This paper investigates a novel problem concerning material delivery in a matrix manufacturing workshop, specifically the multi-automated guided vehicle (AGV) dispatching problem with unloading setup time (MAGVDUST). The objective of the problem is to minimize transportation costs, including travel costs, time penalty costs, AGV costs, and unloading setup time costs. To solve the MAGVDUST, this paper builds a mixed-integer linear programming model and proposes an improved genetic algorithm (IGA). In the IGA, an improved nearest-neighbor-based heuristic is proposed to generate a high-quality initial solution. Several advanced technologies are developed to balance local exploitation and global exploration of the algorithm, including an optimal solution preservation strategy in the selection process, two well-designed crossovers in the crossover process, and a mutation based on Partially Mapped Crossover strategy in the mutation process. In conclusion, the proposed algorithm has been thoroughly evaluated on 110 instances from an actual electronic factory and has demonstrated its superior performance compared to state-of-the-art algorithms in the existing literature.

Industry 4.0 encourages industries to digitise the manufacturing system to facilitate human-robot collaboration (HRC) to foster efficiency, agility and resilience. This cutting-edge technology strikes a balance between fully automated and manual operations to maximise the benefits of both humans and assistant robots (known as cobots) working together on complicated and prone-to-hazardous tasks in a collaborative manner in an assembly system. However, the introduction of HRC poses a significant challenge for assembly line balancing since, besides typical assigning tasks to workstations, the other two important decisions must also be made regarding equipping workstations with appropriate cobots as well as scheduling collaborative tasks for workers and cobots. In this article, the cobot assembly line balancing problem (CoALBP), which just initially emerged a few years ago, is thoroughly reviewed. The 4M1E (i.e., man, machine, material, method and environment) framework is applied for categorising the problem to make the review process more effective. All of the articles reviewed are compared, and their key distinct features are summarised. Finally, guidelines for additional studies on the CoALBP are offered.

In modern industrial manufacturing, there are uncertain dynamic disturbances between processing machines and jobs which will disrupt the original production plan. This research focuses on dynamic multi-objective flexible scheduling problems such as the multi-constraint relationship among machines, jobs, and uncertain disturbance events. The possible disturbance events include job insertion, machine breakdown, and processing time change. The paper proposes a conv-dueling network model, a multidimensional state representation of the job processing information, and multiple scheduling objectives for minimizing makespan and delay time, while maximizing the completion punctuality rate. We design a multidimensional state space that includes job and machine processing information, an efficient and complete intelligent agent scheduling action space, and a compound scheduling reward function that combines the main task and the branch task. The unsupervised training of the network model utilizes the dueling-double-deep Q-network (D3QN) algorithm. Finally, based on the multi-constraint and multi-disturbance production environment information, the multidimensional state representation matrix of the job is used as input and the optimal scheduling rules are output after the feature extraction of the conv-dueling network model and decision making. This study carries out simulation experiments on 50 test cases. The results show the proposed conv-dueling network model can quickly converge for DQN, DDQN, and D3QN algorithms, and has good stability and universality. The experimental results indicate that the scheduling algorithm proposed in this paper outperforms DQN, DDQN, and single scheduling algorithms in all three scheduling objectives. It also demonstrates high robustness and excellent comprehensive scheduling performance.

The present research explores the collective influence of quality assurance and postponement on a hybrid multiproduct replenishing-delivery decision-making. Assume the required multiproduct has a standard (common) component, and our replenishing-delivery model has incorporated a two-phase postponement strategy. The first phase makes all standard components and hires an external supplier to partially provide the required parts to cut short the needed uptime. In contrast, the second phase fabricates the finished multiproduct in sequence. To ensure the desired merchandise quality, we apply a quality-assurance action to the in-house processes to screen and remove scrap items and rework the repairable defects in both stages. Upon completing each merchandise, these products are transported to the customer in n fixed-quantity shipment in fixed-time intervals. We employ math modeling and formulating approaches to gain the overall supply-chain operating expenses comprising subcontracting, fabricating, stock holding, transportation, and customer holding costs. By minimizing system operating expenses, this research determines the optimal replenishing-delivery policy. Lastly, we give a numerical example to demonstrate our study’s applicability and usefulness/capability for facilitating managerial decision-making.

The Inventory Routing Problem (IRP) has been highlighted as a valuable strategy for tackling routing and inventory problems. This paper addresses the IRP but considers the forward delivery and the use of Returnable Transport Items (RTIs) in the distribution strategy. We develop an optimization model by considering inventory routing decisions with RTIs collection (backhaul customers) of a Closed-Loop Supply Chain (CLSC) within a short-term planning horizon. RTIs consider reusable packing materials such as trays, pallets, recyclable boxes, or crates. The RTIs represent an essential asset for many industries worldwide. The solution of the model allows concluding that if RTIs are considered for the distribution process, the relationship between the inventory handling costs of both the final goods and RTIs highly determines the overall performance of the logistics system under study. The obtained results show the efficiency of the proposed optimization scheme for solving the combined IRP with RTIs, which could be applied to different real industrial cases.

Production plans based on Material Requirement Planning (MRP) frequently fall short in reflecting actual customer demand and coping with demand fluctuations, mainly due to the rising complexity of the production environment and the challenge of making precise predictions. At the same time, MRP is deficient in effective adjustment strategies and has inadequate operability in plan optimization. To address material management challenges in a volatile supply-demand environment, this paper creates a make-to-stock (MTS) material production planning model that is based on customer demand and the demand-driven production planning and control framework. The objective of the model is to optimize material planning output under resource constraints (capacity and storage space constraints) to meet the fluctuating demand of customers. To solve constrained optimization problems, the demand-driven material requirements planning (DDMRP) management concept is integrated with the grey wolf optimization (GWO) algorithm and proposed the DDMRP-GWO algorithm. The proposed DDMRP-GWO algorithm is used to optimize the inventory levels, shortage rates, and production line capacity utilization simultaneously. To validate the effectiveness of the proposed approach, two sets of customer demand data with different levels of volatility are used in experiments. The results demonstrate that the DDMRP-GWO algorithm can optimize the production capacity allocation of different types of parts under the resource constraints, enhance the material supply level, reduce the shortage rate, and maintain a stable production process.

This paper explores the impact of retailers' corporate social responsibility (CSR) and manufacturers' overconfidence on manufacturers' carbon reduction in sustainable supply chains. We analyze the profits of manufacturers and retailers under different scenarios and explore the social welfare and environmental impacts under CSR. Our results suggest that retailers' CSR and manufacturers' overconfidence contribute positively to promoting carbon mitigation and reducing environmental impacts under certain conditions. However, with increasing CSR and manufacturer overconfidence levels, manufacturers are more likely to lead to worse environmental impacts and carbon emission reduction. In addition, we show that when the manufacturer's overconfidence level is high, manufacturers and retailers are more profitable and contribute to carbon emission reductions in the manufacturer without overconfidence (retailer without CSR) scenario. Moreover, we find that firms have the higher potential to capture optimal overall social welfare in the presence of retailers with CSR and manufacturer overconfidence.

With the implementation of the Belt and Road Initiative, the inland ports planning is receiving more and more attention. In this work, we aim to determine the scale and function of different potential inland ports in a certain region while considering the cargo flow allocation schemes for the inland ports and seaports in cross-border trade. Unlike previous studies, we consider the dynamic interaction between local government and manufacturing enterprises in the inland port planning process. Based on this, we formulate a bilevel programming model for the considered inland port planning problem, where the upper-level focuses on the local government and the lower-level concentrates on the manufacturing enterprise. To solve the proposed model, we develop a hybrid heuristic algorithm by combining a genetic algorithm and an exact solution method. Furthermore, we conduct a case study of the inland ports planning for the Huaihai Economic Zone in China to verify the applicability of the proposed model and algorithm. The computational results demonstrate that the proposed optimization approach can effectively increase the cross-border transportation market share of inland ports within a limited investment amount and reduce the competition among these inland ports. Our case study also provides valuable management insights on inland port planning in terms of manufacturing enterprises weights, investment limit amount, scale effect, and cargo value weights.

There have been many applications for machine learning algorithms in different fields. The importance of hyperparameters for machine learning algorithms is their control over the behaviors of training algorithms and their crucial impact on the performance of machine learning models. Tuning hyperparameters crucially affects the performance of machine learning algorithms, and future advances in this area mainly depend on well-tuned hyperparameters. Nevertheless, the high computational cost involved in evaluating the algorithms in large datasets or complicated models is a significant limitation that causes inefficiency of the tuning process. Besides, increased online applications of machine learning approaches have led to the requirement of producing good answers in less time. The present study first presents a novel classification of hyperparameter types based on their types to create high-quality solutions quickly. Then, based on this classification and using the hypergradient technique, some hyperparameters of deep learning algorithms are adjusted during the training process to decrease the search space and discover the optimal values of the hyperparameters. This method just needs only the parameters of the previous two steps and the gradient of the previous step. Finally, the proposed method is combined with other techniques in hyperparameter optimization, and the results are reviewed in two case studies. As confirmed by experimental results, the performance of the algorithms with the proposed method have been increased 36.62% and 23.16% (based on the best average accuracy) for Cifar10 and Cifar100 dataset respectively in early stages while the final produced answers with this method are equal to or better than the algorithms without it. Therefore, this method can be combined with hyperparameter optimization algorithms in order to improve their performance and make them more appropriate for online use by just using the parameters of the previous two steps and the gradient of the previous step.

In this work, a probabilistic bid generation problem with the pricing of a bundle of lanes and carrier’s vehicle routing is considered as it is an importation in transportation service procurement. Depending on the network of the vehicle, there exist multiple lanes for traveling between two locations. To solve the bid generation problem efficiently, a two-phase method approach is presented. At the core of the procedure a feasible vehicle routing problem on a multidigraph is solved by an exhaustive search algorithm to enumerate all routes concerning routing constraints and treat each route as a decision variable in the set partitioning formulation. We examine our model both analytically and empirically using a simulation-based analysis.