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This paper aims to introduce a new mixed integer programming formulation and associated heuristic algorithm to solve the Military Nodal Capacity Problem, which is a type of supply chain network design problem that involves determining the amount of capacity expansion required at theater nodes to ensure the on-time delivery of military cargo.

Supply chain network design, mixed integer programs, heuristics and regression are used in this paper.

This work helps analysts at the United States Transportation Command identify what levels of throughput capacities, such as daily processing rates of trucks and railcars, are needed at theater distribution nodes to meet warfighter cargo delivery requirements.

This research assumes all problem data are deterministic, and so it does not capture the variations in cargo requirements, transit times or asset payloads.

This work gives military analysts and decision makers prescriptive details about nodal capacities needed to meet demands. Prior to this work, insights for this type of problem were generated using multiple time-consuming simulations often involving trial-and-error to explore the trade space.

This work merges research of supply chain network design with military theater distribution problems to prescribe the optimal, or near-optimal, throughput capacities at theater nodes. The capacity levels must meet delivery requirements while adhering to constraints on the proportion of cargo transported by mode and the expected payloads for assets.

A comprehensive review of the literature for the problem of lot‐size scheduling (serial and assembly) considering the uncapacitated problem and complicated capacitated assembly manufacturing structure. Analyses the different solution techniques and findings for each product set.

Performance improvement for requirements planning systems is an issue which receives wide interest. Many programming approaches have been proposed to improve material requirements planning procedures. However, most of them appear to be too complex for large manufacturing problems. Alternatively it might be promising to explore the integration of materials requirements planning and capacity requirements planning. Several simple heuristics for integrated requirements planning systems are suggested. Several heuristics are offered to balance the load in the systems and several procedures presented to adjust the planned requirements so that the system will execute more smoothly. An industrial example supports the adequacy of the general concepts provided in this research. Results are presented which demonstrate the adequacy of these heuristics, and illustrate the ease of implementing the procedures into any MRP system.

This paper aims to describe the storage constrained, inbound inventory routeing problem and presents bounds and heuristics for solutions to this problem. It also seeks to analyze various characteristics of this problem by comparing the solutions generated by the two proposed heuristics with each other and with the lower bound solutions.

The proposed heuristics use a sequential decomposition strategy for generating solutions for this problem. These heuristics are evaluated on a set of problem instances which are based on an actual application in the automotive manufacturing industry.

The storage space clearly has a significant effect on both the routeing and inventory decisions, and there are complex and interesting interactions between the problem factors and performance measures.

Facility design decisions for the storage of inbound materials should carefully consider the impact of storage space on transportation and logistics costs.

This problem occurs in a number of different industrial applications while most of the existing literature addresses outbound distribution. Other papers that address similar problems do not consider all of the practical constraints in the problem or do not adequately benchmark and analyze their proposed solutions.

An attempt is made to classify the lot‐sizing problem based on evidence from the literature and current research trends. For future research a mixture of a heuristic method to find a sequence and cycle time and a mathematical program to find lot sizes would be feasible even for fairly large problems. Attempts should be made to apply marginal analysis in practical lot‐sizing problems since it may result in lower cost solutions.

The cognitive composition process of attribute information plays a critical role in heuristic aspects of consumers’ multiattribute preferential decisions. This study examines the effects of attribute information order and attribute information amount on consumers’ multiattribute preferential decisions under the premise of consumers’ limited information processing capacity. An experiment with two separate designs is conducted for testing the different hypotheses. The results support the hypothesis that consumers’ multiattribute preferential decisions are influenced by the amount of attribute information received/processed. The attribute information order is found to affect not only the outcomes of consumer decisions but also the amount of attribute information processed. These findings further suggest that consumers may be more inclined to adopt strategies of a noncompensatory heuristic nature when making multiattribute preferential decisions.

Considers the availability of objective criteria for creativity enhancement from relevant applied research. Uses research to address the problem of creativity within the framework of artificial intelligence. Compares thinking to using a map and discusses creativity in terms of making our own maps rather than just following existing maps. Suggests that creativity includes the individual′s mastering of existing rules, enabling him or her to see when these rules might be broken. It thus follows that basic heuristic competences underlie the creative act. A strategy for stimulating creativity should include a more systematic basis for the development of creative resources, in the concept of heuristic competence.

Polarica is a company in northern Europe buying, refining and selling wild berries and other specialty foods. During the last couple of years the volume of wild berries, mostly blueberries, has increased a lot. This expansion forces Polarica to consider investments in freezing‐in capacity and cold‐storage capacity. A simple heuristic model was constructed, from which it was concluded that additional volumes of frozen blueberries require more storage facilities and it was also recommended that the location should be based on load‐distance analysis. The way this problem is tackled and solved can be copied and hopefully it presents ideas for other similar studies.

In a kitting supply system, the occurrence of material-handling errors is unavoidable and will cause serious production losses to an assembly line. To minimize production losses, this paper aims to present a dynamic scheduling problem of automotive assembly line considering material-handling mistakes by integrating abnormal disturbance into the material distribution problem of mixed-model assembly lines (MMALs).

A multi-phase dynamic scheduling (MPDS) algorithm is proposed based on the characteristics and properties of the dynamic scheduling problem. In the first phase, the static material distribution scheduling problem is decomposed into three optimization sub-problems, and the dynamic programming algorithm is used to jointly optimize the sub-problems to obtain the optimal initial scheduling plan. In the second phase, a two-stage rescheduling algorithm incorporating removing rules and adding rules was designed according to the status update mechanism of material demand and multi-load AGVs.

Through comparative experiments with the periodic distribution strategy (PD) and the direct insertion method (DI), the superiority of the proposed dynamic scheduling strategy and algorithm is verified.

To the best of the authors’ knowledge, this study is the first to consider the impact of material-handling errors on the material distribution scheduling problem when using a kitting strategy. By designing an MPDS algorithm, this paper aims to maximize the absorption of the disturbance caused by material-handling errors and reduce the production losses of the assembly line as well as the total cost of the material transportation.

The continued evolution of influencer marketing has created a need to better understand influencer marketing effectiveness. With brands increasingly partnering with influencers, research is yet to provide an integrated perspective examining the critical role of both parties. This study aims to draw on the source credibility model and signaling theory to explain the mechanisms that matter in influencer marketing effectiveness.

The proposed model of influencer marketing effectiveness is analyzed using partial least squares with data from 281 followers of social media influencers.

The authors establish influencer characteristics of popularity and attractiveness as heuristic cues that inform judgments of influencer efficacy. Further, category involvement and altruistic motives for collaboration are shown to moderate followers’ reliance on these heuristic cues. Then, a sequential mediating effect demonstrates the critical roles of the influencer and partner brand in three desired outcomes: enhanced perception of brand authenticity, enhanced brand engagement and positive attitudes toward influencer posts.

Future research should consider other heuristic cues that could inform influencer efficacy judgments and switch the focus toward the partner brand’s impact on such judgments.

A step-by-step visual framework is presented to help marketers and influencers translate these findings into key responsibilities for developing more effective and collaborative partnerships.

Besides presenting an integrated perspective, signaling theory provides an original lens for explaining influencer marketing effectiveness, addressing the need to expand the theoretical boundaries of influencer marketing research.