Due to awakening environmental awareness and corresponding tightening of environmental protocols in the industrialized world, new production challenges arise. These challenges are to meet the continuously growing worldwide demand for capital and consumer goods while considering the associated economic, environmental, and social aspects. The next generation manufacturing systems must adjust themselves rapidly and cost-effectively. The goal is to respond to changing market needs while minimizing adverse effects on the environment. Reconfigurable Manufacturing Systems (RMSs) - due to its flexibility and characteristics - can increase the system sustainability and responsiveness to satisfy the market needs. In this paper, we propose an environmental oriented multi-objective problem for a sustainable reconfigurable manufacturing system. As design objectives, we consider the total production time, the total production cost and the amount of environmental hazardous wastes. The environmental hazardous wastes considers both liquid hazardous waste and greenhouse gas emissions (GHG). Weighted goal programming is used to tackle this multi-objective problem. The applicability of our approach is illustrated through a numerical example.

Achieving competitiveness in nowadays manufacturing market goes through being cost and time-efficient as well as being environmentally benign. In this paper, three hybrid-meta-heuristics are proposed and compared to solve the single-product multi-unit process plan generation problem. Moreover, in the quest of achieving a sustainable reconfigurable manufacturing system (RMS), three criteria are considered: respectively, the total production cost, the completion time and the greenhouse gases (GHG) emitted by the manufacturing system during the production process. To illustrate the applicability of the approaches, an example is presented and numerical results analysed.

Nowadays, in a hotly competitive manufacturing environment, one of the main challenges for companies is to minimize their costs of production and to be environmentally benign simultaneously. One of the answers lies in the optimal utilization of energy by manufacturing processes. In this paper, we attempt to obtain a partially “sustainable” reconfigurable manufacturing system, by considering the greenhouse gases emission (GHG) as a criterion in addition to the traditional two criteria, respectively, cost and time. In order to do so, we propose a hybrid multi-objective approach, combining the strengths of MOILP ‘Multi-Objective Integer Linear Programming’ and the evolutionary algorithm AMOSA ‘Archived Multi-Objective Simulated-Annealing’ to solve the process plan generation prob- lem of an RMS. More specifically, three criteria are considered, respectively, the total production cost, the completion time and the amount of GHG emitted during the product manufacturing. To illustrate the applicability of the developed approach, a simple example is presented and the numerical results analyzed.

In hotly competitive international industrial and economic environments, supply chain coordination (SCC) is one of active research topics in production and operation management. In this research work, we present a new consensus-based fuzzy goal programming approach for supply chain coordination problem. It is formulated as a multi-criteria group decision making (MCGDM) problem and solved by combining consensus-based goal programming with TOPSIS method in a fuzzy environment. To demonstrate the applicability of the proposed approach, an example of robot selection problem is presented and the numerical results analyzed.

Recent years have seen the development of a new manufacturing paradigm that responses to market changes cost-effectively, called Reconfigurable Manufacturing System (RMS). A RMS consists mainly of reconfigurable machines that give the system the capability to be reconfigurable. Each reconfigurable machine has multiple configurations with different capabilities and functionalities. In this paper we address the problem of process plan generation in Reconfigurable manufacturing systems taking into account the multiconfiguration nature of machines, as well as the state of the manufacturing system in term of already existing process planning. More specifically, we propose an approach that generates process plans with the consideration of other products on the system if they exist. The consideration of previously introduced products is justified by the fact that resources are shared among all products, which affects the overall machine load balancing, The problem is resolved using AMOSA (Archived MultiObjective Simulated Annealing) and the results discussed.

Nowadays, supply chain coordination (SCC) is considered by researchers and practitioners as one of the active research topics in production and operations management (POM). This paper addresses the development of a fuzzy-based possibility measure with goal programming approach dedicated to selection problem. More specifically, the selection problem is formulated as a multi-expert multi-criteria (MEMC) decision making problem and solved by combing consensus-based possibility measure with goal programming in a fuzzy environment. To illustrate the applicability of the proposed approach, a simple example is presented and the numerical results analyzed.

In response to the limitations of existing manufacturing systems and the fast changing environments, reconfigurable manufacturing systems (RMSs) have been proposed as a promising means for companies to manufacture products while meeting individualized customer requirements. In this paper, a simulation based Non dominated Sorting Genetic Algorithm (NSGA-II) approach is proposed to solve the problem of process plans generation for multi-unit single-product type observed in RMS. More specifically, multi-unit macro-level process plans are established in order to schedule different parts/operations on several machines, and where a process plan is associated to each unit in a reconfigurable environment. To demonstrate the applicability of the proposed approach, an illustrative example is presented and the numerical results discussed.

Discounts based on the total value of purchase is a common business practice among the online retailers. The discounts offered could be in the form of rebates, cash backs, gift vouchers, or reward points. The comparison shopping bots provide only price information for individual products but do not consider the total value discounts that could be accrued by buying more products from the same retailer. In this paper, we consider a buyer interested in buying M different items from N retailers who offer total value discounts. The buyer is faced with an optimization problem of choosing the retailers and the items to buy from them to minimize the total buying cost taking into account the total value discounts offered by the retailers. We propose an integer programming formulation for the problem, which can be integrated with the current comparison shopping search engines. As this problem needs to be solved online, we propose a linear programming based heuristic to obtain a nearoptimal solution that could be used with the commercial solvers to accelerate the solution time. Copyright 2008 ACM.

This paper addresses the design of single commodity stochastic distribution networks. The distribution network under consideration consists of a single supplier serving a set of retailers through a set of distribution centers (DCs). The number and location of DCs are decision variables and they are chosen from the set of retailer locations. To manage inventory at DCs, the economic order quantity (EOQ) policy is used by each DC, and a safety stock level is kept to ensure a given retailer service level. Each retailer faces a random demand of a single commodity and the supply lead time from the supplier to each DC is random. The goal is to minimize the total location, shipment, and inventory costs, while ensuring a given retailer service level. The introduction of inventory costs and safety stock costs leads to a nonlinear NP-hard optimization problem. A Lagrangian relaxation approach is proposed. Computational results are presented and analyzed showing the effectiveness of the proposed approach.

e-Procurement is an Internet based business process for sourcing direct or indirect materials. In this paper we propose an auction mechanism called as discount auctions for procuring multiple items. The bid from a supplier consists of individual costs for each of the items and a discount function, which specifies the discount over the number of items. We show that such a bid is more meaningful and cost effective in terms of bid preparation and communication in common procurement scenarios. The bid evaluation problem is modeled as a mixed integer linear program and various structures in the problem that can be exploited for developing algorithms are explored. A heuristic based on linear programming relaxation is proposed to determine a feasible solution to the problem and its closeness to optimality is studied with computational experiments.

A simulation-based multiobjective optimization method is proposed in this paper for joint decision-making on strategic sourcing and inventory replenishment. More specifically, a multiobjective genetic algorithm is developed to determine the optimal supplier portfolio and inventory control parameters in order to reach best compromise of two conflicting criteria: costs and demand fill-rate. Discrete-event simulation is used to provide faithful evaluation of these two criteria. Numerical results on a variant of a real case study are presented.