This paper addresses the permutation flowshop scheduling problem with peak power consumption constraints (PFSPP). The real-time power consumption of the PFSPP cannot exceed a given peak power at any time. First, a mathematical model is established to describe the concerned problem. The sequence of operations is taken as a solution and the characteristics of solutions are analyzed. Based on the problem characteristics, eight heuristics are proposed, including balanced machine-job decoding method, balanced machine-job insert method, balanced job-machine insert method, balanced machine-job group insert method, balanced job-machine group insert method, greedy algorithm, beam search algorithm, and improved beam search algorithm. Similarly, the canonical artificial bee colony algorithm and iterated local search algorithm are modified based on the problem characteristics to solve the PFSPP. A large number of experiments are carried out to evaluate the performance of new proposed heuristics and metaheuristics. The results and discussion show that the proposed heuristics and metaheuristics perform well in solving the PFSPP.

With the rapid development of the courier industry, customers are placing higher demands on the cost and delivery time of courier services. Therefore, this paper focuses on the bi-objective express transportation network design problem (BO-ETNDP) to minimize the operation cost and maximum arrival time. A multi-structure parallel design methodology (MS-PDM) is proposed to solve the BO-ETNDP. In this methodology, all topological structures commonly used in designing transportation networks are sorted out. For each topological structure, a novel bi-objective nonlinear mixed-integer optimization model for BO-ETNDP is developed considering the impact of the hub’s sorting efficiency on the operation cost and arrival time. To solve these models, a preference-based multi-objective algorithm (PB-MOA) is devised, which embeds the branch-and-cut algorithm and Pareto dominance theory in the framework of this ranking algorithm. In the case study, the applicability of the proposed methodology is verified in a real-world leading express company. The results show that our methodology can effectively avoid the limitation of solving the BO-ETNDP with a specific structure. Besides, the suitable topology for designing express transportation networks in different scenarios are explored through the sensitivity analysis.

Among the well-known scheduling problems, the customer order scheduling problem (COSP) has always been of great importance in manufacturing. To reflect the reality of COSPs as much as possible, this study considers that jobs from different orders are classified in various classes. This paper addresses a tri-criteria single-machine scheduling model with multiple job classes and customer orders on which the measurement minimizes a linear combination of the sum of the ranges of all orders, the tardiness of all orders, and the total completion times of all jobs. Due to the NP-hard complexity of the problem, a lower bound and a property are developed and utilized in a branch-and-bound for solving an exact solution. Afterward, four heuristics with three local improved searching methods each and a water wave optimality algorithm with four variants of wavelengths are proposed. The tested outputs report the performances of the proposed methods.

During the spread of the epidemic, the home delivery service (HDS) has been quickly introduced by retailers which helps customers avoid the risk of viral infection while shopping at offline stores. However, the operation cost of HDS is a huge investment for O2O retailers. How to minimize the operating costs of HDS is an urgent issue for the industry. To solve this problem, we outline those management decisions of HDS that have an impact on operating costs, including dynamic vehicle routing, driver sizing and scheduling, and propose an integrated optimization model by comprehensively considering these management decisions. Moreover, the dynamic feature of online orders and the heterogeneous workforces are also considered in this model. To solve this model, an efficient adaptive large neighborhood search (ALNS) and branch-and-cut algorithms are developed. In the case study, we collected real data from a leading O2O retailer in China to assess the effectiveness of our proposed model and algorithms. Experimental results show that our approach can effectively reduce the operating costs of HDS. Furthermore, a comprehensive analysis is conducted to reveal the changing patterns in operating costs, and some valuable management insights are provided for O2O retailers. The theoretical and numerical results would shed light on the management of HDS for O2O retailers.

This paper studies the marketplace channel introduction of contract manufacturers and the response of the platform with an option to introduce a private brand. We develop a game-theoretical model to examine a three-tier e-commerce supply chain including a contract manufacturer (CM), an original equipment manufacturer (OEM) and a platform and derive the equilibrium results. We find that the marketplace channel introduction of the CM and the platform's private brand introduction influence each other. More specifically, marketplace channel encroachment may discourage the platform from introducing a private brand, and this preference is reinforced as the referral fee increases. Interestingly, the introduction of the platform's private brand increases the likelihood of contract manufacturer encroachment, which is mediated by the difference between the two private brands of the CM and platform--as the difference increases, the CM prefers to enter the marketplace channel. Furthermore, only contract manufacturer encroachment (or private brand introduction for the platform) can always benefit the whole supply chain, but the supply chain may be hurt when the platform and the CM perform their strategies simultaneously. In the extension section, in addition to demonstrating the validity of our main results when the CM and the OEM act as a single entity, we also find that the first-mover advantage of the platform may reduce the possibility of the contract manufacturer encroachment.

This paper considers a sales mode selection problem under revenue sharing contracts between resale and agency modes for risk-averse manufacturers with traditional retail channel, direct selling channel, and e-commerce platform channel. By considering the factors including price competition intensity, market share, revenue sharing ratio, commission rate, and degree of risk aversion, we construct leader-follower game models with manufacturers as leaders and traditional retailers and e-commerce platforms as followers. To obtain optimal solutions, we discuss conditions to ensure the upper and lower models to be convex and then give the optimal strategies for all members in the network. Through numerical experiments, we analyze the involved parameters’ impact on sales mode selection strategy and the changing trends of each member's optimal pricing and profit under different sales modes. The numerical results reveal the following revelations: The manufacturer should choose the agency mode when the commission rate is low and the direct selling channel has a large market share. If both the commission rate and degree of risk aversion are high, direct selling channels have a low market share, and price competition intensity is weak, the manufacturer should choose the resale mode. The degree of risk aversion has an effect on each member’s optimal decision. Regardless of which sales mode the manufacturer chooses, the optimal price of each member decreases as the degree of risk aversion increases. Under certain conditions, the manufacturer’s choice of agency mode can create win-win situations with supply chain members.

Automated guided vehicle (AGV) is widely used in automated manufacturing systems as a material handling tool. Although the task scheduling problem with isomorphic AGV has remained a very active research field through the years, too little work has been devoted to the task scheduling problems with heterogeneous AGVs. A coupled task with heterogeneous AGVs is a complex task that needs the cooperation of more than one type of AGVs. In this paper, a manufacturing system with two types of AGVs and three types of tasks is studied. To solve the coupled task scheduling problem with heterogeneous AGVs in this manufacturing system, we introduce two new methods based on the established mathematical model, namely, the decoupled scheduling strategy and coupled scheduling strategy with multi-decision model. The decoupled scheduling strategy is widely used in coupled task scheduling problems. However, there are some situations that the decoupled scheduling strategy cannot solve the problem well. To overcome the problem, the multi-decision point model solves the coupled task scheduling problem without decomposition. In order to ensure the searching speed and searching accuracy, a novel hybrid heuristic algorithm based on simulated annealing algorithm and tabu search algorithm is developed. The simulation experiment results show the proposed coupled scheduling algorithm has priority in coupled task scheduling problems.

In this charge we designated a Hilbert-Johnson process by coupling of heterocyclic N-silylated with benzyl chloride at 100°C using the calcined red algae (CRA) doped with ammonium sulfate (AS), AS@CRA as a heterogeneous catalyst. The resulting examination systems, which included atomic absorption, BET methodology and X-ray diffraction (XRD), scanning electron microscopy (SEM/EDX), and Fourier transform infrared spectroscopy (FT-IR), were employed to describe these catalysts. The effect of catalyst and alkylated agent were extensively studied. This catalyst can also be recycled several times in this condensation, and lastly, we suggested a probable answer mechanism for this process. Moreover, our molecular docking investigation revealed the anti-tuberculosis potential of the synthesized compounds. Notably, the drug isoniazid exhibited higher binding energies compared to the products 2a, 2b, and 2c. Additionally, the ADME study suggests that highly efficacious synthetic compounds may possess anti-tuberculosis properties.

The maximum tardiness reflects the worst level of service associated with customer needs; thus, the principle that seru production reduces the maximum tardiness is investigated, and a model to minimize the maximum tardiness of the seru production system is established. In order to obtain the exact solution, the non-linear seru production model with minimizing the maximum tardiness is split into a seru formation model and a linear seru scheduling model. We propose an efficient cooperative algorithm using a genetic algorithm and an innovative reinforcement learning algorithm (CAGARL) for large-scale problems. Specifically, the GA is designed for the seru formation problem. Moreover, the QL-seru algorithm (QLSA) is designed for the seru scheduling problem by combining the features of meta-heuristics and reinforcement learning. In the QLSA, we design an innovative QL-seru table and two state trimming rules to save computational time. After extensive experiments, compared with the previous algorithm, CAGARL improved by an average of 56.6%. Finally, several managerial insights on reducing maximum tardiness are proposed.

This research focuses on production releasing and routing allocation problems in re-entrant mixed-flow shops. Since re-entrant mixed flow shops are complex and dynamic, many studies evaluate release plans by developing discrete event simulation models and selecting the optimal solution according to the estimation results. However, a high-accurate discrete event simulation model requires a lot of computation time. In this research, we develop an effective multi-fidelity optimization method to address product release planning problems for re-entrant mixed-flow shops. The proposed method combines the advantages of rapid evaluation of analytical models and accurate evaluation of simulation models. It conducts iterative optimization using a low-fidelity mathematical estimation model to find good solutions and searches for the optimal solution via a high-fidelity simulation estimation model. Computational results of large-scale production releasing and routing allocation problems illustrate that the proposed approach is good at addressing large-scale problems in re-entrant mixed-flow shops.