Demand uncertainty obliges all participants through a supply chain to make decisions under uncertainty. These decisions extend across price, investment, production, and inventory quantities. We take account of competition between two supply chains under demand uncertainty. These chains internally are involved in vertical pricing competition; however, they externally participate in horizontal pricing and service level competitions by offering a single-type product to the market. Since firms may have various attitudes against demand uncertainty and its related risks, different risk structures for competitive supply chains are considered. We assume that risk-averse firms are able to decrease demand uncertainty by information gathered from market research. For risk-averse participants in a chain, market research investment is an appropriate ground for vertical coordination, which diminishes risk through a supply chain. Optimal strategies based on game theory are obtained for different risk structures; furthermore, for each structure the effects of risk sensitivity as well as market research efficiency on these optimal strategies are investigated. Finally, we propose two scenarios for information sharing between risk-averse participants.

This work deals with production scheduling problem in an assembly flow shop, having parts machining followed by their subsequent assembly operations. Limited heuristics available on the problem, are based on unrealistic assumption that every part is processed on all machines. In this paper, two heuristics NEH_BB and Disjunctive are proposed to solve assembly flow shop scheduling problem where every part may not be processed on each machine. Exhaustive computational experiments are conducted with 60 trials each. The methods are found to be applicable to large size problems. The objective functions used for comparison are makespan and computational time. Disjunctive method takes very less computational time as compared to NEH_BB and hence claimed to be the better among available approaches for finding solution in assembly flow shop problems.

Control chart pattern (CCP) recognition can act as a problem identification tool in any manufacturing organization. Feature-based rules in the form of decision trees have become quite popular in recent years for CCP recognition. This is because the practitioners can clearly understand how a particular pattern has been identified by the use of relevant shape features. Moreover, since the extracted features represent the main characteristics of the original data in a condensed form, it can also facilitate efficient pattern recognition. The reported feature-based decision trees can recognize eight types of CCPs using extracted values of seven shape features. In this paper, a different set of seven most useful features is presented that can recognize nine main CCPs, including mixture pattern. Based on these features, decision trees are developed using CART (classification and regression tree) and QUEST (quick unbiased efficient statistical tree) algorithms. The relative performance of the CART and QUEST-based decision trees are extensively studied using simulated pattern data. The results show that the CART-based decision trees result in better recognition performance but lesser consistency, whereas, the QUEST-based decision trees give better consistency but lesser recognition performance.

In this paper, a new multi-item inventory system is considered with random demand and random lead time including working capital and space constraints with three decision variables: order quantity, safety factor and backorder rate. The demand rate during lead time is stochastic with unknown distribution function and known mean and variance. Random constraints are transformed to crisp constraints with using the chance-constrained method. The Minimax distribution free procedure has been used to lead proposed model to the optimal solution. The shortage is allowed and the backlogging rate is dependent on the expected shortage quantity at the end of cycle. Two numerical examples are presented to illustrate the proposed solution method.

In this paper, we study a supply chain problem where a whole seller/producer distributes goods among different retailers. The proposed model of this paper is formulated as a more general and realistic form of traditional vehicle routing problem (VRP). The main advantages of the new proposed model are twofold. First, the time window does not consider any lower bound and second, it treats setup time as separate cost components. The resulted problem is solved using a hybrid of particle swarm optimization and simulated annealing (PSO-SA). The results are compared with other hybrid method, which is a combination of Ant colony and Tabu search. We use some well-known benchmark problems to compare the results of our proposed model with other method. The preliminary results indicate that the proposed model of this paper performs reasonably well.

This paper presents a mathematical model for the problem of minimizing the maximum lateness on a single machine when the deteriorated jobs are delivered to each customer in various size batches. In reality, this issue may happen within a supply chain in which delivering goods to customers entails cost. Under such situation, keeping completed jobs to deliver in batches may result in reducing delivery costs. In literature review of batch scheduling, minimizing the maximum lateness is known as NP-Hard problem; therefore the present issue aiming at minimizing the costs of delivering, in addition to the aforementioned objective function, remains an NP-Hard problem. In order to solve the proposed model, a Simulation annealing meta-heuristic is used, where the parameters are calibrated by Taguchi approach and the results are compared to the global optimal values generated by Lingo 10 software. Furthermore, in order to check the efficiency of proposed method to solve larger scales of problem, a lower bound is generated. The results are also analyzed based on the effective factors of the problem. Computational study validates the efficiency and the accuracy of the presented model.

This research aims to present how choosing a suitable layout can improve the performance of virtual cellular manufacturing systems (VCMSs), especially minimizing the material flow between machines required by each family group. To present the efficacy of basic layouts on performances of VCMSs, a multi-objective mathematical model with a goal programming (GP) approach is developed to generate VCMSs based on resource-elements (REs). The formulated model is coded in Lingo software and is run over functional and distributed arrangements of the same machines. The performance and the validity of the developed model are checked by a numerical example taken from the literature. The objective function of the mathematical model is measured for that example over two mentioned layout to compare the performance of the generated systems. Moreover, because of the material handling costs importance, material flows are measured to find the best option as a basic layout for VCMSs. To compare the performance of the generated system with the classical cellular manufacturing system (CMS), cell capacity utilization (CCU) is employed as an independent criterion to evaluate each system. The result illustrates the priority of distributed layouts for generating RE-based VCMSs because of its flexibility, minimizing the objective function for the mathematical model, and smaller material flow by the components. In addition, the generated VCMSs outperforms the classical CMS from the CCU point of view.

Recently, the economic lot scheduling problem (ELSP) with common cycle time and shelf life restrictions has attracted the attention of several researchers. In this paper, a comparative study of solutions given by Xu and Sarker (2003) [Computers & Operations Research 30 (6), 925-938] with the results given by Viswanathan and Goyal (2000) [International Journal of Production Research, 38 (4), 829-836] are presented. Additionally, the paper makes some observations about two mathematical expressions, which contain a technical shortcoming.

Decisions related to acceptance or rejection of orders play an important role in companies engaged in make-to-order production. The incoming orders have a specific delivery date by which the customer expects the due date to be met and the order delivered. In some cases the level of input orders exceeds beyond the existing capacity. In such situations the main concern is to decide which orders must be accepted and which ones rejected taking into account the available production capacity. This paper prioritises the input orders according to a comprehensive and systematic multi criteria decision making (MCDM) model. It then proceeds with making decisions to either accept or reject orders according to the calculated prioritises and production constraints. Ultimately the optimum list of orders for acceptance is determined. The proposed model is a combination of two techniques of Fuzzy Analytical Hierarchy Process (FAHP) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). In this model FAHP is used to determine the weights of criteria and TOPSIS is used for prioritizing the orders. Finally the proposed model is tested for its efficiency by application to a real case.

Determining how a new production cell will function is problematic and can lead to disastrous results if done incorrectly. Discrete-event simulation can provide information on how a line will function before, during, and after the line is in operation. A simulation model can also provide a visual animation of the line to see how product will flow through the line. This paper discusses the development and analysis of a simulation model of a new manufacturing line. The manufacturing cell is a new motor assembly cell. An analysis of the capability of the line for varying demand levels was conducted for the two main motor types produced on the line. An ARENA® simulation model was developed, verified, and validated to determine the daily production and potential problem areas for the various demand levels. The results show that at all but one demand level, the line is capable of producing to within one unit of customer demand if the required number of workers is present. At the highest demand level, the simulation results suggest that the line is not capable of meeting demand. Additional analysis indicates that multiple workstations could prove problematic with minor fluctuations in demand. Problematic workstations were identified for each assembly area and for the line as a whole.