The impact of the COVID-19 prevention and economic interventions on express delivery industry:Evidence from China

As a fundamental logistics service,the express delivery(ED)industry provides production and consumption activities to maintain efficient supply chain operations.It plays an important role in promoting industrial development and maintaining daily life and consumption.This study examined the effects of COVID-19 on the ED industry using ED package delivery data from 31 provinces(autonomous regions and municipalities)in China.Using a difference-in-differences method,the COVID-19 outbreak was found to have a positive effect and epidemic prevention and control(EPC)policies were found to have negative effects on the development of the ED industry.In addition to EPC policies,the Chinese government has implemented other short-term economic interventions(SEI),such as the resumption of work and production(RWP),policies to balance economic growth,and epidemic prevention.This study divided the pandemic period into three stages to analyze the different impacts of EPC and SEI policy changes on the ED industry.The RWP policies adopted during the pandemic positively impacted the volume and income of packages delivered to the ED industry.The conclusions of this study are significant for the ED industry in addressing public health emergencies and for the Chinese government to formulate EPC and SEI policies during epidemic periods.

Blood flow restriction training improves the efficacy of routine intervention in patients with chronic ankle instability

As a new means of rehabilitation,blood flow restriction training(BFRT)is widely used in the field of musculoskeletal rehabilitation.To observe whether BFRT can improve the efficacy of routine rehabilitation intervention in patients with chronic ankle instability(CAI).Twenty-three patients with CAI were randomly divided into a routine rehabilitation group(RR Group)and a routine rehabilitationþblood flow restriction training group(RRþBFRT Group)according to the Cumberland Ankle Instability Tool(CAIT)score.The RR Group was treated with routine rehabilitation means for intervention,and the RRþBFRT Group was treated with a tourniquet to restrict lower limb blood flow for rehabilitation training based on routine training.Before and after the intervention,the CAIT score on the affected side,standing time on one leg with eyes closed,comprehensive scores of the Y-balance test,and surface electromyography data of tibialis anterior(TA)and peroneus longus(PL)were collected to evaluate the recovery of the subjects.Patients were followed up 1 year after the intervention.After 4 weeks of intervention,the RRþBFRT Group CAIT score was significantly higher than the RR Group(19.33 VS 16.73,p<0.05),the time of standing on one leg with eyes closed and the comprehensive score of Y-balance were improved,but there was no statistical difference between groups(p>0.05).RRþBFRT Group increased the muscle activation of the TA with maximum exertion of the ankle dorsal extensor(p<0.05)and had no significant change in the muscle activation of the PL with maximum exertion of the ankle valgus(p>0.05).There was no significant difference in the incidence of resprains within 1 year between the groups(36.36%VS 16.67%,p>0.05).The incidence of ankle pain in the RRþBFRT Group was lower than that in the RR Group(63.64%VS 9.09%,p<0.01).Therefore,four-weeks BFRT improves the effect of the routine intervention,and BFRT-related interventions are recommended for CAI patients with severe ankle muscle mass impairment or severe pain.

COST AND SERVICE-LEVEL-BASED MODEL FOR A SERU PRODUCTION SYSTEM FORMATION PROBLEM WITH UNCERTAIN DEMAND

With increasing demand diversification and short product lifecycles, industries now encounter challenges of demand uncertainty. The Japanese seru production system has received increased attention owing to its high efficiency and flexibility. In this paper, the problem of seru production system formation under uncertain demand is researched. A multi-objective optimization model for a seru production system formation problem is developed to minimize the cost and maximize the service level of the system. The purpose of this paper is to formulate a robust production system that can respond efficiently to the stochastic demand. Sample average approximation （SAA） is used to approximate the expected objective of the stochastic programming. The non-dominated sorting genetic algorithm II （NSGA-II） is improved to solve the multi-objective optimization model. Numerical experiments are conducted to test the tradeoffbetween cost and service level, and how the performance of the seru production system varies with the number of product types, mean and deviation of product volume, and skill-level-based cost.

Review of seru production

Seru production is regarded as a new production mode and derived from the production site of Japanese electronics industry. This production mode is proposed to overcome the low flexibility of the assembly line. Seru production has been successfully implemented in Japanese electronics industry, such as Canon and Sony. Benefits from Seru production include rapid response, good flexibility, and high productivity. Seru production has received extensive attention in academic research and production practice. This study reviews the background, characteristics, types, and operation of seru production. The advantages and applicable scenes of seru production are summarized from the perspective of business practice. We compare seru production and famous production modes, i.e., assembly line, cellular manufacturing, and Toyota Production System. The literature on seru production is surveyed and classified. Furthermore, future research directions are provided.

Solving the rotating seru production problem with dynamic multi-objective evolutionary algorithms

Today's volatile market conditions in electronic industries have lead to a new production system,seru(which is the Japanese pronunciation for cell),and has been widely implemented in hundreds of Japanese and other Asia companies.In particular,the rotating seru has been widely implemented,where workers are fully cross-trained with the same skill level but may be different on the proficiency of performing tasks.The rotating seru production problem,which determines the rotating sequence of workers as well as the assembling sequence of jobs,is difficult to solve due to conflicting objectives and dynamic release of customer demands.To solve this problem,we propose a dynamic multiobjective NSGA-II based memetic algorithm.Moreover,to preserve desirable population diversity and improve the searching efficiency,we propose different problem-specific evolutionary strategies.Finally,we test the performance of our proposed memetic algorithm with other state-of-the-art multi-objective evolutionary algorithms and demonstrate the effectiveness of our proposed algorithm.

An immune system based differential evolution algorithm using near-neighbor effect in dynamic environments

Many real-world problems are dynamic, requiring optimization algorithms being able to continuously track changing optima （optimum） over time. This paper proposes an improved differential evolutionary algorithm using the notion of the near-neighbor effect to determine one individuals neighborhoods, for tracking multiple optima in the dynamic environment. A new mutation strategy using the near-neighbor effect is also presented. It creates individuals by utilizing the stored memory point in its neighborhood, and utilizing the differential vector produced by the ＇near- neighbor-superior＇ and ＇near-neighbor-inferior＇. Taking inspirations from the biological immune system, an immune system based scheme is presented for rapidly detecting and responding to the environmental changes. In addition, a difference- related multidirectional amplification scheme is presented to integrate valuable information from different dimensions for effectively and rapidly finding the promising optimum in the search space. Experiments on dynamic scenarios created by the typical dynamic test instance--moving peak problem, have demonstrated that the near-neighbor and immune system based differential evolution algorithm （NIDE） is effective in dealing with dynamic optimization functions.

Optimal appointment scheduling with no-shows and exponential service time considering overtime work

Appointment systems are used by health clinics to manage access to service providers.In such systems,a specified number of patients are scheduled in advance,but certain patients may not arrive or‘show up’to their appointments.The existence of no-show behaviour influences both the operational cost of the clinics and the waiting time of the patients.In this paper,we determine an optimal schedule that takes no-show behaviour into account to determine the time intervals between patients under the framework of the individual-block/variableinterval rule for minimising the overall cost of the patient waiting time,the practitioner idle time and overtime.Under the condition that the service time of each patient is exponentially distributed,we compare the results with a schedule designed for the same expected number of patients in the absence of no-shows and analyse the effect on the system performance from the perspectives of day-length,expected workload,no-show probability,ratio of overtime costs and no-golf policy.We extend our results to an equally-spaced appointment system,which is commonly used in practice.Our results show that not only do no-shows greatly affect the system performance compared with an appointment system with the same expected workload without no-shows,but they also affect the optimal scheduling behaviours in the dome-shaped distribution.In addition,overtime cannot be eliminated completely even if the day length is adequate for all patients because of the stochastic characteristic of service time.

Minimizing makespan of a production batch within concurrent systems:Seru production perspective

This paper discusses the makespan minimization of a production batch within a specific concurrent system,seru production system.A seru production system consists of multiple independent serus.A seru is a compact assembly origination in which products are assembled from-the-beginning-to-the-end without disruptions.One capability of a seru production system is its responsiveness.A performance measure used to evaluate a seru system’s responsiveness is the makespan of production batches assembled within the seru system.This study addresses the makespan minimization problem through an optimal seru loading policy.The problem is formulated as a min-max integer optimization model.An exact dimension-reduction Algorithm is developed to obtain the optimal allocation that minimizes the makespan.We show that the solution space increases very quickly.In contrast,our algorithm is efficient with a polynomial computational complexity of,where is the total number of serus in a seru system.To verify the usefulness of the developed exact dimension-reduction algorithm,we compare it with a widely practiced greedy algorithm through experiments.We find that our optimal algorithm is robust in most cases and the greedy algorithm is efficient when variability in production efficiencies is high.This result can guide us to adopt different algorithms in different business environments.If the variability in production efficiencies is high,e.g.,new employees and/or new products assembly,the greedy algorithm is efficient.For other cases,our optimal algorithm should be adopted to obtain the minimum makespan.We also extend the method to the application of a rotating seru.

OUTPATIENT CAPACITY ALLOCATION CONSIDERING ADDING CAPACITY TO MATCH HIGH PATIENT DEMAND

This paper focuses on an outpatient capacity allocation problem where the patient demand is quite higher than the supply. We study an adding capacity policy to mitigate the mismatch between supply and demand. Under this policy, the doctor is allowed to add capacity if all regular capacity have been booked. A capacity allocation model is formulated for both possible no-show routine patients and all show-up same-day patients. The purpose is to determine the number of capacity can be added and how to allocate regular capacity among routine patients and same-day patients, towards maximizing the expected profit, which is composed of the expected income minus the cost of weighted expected doctor＇s overload work caused by the adding capacity policy and the cost of rejecting patients. To achieve the aims, we prove the expected profit monotonously decreases when the number of additional capacity exceeds a threshold, and present a two-tier enumeration search algorithm to fred the global optimal solution based on the proof. Numerical results indicate that the proposed policy performs well under different levels of demand higher than supply. The optimal number of the additional capacity is hardly affected by varying total expected patient demand. Additionally, under the change of no-show rate, the number of regular capacity allocated to routine patients becomes more stable, compared with the optimal scheme without considering adding capacity policy.

Dynamic appointment scheduling for outpatient clinics with multiple physicians and patient choice

Mitigating the adverse effects of uncertainty in appointment systems,arising from heterogeneous patient needs and preferences,is critical to the effective use of scarce medical resources and patient satisfaction.This study addresses an online scheduling problem with multiple servers and consideration of patient preference for physicians and their appointment times.The receptionist immediately determines whether a request should be accommodated and offers an appointment time slot for each accepted patient request.The patient may reject an undesirable appointment time slot with a certain probability,or may accept it,but the no-show probability will be higher.A stochastic overbooking model is formulated to maximize the expected profit,which is defined as the revenue generated from accepted requests minus the cost incurred by patients waiting and physicians’overtime.A myopic scheduling policy is developed based on certain structural properties of the objective function.This study advances the study of appointment systems by generating a non-unimodal profit evolution.Moreover,both the decision of accommodating more requests for certain slots and the scheduling of appointments depend on the patient choice rather than the patient type.Further,computational experiments and analysis offer valuable insights into performance improvement in outpatient clinics.