The increase competition and decline economy has increased the relevant importance of having reliable supply chain. The primary objective of many supply chain problems is to reduce the cost of services and, at the same time, to increase the quality of services. In this paper, we present a multi-level supply chain network by considering multi products, single resource and deterministic cost and demand. The proposed model of this paper is formulated as a mixed integer programming and we present two metaheuristics namely MOPSO and NSGA-II to solve the resulted problems. The performance of the proposed models of this paper has been examined using some randomly generated numbers and the results are discussed. The preliminary results indicate that while MOPSO is able to generate more Pareto solutions in relatively less amount of time, NSGA-II is capable of providing better quality results.

In a previously published paper by Khan et al. (2011) [Khan, M., Jaber, M.Y., & Bonney M. (2011). An economic order quantity (EOQ) for items with imperfect quality and inspection errors, International Journal of Production Economics, 133, 113-118], we found that there is a contradiction between the cycle length and the holding cost per cycle. To obtain the cycle length, the authors assumed that the returned items from the market were replaced with good quality items. However, for the holding cost per cycle, the authors implicitly assumed that the returned items were not replaced by good quality items. In this note, we first point out the contradiction. Then we fix this flaw and develop a corrected EOQ.

This study investigates optimal pricing and inventory policies for non-instantaneous deteriorating items with permissible delay in payment. The demand rate is as known, continuous and differentiable function of price while holding cost rate, interest paid rate and interest earned rate are characterized as independent fuzzy variables rather than fuzzy numbers as in previous studies. Under these general assumptions, we first formulated a fuzzy expected value model (EVM) and then some useful theoretical results have been derived to characterize the optimal solutions. An efficient algorithm is designed to determine the optimal pricing and inventory policy for the proposed model. The algorithmic procedure is demonstrated by means of numerical examples.

One of the techniques, which are used for Multiple Criteria Decision Making (MCDM) is the permutation. In the classical form of permutation, it is assumed that weights and decision matrix components are crisp. However, when group decision making is under consideration and decision makers could not agree on a crisp value for weights and decision matrix components, fuzzy numbers should be used. In this article, the fuzzy permutation technique for MCDM problems has been explained. The main deficiency of permutation is its big computational time, so a Tabu Search (TS) based algorithm has been proposed to reduce the computational time. A numerical example has illustrated the proposed approach clearly. Then, some benchmark instances extracted from literature are solved by proposed TS. The analyses of the results show the proper performance of the proposed method.

Manpower planning using annualised hours is an effective tool where seasonal demand for staff in industry exists. In annualised hours (AH) workers are contracted to work for a certain number of hours per year. The workers are associated with relative efficiency for different types of tasks. This paper proposes a Mixed Integer linear Programming (MILP) model to solve an annualised working hours planning problem when spike in demand exists. The holiday weeks for the workers are considered as partially individualised. If a worker has been assigned with more than one type of working week in a week, this will be compensated with one or more holiday week. The performance of the model is demonstrated with an example. It can be seen that this type of modelling helps to meet the spikes in demand with less capacity shortage compared with one working week in a week.

This paper considers preemption and idle time are allowed in a single machine scheduling problem with just-in-time (JIT) approach. It incorporates Earliness/Tardiness (E/T) penalties, interruption penalties and holding cost of jobs which are waiting to be processed as work-in-process (WIP). Generally in non-preemptive problems, E/T penalties are a function of the completion time of the jobs. Then, we introduce a non-linear preemptive scheduling model where the earliness penalty depends on the starting time of a job. The model is liberalized by an elaborately–designed procedure to reach the optimum solution. To validate and verify the performance of proposed model, computational results are presented by solving a number of numerical examples.

Supply chain is a network of organizations that work together and performs various business functions such as procurement of raw materials, converting the raw material into semi-finished or finished goods and distributing the same to their ultimate customers. Presence of bullwhip effect in a supply chain is costly and degrades the performance of the supply chain. Reduction in bullwhip effect can improve the efficiency or profitability of a supply chain. The objective of this paper is to know the impact of imperfect Advance Demand Information (ADI) sharing on bullwhip effect in a four-stage serial supply chain and to evaluate the supply chain performance by conducting an experiment similar to the beer distribution game. The performance measures used are variance of orders placed by each stage, fill rate, total inventory at each stage and total holding cost of the supply chain. Results show that imperfect ADI improves the performance of the supply chain. The performance of the supply chain is also evaluated under order up to level (R, S) policy with safety stock and it is found that the performance of supply chain is better when inventory policy used.

This paper addresses scheduling a set of weighted jobs on a single machine in presence of release date for delivery in batches to customers or to other machines for further processing. The problem is a natural extension of minimizing the sum of weighted flow times by considering the possibility of delivering jobs in batches and introducing batch delivery costs. The classical problem is NP-hard and then the extended version of the problem is NP-hard. The objective function is that of minimizing the sum of weighted flow times and delivery costs. The extended problem arises in a real supply chain network by cooperation between two layers of chain. Structural properties of the problem are investigated and used to devise a branch-and-bound solution scheme. Computational experiments show the efficiency of suggested algorithm for solving instances up to 40 jobs.

In response to increasing inflexible customer demands and to improve the competitive advantage, industrial organizations have to adopt strategies to achieve cost reduction, continual quality improvement, increased customer service and on-time delivery performance. Selection of the most suitable plant or facility layout design for an organization is one among the most important strategic issues to fulfill all these above-mentioned objectives. Nowadays, many industrial organizations have come to realize the importance of proper selection of the plant or facility layout design to survive in the global competitive market. Selecting the proper layout design from a given set of candidate alternatives is a difficult task, as many potential qualitative and quantitative criteria need to be considered. This paper proposes a Euclidean distance based approach (WEDBA) as a multiple attribute decision making method to deal with the complex plant or facility layout design problems of the industrial environment. Three examples are included to illustrate the approach.

Supply chain coordination as an effective tool plays an important role in improving supply chain performance. In this article, a two-level supply chain with one manufacturer and two retailers is considered. The order quantity that retailers are faced with depends on the amount of advertisements and both retailers compete with each other on advertising. The Stackelberg game is established between manufacturer and retailers such that the manufacturer and the retailers play the leader and the follower roles, respectively. First, the manufacturer determines the wholesale prices for retailers and instead, the retailers determine the order quantity and advertising level, simultaneously. The manufacturer produces one kind of product and delivers it to retailers before the beginning of selling season. Retailers can affect the order quantity regarding the demand dependency on advertising level through the incurred costs from the advertising. In this paper, we show that we can achieve the desirable supply chain coordination through using combined quantity discount and advertising cost sharing contracts. We also consider the win-win situation for all the members of the supply chain.