Manufacturers today need to optimize their fabrication runtime decision by meeting short customer order due dates externally and managing the potentially unreliable machines and manufacturing processes internally. Outsourcing and overtime are commonly utilized strategies to expedite fabricating time. Additionally, detailed analyses and necessary actions on inevitable product defects (i.e., removal of scraps) and equipment breakdowns (such as machine repairing) are prerequisites to fabrication runtime planning. Motivated by assisting today’s manufacturers decide the best batch runtime plan under the situations mentioned above, this study applies mathematical modeling to a hybrid fabrication problem that incorporates partial overtime and outsourcing, inevitable product defects, and a Poisson-distributed breakdown. We develop a model to accurately represent the problem’s characteristics. Formulations and detailed model analyses allow us to find the cost function first. Differential equations and algorithms help us confirm the gain function’s convexity and find the best runtime decision. Lastly, we use numerical illustrations to show our study’s applicability by revealing in-depth crucial managerial information of the studied problem.

The growing popularity of aggregation platforms has attracted widespread attention in the ride-hailing market in recent years. In order to obtain additional orders by charging commissions and slotting fees, many ride-hailing platforms choose to bundle with aggregation platforms. Unlike traditional reseller electronic channels, the bundle channels may affect pricing of platforms, service levels of drivers, market demands and they may further impact on profits. These different attitudes raise an interesting and key question about the influence of bundle channels in ride-hailing platforms. In this paper, we propose an analytical framework for pricing strategies of ride-hailing and aggregation platforms under bundle mode and analyze their pricing process from the perspective of Nash and Stackelberg games, where the platforms serve as leaders to determine optimal prices through Nash equilibrium and the drivers serve as followers to provide optimal service levels. Through sensitivity analysis of service levels and costs, we capture the distribution trends of profits between the platforms. Based on some numerical examples and results analysis, some interesting managerial insights on pricing of ride-hailing and aggregation platforms are gained.

Social dilemmas describe conflict situations between immediate self-interest and longer-term collective interests. In these situations, it is better that all players work together to attain a common goal, but individuals may threaten the best payoff of the group by free-riding. Human behavior in a pandemic is one example of a social dilemma but wait-and-see games and relying on herd immunity to get a free ride generates a threat of continuing the pandemic. This study aims to use probabilistic incentives given by a third party as a mechanism to inhibit free-riding behavior by promoting cooperation in the volunteer dilemma game. For more realistic human behavior simulation, we use an agent-based model of network topology. When the parameters of the problem change gradually, an abrupt jump in the cooperation rate may happen and lead to a significant shift in the outcome. Catastrophe theory is a valuable approach to survey these nonlinear changes. This study tries to give some managerial insights to the decision-makers to find the minimum level of necessary effort in which the cooperation dominates the defection.

The study examines the combined effect of rework, multiple shipments, postponement, and overtime producing common-component on a multiproduct vendor-client incorporated system. Clients’ product demand trend turns to diversity, quality, and rapid response in the current supply-chain environment. Under such a stiff competitive environment, today’s manufacturers must effectively plan their multi-item fabrication to boost utilization and product quality, minimize total relevant costs, and meet short given order lead time. By considering the commonality of the finished goods, required quality, and completion lead time, this study presents an exact model featuring rework of defects, multiple shipments, postponement, overtime producing the mutual component, and satisfying the market needs. Through the techniques of explicitly modeling, formulating, and system cost minimization, this study simultaneously derives the optimal cycle-time and shipping frequency for the studied problem. A numerical example helps show how our model works for any given parameter values and how the variation in single and multiple factors of the problem affects the crucial system performances (e.g., total uptimes, each relevant cost, utilization, total cost, etc.) A wide variety of today’s industries (e.g., automotive, household goods, etc.) and their related supply chains can utilize our decisional model to reveal in-depth managerial insights for planning their fabrication and shipments.

In this research integrated scheduling of machines and automated guided vehicles (AGVs) in a flexible job shop environment is addressed. The scheduling literature generally ignores the transportation of jobs between the machines and when considered typically assumes an unlimited number of AGVs. In order to comply with Industry 4.0 requirements, today’s manufacturing systems make use of AGVs to transport jobs between the machines. The addressed problem involves simultaneous assignment of operations to one of the alternative machines, determining the sequence of operations on each machine and assignment of transportation operations between machines to an available AGV. We present a Microsoft Excel® spreadsheet-based solution for the problem. Evolver®, a proprietary GA is used for the optimization. The GA routine works as an add-in to the spreadsheet environment. The flexible job shop model is developed in Microsoft Excel® spreadsheet. The assignment of AGV is independent of the GA routine and is done by the spreadsheet model while the GA finds the assignment of operations to the machines and then finds the best sequence of operations on each machine. Computational analysis demonstrates that the proposed method can effectively and efficiently solve a wide range of problems with reasonable accuracy. Benchmark problems from the literature are used to highlight the effectiveness and efficiency of the proposed implementation. In most of the cases the proposed implementation can find the best-known solution found by previous studies.

This paper recommends a new kind of assembly line rebalancing and worker assignment problem, taking ergonomic risks into account. Assembly line rebalancing problem (ALRBP) occurs when a current line must be rebalanced due to conditions such as changes in demand, production processes, product design, or quality issues. Although there are several research attempts on ALRBP in the relevant literature, only a few studies consider workers as unique individuals. This paper aims to solve the double reassignment problem: tasks to workers and workers to stations, considering ergonomic risk factors. This paper is the first study that comprises worker assignment and ergonomic constraints in ALRBP literature to the best of our knowledge. Objectives of our novel problem are to minimize rebalancing cost, which includes transportation of tasks and workers and minimize stations' ergonomic risk factors. A randomized constructive rule-based heuristic approach is developed to cope with the problem. The proposed solution approach is applied to benchmark data, and obtained results are promising. Moreover, the proposed solution approach is implemented in an automotive parts manufacturing plant.

This research proposes a dynamic decision-making framework for a hybrid production system that incorporates manufacturing and remanufacturing procedures into a closed-loop supply chain network with merchandise substitution and shortages within traditional markets (TM) and electronic markets (EM). In particular, we develop models of profit maximization and equilibrium analysis by using calculus with dynamic programming under four business schemes, including a manufacturing-only model within TM/EM and a hybrid remanufacturing model within TM/EM. Dynamic decision-making planning was taken for brand-new and like-new decayed merchandise in hybrid production systems. The results demonstrate that solutions generated within EMs surpass those within TMs in terms of maximizing profits. Further, the hybrid remanufacturing model did not surpass the manufacturing-only model under a general setting, but had better performance under certain conditions, including intense competition, a smaller remanufacturing cost, a larger brand-new merchandise market size, and a smaller like-new merchandise market size.

As a useful strategy to improve the flexibility of the system to manage uncertainty in supply and demand and to improve the sustainability of the supply chain, vendor-managed inventory (VMI) programs have attracted widespread attention in the field of supply chain management. However, a growing body of empirical literature has shown that participants’ decisions deviate significantly from the standard theoretical predictions. Under a VMI program, the supplier bears not only the production cost, but also the risk of leftover inventory. Moreover, the inequality among participants and different personalities of decision-makers in VMI programs may lead to the divergence of decision-making. To understand the supplier’s replenishment decision in view of the behavioral pattern, we propose a new inventory model for the supplier with the focus theory of choice. The proposed model conceives that the retailer evaluates each replenishment quantity based on the most salient demand for him/her instead of calculating the expected utility. By employing this inventory model, we construct a two-tier supply chain model with revenue-sharing contract and theoretically derive the optimal sharing percentage of the revenue and replenishment quantity. Results analysis gains managerial insights into the strategic selection of the retailer who faces suppliers with different personalities. Comparisons between the classic revenue-sharing contract model and the proposed model are also carried out by illustrative examples. This research provides a new perspective to analyze individual supplier’s behavior in a VMI program with revenue-sharing contracts.

In transportation, the multi-depot heterogeneous fleet vehicle routing problem with time windows (MDHFVRPTW) is one of the hard-to-solve real-life problems. In the study, a new node-based MDHFVRPTW has been developed. Unlike other studies in the literature, heterogeneous fleets including both airline and roadway vehicles are used for routing. In the model, real-life data of the airline and roadway are taken into consideration. In particular, important aviation constraints such as the range of the aircraft, landing and take-off cycle (LTO) cost according to the engine type, and the penalty cost are presented in the model. The problem is analysed by using narrow and wide time windows, which is the realization of fast and normal demand. A new hybrid genetic algorithm with variable neighborhood search (HGA-VNS) has been proposed for the solution of the MDHFVRPTW model. In the solution of the model, remarkable results have been obtained with the HGA-VNS algorithm compared to the genetic algorithm and off-the-shelf solvers. Also, the HGA-VNS algorithm has been tested with small and large-scale instances and compared with other studies in the literature. It is thought that the proposed MDHFVRPTW model and the developed HGA-VNS algorithm will bring a different perspective to transportation.