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In this study, we present a flexible delivery routing problem faced by logistics service providers in which some customers may receive their packages indirectly. Customers are supposed to get their packages directly from a delivery service provider or be delivered to indirectly using a nearby station drop and a subsequent separate last-mile delivery service provider that finally delivers their goods. We address the problem of determining whether deliveries to customers should be fulfilled directly or indirectly in this situation. We present the mathematical programming model and propose a near-optimal heuristic approach based on a memetic algorithm. In addition, we evaluate the numerical performance of the proposed algorithm and perform various parametric analyses to gain managerial insights from the model.

In this paper, we propose learning-based adaptive control based on reinforcement learning for the booking policy in sea cargo revenue management. The problem setting is that the demand distribution is unknown while the historical data is available, and the problem is formulated as a stochastic dynamic programming model. We demonstrate the existence of an optimal control limit policy and investigate the important properties and optimal policy structures of the model. We then propose a reinforcement learning approach for the data-driven approximation of the optimal booking policy to maximize shipping line revenue. The performance of the proposed approach is very close to that of the optimal policy and superior to that of the EMSR-b algorithm.

In this paper, we examine an optimization problem for a shipping company that provides an on-demand delivery service in which its customers visit a specified location called a delivery center (e.g., a convenience store or Amazon locker) to pick up their items themselves, rather than delivering the products purchased online to the customers directly. In this problem, we select delivery centers that are accessible for all customers and determine the optimal delivery route that minimizes the costs proportional to the distance traveled by the delivery vehicle, the delivery time, and costs. This is an extension of the traveling salesman problem with a time window constraint, which simultaneously determines the locations of the delivery centers for customer pickup and the delivery route. To solve this problem and to improve managerial practice, we propose a mathematical programming model and an efficient two-phase solution algorithm based on an ant colony heuristic that is better suited to reality. By addressing on-demand delivery optimization problems, shipping companies can guarantee customer satisfaction while reducing shipping costs.

Automated material handling systems (AMHSs) in semiconductor fabrication facilities (fabs) are inherently capital intensive because they are moving toward full automation. However, in addition to being capital intensive, full automation can come at the cost of compromised performance or instability when abnormal events occur. Vehicular congestion is one example of an abnormal event and is a recurring problem in fabs that reduces production efficiency. In this paper, motivated by a material handling system design problem when constructing a new semiconductor fabrication plant in practice, we present a model for optimizing the location of overhead hoist transport lifters, which have proven to be a suitable addition to AMHSs for resolving bottlenecks caused by heavy congestion. To do so, we study a capacitated facility location problem (CFLP) that incorporates real-life constraints and consider the interactions between lifters of differing types. We first propose a hybrid approach that combines a genetic algorithm with a depth-first search (DFS) based on memorization to approximate the optimum positions for the installation of the lifters. We then conduct a numerical experiment to compare the performance of our approach with optimal solutions in small- to medium-sized facilities and perform a sensitivity analysis for the important parameters involved. Finally, an experimental study based on real data from semiconductor fabs is conducted to demonstrate the applicability and usefulness of the proposed model.

With the growing interest and popularity of electric vehicles (EVs), the electrification of buses has been progressing recently. To achieve the seamless operation of electric buses (e-Buses) for public transportation, some bus stations should play the role of battery swapping station due to the limited travel range of e-Buses. In this study, we consider the problem of locating battery swapping stations for e-Buses on a passenger bus traffic network. For this purpose, we propose three integer programming models (set-covering-based model, flow-based model and path-based model) to model the problem of minimizing the number of stations needed. The models are applied and tested on the current bus routes in the Seoul metropolitan area of South Korea.

This paper investigates several incentive mechanisms for collaborative product quality improvement in a buyer-driven supply chain, and the impacts of those mechanisms on supply chain performance. The buyer, the Stackelberg leader, determines the sales price of a product while the supplier is responsible for production and product quality determination. We develop analytical models incorporating two reward schemes to better understand how the buyer can facilitate the supplier’s quality improvement efforts. We offer managerial insights and practical guidelines for implementing quality management in the supply chain, derived from both an analytical comparison and numerical experiments.

Organ transplants are essential for many end-stage organic disease patients. Unfortunately, because of medical or biological incompatibilities, not all donors can donate to their intended recipients. These incompatibilities can be overcome by organ exchange programs, which find new compatible donor–patient pairs by exchanging donors between patients. Organ exchange programs have become prevalent in the last decade for kidneys, and liver exchanges have also been increasing steadily. However, despite the growing number of liver exchanges, since the procedure is relatively new, there is a lack of studies attempting to optimize exchange plans through mathematical programming. This paper develops a new integer programming model for liver exchange programs that takes into account the unique characteristics of liver transplantation. In addition, a new enhanced model is obtained by applying the reformulation-linearization technique (RLT), which provides tight linear programming (LP) relaxation bounds and is computationally efficient.

This paper investigates the joint decision on inventory and refurbishing strategies in a closed-loop supply chain, consisting of a manufacturer responsible for the production and first-market sales and a third-party refurbisher responsible for the refurbishment and second market. Based on a game-theoretic model, we investigate the effects of supply chain structures on the production operations, sales price of a refurbished product, profits and environmental performance. Through numerical analyses, we reveal that the integrated system yields higher first-market sales and a larger production lot size, while the decentralized one shows higher second-market sales and a larger refurbishing lot. Interestingly, the integrated system sets the higher price for the refurbished item due to channel conflict issues. Efforts to increase the second-market sales can harm the overall supply chain profit by leading to the decrease in the higher-margin first-market sales. We also show that the difference in pricing decisions and inventory holding cost components allows a better operational performance of the third-party refurbisher's process in the decentralized system. Then, it leads to the better environmental performance in the decentralized system, enhancing the refurbishing rate and sales of refurbished items while reducing scrap costs. By investigating the overall performance of integrated and decentralized supply chains, we offer important implications to practicing supply chain managers by revealing how the third-party refurbisher can help the sustainable operations of a closed-loop supply chain. We also contribute to the body of knowledge by incorporating pricing and lot sizing issues into the context of closed-loop supply chain management.

This study aimed to resolve a real-world traffic problem in a large-scale plant. Autonomous vehicle systems (AVSs), which are designed to use multiple vehicles to transfer materials, are widely used to transfer wafers in semiconductor manufacturing. Traffic control is a significant challenge with AVSs because all vehicles must be monitored and controlled in real time, to cope with uncertainties such as congestion. However, existing traffic control systems, which are primarily designed and controlled by human experts, are insufficient to prevent heavy congestion that impedes production. In this study, we developed a traffic control system based on machine learning predictions, and a routing method that dynamically determines AVS routes with reduced congestion rates. We predicted congestion for critical bottleneck areas, and utilized the predictions for adaptive routing control of all vehicles to avoid congestion. We conducted an experimental evaluation to compare the predictive performance of four popular algorithms. We performed a simulation study based on data from semiconductor fabrication to demonstrate the utility and superiority of the proposed method. The experimental results showed that AVSs with the proposed approach outperformed the existing approach in terms of delivery time, transfer time, and queuing time. We found that adopting machine learning-based traffic control can enhance the performance of existing AVSs and reduce the burden on the human experts who monitor and control AVSs.

Kidney exchange programs, which allow a potential living donor whose kidney is incompatible with his or her intended recipient to donate a kidney to another patient in return for a kidney that is compatible for their intended recipient, usually aims to maximize the number of possible kidney exchanges or the total utility of the program. However, the fairness of these exchanges is an issue that has often been ignored. In this paper, as a way to overcome the problems arising in previous studies, we take fairness to be the degree to which individual patient-donor pairs feel satisfied, rather than the extent to which the exchange increases social benefits. A kidney exchange has to occur on the basis of the value of the kidneys themselves because the process is similar to bartering. If the matched kidneys are not of the level expected by the patient-donor pairs involved, the match may break and the kidney exchange transplantation may fail. This study attempts to classify possible scenarios for such failures and incorporate these into a stochastic programming framework. We apply a two-stage stochastic programming method using total utility in the first stage and the sum of the penalties for failure in the second stage when an exceptional event occurs. Computational results are provided to demonstrate the improvement of the proposed model compared to that of previous deterministic models.

This study investigates a strategic alliance as a horizontal cooperation in the logistics and transportation industries by considering various sharing rules with a cooperative game approach. Through forging a strategic alliance, carriers gain extra benefits from resource sharing and high efficiency resource utilization. In particular, our research focuses on the cost savings from using larger vehicles utilizing collective market demand and regarding them as benefits of cooperation. The model conceptualizes the characteristic function of cost savings by coalitions that take into account the hub-spoke network which is common in transportation services. To share the improved profits fairly between members, we use different allocation schemes: the Shapley value, the core center, the &tau, value, and the nucleolus. By analyzing those cooperative game theoretic solutions employing an alliance composed of three carriers, we investigate whether satisfaction in this specific coalition provides an incentive for carriers to join such a coalition. Our results from the analysis, with respect to fair allocation schemes, provide a practical and academic foundation for further research.

Kidney exchange allows a potential living donor whose kidney is incompatible with his intended recipient to donate a kidney to another patient so that the donor’s intended recipient can receive a compatible kidney from another donor. These exchanges can include cycles of longer than two donor–patient pairs and chains produced by altruistic donors. Kidney exchange programs (KEPs) can be modeled as a maximum-weight cycle-packing problem in a directed graph. This paper develops a new integer programming model for KEPs by applying the reformulation-linearization technique (RLT) to enhance a lower bound obtained by its linear programming (LP) relaxation. Given the results obtained from the proposed model, the model is expected to be utilized in the integrated KEP IT (Information Technology) healthcare platform to obtain plans for optimized kidney exchanges.

In this paper, we examine a sustainable economic order quantity (S-EOQ) problem with a stochastic lead-time and multi-modal transportation options. With the S-EOQ, decisions of order quantities, as well as a reorder point could be influenced by various factors, including unit price, stock-out cost, lead-time variability and emission costs. For a better understanding, we present a mathematical model of the concomitant S-EOQ problem and, using numerical experiments, explore various scenarios to determine the effects of incorporating sustainability considerations into the traditional inventory model on operational decisions including the choice of transportation modal combination and the sourcing decisions

In this paper, we study the optimal policies of retailers who operate their inventory with a single period model (i.e., newsvendor model) under a free shipping offer where a fixed shipping fee is exempted if an order quantity is greater than or equal to a given minimum quantity. Zhou et al. (2009) have explored this model, and we further investigate their analysis for the optimal ordering policies which they did not sufficiently develop. Based on the investigation, we extend the base model in order to deal with the practically important aspect of inventory management when the exact distribution function of demand is not available. We incorporate the aspect into the base model and present the optimal policies for the extended model with a numerical example. Finally, we conduct extensive numerical experiments to evaluate the performance of the extended model and analyze the impacts of minimum free shipping quantity and the fixed shipping fee on the performance.

For companies facing challenge of managing unreliable supply sources, one of the operational goals is to increase the overall profit by improving performance of uncertain supply. We here develop stochastic decision frameworks which evaluate necessary information about uncertainties and help newsvendor and suppliers to enhance supply reliability. We first analyze the newsvendor’s purchasing decision under supply uncertainty and the derived decision framework is used to identify stochastic dominance conditions. We found that partial supply risk information is sufficient to determine regular ordering quantity, but to improve the overall profit, it is important to gather more information about stochastic dominance conditions.

We investigate how the choice of coopetition of the simultaneous pursuit of collaboration and competition dynamically impacts both the participating firms and also the other self-developing ones in the same market. A conceptual framework of mathematical models obtained from the arguments and insights in the literature is used to undertake an in-depth study through a multiperiod analysis from 2013 to 2020 of an exemplar case of coopetition, the two concurrently ongoing coopetition partnerships in the US electric vehicle (EV) market, the Tesla Motors-Daimler AG alliance and the Tesla Motors-Toyota alliance and the other firms which are not involved in coopetition.

We analyze the impact of transshipments among retailers on their inventory replenishment decisions when products are perishable. Previous studies have shown that transshipments improve their operational efficiencies through inventory reallocation. However, these studies have not investigated transshipments for perishable products having a limited lifetime. In this paper, we consider a centrally managed firm owning several retail stores, which coordinates their replenishment policies along with the utilization of transshipments for perishable goods of a two-period lifetime. For this, the single-period planning horizon is analyzed, with a general solution approach for the optimal replenishment and transshipment decisions. We further present a designated solution approach for a two-location system as a special case and illustrate numerical examples in order to highlight the potential value of implementing transshipments for perishable products.

We investigate the value of choosing the next stop to visit in a multistop trip based on current traffic conditions to minimize the expected total travel time of the tour. We model this problem as a Markov decision process. Numerical examples are presented that indicate that the value of using current traffic information for dynamic tour determination can be significant.

A method of fault detection and classification (FDC) for semiconductor manufacturing equipment e-diagnostics using equipment data is presented. Detecting faulty processes, identifying any anomaly at their onsets, and rapidly classifying the root cause of the fault are crucial for maximizing equipment utilization in current semiconductor manufacturing; however, tool data acquired from production equipment contains much information that is often challenging to analyze due to its sheer volume and complexity. In this paper, modular neural network (MNN) modeling is presented as a method for fault detection modeling in plasma etching. Based on the result from the MNN modeling, a tool data set is grouped according to its related subsystems, and FDC is performed using Dempster-Shafer (D-S) theory to address the uncertainty associated with fault diagnosis. Subsystem level fault detections, such as radio frequency (RF) power source module, RF power bias module, gas delivery module, and process chamber module, are presented by combining related parameters, and successful fault detection is achieved. The evidential reasoning of RF probe is also beneficial for the detection of chamber leak simulation, and the classification of fault is made by further investigating voltage signal of RF probe. Successful fault detection in subsystem level with zero missed alarms was demonstrated using D-S theory of evidential reasoning, and the classification for finding root cause of the fault is presented in the chamber leak fault simulation. We realized that successful FDC can be accomplished by combining various related information and by incorporating engineering expert knowledge.

We determine the value of monitoring perishable freight in-transit for a single vehicle traveling from an origin to a destination. We develop a computationally practical approach for determining the optimal expected cost function and an optimal policy, based on an infinite horizon partially observed Markov decision process model. Structural properties of the optimal expected cost function and optimal policy are determined. These results can lend insight when deciding whether to acquire the capacity to monitor freight status in transit and what actions to take, based on the data from the in-transit monitoring, that optimally increase expected supply chain productivity.

We consider a discrete state, discrete decision epoch inventory replenishment control problem under supply uncertainty. We assume that there is no backlogging, the single period demand d is deterministic, and once an item is placed in inventory, it will not perish. If a units of the product are ordered, then α units are placed into inventory with probability P(α|a), where $\sum_{\alpha=0}^{a}P(\alpha|a)=1$ . Let z=d−x, where x is the current inventory level. For the infinite horizon, total discounted cost criterion, we present conditions that guarantee that an optimal replenishment policy δ ∗ is such that δ ∗(z)=0 for z≤0, δ ∗(z)≥z≥0, and δ ∗(z)−z is monotonically non-decreasing for z≥0. Such a “staircase” structure has a simple parametric description, which can help to accelerate value iteration and policy iteration.

District heating is a system of distributing heated water from centralized facilities to local homes and buildings. In this paper, we model the distribution planning problem as a supply chain planning problem and propose an explicit column generation algorithm to handle large-scale data and nonlinear constraints. The algorithm is successfully applied to a Korean district heating company and computational experiments show that the integrated operation of the district heating network increases the total profit compared to previous isolated networks.