A genetic algorithm heuristic approach to general outsourcing capacitated production planning problems

This paper addresses a dynamic capacitated production planning (CPP) problem with consideration of outsourcing. Specifically, the outsourcing problem considered in this paper has the following features: (1) all demands are met by production or outsourcing without postponement or backlog, (2) production, inventory, and outsourcing levels all have a limit, and (3) the cost functions are considered arbitrarily and time-varying. These features come together, leading to a so-called general outsourcing CPP problem. In our previous work, an algorithm with pseudo-polynomial time complexity was developed, which includes a formation of a feasible solution region and then a search procedure using dynamic programming techniques. Due to the computational complexity with such an approach, only small and medium problems can be solved in a practical sense. In this paper, we present a genetic algorithm (GA) approach to the same problem. The novelty of this GA approach is that the idea of the feasible solution region is used as a heuristic to guide the searching process. We present a computational experiment to show the effectiveness of the proposed approach.     

Production scheduling with outsourcing scenarios: a mixed integer programming and efficient solution procedure

When market demand exceeds the company's capacity to manufacture, outsourcing is commonly considered as an effective alternative option. In traditional scheduling problems, processing of received orders is just possible via in-house resources, while in practice, outsourcing is frequently found in various manufacturing industries, especially in electronics, motor and printing companies. This paper deals with the scheduling problem, minimising the cost of outsourcing and a scheduling measure represented by weighted mean flow time, in which outsourcing of manufacturing operations is allowed through subcontracts. Each order can be either scheduled for in-house production or outsourced to an outside supplier in order to meet customer due dates. In this problem, not only should the sequence of orders be determined, but also decision on picking the jobs for outsourcing, selecting the appropriate subcontractor, and scheduling of the outsourced orders are considered as new variables. To formulate the given problem, four different outsourcing scenarios are derived and mixed integer programming models are developed for each one separately. Furthermore, to solve the suggested problem, a computationally effective team process algorithm is devised and then a constraint handling technique is embedded into the main algorithm in order to ensure satisfaction of customer due dates. Numerical results show that the suggested approach possesses high global solution rates as well as fast convergence.     

A method for man hour optimisation and workforce allocation problem with discrete and non-numerical constraints in large-scale one-of-a-kind production

In one-of-a-kind production (OKP), how to coordinate workforce allocations to obtain reasonable man-hour has the practical significance. However, plenty of variables and complicated operation relationships are involved, which implies the man-hour optimisation in OKP belongs to a constrained mixed discrete optimisation problem. In this article, to deal with the man-hour optimisation in large-scale OKP which often refers to complex production, e.g. shipbuilding, we introduce a top-down refinement method to specialise the product design and production decomposition in OKP, which also indicates the operation relationships of the interim product production processes. Consequently, we suggest three basic task structures which are with wide adaptability in OKP industry: tandem structure, parallel structure and double-level-nested parallel structure. Meanwhile, for the double-level-nested parallel structure, a method based on matrix real-coded genetic algorithm and dynamic programming is presented to solve the man-hour optimisation and labour force allocation problem. Through the case studies, including an industrial implementation in the shipbuilding interim product (i.e. a hull block) production, our optimisation method demonstrates significant potential to improve the production efficiency.     

Operator allocation planning for reconfigurable production line in one-of-a-kind production

In a one-of-a-kind production (OKP) company, the operation routing and processing time of an order are usually different from the others due to high customisation. As a result, an OKP company needs to dynamically adjust the production resources to keep the production lines reconfigurable. Through a proper assignment of operators in different sections of a production line, bottlenecks and operator re-allocation during production can be reduced effectively. In this paper, a mathematical model is introduced for optimal operator allocation planning on a reconfigurable production line in OKP. The optimisation objectives are to minimise the total number of the operators, total job earliness and tardiness, and the average work-in-process storage. A branch-and-bound algorithm with efficient pruning strategies is developed to solve this problem. The proposed model and the algorithm are empirically validated by using the data of a windows and doors manufacturing company. A software system based on the proposed approach has been implemented in the company.     

A shuffled frog-leaping algorithm for job shop scheduling with outsourcing options

In this paper, job shop scheduling problem with outsourcing options is considered and a novel shuffled frog-leaping algorithm (SFLA) is presented to minimise total tardiness under condition that total outsourcing cost does not exceed a given upper bound. In SFLA, a tournament selection-based method is used to decompose the whole population into some memeplexes, the search process in each memeplex is done on the best solution of the memeplex and composed of the global search step and the multiple neighbourhood search step. SFLA is tested on a number of instances and compared with some methods from the literature. Computational results validate the promising performance of SFLA on the considered problem.     

Production outsourcing: A linear programming model for the Theory-Of-Constraints

This paper presents an analysis of the outsourcing problem. Pertinent variables are identified and the relationships between them are defined. We formulate the outsourcing problem as a Linear-Programming (LP) problem and identify an analytical solution. We proceed with an example examining three decision models: standard cost accounting, standard Theory-Of-Constraints (TOC) and our own solution. The model enables managers to determine which products to manufacture and which to outsource. The solution of the LP formulation enables managers to apply the model by computing an operational ratio, without having to solve a linear programming problem. The final model is simpler to apply and requires the computation of fewer variables than other prevalent models.     

Automatic generation of a section building planning for constructing complex ships in European shipyards

Efficient planning of the section building process is important for European shipyards since delays in this process can disrupt the on-time delivery of a ship. Automatically generating production schedules of the section building process can result in higher quality schedules compared to those created manually. Recently, the production processes of European shipyards have shifted to focus heavily on outsourcing and outfitting, yet existing automatic planning methods for section building fail to sufficiently consider these factors. This paper develops a mathematical model of the section building process which includes the effects of outfitting and outsourcing. The objective of this model is to simultaneously minimise the fluctuations in workload and the number of outsourced man-hours. The mathematical model was solved by implementing the non-dominated sorting generic algorithm-II (NSGA-II) using a custom heuristic as the fitness function. Due to the multi-objective nature of the problem definition and solution approach, a Pareto front of optimal solutions is created instead of a single, best solution. A test case showed that gains in both objectives are achievable compared to the planning developed manually. Implementing the Section Building Planning methodology developed in this paper could potentially improve the efficiency and controllability of the overall shipbuilding process.     

Identification of the optimal product configuration and parameters based on individual customer requirements on performance and costs in one-of-a-kind production

One-of-a-kind production (OKP) aims at manufacturing products based on the requirements from individual customers while maintaining the high quality and efficiency of mass production. This research addresses the issues in identifying the optimal product configuration and its parameters based on individual customer requirements on performance and costs of products. In this work, variations of product configurations and parameters in an OKP product family are modeled by an AND-OR tree and parameters of the nodes in this tree. Different product configurations with different parameters are evaluated by performance and cost measures. These evaluation measures are converted into comparable customer satisfaction indices using the non-linear relations between the evaluation measures and the customer satisfaction indices. The optimal product configuration and its parameters with the maximum overall customer satisfaction index are identified by genetic programming and constrained optimization. A case study to identify the optimal configuration and its parameters of window products in an industrial company is used to demonstrate the effectiveness of the introduced approach.     

Models and optimisation approaches for scheduling steelmaking–refining–continuous casting production under variable electricity price

This paper studies the steelmaking–refining–continuous casting (SRCC) scheduling problem with considering variable electricity price (SRCCSPVEP). SRCC is one of the critical production processes for steel manufacturing and energy intensive. Combining the technical rules used in iron-steel production practice, time-dependent electricity price is considered to reduce the electricity cost and the associate production cost. A decomposition approach is proposed for the SRCCSPVEP. Without considering the electrical factor, the first phase applies the mathematical programming method to determine the relative schedule plan for each cast. In the second phase, we formulate a scheduling problem of all casts subject to resource constraint and time-dependent electricity price. A heuristic algorithm combined with the constraint propagation is developed to solve this scheduling problem. To investigate and measure the performance of the proposed approach, numerous instances are randomly generated according to the collective data from a well-known iron-steel plant in China. Computational results demonstrate that our algorithm is very efficient and effective in providing high-quality scheduling plans, and the electricity cost can be reduced for the iron-steel plant.     

An effective approach to multi-item capacitated dynamic lot-sizing problems

In this study we solve the multi-item capacitated dynamic lot-sizing problem, where each item faces a series of dynamic demands, and in each period multiple items share limited production resources. The objective is to find the optimal production plan so as to minimise the total cost, including production cost, inventory holding cost, and fixed setup cost. We consider both single-level and multi-level cases. In the multi-level case, some items are consumed in order to produce some other items and therefore items face internally generated demand in addition to external demands. We propose a simple three-stage approach that is applicable to both classes of problems. In the first stage we perform preprocessing, which is designed to deal with the difficulty due to the joint setup cost (a fixed cost incurred whenever production occurs in a period). In the second stage we adopt a period-by-period heuristic to construct a feasible solution, and in the final stage we further improve the solution by solving a series of subproblems. Extensive experiments show that the approach exhibits very good performance. We then analyse how the superior performance is achieved. In addition to its performance, one appealing feature of our method is its simplicity and general applicability.     

Uncapacitated production planning with multiple product types, returned product remanufacturing, and demand substitution

This paper investigates an uncapacitated multi-product production planning problem with returned product remanufacturing and demand substitution, where no backlog and no disposal are allowed. Both the production of new products and the remanufacturing of returned products are considered to meet time-varying demands in a finite time horizon. Setup costs are taken into account when a new product is manufactured or a returned product is remanufactured. The problem is to determine when and how many returned products are remanufactured and new products are manufactured so as to minimize the total cost, including manufacturing, remanufacturing, holding and substitution costs. We first develop an optimization model to formulate the problem. We then propose a dynamic programming approach to derive the optimal solution in the case with large quantities of returned products. We further propose an approximate approach for the general problem to compute a near-optimal solution. The proposed approaches are evaluated by computational experiments and the effectiveness of the approximate approach is verified. Some managerial insights regarding the effects of remanufacturing/substitution are also obtained from the computational studies. This work was partly supported by the National Science Foundation of China under Grant No. 70329001, 70321001, China Postdoctoral Science Foundation under Grant No. 2003034020, 70501014, SRF for ROCS, SEM (2004) and Hong Kong Research Grants Council under Earmarked Grant No. CUHK 4170/03E. We wish to express our sincere appreciation to the Special Issue Editor and the two referees for their constructive comments and suggestions to help improve our paper.     

A Secure Rotation Invariant LBP Feature Computation in Cloud Environment

In the era of big data, outsourcing massive data to a remote cloud server is a promising approach. Outsourcing storage and computation services can reduce storage costs and computational burdens. However, public cloud storage brings about new privacy and security concerns since the cloud servers can be shared by multiple users. Privacy-preserving feature extraction techniques are an effective solution to this issue. Because the Rotation Invariant Local Binary Pattern (RILBP) has been widely used in various image processing fields, we propose a new privacy-preserving outsourcing computation of RILBP over encrypted images in this paper (called PPRILBP). To protect image content, original images are encrypted using block scrambling, pixel circular shift, and pixel diffusion when uploaded to the cloud server. It is proved that RILBP features remain unchanged before and after encryption. Moreover, the server can directly extract RILBP features from encrypted images. Analyses and experiments confirm that the proposed scheme is secure and effective, and outperforms previous secure LBP feature computing methods.     

Simultaneous supply and production planning

This paper considers a novel production and logistics planning problem called the Economic Lot and Supply Scheduling Problem. The problem combines the Economic Lot-Sizing Problem and the Vehicle Routing Problem into a simultaneous problem-solving approach. The considered problem is observable in several industrial areas, e.g. in retailing and in the automotive industry. Assuming a certain delivery policy the paper presents a complete mathematical model and a ?-exact solution procedure. Analytical results for a broad range of test instances are calculated which demonstrate new insights into the considered planning problem. The results emphasise the economic advantage of the proposed simultaneous modeling and solution approach, especially for certain parameter settings.     

考虑运输能力约束的VMI补货发货动态批量研究

考虑由一个供应商和多个零售商构成的VMI供应链,在供应商运输能力有限、可采用外包策略的情况下,研究了动态需求环境下供应链补货及发货批量策略问题.分析得出供应商最优补货及发货期结构满足"零库存补货"性质,进而利用动态规划方法提出一个多项式算法优化补货及发货策略,其计算复杂度为O(T3),且通过平衡各种成本,可得每个发货周...     

Productivity measurement of the manufacturing process for outsourcing decisions: the case of a Taiwanese printed circuit board manufacturer

Outsourcing is used to alleviate capacity shortages and increase production flexibility. Generally, the outsourcing decision of manufacturing processes is determined by the capacity utilization rate and focus on individual process. In this article, multicriteria are considered by applying the data envelopment analysis to evaluate the relative efficiencies of all the manufacturing processes to help to assess the allocation of a company's resources. The Malmquist productivity index (MPI) is further employed to measure the productivity changes of each process, on which the outsourcing decision can be based. The empirical case of a Taiwanese printed circuit board manufacturer is applied to illustrate the outsourcing decision process. According to the outsourcing decision matrix proposed in this article, the manufacturing process with low efficiency score and MPI value was identified as a high priority for outsourcing. The outcome shows that the manufacturing process with a higher investment of fixed assets does not guarantee better efficiency. If a company follows the outsourcing decision suggested by the proposed approach, the capacity utility rate and the production quantity per person can be increased. The results and analysis model can provide an alternative for managerial thinking and practical application for outsourcing decisions.     

Pre-positioning inventory and service outsourcing of relief material supply chain

Service outsourcing is very common in a commercial supply chain, and in humanitarian relief area, the transportation service is usually outsourced. To practice relief supply more effectively, it seems essential to enlarge outsourcing from shipping to more areas, and private enterprises could play a vital role. This paper examines the optimal pre-disaster order quantity of a certain relief commodity, based on a two-stage coordinated approach. Our findings show that the delay cost, shortage penalty cost, risk of supply shortage, salvage value, expected perishable rate, unit inventory cost and reactive price have significant impacts on the optimal amount of propositioned inventory. Moreover, the outsourcing strategies differ by types of relief commodities. For perishable supplies, proactive or reactive outsourcing would improve the benefits of buyer and supplier simultaneously. As for imperishable supplies, it is better to combine proactive insourcing approach and reactive outsourcing strategy. In view of some supplies whose monitoring cost is high, the insourcing approach is much better than the outsourcing approach.     

Stochastic economic lot sizing and scheduling problem with pitch interval,reorder points and flexible sequence

This paper presents a solution for a class of the stochastic economic lot sizing scheduling problem that is typical of the replenishment pull system proposed by the lean manufacturing approach. In this class, lots of any product are produced in fixed intervals called pitch. The proposed solution uses flexible production sequences and reorder points that are compatible with the concepts of supermarket and level production. It adopts the queuing discipline obtained from a fluid model that approximates the stochastic process of arrival and production orders. Given the queuing discipline, an iterative algorithm returns a near-optimal solution for the system. The proposed approach allows us possible to differentiate inventory cost and service levels by product, and the stock required is lower than that required by the discipline ‘first stock out, first out’. The algorithm is fast and stable, allowing its frequent use in real-world instances.     

A gradient search and column generation approach for the build–pack planning problem with approved vendor matrices and stochastic demand

We study a production planning problem of product assembly with random demand, where the customers choose their preferred suppliers for pairs of inter-dependent components through the approved vendor matrix. The problem is to develop production plans that minimise the expected total shortage and holding costs while observing the matrix restrictions and limited component supplies. We provide a mathematical programming formulation of the problem with a large number of decision variables, whose cost function is the solution of a parametric stochastic transportation problem. We present a gradient-based interior-point approach to solve this problem where the gradient is estimated by the shadow price from the solution of such a transportation problem. A column generation scheme is integrated into the approach to handle the large problem issue. Computational results show that our algorithm significantly improves the computational time when compared with the approach without column generation. In addition, we also discuss some extensions of the basic problem to the multi-period rolling horizon case.     

A parallel genetic algorithm for dynamic cell formation in cellular manufacturing systems

Instead of using expensive multiprocessor supercomputers, parallel computing can be implemented on a cluster of inexpensive personal computers. Commercial accesses to high performance parallel computing are also available on the pay-per-use basis. However, literature on the use of parallel computing in production research is limited. In this paper, we present a dynamic cell formation problem in manufacturing systems solved by a parallel genetic algorithm approach. This method improves our previous work on the use of sequential genetic algorithm (GA). Six parallel GAs for the dynamic cell formation problem were developed and tested. The parallel GAs are all based on the island model using migration of individuals but are different in their connection topologies. The performance of the parallel GA approach was evaluated against a sequential GA as well as the off-shelf optimization software. The results are very encouraging. The considered dynamic manufacturing cell formation problem incorporates several design factors. They include dynamic cell configuration, alternative routings, sequence of operations, multiple units of identical machines, machine capacity, workload balancing, production cost and other practical constraints.     

Statistics and Related Topics,Proceedings of the International Symposium on Statistics and Related Topics Held in Ottawa,Canada, May 1980

In some manufacturing situations, the assumption of a long production run may not be appropriate. For example, job shops typically will not have the benefit of large production runs. Much of the literature on the economic design of control charts, however, assumes an effectively infinite production run. A finite-horizon or short-production-run version of an economic-process-control model of Bather and Box and Jenkins is considered here. An algorithm is derived that allows implementation of this model and adjustment strategy for the short-production-run case. The solution to the control problem is consistent with traditional statistical process control philosophy in that process adjustment is called for only when the process mean is substantially off target. The control or adjustment limits for this model are time-varying and depend on the break-even points between quadratic cost for being off target and fixed adjustment cost. It is shown that the length of the production run can greatly influence the control or adjustment strategy. Use of control limits based on the assumption of an infinite-run process can significantly increase total expected cost.