The drum–buffer–rope (DBR) is a scheduling mechanism under the Theory of Constraints (TOC) philosophy. In DBR, ‘drum’ is a production schedule on the capacity-constrained resources (CCRs), which controls the speed of production for the whole system; ‘rope’ is a mechanism to release the required material to the CCRs; and ‘buffer’ is used to protect the CCRs from starvation due to statistical fluctuations. For a non-identical parallel machine flow-shop environment, estimating an efficient rope and time buffer for DBR implementation is not an easy task because of the complexity of non-identical parallel machine loading. This paper proposes a new scheduling method, which is called the modified DBR (MOD-DBR). It applies a backward finite capacity scheduling technique, including machine loadings and detail scheduling, instead of the rope mechanism in DBR. The scheduling performances of MOD-DBR are evaluated under variable processing time situations. The experimental results indicate that the MOD-DBR without a time buffer outperformed the DBR with a considerable level of buffer on the average flow time, while they have the same performance on tardiness, constraint resource utilization, and throughput.

While public service motivation (PSM) and teamwork are widely recognized as crucial drivers for effective public service delivery, researchers primarily analyze these factors independently and at a personal level. The existing literature rarely explores the interplay between PSM, the project team learning process (PTLP), and psychological safety (PS) within research and development (R&D) project teams, particularly in national R&D organizations. This study addresses this gap by proposing a theoretical model that examines the combined effect of individual motivation and team collaboration, mediated by PS, on R&D PTLP. Additionally, it investigates the moderating influence of project management (PM) styles—fully agile and partially agile—on these relationships. The proposed method utilizes partial least squares structural equation modeling (PLS-SEM) for quantitative data analysis. Our findings revealed a positive relationship between PSM, PS, and R&D PTLP, with PS acting as a significant mediator. Notably, the relationship between PSM and R&D PTLP was stronger under fully agile project management compared to partially agile settings. These findings suggest that both project teams and organizations should prioritize promoting PS and consider the moderating effects of project management styles to foster a sustainable R&D team learning process, particularly within national R&D institutions.

This paper introduces the demand-driven assembly line rebalancing problem (DDALRP) and proposes a non-linear, multi-objective, combinatorial optimization model to solve it. A DDALRP arises whenever the production output of the assembly line (AL) must be continuously readjusted along a planning horizon in order to satisfy as much as possible a given demand forecast; thus, dealing not with a one-time rebalance, but with a multi-period rebalance, fact that exponentially increases the complexity and combinatorial nature of the problem. Adapting or regulating the production output of the AL to a particular demand forecast or production plan is a relatively new idea in the assembly line balancing (ALB) / rebalancing (ALR) literature; and the novelty of this work is the rebalancing mechanism employed to solve the problem: we address the problem by reallocating workers to stations, taking into consideration their learning and forgetting (L&F) curves. Our proposed model was solved by implementing a genetic algorithm (GA) in 162 cases (three problem instances under 54 scenarios each), which produced useful insights about the dynamics of worker reallocation under different situations: optimistic, most-likely, pessimistic L&F coefficients; experienced and inexperienced workers; and different demand scenarios.

This paper aims to develop a finite capacity material requirement planning (FCMRP) system based on TOC philosophy (TOC-MRP) for multi-stage assembly factory that has some bottleneck stations. The proposed TOC-MRP system tries to load and schedule operations on bottleneck stations in a manner that they are free of idle time and overtime. The schedules on non-bottleneck stations will be arranged until they are not conflicting with those on the bottleneck stations. The non-bottleneck stations are allowed to have idle time and overtime if necessary. To analyse whether TOC-MRP is effective, it is compared with a FCMRP method that does not adopt TOC philosophy. The experimental results reveal that the TOC-MRP outperforms the FCMRP without TOC philosophy.

This paper proposes a Delivery Service Management (DSM) system for Small and Medium Enterprises (SMEs) that own a delivery fleet of pickup trucks to manage Business-to-Business (B2B) delivery services. The proposed DSM system integrates four systems: Delivery Location Positioning (DLP), Delivery Route Planning (DRP), Arrival Time Prediction (ATP), and Communication and Data Sharing (CDS) systems. These systems are used to pinpoint the delivery locations of customers, plan the delivery route of each truck, predict arrival time (with an interval) at each delivery location, and communicate and share information among stakeholders, respectively. The DSM system deploys Google applications, a GPS tracking system, Google Map APIs, ATP algorithms (embedded in Excel Macros), Line, and Telegram as supporting tools. To improve the accuracy of the ATP system, three techniques are applied considering driver behaviors. The proposed DSM system has been implemented in a Thai SME. From the process perspective, the DSM system is a systematic procedure for end-to-end delivery services. It allows the interactions between planner-driver decisions and supporting tools. The supporting tools are simple, can be easily used with little training, and require low capital expenditure. The statistical analysis shows that the ATP algorithm with the three techniques provides high accuracy. Thus, the proposed DSM system is beneficial for practitioners to manage delivery services, especially for SMEs in emerging countries.