To prevent excess unsold goods caused by market fluctuations, retailers can redistribute surplus commodities among their stores to maximise profits. This paper introduces a novel approach to model the common real-world problem of multi-commodity inventory allocation and redistribution. Our unified approach integrates the two problems and seeks to optimise maximum profit. The study encompasses various factors such as inventory capacity, reallocation constraints, vehicle capacity, time windows for pickup and delivery, and a homogeneous fleet of vehicles. We propose two mixed-integer programming paradigms, the integrated and sequential formulations, along with an improved variable neighborhood search (IVNS) algorithm to solve the problem. Computational results demonstrate the effectiveness of the IVNS algorithm, while further analysis highlights the pros and cons of the two formulation paradigms. Notably, the integrated formulation yields superior solutions at the expense of increased computational time.

Background: Improper disposal of urban medical waste is likely to cause a series of neglective impacts. Therefore, we have to consider how to improve the efficiency of urban medical waste recycling and lowering carbon emissions when facing disposal. Methods: This paper considers the multi-cycle medical waste recycling vehicle routing problem with time windows for preventing and reducing the risk of medical waste transportation. First, a mixed-integer linear programming model is formulated to minimize the total cost consisting of the vehicle dispatch cost and the transportation costs. In addition, an improved neighborhood search algorithm is designed for handling large-sized problems. In the algorithm, the initial solution is constructed using the Clarke–Wright algorithm in the first stage, and the variable neighborhood search algorithm with a simulated annealing strategy is introduced for exploring a better solution in the second stage. Results: The computational results demonstrate the performance of the suggested algorithm. In addition, the total cost of recycling in the periodic strategy is lower than with the single-cycle strategy. Conclusions: The proposed model and algorithm have the management improvement value of the studied medical waste recycling vehicle routing problem.

In order to improve the sorting efficiency of automatic guided vehicle (AGV) in the logistics sorting center, an optimized model was proposed considering the characteristics of power consumption and charging demand in the sorting process of electric-driven AGVs. On the basis of considering of the AGVs’ remaining power and package delivery time window, a mixed integer programming (MIP) model with the minimization of the sorting operation cycle and a corresponding constrained programming (CP) model were formulated. In CP model, the interval variables were used to describe the performance of tasks and the change of electric quantity was recorded by using cumulative function. The computational results show that the CP model has better performance compared with the MIP model.Adopting mixed integer programming and constrained programming to formulate the AGV scheduling model can effectively improve the sorting efficiency, reduce the operating cost of enterprises, and provide an alternative solution for the AGV scheduling problem with more constraints.

为了满足快时尚品连锁门店多品类的销售需求,综合考虑载重限制、多货品、门店相互调货以及服务时间窗等因素,研究了同时取送货车辆路径问题.为了有效降低库存管理和运输成本,通常鼓励门店间的货品相互调配,且需要控制用车数.为此,以最小化车辆数为第一优化目标,最小化转运成本(运输成本和仓库处理成本)为第二优化目标,构建了混合整数规划模型,并设计了两阶段启发式求解算法.算法采用最短路径插入规则生成初始解,并配合8个邻域操作算子进行迭代搜索.采用该算法求解标准算例,对比文献中结果表明,其具有较好的寻优能力.基于某女鞋连锁门店的实际运营数据设计了72个算例,计算分析表明,该算法的求解能力与效率均优于整数规划模型.

In order to solve the single machine scheduling problem, and improve the reliability and stability of the equipment, a single machine scheduling model considering piecewise linear deterioration and maintenance unavailability time was established according to the characteristics of continuous fault detection and discrete fault detection. Based on the system reliability theory, the single machine scheduling problem considering machine reliability was studied, and the advantages and disadvantages of maintenance strategy were compared by unified processing of decision conditions. The influence of related parameter changes on production scheduling optimization was determined by single factor and two factor adjustment analysis. The results show that the solution time of the model increases exponentially with the decreasing direction of unit delay cost, and the greater the unit delay cost, the faster the solution speed. The change of delay cost per unit time will not cause a significant change in maintenance cost, and the cost function does not have jumping nodes in flexible periodic maintenance, so it can not be adjusted adaptively. Ratio of preventive maintenance time to fault minor maintenance time has great influence on maintenance decision under discrete detection. The unified processing method of decision conditions can reduce the calculation time and detection cost, and better solve the problems of excessive maintenance or insufficient maintenance in discrete fault detection. These will help to reduce operating costs and improve economic benefits.