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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2004.11.012 Byline: S.A. Torabi (a), S.M.T. Fatemi Ghomi (b), B. Karimi (b) Keywords: Flexible flow line; Lot and delivery scheduling; Supply chain; Hybrid genetic algorithm; Finite horizon Abstract: In this paper, we investigate the lot and delivery scheduling problem in a simple supply chain where a single supplier produces multiple components on a flexible flow line (FFL) and delivers them directly to an assembly facility (AF). It is assumed that all of parameters such as demand rates for the components are deterministic and constant over a finite planning horizon. The main objective is to find a lot and delivery schedule that would minimize the average of holding, setup, and transportation costs per unit time for the supply chain. We develop a new mixed integer nonlinear program (MINLP) and an optimal enumeration method to solve the problem. Due to difficulty of obtaining the optimal solution in medium and large-scaled problems, a hybrid genetic algorithm (HGA) is also developed. The proposed HGA incorporates a neighborhood search (NS) into a basic genetic algorithm that enables the algorithm to perform genetic search over the subspace of local optima. The two proposed solution methods are compared on randomly generated problems, and computational results show that the performance of HGA is very promising because it is able to find an optimal or near-optimal solution for majority of the test problems. Author Affiliation: (a) Department of Industrial Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran (b) Department of Industrial Engineering, Amirkabir University of Technology, Hafez Ave No. 424, 15916-34311 Tehran, Iran Article History: Received 18 February 2004; Accepted 3 November 2004

Most of current logistics network design models in the literature typically assume that facilities are always available and absolutely reliable while in practice, they are always subject to several operational and disruption risks. This paper proposes a reliable closed-loop supply chain network design model, which accounts for both partial and complete facility disruptions as well as the uncertainty in the critical input data. The proposed model is of mixed integer possibilistic linear programming type that aims to minimise simultaneously the total cost of opening new facilities and the expected cost of disruption scenarios. An enhanced possibilistic programming approach is proposed to deal with the epistemic uncertainty in input data. Furthermore, thep-robustness criterion is used to limit the cost of disruption scenarios and protect the designed network against random facility disruptions. Several numerical experiments along with sensitivity analyses on uncertain parameters are conducted to illustrate the significance and applicability of the developed model as well as the effectiveness of the proposed solution approach. Our results demonstrate that operational and disruption risks considerably affect the whole structure of the designed network and they must be taken into account when designing a reliable closed-loop logistics network. [ABSTRACT FROM AUTHOR]

In this study, we address a new variant of supplier selection problem named maintenance supplier selection problem faced by a manufacturer. The production system consists of different multi-component equipments whose maintenance activities require several components (parts) each of which could be provided by multiple suppliers. A multi-objective mathematical model is developed to decide about the supply base of each part as well as the purchasing quantity of each part from each selected supplier. The model accounts for the total life cycle costs of purchased parts and various risks threatening the candidate suppliers. A fuzzy/soft lexicographic goal programming approach with soft priorities between objectives is proposed to enable the decision-maker to make preferred trade-offs between objectives by which the effects of various risks in each phase of life cycle of procured parts are investigated. The capability and effectiveness of the proposed model is validated through a case study. Some sensitivity analyses are also carried out for investigating the impact of cost, risk and objectives’ priorities on the final preferred compromise solution. Finally, some managerial insights and concluding remarks are provided. [ABSTRACT FROM PUBLISHER]

In retail supply chains, ordering and pricing policies for the retailer and shipment plan for the supplier are the most important decisions. These policies are often conducted either individually or sequentially with poor overall performance for the whole supply chain resulting to extra inventory and other deficiencies. In this paper, an integrated marketing-inventory model in a two-echelon supply chain model is developed involving discount promotion, customer behaviour more realistically and operations aspects to determine optimal ordering, shipping and pricing quantities simultaneously. An efficient analytical solution procedure and a Particle Swarm Optimisation solution algorithm are also developed. Finally, a number of numerical tests are conducted to approve the interesting theoretical results of the analytical approach. [ABSTRACT FROM PUBLISHER]

Large firms usually employ the well-known ABC inventory classification technique to have an efficient control on a huge amount of inventory items. While the traditional inventory classification method only considers one criterion, namely the annual dollar usage to classify inventory items, the recent literature shows a focus on multi-criteria inventory classification (MCIC) techniques. Surprisingly, among several methods that have been developed for the MCIC problem, there are just a few, which clearly account for qualitative criteria, while most of the affecting criteria are of qualitative type in nature. This paper presents a modified linear optimisation method that enables inventory managers to classify a number of inventory items in the presence of both qualitative and quantitative criteria without any subjectivity. Furthermore, an efficient procedure is proposed to maximise the minimum importance attached to various criteria leading to improvement of discriminating power among inventory items. For validating the proposed method, it is applied on a case study taken from the literature and a comparative study with the existing competent methods is also provided. [ABSTRACT FROM PUBLISHER]

In this paper, a hybrid cellular manufacturing (HCM) system is presented in which the main sources of uncertainty, e.g. the demands of parts and unit costs are treated as fuzzy numbers in the form of possibilistic distributions. The basic concept of HCM is that high variation in demand might disturb cell efficiency, so forming cells with only those parts that have stable demand, will profit. Thus, to design stable and robust manufacturing cells, a two-phase method is proposed in which a fuzzy adaptive ranking method is first applied to identify those parts with low and non-repetitive demands (i.e. the special parts) which will then be assigned to a functional cell. Afterwards, an interactive possibilistic programming model is applied to cell formation of remaining regular parts while considering both part sequences and multiple routes. To show the capability and usefulness of the proposed method, an illustrative example is also provided. Finally, concluding remarks are reported. [ABSTRACT FROM PUBLISHER]

In multi-site (parallel) manufacturing systems, each selling region (SR) is usually allocated to a specific manufacturing site (MS) in order to minimise the transportation costs, simplify the planning process, etc. However, such manufacturing systems usually encounter great drawbacks in terms of over-achievement or under-achievement of the forecasted demand due to isolation of the MSs. To cope with this drawback, this paper proposes a novel framework for preparing an optimal aggregate production plan by interconnecting the MSs through lateral trans-shipment. In the presence of fluctuating and dynamic demands, we apply some classic strategies including the inventory holding, back-ordering, and additional capacity options (i.e. overtime and changing workforce level) as well as lateral trans-shipment of products among MSs as the feasible strategies undertaken. Maximising the total profit and minimising the manufacturing lead time are considered as two managerial goals. Due to ambiguousness of some critical parameters as well as vagueness of objectives’ target values, a fuzzy goal programming (FGP) approach with imprecise goal hierarchy is developed for modelling the aggregate production-distribution planning (APDP) problem with trans-shipment. The proposed FGP model is then converted to an equivalent crisp one by combining the two recently developed fuzzy programming approaches. An illustrative example inspired by a real case study is provided to show the usefulness and applicability of the proposed model. [ABSTRACT FROM PUBLISHER]

Lotsizing in capacitated pure flow shop with sequence-dependent setups has been considered in this paper. An exact formulation of the problem is provided as a mixed-integer program. It is well known that the capacitated lotsizing and scheduling problem (CLSP) is NP-hard. The introduction of serially arranged machines and sequence-dependent setups makes the problem even more complicated. Five MIP-based heuristics based on iterative procedures are provided. The first three heuristics are based on the original model but to solve non-small instances of problem, the last two heuristics are based on permutation flow shop problem which ignores the majority of combinations. To test the accuracy of heuristics, two lower bounds are developed and compared against the optimal solution. The trade-offs between solution quality and computational times of heuristics are also provided. [ABSTRACT FROM AUTHOR]

This study proposes a novel hierarchical production planning (HPP) structure for make-to-order (MTO) companies. The proposed HPP structure contains three decision making levels: (1) the order entry level, (2) the order release level and (3) the order sequencing/dispatching level. The objective is to manage the delivery dates of arriving orders in order to reach short and reliable delivery dates using appropriate decision making techniques at each level. At the first level, the main decision concerns the acceptance or rejection of new arriving orders subject to some relevant limitations (e.g. capacity constraint). At this level, an extended version of our previously proposed model (Ebadian, M., et al., 2007. A new decision making structure for the order entry stage in make-to-order environments. International Journal of Production Economics, 111, 351-367.) is applied. Besides rejecting some of the low priority orders, determination of delivery dates and prices for the non-rejected orders constitute the other outputs of this level. The second level is related to releasing the accepted orders to the shop floor. The decision making problems at this level include which orders and when they can be released. We develop a new order release method which improves the delivery date performance through generating a smoothed production schedule. Finally, at the third level, a modified dispatching rule is proposed to sequence the existing orders at each workstation so that the previously agreed delivery dates can be met. The proposed models at the second and third levels are validated through a number of numerical experiments conducted by simulation studies and the corresponding results are discussed in details. [ABSTRACT FROM AUTHOR]