Periodic product distribution from multi-depots under limited supplies

Physical distribution is one of the key functions in logistics systems, involving the flow of products from manufacturing plants or distribution centers through the transportation network to consumers. It is a very costly function, especially for the distribution industries. While maintaining the desired customer service levels, an effort is made in this paper to improve distribution strategies and reduce the distribution cost for the multi-product, multi-depot periodic distribution problem. In industry practice, depots typically operate independently and solely within their own territories. However, it may be beneficial to allow those depots to operate interdependently, particularly when the product supplies are limited at some depots. In such cases, the distributors may satisfy customers' requests by delivering products from other depots that hold more supplies. In particular, the impact of interdependent operations among depots, which has not previously been addressed in the context of industrial applications, is investigated in this research. A mixed-integer linear programming model is formulated to represent the multi-product, multi-depot periodic distribution problem. Three tabu search heuristics with different long-term memory applications are developed to solve the problem. The performance of the heuristics is evaluated by comparing the solutions obtained with the optimal solutions or lower bounds from the regular branch-and-bound method as well as a fast technique to find a lower bound that is developed in this research. The heuristics provide optimal/good quality solutions in a much shorter time. A randomized complete block design is applied to test the performance of the heuristics on various problem structures. The experimental results show that the tabu search heuristic that incorporates the use of a long-term memory in the diversification process outperforms the other heuristics. The heuristic is further applied to investigate the impact of interdependent operations among depots. The results reveal that interdependent operations among depots provide significant savings in costs over independent operations among depots, especially for large-size problems.     

Daily scheduling of caregivers with stochastic times

This paper addresses a daily caregiver scheduling and routing problem arising in home health care or home care service providers. The problem is quite challenging due to its uncertainties in terms of travel and service times derived from changes in road traffic conditions and customer health status in practice. We first model the problem as a stochastic programme with recourse, where the recourse action is to skip customers without services if the caregiver arrives later than their latest starting service time (i.e. hard time window requirements). Then, we formulate the problem as a set partitioning model and solve it with a branch-and-price (B&P) algorithm. Specifically, we devise an effective discrete approximation method to calculate the arrival time distribution of caregivers, incorporate it into a problem-specific label algorithm, and use a removal-and-insertion-based heuristic and the decremental state-space relaxation technique to accelerate the pricing process. Finally, we conduct numerical experiments on randomly generated instances to validate the effectiveness of the discrete approximation method and the proposed B&P algorithm.     

Two-echelon spare parts inventory system subject to a service constraint

In this paper, we consider a spare parts inventory problem faced by a manufacturer of electronic machines with expensive parts that are located at various customer locations. The parts fail infrequently according to a Poisson process. To serve customers when a failure occurs, the manufacturer operates a central warehouse and many field depots that stock spare parts. The central warehouse acts as a repair facility and replenishes stock at the field depots. There is a centralized decision-maker who manages the inventory in both the central warehouse and the field depots. We develop a continuous review, base stock policy for this two-echelon, multi-item spare parts inventory system. We formulate a model to minimize the system-wide inventory cost subject to a response time constraint at each field depot. We present an efficient heuristic algorithm and study its computational effectiveness.     

A DISCRETE LOCATION ASSIGNMENT PROBLEM WITH CONGESTION

Given a set of sites, this paper deals with the selection of sites for installing facilities and the assignment of customers to them. The customers have random demands for service and form finite source queues at the installed facilities. The objective is to minimize the expected total cost per unit of time consisting of travel cost, waiting cost for service, and fixed cost for installing and maintaining the facilities. A heuristic algorithm is developed. Computational results supporting the efficiency and accuracy of the heuristic are included. Under specific conditions, the problem is reduced to a linear integer programming problem and is solved by a branch and bound algorithm.     

Green permutation flowshop scheduling problem with sequence-dependent setup times: a case study

Increasing global energy consumption, large variations in its cost and the environmental degradation effects are good reasons for the manufacturing industries to become greener. Green shop floor scheduling is increasingly becoming a vital factor in the sustainable manufacturing. In this paper, a green permutation flowshop scheduling problem with sequence-dependent setup times is studied. Two objectives are considered including minimisation of makespan as a measure of service level and minimisation of total energy consumption as a measure of environmental sustainability. We extend a bi-objective mixed-integer linear programming model to formulate the stated problem. We develop a constructive heuristic algorithm to solve the model. The constructive heuristic algorithm includes iterated greedy (CHIG) and local search (CHLS) algorithms. We develop an efficient energy-saving method which decreases energy consumption, on average, by about 15%. To evaluate the effectiveness of the constructive heuristic algorithm, we compare it with the famous augmented ?-constraint method using various small-sized and large-sized problems. The results confirm that the heuristic algorithm obtains high-quality non-dominated solutions in comparison with the augmented ?-constraint method. Also, they show that the CHIG outperforms the CHLS. Finally, this paper follows a case-study, with in-depth analysis of the model and the constructive heuristic algorithm.     

Optimal and Heuristic Facility Phase-out Strategies

This paper presents an efficient branch and bound algorithm and near optimal heuristic algorithms for solving the problem of withdrawing inventory and/or service facilities for a good or service whose overall demand is declining over time. In particular, this paper models the problem faced by a manager who must consider closing up to M initially open and operating support facilities as demand shifts and declines over a T period planning horizon. The criterion is minimization of total estimated discounted costs. The costs considered are the variable operating cost at each facility, the transportation costs between facilities and demand centers, and the costs to operate and close each facility. Computational results are presented for both the optimum finding and heuristic algorithms.     

面向下一代网络业务部署的多维负载平衡算法

分析了用于解决下一代网络业务部署问题的已有负载平衡算法的不足,提出了一种启发式的多维负载平衡优化算法(MLOASD),并通过理论分析和实验检测证明了该算法的有效性.结果表明,用该算法能够获得系统负载平衡的近似最优的业务部署方案,其性能优于已有算法,具有实用性.     

一种新型的动态路由和波长分配综合算法

讨论了WDM光网中，在动态业务流量和有限范围波长变换情况下的动态路由和波长分配问题。基于Moone-Dijkstra算法，考虑到动态波长变换的可能和限制，提出了一种新型的、可实现动态最小代价路由和最佳虚波长通道的综合启发式算法(DMC-OVWP)。该算法对路由子问题和波长分配子问题既相互独立，又相互结合，优化了RWA。以中国教育和科研计算机网(CERNET)为拓扑背景，基于本算法进行了计算机仿真，并对实验结果进行了比较分析，证明本算法可充分利用网络信息获取较低的阻塞率。     

DESIGNING CELLULAR MANUFACTURING SYSTEMS: BRANCH-AND-BOUND AND A* APPROACHES

In this paper, heuristic and optimal algorithms for solving the group technology problem are presented. The heuristic algorithm is based on a branch-and-bound concept. A quadratic programming model for the machine grouping problem is formulated. The A* algorithm is developed for optimal solving of the machine grouping problem. The performance of the heuristic branch-and-bound method and the A* algorithm is compared with several existing heuristics.     

Single machine scheduling problem with batch setups involving positional deterioration effects and multiple rate-modifying activities

This article considers a single machine scheduling problem with batch setups, positional deterioration effects, and multiple optional rate-modifying activities to minimize the total completion time. This problem is formulated as an integer quadratic programming problem. In view of the complexity of optimally solving this problem, a two-phase heuristic algorithm is proposed where an optimal but non-integer solution is obtained in the first phase by solving a continuous relaxed version of the problem. This solution serves as a lower bound for the optimal value of the total completion time. The second phase of the algorithm generates an integer solution using a simple rounding scheme that is optimum or very close to optimum for this problem. Empirical evaluation and comparison with an existing heuristic algorithm show that the proposed heuristic algorithm is substantially more effective in solving large-size problem instances.     

Route computation in large road networks: a hierarchical approach

The optimal route between a given origin and destination needs to be computed in a fast and efficient manner in dynamic route guidance systems. Conventional routing algorithms have been found to be inadequate when applied directly to large road networks. Algorithms based on the concept of hierarchical abstraction make use of the knowledge about the road network to reduce search and provide near-optimal solutions. A generic procedure is presented for organising a given road network as a multiple-layer hierarchy. An efficient hierarchical routing algorithm is proposed, which breaks down the route search into a number of individual searches in small sub-networks. The algorithm incorporates a heuristic layer-switching technique to improve its performance without compromising the accuracy. The hierarchical routing algorithm was tested on the road network of Singapore and the solutions were found to be comparable to the optimal least-cost paths.     

Robust economic-statistical design of X-bar control chart

Control charts are developed to monitor the service and production processes. The fact that many processes have uncertain parameters is a barrier to obtain the best design of the control charts. In this paper, economic statistical design (ESD) of the X-bar control chart utilising robust optimisation approach that considers interval estimates of uncertain parameters is investigated. A heuristic algorithm is developed to obtain the robust scheme of the control chart. Robust design for an industrial problem is compared with traditional ESD, and heuristic design. Numerical analyses and simulation study show that the proposed X-bar control chart offers a better approach and more reliable solutions for practitioners.     

Optimisation of the multi-depots pick-up and delivery problems with time windows and multi-vehicles using PSO algorithm

Many sectors in the transport industry are concerned about the vehicle routing problem (VRP), hence the growing interest of researchers for this type of problem and its variants. This is due essentially to its many real applications in logistics for the transport of goods. The originality and contribution of our work is that we have dealt a problem that combines several variants: multiple vehicles (m), multiple depots (MD), pickup and delivery problem (PDP) with time windows (TW). Hence the notation of our problem: m-MDPDPTW. In this paper, we present the m-MDPDPTW, which is an optimisation problem belonging to the category of NP Hard problems. This problem must meet requests for transport between customers and suppliers satisfying precedence, capacity and time constraints. The goal is to find the best solution, which is the best route minimising the total travelled distance. To solve and optimise our m-MDPDPTW, we have developed a new algorithm based on the particle swarm optimisation (PSO) method. The performance of this new approach is tested on data set instances of Li and Lim's benchmark problems in which we have added multiple depot locations. Comparing with prior works, our proposed approach gave better results by decreasing the distance for several studied instances.     

Solution algorithms for single-machine group scheduling with ready times and deteriorating jobs

This article considers the single-machine group scheduling problem with deterioration effect and ready times. The objective of this problem is to determine the sequence of groups and the sequence of jobs to minimize the makespan. To solve the problem, an algorithm based on enumeration, an heuristic algorithm and a branch-and-bound algorithm are developed and exhaustively tested. The computational results show that the performance of the heuristic algorithm is fairly accurate in obtaining near-optimal solutions and the branch-and-bound algorithm is very effective in obtaining optimal solutions.     

A hybrid adaptive large neighbourhood search for multi-depot open vehicle routing problems

In this paper we address the multi-depot open vehicle routing problem (MDOVRP), a complex and difficult problem arising in several real-life applications. In the MDOVRP vehicles start from several depots and do not need to return to the depot at the end of their routes. We propose a hybrid adaptive large neighbourhood search algorithm to solve the MDOVRP coupled with improvement procedures yielding a hybrid metaheuristic. The performance of the proposed metaheuristic is assessed on various benchmark instances proposed for this problem and its special cases, containing up to 48 customers (single-depot version) and up to six depots and 288 customers. The computational results indicate that the proposed algorithm is very competitive compared with the state-of-the-art methods and improves 15 best-known solutions for multi-depot instances and one best-known solution for a single-depot instance. A detailed sensitivity analysis highlights which components of the metaheuristic contribute most to the solution quality.     

A Heuristic Programming Algorithm for Warehouse Location

A new heuristic programming method of solving a particular type of warehouse location problem is presented. The problem is to allocate K or less facilities to N possible locations so as to service M demand centers at minimum cost. The algorithm presented is suitable for hand calculation of medium-size problems (50 × 50) or when computerized will readily solve large-scale problems of the order of (600 × 600); i.e., 600 demand centers and 600 possible locations.     

Joint optimisation of capacity and safety stock allocation

We study the joint optimisation of capacity and safety stock allocation in assembly systems. Particularly, we consider capacitated systems with base-stock policies and periodic review. Capacity allocation is restricted by budget constraints, which can connect multiple systems. Our objective is to minimise overall inventory holding costs while satisfying service level as well as budget constraints. We propose an algorithm to jointly approximate optimal capacity allocation and base-stock levels. To this end, we introduce a set of convex approximations for this non-convex optimisation problem. In order to solve the resulting convex programmes, we analytically compute sample path derivatives via infinitesimal perturbation analysis. By iteratively adapting the approximations, we achieve good capacity allocations and base-stock levels for the original problem. Furthermore, we introduce a heuristic to allocate capacity, which originates from link capacity allocation in communication networks and use it as a benchmark. The algorithm is applied to small illustrative examples as well as cases motivated by the semiconductor manufacturing process at IBM Systems. It turns out that particularly for high utilisation levels, our algorithm can achieve significant improvements compared to the capacity allocation heuristic.     

High-performing heuristics to minimize flowtime in no-idle permutation flowshop

In this article, the issue of production scheduling in a no-idle flowshop environment is addressed. An extensive literature review has shown that there are no heuristics specifically proposed for this problem, especially when it comes to constructive heuristic methods. In this context, this article proposes a highly efficient simple constructive heuristic to minimize the total flowtime criterion. The proposed heuristic was embedded in the high performance iterated greedy algorithm. Computational results and statistical analysis show that the proposed heuristic overperformed the main constructive methods found up to now. In addition, it is observed that the integration of the proposed heuristic with the iterated greedy algorithm provides the most efficient metaheuristic for the problem.     

Hybrid genetic algorithm for the economic lot-scheduling problem

The economic lot-scheduling problem (ELSP) is an important production scheduling problem that has been intensively studied over 40 years. Numerous heuristic algorithms have been developed since the problem is NP-hard. Dobson's heuristic has been regarded as the best in its performance. The present paper provides a hybrid genetic algorithm based on the time-varying lot sizes approach in the ELSP literature. Numerical experiments show that the hybrid genetic algorithm outperforms Dobson's heuristic.