DYNAMIC ADVANCED PLANNING AND SCHEDULING WITH FROZEN INTERVAL FOR NEW ORDERS

A dynamic advanced planning and scheduling （DAPS） problem is addressed where new orders arrive on a continuous basis. A periodic policy with frozen interval is adopted to increase stability on the shop floor. A genetic algorithm is developed to find a schedule at each rescheduling point for both original orders and new orders that both production idle time and penalties on tardiness and earliness of orders are minimized. The proposed methodology is tested on a small example to illustrate the effect of the frozen interval. The results indicate that the suggested approach can improve the schedule stability while retaining efficiency.

Discrete logistics network design model under interval hierarchical OD demand based on interval genetic algorithm

Aimed at the uncertain characteristics of discrete logistics network design,an interval hierarchical triangular uncertain OD demand model based on interval demand and network flow is presented.Under consideration of the system profit,the uncertain demand of logistics network is measured by interval variables and interval parameters,and an interval planning model of discrete logistics network is established.The risk coefficient and maximum constrained deviation are defined to realize the certain transformation of the model.By integrating interval algorithm and genetic algorithm,an interval hierarchical optimal genetic algorithm is proposed to solve the model.It is shown by a tested example that in the same scenario condition an interval solution[3275.3,3 603.7]can be obtained by the model and algorithm which is obviously better than the single precise optimal solution by stochastic or fuzzy algorithm,so it can be reflected that the model and algorithm have more stronger operability and the solution result has superiority to scenario decision.

Modelling medium-and long-term purchasing plans for environment-orientated container trucks:a case study of Yangtze River port

The transportation sector is the most significant contributor to anthropogenic greenhouse gas(GHG)emissions.Particularly,maritime transportation,which is predominantly powered by fossil-fuel engines,accounts for more than 90%of world freight movement and emits 3%of global carbon dioxide(CO_(2))emissions.China is the world’s largest emitter of CO_(2 )and plays a key role in mitigating global climate change.In order to tackle this pressing concern,this study analyses the port’s throughput,the current number of trucks and their emissions during the container truck purchasing process.Previous studies about container truck purchasing plans mostly focused on the trucks’price and port needs.The objective of this study is to minimize the total cost of a port’s inland transportation using optimization technique such as the interval uncertainty planning model to convert container truck emissions into social costs.The study considers the port of Yangtze as a case study.The study has designed two scenarios.(i)The base scenario(business-asusual,BAU)is used to quantify the relationship between pollutant emissions and system cost.In the base scenario,no environmental control facilities are used during the planning period,and there is no need to purchase new energy container trucks.(ii)The expected scenario(Scenario A)is for three planning periods.In Scenario A,the emissions levels are required to remain at the same level as the first planning period during the whole planning period.By solving the above model,the number of all truck types,system cost,container throughput and truck emissions in the port area were analysed.The results showed that if no emission reduction control measures are implemented in the next 9 years,the growth rate of pollutants in the port area could reach 20%.In addition,the findings showed clearly that truck emissions are reduced by purchasing new energy trucks and restricting the number of fossil-fuel(diesel)trucks.This study could also help to minimize system costs associated with port planning and management.