Decomposition heuristic to minimize total cost in a multi-level supply chain network

In this paper, the distribution planning model for the multi-level supply chain network is studied. Products which are manufactured at factory are delivered to customers through warehouses and distribution centers for the given customer demands. The objective function of suggested model is to minimize logistic costs such as replenishment cost, inventory holding cost and transportation cost. A mixed integer programming formulation and heuristics for practical use are suggested. Heuristics are composed of two steps: decomposition and post improving process. In the decomposition heuristics, the problems are solved optimally only considering the transportation route first by the minimum cost flow problem, and the replenishment plan is generated by applying the cost-saving heuristic which was originally suggested in the manufacturing assembly line operation, and integrating with the transportation plan. Another heuristic, in which the original model is segmented due to the time periods, and run on a rolling horizon based method, is suggested. With the post-improving process using tabu search method, the performances are evaluated, and it was shown that solutions can be computed within a reasonable computation time by the gap of about 10% in average from the lower bound of the optimal solutions.     

Heuristics for the single machine scheduling problem with quadratic earliness and tardiness penalties

In this paper, we consider the single machine scheduling problem with quadratic earliness and tardiness costs, and no machine idle time. We propose several dispatching heuristics, and analyse their performance on a wide range of instances. The heuristics include simple and widely used scheduling rules, as well as adaptations of those rules to a quadratic objective function. We also propose heuristic procedures that specifically address both the earliness and the tardiness penalties, as well as the quadratic cost function. Several improvement procedures were also analysed. These procedures are applied as an improvement step, once the heuristics have generated a schedule.     

A performance comparison of heuristics for the total weighted tardiness problem

The single machine total weighted tardiness problem is an NP-hard problem that requires the use of heuristic solution procedures when more than 50 jobs are to be scheduled. In the literature, a well-tuned simulated annealing method and a descent heuristic with zero interchanges (DESO) both generated the best solutions for a large set of randomly generated problems. Due dates are generated by defining two parameters: the relative range of due dates (RDD) and the average tardiness factor (TF). In this paper, we define several heuristics based on dynamic programming and then use these and DESO heuristics to solve 50-job, 100-job, 200-job, and 500-job problems.     

Goal distance estimation for automated planning using neural networks and support vector machines

Many of today’s most successful planners perform a forward heuristic search. The accuracy of the heuristic estimates and the cost of their computation determine the performance of the planner. Thanks to the efforts of researchers in the area of heuristic search planning, modern algorithms are able to generate high-quality estimates. In this paper we propose to learn heuristic functions using artificial neural networks and support vector machines. This approach can be used to learn standalone heuristic functions but also to improve standard planning heuristics. One of the most famous and successful variants for heuristic search planning is used by the Fast-Forward (FF) planner. We analyze the performance of standalone learned heuristics based on nature-inspired machine learning techniques and employ a comparison to the standard FF heuristic and other heuristic learning approaches. In the conducted experiments artificial neural networks and support vector machines were able to produce standalone heuristics of superior accuracy. Also, the resulting heuristics are computationally much more performant than related ones.     

Efficient constructive and composite heuristics for the Permutation Flowshop to minimise total earliness and tardiness

In this paper we address the problem of scheduling jobs in a permutation flowshop with a just-in-time objective, i.e. the minimisation of the sum of total tardiness and total earliness. Since the problem is NP-hard, there are several approximate procedures available for the problem, although their performance largely depends on the due dates of the specific instance to be solved. After an in-depth analysis of the problem, different cases or sub-problems are identified and, by incorporating this knowledge, four heuristics are proposed: a fast constructive heuristic, and three different local search procedures that use the proposed constructive heuristic as initial solution.The proposed Prod. Type: FLPheuristics have been compared on an extensive set of instances with the best-so-far heuristic for the problem, as well as with adaptations of efficient heuristics for similar scheduling problems. The computational results show the excellent performance of the proposed algorithms. Finally, the positive impact of the efficient heuristics is evaluated by including them as seed sequences for one of the best metaheuristic for the problem.     

An inventory replenishment policy for deteriorating items with shortages and partial backlogging

It has long been assumed that the shortages in inventory systems are either completely backlogged or totally lost. However, it is more reasonable to characterize that the longer the waiting time for the next replenishment, the smaller the backlogging rate would be. Moreover, the opportunity cost due to lost sales should be considered since some customers would not like to wait for backlogging during the shortage periods. Without considering these two realistic conditions, study on the inventory modeling for deteriorating items with shortages and partial backlogging cannot be complete and general. In the present article we define an appropriate time-dependent partial backlogging rate and introduce the opportunity cost due to lost sales. Numerical examples are also presented to illustrate the effects of changes in backlogging parameter and unit opportunity cost on total cost and the optimal number of replenishments.Scope and purposeIn a recent article published in this Journal, Giri et al. (Comput. Oper. Res. 27 (2000) 495–505) implemented an existing procedure to the inventory problem of Hariga and Al-Alyan (Comput. Oper. Res. 24 (1997) 1075–83) which concerns with lot-sizing heuristic for deteriorating items with shortages allowed in all cycles except the last one. Giri et al. deviated from the traditional practice and suggested a new policy allowing shortages in all cycles over a finite planning horizon. Their numerical results indicated the proposed policy is cheaper to operate with a cost reduction up to 15%. However, they did not consider the opportunity cost due to lost sales that happen because customers would not like to wait for backlogging. Moreover, for many products with growing sales, the length of the waiting time for the next replenishment is the main factor for determining whether the backlogging will be accepted or not, and the backlogging rate is expected to be time-dependent. Thus the assumption made in Giri et al. that the backlogging rate is a fixed fraction of the total amount of shortages is not reasonable.The purpose of this paper is to present a more realistic discussion of the inventory problem for deteriorating items with time-varying demands and shortages over a finite planning horizon. In contrast to the model by Giri et al., we define an appropriate partial backlogging rate and introduce the opportunity cost due to lost sales. We attempt to complement their model as a practical and general solution for inventory replenishment problems. With these extensions, the scope of applications of the present results is expanded.     

A generalized exchange heuristic for the capacitated vehicle routing problem

We consider the problem of dispatching the minimum number of vehicles from a central depot to make deliveries to a set of clients with known demands. The objective is to minimize the total distance travelled, subject to vehicle capacity requirements. We present a new heuristic algorithm for solving this problem. The algorithm is based on generalized edge-exchange search procedures, and relaxation of the capacity requirements. Computational results, based upon standard test problems with up to 249 customers, indicate that our heuristic compares favourably with known heuristics in terms of solution quality.     

Concurrent Scheduling: Efficient Heuristics for Online Large-Scale Data Transfers in Distributed Real-Time Environments

The static staging heuristics proposed in the literature for staging the data items associated with real-time distributed applications adhere to a method by which only one data item is transferred in each communication step to optimize a specific cost function. In this paper, we first propose the extended partial path (EPP) algorithm based on the same method. In terms of maximizing the number of satisfied requests, we have analytically shown that EPP has a performance that is equal to or greater than the partial path heuristic (PPH) introduced previously, thanks to excluding the data items that cannot be satisfied by PPH from scheduling and scheduling the satisfiable data-items along their extended paths. In contrast to EPP and other data staging heuristics proposed, we develop the concurrent scheduling (CS) heuristic which allows simultaneous transfer of more than one data item in an organized fashion, thereby improving the overall performance of the staging system. At the heart of the CS heuristic are EPP and the local priority assignment method devised for solving the conflicts between data items at the intermediate nodes. The extensive simulation results further confirm the superiority of the CS heuristic over PPH     

Information technology capital budgeting using a knapsack problem

In this paper, we describe an information technology capital budgeting (ITCB) problem, show that the ITCB problem can be modeled as a 0–1 knapsack optimization problem, and propose two different simulated annealing (SA) heuristic solution procedures to solve the ITCB problem. Using several simulations, we empirically compare the performance of two SA heuristic procedures with the performance of two well‐known ranking methods for capital budgeting. Our results indicate that the information technology (IT) investments selected using the SA heuristics have higher after‐tax profits than the IT investments selected using the two ranking methods.     

Heuristics for minimizing maximum lateness on a single machine with family-dependent set-up times

We address the problem of scheduling a single machine subject to family-dependent set-up times in order to minimize maximum lateness. We present a number of local improvement heuristics based on the work of previous researchers, a rolling horizon heuristic, and an incomplete dynamic programming heuristic. Extensive computational experiments on randomly generated test problems compare the performance of these heuristics. The rolling horizon procedures perform particularly well but require their parameters to be set based on problem characteristics to obtain their best performance.     

A case-based approach to heuristic planning

Most of the great success of heuristic search as an approach to AI Planning is due to the right design of domain-independent heuristics. Although many heuristic planners perform reasonably well, the computational cost of computing the heuristic function in every search node is very high, causing the planner to scale poorly when increasing the size of the planning tasks. For tackling this problem, planners can incorporate additional domain-dependent heuristics in order to improve their performance. Learning-based planners try to automatically acquire these domain-dependent heuristics using previous solved problems. In this work, we present a case-based reasoning approach that learns abstracted state transitions that serve as domain control knowledge for improving the planning process. The recommendations from the retrieved cases are used as guidance for pruning or ordering nodes in different heuristic search algorithms applied to planning tasks. We show that the CBR guidance is appropriate for a considerable number of planning benchmarks.     

An improved heuristic for the capacitated arc routing problem

The capacitated arc routing problem (CARP) is an important and practical problem in the OR literature. In short, the problem is to identify routes to service (e.g., pickup or deliver) demand located along the edges of a network such that the total cost of the routes is minimized. In general, a single route cannot satisfy the entire demand due to capacity constraints on the vehicles. CARP belongs to the set of NP-hard problems; consequently numerous heuristic and metaheuristic solution approaches have been developed to solve it. In this paper an “ellipse rule” based heuristic is proposed for the CARP. This approach is based on the path-scanning heuristic, one of the mostly used greedy-add heuristics for this problem. The innovation consists basically of selecting edges only inside ellipses when the vehicle is near the end of each route. This new approach was implemented and tested on three standard datasets and the solutions are compared against: (i) the original path-scanning heuristic; (ii) two other path-scanning heuristics and (iii) the three best known metaheuristics. The results indicate that the “ellipse rule” approach lead to improvements over the three path-scanning heuristics, reducing the average distance to the lower bound in the test problems by about 44%.     

Integrating the lot-sizing and sequencing decisions for scheduling a capacitated flow line

In this paper we consider a general problem of scheduling a single flow line consisting of multiple machines and producing a given set of jobs. The manufacturing environment is characterized by sequence dependent set-up times, limited intermediate buffer space, and capacity constraints. In addition, jobs are assigned with due dates that have to be met. The objectives of the scheduling are: (1) to meet the due dates without violating the capacity constraints, (2) to minimize the makespan, and (3) to minimize the inventory holding costs. While most of the approaches in the literature treat the problem of scheduling in flow lines as two independent sub-problems of lot-sizing and sequencing, our approach integrates the lot-sizing and sequencing heuristics. The integrated approach uses the Silver-Meal heuristic (modified to include lot-splitting) for lot-sizing and an improvement procedure applied to Palmer's heuristic for sequencing, which takes into account the actual sequence dependent set-up times and the limited intermedite buffer capacity. We evaluate the performance of the integrated approach and demonstrate its efficacy for scheduling a real world SMT manufacturing environment.     

Problem reduction heuristic for the 0-1 multidimensional knapsack problem

This paper introduces new problem-size reduction heuristics for the multidimensional knapsack problem. These heuristics are based on solving a relaxed version of the problem, using the dual variables to formulate a Lagrangian relaxation of the original problem, and then solving an estimated core problem to achieve a heuristic solution to the original problem. We demonstrate the performance of these heuristics as compared to legacy heuristics and two other problem reduction heuristics for the multi-dimensional knapsack problem. We discuss problems with existing test problems and discuss the use of an improved test problem generation approach. We use a competitive test to highlight the performance of our heuristics versus the legacy heuristic approaches. We also introduce the concept of computational versus competitive problem test data sets as a means to focus the empirical analysis of heuristic performance.     

Production planning in additive manufacturing and 3D printing

Additive manufacturing is a new and emerging technology and has been shown to be the future of manufacturing systems. Because of the high purchasing and processing costs of additive manufacturing machines, the planning and scheduling of parts to be processed on these machines play a vital role in reducing operational costs, providing service to customers with less price and increasing the profitability of companies which provide such services. However, this topic has not yet been studied in the literature, although cost functions have been developed to calculate the average production cost per volume of material for additive manufacturing machines.In an environment where there are machines with different specifications (i.e. production time and cost per volume of material, processing time per unit height, set-up time, maximum supported area and height, etc.) and parts in different heights, areas and volumes, allocation of parts to machines in different sets or groups to minimize the average production cost per volume of material constitutes an interesting and challenging research problem. This paper defines the problem for the first time in the literature and proposes a mathematical model to formulate it. The mathematical model is coded in CPLEX and two different heuristic procedures, namely ‘best-fit’ and ‘adapted best-fit’ rules, are developed in JavaScript. Solution-building mechanisms of the proposed heuristics are explained stepwise through examples. A numerical example is also given, for which an optimum solution and heuristic solutions are provided in detail, for illustration. Test problems are created and a comprehensive experimental study is conducted to test the performance of the heuristics. Experimental tests indicate that both heuristics provide promising results. The necessity of planning additive manufacturing machines in reducing processing costs is also verified.     

The application of single-pass heuristics for U-lines

U-lines have been adopted in many manufacturing settings as part of JIT implementation. In this paper, we examine the applicability of existing straight-line heuristics for obtaining a balance on a U-line. We modify 13 single-pass heuristics and study the effectiveness of various heuristics under different problem conditions. An extensive computational study is carried out to help identify the best heuristics. In addition, we compare recent U-line procedures with a single-pass heuristic using some literature problems. Based on a single-pass heuristic, we compare the configurations of a straight- and U-line.     

Adaptive selection of heuristics for assigning time slots and rooms in exam timetables

This paper presents an iterative adaptive approach which hybridises bin packing heuristics to assign exams to time slots and rooms. The approach combines a graph-colouring heuristic, to select an exam in every iteration, with bin-packing heuristics to automate the process of time slot and room allocation for exam timetabling problems. We start by analysing the quality of the solutions obtained by using one heuristic at a time. Depending on the individual performance of each heuristic, a random iterative hyper-heuristic is used to randomly hybridise the heuristics and produce a collection of heuristic sequences to construct solutions with different quality. Based on these sequences, we analyse the way in which the bin packing heuristics are automatically hybridised. It is observed that the performance of the heuristics used varies depending on the problem. Based on these observations, an iterative hybrid approach is developed to adaptively choose and hybridise the heuristics during solution construction. The overall aim here is to automate the heuristic design process, which draws upon an emerging research theme which is concerned with developing methods to design and adapt heuristics automatically. The approach is tested on the exam timetabling track of the second International Timetabling Competition, to evaluate its ability to generalise on instances with different features. The hyper-heuristic with low-level graph-colouring and bin-packing heuristics approach was found to generalise well over all the problem instances and performed comparably to the state of the art approaches.     

Analysis of Heuristics for Number Partitioning

We illustrate the use of phase transition behavior in the study of heuristics. Using an "annealed" theory, we define a parameter that measures the "constrainedness" of an ensemble of number partitioning problems. We identify a phase transition at a critical value of constrainedness. We then show that constrainedness can be used to analyze and compare algorithms and heuristics for number partitioning in a precise and quantitative manner. For example, we demonstrate that on uniform random problems both the Karmarkar–Karp and greedy heuristics minimize the constrainedness, but that the decisions made by the Karmarkar–Karp heuristic are superior at reducing constrainedness. This supports the better performance observed experimentally for the Karmarkar–Karp heuristic. Our results refute a conjecture of Fu that phase transition behavior does not occur in number partitioning. Additionally, they demonstrate that phase transition behavior is useful for more than just simple benchmarking. It can, for instance, be used to analyze heuristics, and to compare the quality of heuristic solutions.     

Assembly line balancing using genetic algorithms

Assembly Line Balancing (ALB) is one of the important problems of production/operations management area. As small improvements in the performance of the system can lead to significant monetary consequences, it is of utmost importance to develop practical solution procedures that yield high-quality design decisions with minimal computational requirements. Due to the NP-hard nature of the ALB problem, heuristics are generally used to solve real life problems. In this paper, we propose an efficient heuristic to solve the deterministic and single-model ALB problem. The proposed heuristic is a Genetic Algorithm (GA) with a special chromosome structure that is partitioned dynamically through the evolution process. Elitism is also implemented in the model by using some concepts of Simulated Annealing (SA). In this context, the proposed approach can be viewed as a unified framework which combines several new concepts of AI in the algorithmic design. Our computational experiments with the proposed algorithm indicate that it outperforms the existing heuristics on several test problems.     

基于时序一致的工作流费用优化方法

针对效用网格下的工作流时间约束-费用优化问题,分层算法将工作流进行分层并逐层进行优化调度,取得了良好效果.然而,这类分层算法由于缺乏更有效的截止时间确定策略来保证时间约束而使得算法的适用性受限.在已有算法截止期约束的逆向分层算法(deadline bottom level,DBL)的基础上,研究工作流的时序特征,并基于任务的一致性状态对费用进行优化,提出了基于时序一致的截止期约束逆向分层算法(temporal consistency based deadline bottom level,TCDBL).TCDBL通过一致性时间点来保证时间约束,解决了DBL的适用性受限问题;同时基于各层并行度分配冗余时间,基于宽松时间约束策略进行费用优化,达到了进一步减少工作流执行费用的目标.实验结果表明TCDBL的费用优化效果比DBL改进了约14%.