Asia-Pacific Journal of Operational Research

Currently, distributed supply chain production planning (SCPP) is a practical problem affecting global supply chain production. Distributed SCPP is a complex constraint satisfaction problem (CSP) comprising a series of constraints intra- and inter-production members on orders, capacity, and materials. Previous studies have proposed various advanced planning and scheduling (APS) algorithms for companies to solve internal CSPs related to production planning. Distributed SCPP problems additionally require a coordination approach to resolve conflicts among the production plans of individual production members. Several distributed CSP (DCSP) algorithms have been proposed to guide the coordination for various DCSP conditions and objectives. Based on these techniques, we propose a coordination approach for achieving consistency among individual production plans to develop an integral supply chain production plan. First, we employed an APS system to resolve the internal production planning constraints and to develop a feasible production plan for each production member. Next, we developed a hybrid DCSP coordination algorithm based on agent-ordering asynchronous weak-commitment search (AWS) algorithm and asynchronous incremental relaxation (IR) algorithm concepts to facilitate production planning coordination and resolve constraint conflicts among individual production plans. Finally, a thin-film transistor liquid crystal display (TFT-LCD) supply chain was used to illustrate and verify the proposed coordination approach.

Previous studies show that the concept of L^♮-convexity is helpful in characterizing the optimal policy for some inventory models with positive leadtimes. Such examples include the lost-sales inventory model by Zipkin (2008). On the structure of lost-sales inventory models. Operations Research, 56(4) 937–944. and the inventory-pricing model by Pang et al. (2012). A note on the structure of joint inventory-pricing control with leadtimes. Operations Research, 60(3), 581–587. However, when taking batch ordering into account, L^♮-convexity does not work anymore. In this paper, we extend L^♮-convexity to a more general concept termed as Q-jump-L^♮-convexity and apply it to batch ordering inventory models including a lost-sales inventory model and an inventory-pricing model with batch ordering and positive leadtimes. By utilizing this new concept, we can partially characterize the structure of the optimal policies for both the models. Moreover, we are able to evaluate the sensitivity of the optimal decisions with respect to system states. Our results can also be applied to the serial and the assembly inventory systems with lost-sales and batch ordering.

We consider the scheduling problem in which two agents, each with a set of jobs, compete to perform their respective jobs on a single machine under a group technology (GT) environment. The jobs of agents are classified into groups according to their production similarities in advance, all jobs of the same group are required to be processed contiguously on the machine. A sequence-independent setup time precedes the processing of each group. We propose a polynomial time solution for the problem of minimizing the maximum regular cost of one agent, subject to an upper bound on the maximum regular cost of the second agent. We also show that the problem of minimizing the total completion time of the first agent, subject to an upper bound on the maximum lateness of the second agent is strongly -hard. The case where all groups of the first agent have the same number of jobs is shown to be polynomially solvable.

This paper considers a batch delivery scheduling problem in which n independent and simultaneously available jobs are to be processed on m unrelated or uniform parallel machines. The jobs scheduled on the same machine are delivered in batches to customers and the delivery date of a batch equals the completion time of the last job in the batch. The number of jobs in each delivery batch is constrained by the batch size, and the cost of delivering a batch depends not only on the number of jobs in the batch but also on the machine on which the batch is processed. The objective is to find jointly the optimal number of batches on each machine, the optimal assignment of jobs to the batches, and the optimal job processing sequence to minimize the sum of total flowtime, total holding time, and delivery costs. When the number of batches has a fixed upper bound, we present polynomial-time algorithms to solve the problems with unrelated and uniform parallel machines. If the bound constraint on the number of batches is relaxed, we provide polynomial-time algorithms to solve two special cases of the problem with uniform parallel machines.

The lot-streaming flow shop scheduling problem plays an important role in modern industry. This paper addresses this problem with the objective of minimizing the total weighted earliness and tardiness penalties and then proposes a simple but effective iterated local search (ILS) algorithm. In the proposed ILS algorithm, an adapted Nawaz–Enscore–Ham (NEH) heuristic is used to generate an initial solution. A local search procedure based on the insertion neighborhood is employed to perform local exploitation. A simulated-annealing-typed acceptance criterion is utilized to determine the start point for next iteration. Extensive experiments are conducted to compare the proposed ILS algorithm with some existing algorithms. The computational results and comparisons demonstrate the effectiveness of the proposed ILS algorithm.

Scheduling with learning effects has received lots of research attention lately. However, the multiple-agent setting with learning consideration is relatively limited. On the other hand, the actual processing time of a job under an uncontrolled learning effect will drop to zero precipitously as the number of the jobs already processed increases. This is rather absurd in reality. Based on these observations, this paper considers a single-machine two-agent scheduling problem in which the actual processing time of a job depends not only on the job's scheduled position, but also on a control parameter. The objective is to minimize the total weighted completion time of jobs from the first agent with the restriction that no tardy job is allowed for the second agent. A branch-and-bound algorithm incorporated with several dominance properties and lower bounds is proposed to derive the optimal solution for the problem. In addition, genetic algorithms (GAs) are also provided to obtain the near-optimal solution. Finally, a computational experiment is conducted to evaluate the performance of the proposed algorithms.

The thin flim transistor liquid crystal display (TFT-LCD) module factories are in charge of the last process of the TFT-LCD supply chain, i.e., assembling LCD panels (produced from the former process in the supply chain) as well as electronic components into TFT-LCD panels or the finished products using TFT-LCD panels. Since the variation of machine processes and production techniques at the former process of the supply chain results in a variety of qualities of LCD panels, the finished products assembled with those LCD panels of different qualities are also of different qualities, according to which they are classified into different grades. This paper models the material allocation system for a TFT-LCD module factory based on the make-to-stock (MTS) production, in which one of the main designs is to allow the flexibility that the finished products of higher grades are able to be downgraded to respond to the drastic variation of customer requirements for each grade of finished products. The system is modelled in detail by an integer programming approach that takes into account not only downgraded products but also the customer requirements for the quality qualification rate and the lower bound of non-dot-defect rate of products, to maximize the total profit of the product portfolio. The model is evaluated by conducting an experimental study for a numerical example, which turns out that the proposed system looks promising as it determines a feasible product portfolio that decreases both the inventory of LCD panels and the volume of unqualified products while maintaining a high profit.

This research uses the fuzzy Delphi and Analytical Hierarchy Process (FAHP) to analyze the key factors involved in evaluating and screening industry managers of intellectual property (IP) rights within Taiwan. Firms can utilize the key factors of human resource management to provide self-growth opportunities and obtain competitive advantage opportunities that can expand their global market share. This study summarizes the evaluation and screening criteria used for IP rights managers via a questionnaire distributed to 29 management experts, from the governmental departments related to IP rights body, and scholars with backgrounds in IP rights related teaching, who had actually participated in IP rights managers' specialty decisions, and who had employed fuzzy Delphi screening criteria, uses Analytic Hierarchy Process (AHP) calculate relative weight. The findings include five key competency factors: Evaluating IP rights, locating the core patent group, calculating the chance that patents will be granted, deciding the best time to patent, and deciding whether to apply for patents inland or overseas.

Computer networks are important infrastructures required by many modern corporations today. Maintaining a reliable computer network becomes an important issue in daily business operations. A computer network usually consists of components (including links or vertices) that may have several states due to failure, partial failure or maintenance, making it a multi-state computer network (MCN). This paper proposes a novel approach to create an optimal component quality plan in MCN. Finding the optimal component quality plan for an MCN requires searching for an optimal plan such that each component in the network has the proper level of quality while the network maintains highly reliable functionality. Because the costs of quality components are diversified, an appropriate plan for component quality distribution cannot only greatly reduce the cost of network installation but also maintain the network reliability. Another important consideration is that high-quality and expensive components may not increase network reliability if they are not placed appropriately. A novel heuristic approach is proposed to efficiently search for such plan. A comparison with the implicit enumeration method is conducted. The results show that the proposed approach is very effective and efficient. Some numerical examples are illustrated and explained in detail in this paper.

With the rapid development of the Internet, many manufacturers nowadays are increasingly adopting a dual-channel strategy to sell their products. In this paper, we present an analytical framework in a single-channel supply chain and a dual-channel supply chain, respectively. We compare the optimal service levels under different scenarios to investigate the impacts of bidirectional free-riding and service competition on members' decisions. In order to realize system optimization, we propose contracts to coordinate the decentralized supply chain under different cases. We find that when a new channel is added, the retailer will always increase his service level to compete with the manufacturer, while the manufacturer needs to take the relationship with the retailer into consideration and decides whether to increase or decrease her service level. We also find that the contracts for different cases can make the optimal solutions the same as those under the centralized scenario by adjusting service levels based on the relative size of competition effect and spillover effect. Finally, we conduct numerical examples to verify the existence of Pareto improvement intervals and derive some managerial insights.