Application of a hybrid simulated annealing-mutation operator to solve fuzzy capacitated location-routing problem

In the field of supply chain management and logistics, using vehicles to deliver products from depots to customers is one of the major operations. Before using vehicles, optimizing the location of depots is necessary in a location-routing problem (LRP). Also, before transportation products, optimizing the routing of vehicles is required so as to provide a low-cost and efficient service for customers. In this paper, the mathematical modelling of LRP is developed according to the existing condition and constraint in the real world. Maximum travelling time constraint is added, and we apply fuzzy numbers to determine customer demands, travelling time and drop time. The objective is to open a subset of depots to assign customers to these depots and to design vehicle routes, in order to minimize both the cost of open depots and the total cost of the routes. The proposed problem is modelled as a fuzzy linear programming (FLP), by applying the fuzzy ranking function method; the proposed FLP is converted to an exact linear programming (LP). A Lingo solver is used to solve this LP model in very small size. LRP is an non-deterministic polynomial-time hard (NP-Hard) problem, and because of the limitation of Lingo solver in solving medium, and large-size numerical examples, a hybrid algorithm including simulated annealing and mutation operator is proposed to solve these numerical examples. Also, a heuristic algorithm is proposed to find a suitable initial solution which is used in hybrid algorithm. At the end, a different analysis of the applied algorithm and a proposed model are introduced.     

Modeling and solving a one-supplier multi-vehicle production-inventory-distribution problem with clustered retailers

This paper considers a supply chain management problem which integrates production, inventory, and distribution decisions. The supply chain is composed of one supplier production facility and several retailers located in a given geographic region. The supplier is responsible for the production and the replenishment of the inventory of retailers, in a vendor managed inventory (VMI) context. The distance between retailers is negligible compared to the distance between the supplier and the retailers’ region. Thus, for each vehicle, there is a major fixed cost for traveling to the cluster of retailers and a minor fixed cost for visiting each individual retailer. The problem consists of determining quantities to be produced, quantities to be delivered to retailers, vehicles to be used, and retailers to be serviced by each vehicle. This problem is an extension of the one warehouse multi-retailer problem with the consideration of production planning and storage and vehicle capacity limitations in addition to fixed vehicle utilization costs and retailer servicing costs. The objective is to minimize a total cost composed of production, transportation, and inventory holding costs at the supplier and at the retailers. Two mixed integer linear programming formulations are proposed and six families of valid inequalities are added to strengthen these formulations. Two of these families are new and the others are adapted from the literature. The numerical results show that the valid inequalities considerably improve the quality of the formulations. Moreover, the parameters that influence the most computational times are analyzed.     

A hybrid simulated annealing for the single-machine capacitated lot-sizing and scheduling problem with sequence-dependent setup times and costs and dynamic release of jobs

Single-machine capacitated lot-sizing and scheduling problem (SMCLSP) is one of the most famous fields of research in scheduling area. What makes this problem particularly difficult to solve in many real situations is large and sequence-dependent setups and also multiple criterions that must be considered. In this paper, such a problem is formulated and a hybrid simulated annealing for this problem is presented. Also, lower bounds are obtained from solving the problem relaxation, and they are compared with the optimal solutions to estimate the goodness of them. As decision criteria, all of important costs imposed by the solution are considered and also infeasible sequences are mentioned. There is a ceiling value for the manufacturer's cost of each job and according to it, some sequences between jobs are maybe infeasible. It showed the superiority of hybrid simulated annealing (HSA) compared with one of the efficient recent heuristics. The average deviation of the HSA from the corresponding optimal solution for small-size problems ranges from 0% to 10% and from the corresponding lower bound ranges for large-size problems ranges from 11% to 21%. The presented HSA is capable to solve large instances that are mostly compatible with the real-world problems.     

Optimizing bi-objective redundancy allocation problem with a mixed redundancy strategy

Redundancy Allocation Problem (RAP) is a challenging subject which has attracted the attention of many authors. Generally, in the RAP there are two strategies for using the redundant components: active and standby. In this paper a new redundancy strategy, called mixed redundancy, is introduced and considered in a multi-objective optimization RAP. Results demonstrate that the new strategy increases the reliability value of the system considerably. This improvement can be very important for system designers, because the reliability of any systems with the structure of redundant components can be increased by changing the redundancy strategy, not by only adding redundant component. Moreover, this improvement dose not increases the cost and other known physical characteristics of the system.     

不确定需求和旅行时间下的车辆路径问题

针对一类不确定需求和旅行时间下的随机车辆路径问题,建立了一个随机规划模型,提出了一种带有自适应机制的改进遗传算法。该算法引入自适应选择机制,采用了新的交叉算子。选取两种不同规模的随机车辆调度问题,分别采用该算法和基于边重组的改进遗传算法进行求解,并通过对计算结果进行对比分析,分别针对自适应选择机制和新的交叉算子做了讨论。结果表明,所提算法不仅取得了更好的优化结果,而且具有更快的收敛速度。     

A capacitated production planning problem for closed-loop supply chain with remanufacturing

This paper addresses a dynamic capacitated production planning problem in steel enterprise employing a closed-loop supply chain strategy, in which the remanufacturing process is applied. Particularly, the remanufacturing problem considered in this paper is obviously different from the typical lot-sizing problems, within which all demands are met by production or remanufacturing without backlogs; the production, inventory, and remanufacturing levels all have limits; both the production and remanufacturing setup cost functions are arbitrary and time-varying; the objective is to minimize the total cost. Firstly, the closed-loop supply chain with remanufacturing model is formulated. Then, the genetic algorithm heuristic approaches are proposed to solve the NP-hard problem. Finally, a computational experiment is presented which can solve the 200 size problem efficiently. Furthermore, the comparisons against the branch and bound method show the effectiveness and efficiency of the proposed approach for solving the large size remanufacturing problem.     

双目标函数下需要安装时间的平行多功能机排序问题

讨论了双目标函数下需要安装时间的平行多功能机排序问题。在该问题中,每个工件对应机器集合的一个子集,且每个工件只能在相应子集中的任一台机器上加工,工件分组,不同组中的工件连续加工需要安装时间,目标函数为极小化最大完工时间和安装次数。根据实际应用背景确定双目标排序问题的形式,并证明了该问题是NP—难的。设计了一个求启发式有效解的算法,首先按照特定的规则将所有工件组都整组地安排到各台机器上,然后逐步改进最大完工时间和拆分工件组,从而得到一系列的启发式有效解。实验表明,该算法是实用而有效的。     

New heuristic methods for the single-source capacitated multi facility Weber problem

In this paper, a generalized variant of the multi facility Weber problem is proposed, where each customer must be satisfied by a single capacitated facility. To solve the problem, the allocation phase of the alternate location–allocation (ALA) is strengthened by a priority-based approach. Computational experiments are implemented on a set of random test instances, along with a set of instances taken from the literature. Computational results compared with GAMS/BARON indicate that the priority-based ALA solves large-scale instances within promising computational time and outperforms GAMS/BARON in terms of solution quality. This success rate is achieved in nearly 80 % of studied cases. Finally, some guidelines for future research are presented.     

利用量子粒子群算法求解单级多资源约束生产批量计划问题

针对单级多资源约束生产批量计划问题,提出了基于量子粒子群算法求解该问题的方法。此算法将量子强大的领域搜索能力和基本粒子群算法(PSO)通过跟踪极值更新粒子的功能结合,能够改善粒子群算法后期搜索速度慢的问题。通过对其他文献的实例进行计算与比较,结果表明,在求解单级多资源约束生产批量计划问题时,量子粒子群算法(QP-SO)要优于退火惩罚混合遗传算法和传统的遗传算法。     

考虑运输成本的单级多资源约束生产批量问题研究

构建了考虑运输成本的单级多资源约束生产批量计划模型,并提出了用于求解该问题的二进制粒子群算法,阐明了该算法的具体实现过程。通过对仿真实例进行计算和结果比较,表明了在生产批量计划决策时考虑运输成本可以大大降低运输费用,从而降低企业运行的总费用。     

车门锈蚀问题分析解决

通过汽车车门生锈的质量案例,分析车门生锈的的根本原因,并对车门折边胶涂布质量进行改进、检查和验证,建立车门折边防腐工艺设计要求。     

网络结构变更及问题的排查方法

由于学校机房的扩充,网络结构发生变化,在配置新网络结构的过程中,遇到了问题,通过替换、排除、逐点定位等方法,最终解决了故障;该文将解决问题的思路方法介绍给大家,供同行借鉴。     

The berth allocation problem with stochastic vessel handling times

In this paper, the berth allocation problem with stochastic vessel handling times is formulated as a bi-objective problem. To solve the resulting problem, an evolutionary algorithm-based heuristic and a simulation-based Pareto front pruning algorithm is proposed. Computational examples show that the proposed approach provides solutions superior to the ones where the expected value of the vessel handling times is used.     

LX型电动单梁悬挂起重机运行扭摆问题的处理

电动单梁悬挂起重机普遍存在着起动、停车扭摆及不同步的问题,针对这些问题论述其产生原因及处理方法。     

Iterated hybrid metaheuristic algorithms for unrelated parallel machines problem with unequal ready times and sequence-dependent setup times

The problem considered in this paper involves a set of independent jobs on unrelated parallel machines with sequence-dependent setup times and unequal ready times, and the objective is to minimize the weighted number of tardy jobs. Iterated hybrid metaheuristic algorithms are proposed to address this problem. The algorithms begin with effective initial solution generators to generate initial feasible solutions; then, hybrid metaheuristics are applied to improve the initial solutions, integrating the principles of the variable neighborhood descent approach and tabu search. If the search becomes trapped at a local optimum, a perturbation operator is developed to help the search escape. To evaluate the performance of the suggested algorithms, heuristic rules and iterated local search algorithms are examined and compared. Computational experimental results show that the proposed algorithms outperform the other heuristics.     

A fuzzy bi-objective mixed-integer programming method for solving supply chain network design problems under ambiguous and vague conditions

Supply chain (SC) network design problems are complex problems with multi-layer levels and dynamic relationships which involve a considerable amount of uncertainty concerning customer demand, facility capacity, or lead times, among others. A large number of optimization methods (i.e., fuzzy mathematical programming, stochastic programming, and interval mathematical programming) have been proposed to cope with the uncertainties in SC network design problems. We propose a fuzzy bi-objective mixed-integer linear programming (MILP) model to enhance the material flow in dual-channel, multi-item, and multi-objective SCs with multiple echelons under both ambiguous and vague conditions, concurrently. We use a computationally efficient ranking method to resolve the ambiguity of the parameters and propose two methods for resolving the vagueness of the objective functions in the proposed fuzzy MILP model. The preferences of the decision makers (DMs) on the priority of the fuzzy goals are represented with crisp importance weights in the first method and fuzzy preference relations in the second method. The fuzzy preference relations in the second method present a unique practical application of type-II fuzzy sets. The performance of the two methods is compared using comprehensive statistical analysis. The results show the perspicuous dominance of the method which uses fuzzy preference relations (i.e., type-II fuzzy sets). We present a case study in the food industry to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms. To the best of our knowledge, a concurrent interpretation of both ambiguous and vague uncertainties, which is applicable to many real-life problems, is novel and has not been reported in the literature.     

A genetic algorithm for the simultaneous lot sizing and scheduling problem in capacitated flow shop with complex setups and backlogging

In this paper the capacitated lot sizing and scheduling problem with sequence-dependent setups, setup carryover, and backlogging has been studied. The problem can be formulated as a mixed-integer program. Most lot sizing problems are hard to solve, especially in medium and large scale. In recent years, to deal with the complexity and find optimal or near-optimal results in reasonable computational time, a growing number of researchers have employed metaheuristic approaches to lot sizing problems. One of the most popular metaheuristics is genetic algorithm which has been applied to different optimization problems successfully. Therefore, we have developed a genetic algorithm to solve this model. To test the accuracy of the genetic algorithm, a lower bound is developed and compared against the genetic algorithm. In computational experiments, proposed genetic algorithm performed extremely well. It is concluded that the genetic algorithm is efficient and effective for this problem.