics and show that the best of the newly proposed heuristics is 95% more efficient than the existing one in the literature under the same CPU time.

Due to the deep integration of modern information technology, supply chain management has moved into a new stage of a smart supply chain. Considering the dual smart innovation of the manufacturer's production and retailer’s service, the manufacturer-led Stackelberg game model is constructed in the smart supply chain. Under the single and coordinated government subsidy strategies, the optimal decisions of the smart supply chain are researched, and the impacts of manufacturers' risk aversion on the government subsidy strategies and supply chain decisions are analysed. In addition, the efficiencies of different government subsidy strategies are compared and analysed by numerical simulation. Finally, the results show that: (i) The moderate risk aversion by the manufacturer can improve social welfare and help provide consumers with more affordable products. (ii) The government expenditure and product prices are highest under the coordinated subsidy strategy. (iii) Subsidising manufacturers is more beneficial than subsidising retailers among the two single government subsidy strategies. (iv) In general, the coordinated government subsidy strategy is more effective than the single subsidy strategy for the innovative development of a smart supply chain. In conclusion, the research provides a significant practical reference for jointly building the smart supply chain.

As a result of the rapidly increasing distribution network, the toxic gases emitted by the vehicles to the environment have also increased, thus posing a threat to health. This study deals with the problem of determining green vehicle routes aiming to minimize CO2 emissions to meet customers' demand in a supermarket chain that distributes fresh and dried products. A new method based on clustering algorithms and Hopfield Neural Network is proposed to solve the problem. We first divide the large-size green vehicle routing problem into clusters using the K-Means and K-Medoids algorithms, and then the routing problem for each cluster is found using the Hopfield Neural Network, which minimizes CO2 emissions. A real-life example is carried out to illustrate the performance and applicability of the proposed method. The research concludes that the proposed approach produces very encroaching results.